research paper on land use land cover

• Open access
• Published: 14 March 2024

Assessment of land use land cover change and its effects using artificial neural network-based cellular automation

• Nishant Mehra   ORCID: orcid.org/0000-0001-6069-8103 1 &
• Janaki Ballav Swain 1  

Journal of Engineering and Applied Science volume  71 , Article number:  70 ( 2024 ) Cite this article

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Metrics details

The challenge of urban growth and land use land cover (LULC) change is particularly critical in developing countries. The use of remote sensing and GIS has helped to generate LULC thematic maps, which have proven immensely valuable in resource and land-use management, facilitating sustainable development by balancing developmental interests and conservation measures. The research utilized socio-economic and spatial variables such as slope, elevation, distance from streams, distance from roads, distance from built-up areas, and distance from the center of town to determine their impact on the LULC of 2016 and 2019. The research integrates Artificial Neural Network with Cellular Automta to forecast and establish potential land use changes for the years 2025 and 2040. Comparison between the predicted and actual LULC maps of 2022 indicates high agreement with kappa hat of 0.77 and a percentage of correctness of 86.83%. The study indicates that the built-up area will increase by 8.37 km 2 by 2040, resulting in a reduction of 7.08 km 2 and 1.16 km 2 in protected and agricultural areas, respectively. These findings will assist urban planners and lawmakers to adopt management and conservation strategies that balance urban expansion and conservation of natural resources leading to the sustainable development of the cities.

Introduction

The demographic projections suggest that the Central and Southern Asia are poised to emerge as the world’s most populous region by 2037 [ 1 ]. Furthermore, India surpassed China to become the most populous country in the year 2023, and prevailing indications anticipate the persistence of this demographic trend for several decades [ 2 ]. The unrestrained expansion of built-up areas is majorly propelled by a substantial increase in population which ultimately leads to land use land cover (LULC) changes [ 3 , 4 , 5 ].

The significant characteristics of urban sprawl are a rapid decrease in vegetated areas [ 6 , 7 ], random and unplanned growth [ 8 , 9 ], increased economic activities in higher elevations [ 10 , 11 , 12 ], land cover change in agricultural areas [ 13 , 14 , 15 , 16 ], and increase in urban heat island [ 17 , 18 , 19 ]. This has created environmental, ecological, economic, and social challenges [ 8 ]. The changes, geographical and climatic, occurring in Himalayan cities call for special attention due to the geo-morphological, topographical, and seismic constraints [ 7 , 10 , 20 , 21 ]. Thus, the monitoring of spatio-temporal expansion of the cities and accurate prediction of LULC change is vital for ecosystem conservation and sustainable development management strategies to be implemented in these regions [ 22 ]. As per the year-wise records shared by the Department of Economics and Statistics, State Government of Himachal Pradesh in India, the class III cities having a population of less than 50,000 in the state were found to be more vulnerable to urban sprawl due to saturation in capital city Shimla, and thus, there is a pressing need to balance economic development with sustainable environmental practices.

The integrated use of remote sensing and GIS has helped immensely in the management of land and natural resources and in understanding the complex linkages between spatial patterns and processes responsible for change [ 7 , 23 , 24 , 25 ]. Thus, the modeling and accurate prediction of urban sprawl has been inviting the attention of various researchers [ 26 , 27 ], and the use of modern self-learning algorithms has further improved the accuracy of these models [ 28 , 29 , 30 , 31 ]. The understanding of dynamic changes occurring in the region and the incorporation of driving factors also improves the accuracy of these models [ 26 ].

Cellular automata (CA)-based models are spatially explicit models (SEM) that work on a simple premise that the future state of a land cover type is dependent on the past local interactions between the different land covers [ 22 , 26 ]. The model’s popularity in GIS grew immensely in the 1980s, catalyzed by pivotal contributions from Wolfarm [ 32 ], Michael Batty and Xie [ 33 ], and Batty et al. [ 34 ]. The accuracy of the model was dependent upon the temporal scale of maps, neighboring cells, and transition rules [ 35 , 36 ]. Batty [ 34 ], Leao [ 37 ], and Lagarias [ 38 ] found them to be powerful spatial dynamic models. The open structure, simplicity, good spatial resolution, and integration with other knowledge-driven models make it an appropriate choice for urban sprawl studies [ 22 , 26 , 35 , 39 ]. However, the model is dependent upon spatial data only and is limited in implementing driving forces which is important for complex processes and accurate simulation [ 22 , 26 ]. The non-uniform cell space, dynamic neighborhood classes, and non-stationary transition rules offer opportunities for modification in the original CA structure to make it applicable for real-time complex urban sprawl studies [ 22 , 35 ]. This makes it necessary to integrate CA with other models.

To address the inherent constraints in the individual models, various researchers have employed hybrid models like CA–Markov model [ 40 ] and CA-ANN model [ 41 ]. The integration of spatial patterns with the processes responsible for causing changes in landforms is imperative for the accurate prediction and modeling of land cover changes [ 24 ]. Artificial neural networks (ANN) can identify and analyze the complex inter-relationship between causative factors and complex patterns [ 26 , 42 ]. The architecture of ANN simulates and behaves in a similar pattern as the human brain and nervous system [ 43 , 44 , 45 ]. ANN can deal with incomplete data, does not assume the distribution of input data, and can detect potential inter-dependencies between driving factors [ 46 , 47 ]. Multi-layer perceptron (MLP)-ANN, consists of input layers, hidden layers, and an output layer, and is the widely used model in ANN because it is fast, accurate, and can infer and forecast outcomes derived from inputs that it has not encountered previously, exhibiting the capacity for extrapolation and prognostication [ 48 ]. Researchers have adeptly employed CA-ANN models to address spatial-dynamic complexities and driving factors, enhancing the robustness and realism of modeling for accurate prediction and estimation of land cover changes [ 18 , 39 , 42 , 49 , 50 ].

The study aims to model LULC change using MLP-ANN and cellular automation simulation in the city of Dharamshala, one of the fastest-growing cities in the state of Himachal Pradesh, India. The results are expected to act as a road map for urban planners and policymakers for sustainable development of the city. The research used the MOLUSCE plugin, as a tool to predict and assess the transformations occurring in each LULC type in the study area. In the study, LULC maps of 2016 and 2019 were used as independent variables in the model to simulate and validate the LULC map of 2022, and thereafter, LULC maps of 2025 and 2040 were predicted.

The research locale encompasses Dharamshala, situated in the state of Himachal Pradesh, India, as illustrated in Fig.  1 . Positioned within the Western Himalayas, the city graces the southern inclines of the principal regional Dhauladhar mountain range (V. Gupta et al., [ 51 ]). Geographically, the study vicinity spans from 32° 9′ 52″ N to 32° 15′ 58″ N in latitude and 76° 17′ 22″ E to 76° 23′ 09″ E in longitude, encompassing an expanse of 42.7 km 2 . Elevation within this area exhibits variability, ranging from 790 m in the southwest to an altitude of 2130 m above mean sea level (AMSL) in the north. The region has a humid subtropical climate and experiences a mean annual temperature of about 19.1 ± 0.5 °C. The zenith of temperature occurs in June with an average of 32 °C, while the nadir registers in January with an average of 10 °C. The northern parts of the region also receive heavy snowfall during winter. Geologically, the region forms a part of the Outer Himalayas with a predominant geological composition comprising sandstone, characterized by alternating bands of clays, shale, and siltstones (V. Gupta et al., [ 51 ]).

Study area, Dharamshala city

The city is the winter capital of the state of Himachal Pradesh and the headquarters of the Central Tibetan Administration. The city is a famous hill station destination, both for national and international visitors. Further, it is also the administrative headquarters of Kangra district. The city was declared a municipal corporation in the year 2015 by merging 9 adjacent villages and has ever since witnessed rapid urbanization. It is one among the 100 cities in India and the only city in the state of Himachal Pradesh chosen in the year 2016 to be developed under the National Smart Cities Mission by the Government of India.

A dramatic rise in urban spaces has been witnessed in the city from the year 2016 onwards, and there exists an inherent imperative to address the recent alterations that have manifested within this geographical area through a scientific lens. The time scale chosen in the study corresponds to the maximum socio-economic changes occurring in the city due to the formation of municipal limits, hosting of international cricket matches and also serving as the residence of His Holiness Dalai Lama.

The simulation’s correctness is determined by the quality of the data and criteria used in the investigation [ 26 , 35 , 39 ]. The month of May is characterized by sunny days with no or little rainfall in the region; thus, all the temporal satellite imageries were chosen from this month to negate the impacts of phenological effects and cloudy pixels [ 52 ]. The ancillary data included a draft town and country planning (TCP) report of Dharamshala city and ground truth points (using GPS) for assistance and validation in image classification.

The study incorporated LULC maps of 2016, 2019, and 2022 and digital elevation model (DEM), the details of which are given in Table  1 . Multi-temporal Landsat 8 Operational land Imager (OLI) satellite imageries for the years 2016, 2019, and 2022 were used, the description of which is shown in Table  2 . A hybrid approach involving a Maximum Likelihood Classifier (MLC) and thereafter adopting post-classificaton improvement measures using vegetation indices was used in the research study to create LULC maps of 2016, 2019, and 2022 with each LULC map attaining an overall accuracy surpassing 85% and kappa hat showing substantial agreement. The selection of the Maximum Likelihood Classifier was based on the topographical challenges and spectrally homogeneous attributes of the land cover classes under investigation. The correction of the land cover classes through visual interpretation becomes essential by utilizing high-resolution satellite imagery obtained from Google Earth and Planet Scope [ 53 , 54 ].

The riverine sources, in this part of the Himalayan region, are characterized by the presence of boulders and cobbles, and thus, the chances of overlapping spectral characteristics for the built-up areas and water bodies were likely. The Strahler order algorithm available in SAGA was used to accurately delineate the water bodies.

Various researchers have included slope, elevation, and aspect, as geospatial parameters; population density as the socio-economic parameter; and spatial variables such as distance from the water bodies, roads, built-up areas, and from the center of town for simulation [ 18 , 30 , 31 , 39 , 42 , 49 , 50 ]. After checking different combinations of socio-economic and physical factors, the simulated LULC map of 2022 showed the best performance by considering five parameters that included slope, distance from streams, distance from roads, distance from built-up areas, and distance from the center of town. The explanatory maps having the shp data format were converted to a raster and then Euclidean distance was calculated in QGIS to create a raster data type. The explanatory maps in GeoTIFF format were also created using Euclidean distance in QGIS.

The methodological workflow for the area under investigation is summarized in Fig.  2 . The MOLUSCE plugin available in QGIS 2.18 was used for the simulation of land cover change in 2022.

Methodological workflow and data analysis

The transition probabilities derived from MLP-ANN learning processes are fed into CA to predict and estimate the LULC changes in this hybrid model of CA-ANN [ 31 , 49 ].

Image pre-processing

The satellite imageries of 2016, 2019, and 2022 were transformed to spectral radiance values, and the Dark Object Subtraction (DOS) in the semi-automatic classification (SCP) plugin in QGIS was used for performing atmospheric correction. Thereafter, the images were mosaicked, and an image subset was performed using the shapefile of the municipal corporation limits of Dharamshala city. The shape file of municipal limits was geometrically corrected with the use of ground control points (GCP) selected using GPS. This was executed in a manner that ensured the Root mean Squared Error (RMSE) attained a value of less than half of a pixel [ 55 ].

Modified Anderson’s LULC classification system was adopted to produce thematic maps comprising five LULC classes, Protected areas (PA), Agricultural areas (AA), Built-up Areas (BA), Barren land (BL), and Water bodies (WB), as shown in Table  3 , for the years 2016, 2019, and 2022. Supervised classification using MLC was used for the creation of the five land cover classes [ 7 , 20 , 53 , 56 , 57 ]. The forests are protected under Indian Forest Act, 1927, and the tea plantations are protected under Himachal Pradesh Ceiling on Land Holdings Act, 1972, and thus were classified under the protected areas (PA).

The LULC maps for 2016 and 2019 are taken as input and establish the spatio-temporal dynamics of the region. The MOLUSCE plugin was used to create a transition map between 2016 and 2019 showing the percentage change occurring in each of the five land cover types for the period from 2016 to 2019.

For using the CA model, the region should be a discrete grided area, with each cell specifying a land cover type. The driving factors could be categorized as having different spatial attributes, such as distance parameters, physical properties, and neighborhood relationships [ 58 ]. The distance parameter includes distance from the streams, roads, built-up areas, and from the center of town. Physical properties include slope and elevation. Neighborhood relationships involve the percentage area of a land cover type around the cell of interest. The explanatory maps are extracted in a raster format (Fig.  3 ).

Explanatory map: slope, distance from streams, distance from roads, distance from built-up areas, distance from the center, and elevation

The transition functions are non-linear and represent the relationship between driving factors and transformation probabilities of land cover type [ 26 , 39 ]. ANN model is trained on explanatory maps, and then the transition probabilities are established for the CA model. The prediction of transition probabilities from the current land use type to different LULC categories at the subsequent time point, denoted as “ t  + 1,” was determined by taking into account the current LULC classification of a specific cell as well as the neighboring cells at time t .

Based on spatio-temporal dynamics and the impact of driving factors, the simulation is initially performed for the year 2022, and based on the performance of the model, the predictions are thereafter made for the years 2025 and 2040 in the iterative steps of two and six, respectively, in the model.

Evaluating correlation and transition analysis

The examination of correlation among the driving factors was executed using the Cramer coefficient, also known as the Cramer V method, particularly suitable for contingency tables larger than 2 × 2. The outcomes span a range of 0 to 1, where elevated values signify a heightened correlation amid the driving factors. A coefficient surpassing 0.15 indicates a substantial explanatory potency of variables [ 49 ]. The correlation matrix is shown in Table  4 .

The changes (in area and percentage) occurring in the land cover classes for the period 2016 to 2019 are shown in Table  5 . The transition matrix, shown in Table  6 , helps compare and understand temporal transformations occurring in the region, without the impact of physical and socio-economic driving factors. Within the matrix’s diagonal, the constituent elements signify the magnitude of class constancy, portraying the persistence of specific land cover categories. Conversely, the off-diagonal entries encapsulate the dimensions of shifts occurring between distinct classes [ 18 ]. The values proximate to 1 are present in the diagonal entries, signifying the stability of the corresponding land cover types for the chosen period.

Transition potential modeling

The transformations occurring in a region are a highly complex process dependent on spatio-temporal changes and driving factors responsible for the changes [ 26 , 31 ]. The geographical phenomenon although non-linear and stochastic but have fractal properties [ 59 ] and machine learning algorithms, like MLP-ANN, can be very useful in the identification of these changes [ 45 , 60 ]. The transition function pertaining to the alteration in LULC delineates the association linking the driving factors with the probabilities of conversion, specifically discerning whether cells will shift towards a particular land use/cover classification. The multi-layer feed-forward approach of the model is trained using the error back propagation, wherein the network parameters are modified as per the output error demands [ 48 , 58 , 61 ]. The learning curve for the ANN-MLP is shown in Fig.  4 .

Neural network learning curve

In LULC simulation, the cross-tabulation matrix, also referred to as a contingency table, error matrix, or confusion matrix, stands as an extensively utilized approach for the evaluation of outcomes [ 62 ]. Cross-tabulation facilitates a comparative analysis between the outcomes projected by the model and the observed outcomes [ 63 ]. In this matrix, each row corresponds to the anticipated category, while each column signifies the factual category, thereby showcasing discrepancies in the cells, often expressed as errors represented in percentages or areas [ 27 , 64 ].

The assessment of accuracy was conducted utilizing overall accuracy and kappa hat statistics as the metrics of evaluation. Both metrics use the confusion matrix for calculation purposes. The determination of overall accuracy involves the consideration of diagonal elements only within the confusion matrix, while the kappa hat also considers non-diagonal elements and thus incorporates omission and commission errors [ 64 ]. Kappa hat evaluates the land modeling performance excluding chance agreement [ 65 ], with values ranging from 0.41 to 0.60 categorized as “moderate agreement” and 0.61 to 0.80 as “substantial agreement” [ 27 , 66 ].

Several simulations with different combinations of exploratory maps were performed, as shown in Table  7 . The combination consisting of the parameters distance from built-up areas, distance from roads, distance from the center of town, elevation, slope, and distance from streams showed the maximum accuracy and was chosen in the research study to prognosticate the LULC for the year 2022. The simulated and actual maps were compared with the accuracy metric kappa having a value of 0.77 denoting a notable concordance between both the maps and accuracy was found to be 86.83%. It can be concluded from these that the explanatory variables chosen had a great influence on the prediction of LULC classes. The maps for the years 2025 and 2040 were predicted after running two and seven iterations in CA, respectively.

Results and discussion

The LULC distribution for the years 2016, 2019, and 2022 is shown in Table  8 . Table 9 shows the transition undergoing area-wise and percentage-wise for each LULC class from 2016 to 2019 and 2019 to 2022. The positive values show the increase in that land cover class, while the negative values indicate the decrease for a particular land cover class. The spatio-temporal distribution of LULC classes for the years 2016, 2019, and 2022 are shown in Fig.  5 . It can be observed that protected areas had undergone the maximum transition from the year 2016 to 2022 with a reduction of 11.85% and a decrease of 5.04 km 2 in area. The built-up areas had increased considerably by 14.54% and 6.18 km 2 in area. The agricultural areas had also decreased by 2.73% and 1.16 km 2 in area and a slight increase in barren land is also observed. This signifies the impact of anthropogenic and socio-economic activities in the city and the rapid conversion of this hill station into a concrete jungle. The results also indicate widespread encroachments and abeyance of legislation.

LULC maps for the years 2016, 2019, and 2022

The increase in built-up areas and barren land for the period 2016–2022 is primarily related to the increasing human population and tourist inflow in the city, leading to additional need for residential and commercial spaces. This led to high pressure on the protected areas and agricultural areas, which had suffered maximum depreciation for this period.

The region lying at an altitude of less than 1500 m remained the most critical with maximum changes in LULC classes being witnessed there. The built-up areas, agricultural areas, and protected areas showed maximum transition in this region. The main reason for this could be attributed to the better transportation facilities, road connectivity, suitable climatic conditions for living and agricultural practices, commercial establishments, and more population concentration in this region. Higher altitude regions, because of terrain and other geographical constraints, are less vulnerable to built-up areas. Thus, the city requires greater concern and attention from policymakers and environmentalists to pave the way for a balanced, holistic, and sustainable development model.

The simulation and accurate prediction of LULC become necessary to understand the trend and direction of urban sprawl. The LULC maps of 2025 and 2040 were prepared using CA modeling, and the spatial distribution of these LULC maps is shown in Fig.  6 . Six driving factors, distance from built-up areas, distance from roads, distance from the center of town, elevation, slope, and distance from streams, were chosen for the modeling.

Predicted LULC maps for the years 2025 and 2040

The LULC change analysis of the maps from 2016 to 2025 and 2016 to 2040 is shown in Tables  10 and 11 . The results indicate the continuation of the trend of increase in the built-up areas and a decrease in protected areas for the year 2025. However, the increase in built-up areas will saturate after 2025, and the percentage increase in built-up areas for 3 years will be reduced as compared to the previous 3-year transition. This could be attributed to the fact that most of the usable and productive areas for construction will be exhausted.

The hilly areas offer geographical and topographical constraints for construction, and thus, the ideal locations for construction are usually those located at mid-altitudes and having less slope. The seismicity of the area is another challenge. All these factors will lead to construction in high seismic and landslide-prone areas, which would present a significant impediment to the well-being and security of the inhabitants. Another important observation from the findings was that the transition of built-up areas on the temporal scale is usually restricted to mid and south-eastern regions of the study area. The region has witnessed urban sprawl in these pockets and will remain a critical region in the future.

The swift expansion of urbanized regions, stemming from demographic expansion and the influx of tourists, emphasizes the critical significance of implementing sustainable urban planning strategies. Effective land-use management strategies should be implemented by policymakers and urban planners involving the promotion of efficient land use, reducing urban sprawl, and preserving green spaces, contributing to the attainment of Sustainable Development Goal (SDG) 11, which focuses on creating sustainable cities and communities.

The decline in protected areas is a matter of concern as it poses a threat to biodiversity and ecosystems. Strict implementation of legislation, with the involvement of environmentalists and policymakers, can help protect and restore these areas, thus preserving biodiversity and ensuring the long-term sustainability of natural resources. This effort directly relates to SDG 15, which focuses on maintaining and enhancing life on land.

Land-use planning plays a crucial role in fostering responsible consumption and production patterns. By optimizing land use and preventing further encroachment on protected areas, policymakers can contribute to sustainable resource management and reduce the environmental impact of human activities, which aligns with the objectives of SDG 12, aiming to ensure responsible consumption and production.

The increasing population and tourists will remain the major driving factors for the change. The decrease in agricultural areas indicates a shift in agriculture practice, which lately has been the preferred occupation of the residents. Further, the decrease in protected areas indicates the persistent encroachments and abeyance of legislation. In order to address the decreasing agricultural areas, it is crucial to promote sustainable farming practices and increase agricultural productivity to address the escalating requirements of sustenance. This can be accomplished through the implementation of innovative techniques, support for small-scale farmers, and ensuring food security for all, thereby working towards achieving Zero Hunger (SDG-2).

Conclusions

The study applied ANN-based CA approach for prediction of land cover classes which showed substantial agreement between the simulated and the actual LULC map, with the accuracy metric kappa showing a value of 0.77. The model incorporated six driving factors, out of which four were socio-economic spatial parameters, distance from built-up areas, roads, center of town, and streams; while two were geospatial parameters, elevation, and slope. These criteria combinations performed the best in the CA-ANN model showing the highest value of accuracy of 86.83%.

The selection of these factors was based on their potential influence on the study’s outcomes. For instance, proximity to built-up areas may impact pollution levels and development rates, while distance from roads may correlate with traffic noise and urbanization patterns. Elevation and slope could affect water resource accessibility, and proximity to streams might indicate water source quality.

The study predicts that the built-up areas will increase by 17.84% in the year 2025 and 19.69% by the year 2040. The protected areas will decrease by 14.75% and 16.66%, agricultural areas by 2.81% and 2.72%, and barren land by 0.29% and 0.31% for the years 2025 and 2040, respectively.

The rapid increase in population and tourism has led to a significant rise in built-up areas, creating an urgent demand for more land and putting undue pressure on protected areas and agricultural areas. Strict implementation of legislation is necessary to prevent further encroachments in the protected areas. Studying the critical land-use classes in terms of socio-ecological and environmental concerns is valuable for balancing environmental pressures and conservation interventions. The findings can offer guidance to administrators, policymakers, agricultural practitioners, and urban planners in formulating methodologies for sustainable land-use planning and management, fostering the optimal utilization of natural resources.

Availability of data and materials

The data used in the study was downloaded from USGS ( https://earthexplorer.usgs.gov/ ) and is available openly. It is further declared that the data related to the study will be shared upon request.

It is further certified that the research complies with ethical standards, there was no funding for this research, and there are no potential conflicts of interest (financial or non-financial).

Abbreviations

Land use land cover

Cellular automata

Artificial neural network

Multi-layer perceptron

Operational Land Imager

Thermal infrared sensor

Protected areas

Agricultural areas

Built-up areas

Barren land

Water bodies

Maximum likelihood classifier

Modules for land use change

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Mehra, N., Swain, J.B. Assessment of land use land cover change and its effects using artificial neural network-based cellular automation. J. Eng. Appl. Sci. 71 , 70 (2024). https://doi.org/10.1186/s44147-024-00402-0

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• Artificial neural network (ANN)
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• Cellular automata (CA)
• Land use land cover (LULC)

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A comprehensive review of land use and land cover change based on knowledge graph and bibliometric analyses, 1. introduction, 2. data sources and methods, 2.1. data source, 2.2. methods, 3. results of bibliography analysis, 3.1. number of published articles and citation trends, 3.2. top publication sources, 3.3. highly productive institutions and countries/regions, 3.4. scientific collaboration, 3.5. networking analysis using the key research terms, 4. discussion, 4.1. data sample, 4.1.1. pixel-level lulc remote sensing classification dataset, 4.1.2. object-level lulc remote sensing classification dataset, 4.2. deep learning model, 4.2.1. convolutional neural networks model, 4.2.2. fully convolutional networks model, 4.2.3. recurrent neural network, long short term memory network and gated recurrent unit model, 4.2.4. autoencoder model, 4.2.5. adversarial extension model, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Rong, C.; Fu, W. A Comprehensive Review of Land Use and Land Cover Change Based on Knowledge Graph and Bibliometric Analyses. Land 2023 , 12 , 1573. https://doi.org/10.3390/land12081573

Rong C, Fu W. A Comprehensive Review of Land Use and Land Cover Change Based on Knowledge Graph and Bibliometric Analyses. Land . 2023; 12(8):1573. https://doi.org/10.3390/land12081573

Rong, Caixia, and Wenxue Fu. 2023. "A Comprehensive Review of Land Use and Land Cover Change Based on Knowledge Graph and Bibliometric Analyses" Land 12, no. 8: 1573. https://doi.org/10.3390/land12081573

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• Published: 20 June 2019

Land use and land cover change effect on surface temperature over Eastern India

• Partha Pratim Gogoi 1 ,
• V. Vinoj   ORCID: orcid.org/0000-0001-8573-6073 1 ,
• D. Swain   ORCID: orcid.org/0000-0001-6324-4107 1 ,
• G. Roberts 2 ,
• J. Dash 2 &
• S. Tripathy 3  

Scientific Reports volume  9 , Article number:  8859 ( 2019 ) Cite this article

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• Atmospheric science
• Climate change

Land use and land cover (LULC) change has been shown to have significant effect on climate through various pathways that modulate land surface temperature and rainfall. However, few studies have illustrated such a link over the Indian region using observations. Through a combination of ground, satellite remote sensing and reanalysis products, we investigate the recent changes to land surface temperature in the Eastern state of Odisha between 1981 and 2010 and assess its relation to LULC. Our analysis reveals that the mean temperature of the state has increased by ~0.3 °C during the past three decades with the most accelerated warming (~0.9 °C) occurring during the recent decade (2001 to 2010). Our study shows that 25 to 50% of this observed overall warming is associated with LULC. Further we observe that the spatial pattern of LULC changes matches well with the independently estimated warming associated with LULC suggesting a physical association between them. This study also reveals that the largest changes are linked to changing vegetation cover as evidenced by changes to both LULC classes and normalized difference vegetation index (NDVI). Our study shows that the state has undergone an LULC induced warming which accounts for a quarter of the overall temperature rise since 2001. With the expected expansion of urban landscape and concomitant increase in anthropogenic activities along with changing cropping patterns, LULC linked changes to surface temperature and hence regional climate feedback over this region necessitates additional investigations.

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Introduction.

The surface temperatures are increasing globally as a consequence of anthropogenic climate change. However, it is known that observed changes are a result of both climate forcing and numerous other feedbacks including LULC. The LULC could change as a response to climate and also act as a feedback. In addition to these natural forcing and feedback cycles, there are also additional aspects that are linked to anthropogenic activities. This results in further modification to the LULC and meteorological responses thereupon 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 . These LULC changes and their effects are mostly discernible over regions having higher population density, industrialization, urbanization, deforestation, agricultural diversification etc. Thus, the most visible effect of anthropogenic activities regionally and locally are changes in the LULC which modifies the surface energy balance which in turn affects the surface temperature altering the region’s micro-climate 5 , 8 , 11 , 12 , 13 , 14 , 15 , 16 , 17 .

The changes in LULC also modulate the incidence of heat/cold waves, clouds and rainfall patterns 18 , 19 , 20 , 21 , 22 , 23 , 24 . In addition, LULC change have also been linked to atmospheric aerosol emissions 20 , 25 , 26 which can modify the surface temperature through both direct and indirect effects, thereby modulating rainfall which can also result in droughts or floods through changes to extreme events under certain favorable circumstances 18 .

Over the Indian region, there are only a few scientific investigations that have attempted to discern LULC induced temperature changes, but they are either limited to the major metropolitan cities 6 , 11 , 20 , 23 , 27 , 28 , 29 , 30 , 31 , 32 , 33 or have only focused on aspects related to urbanization 3 , 4 , 34 , 35 , 36 , 37 , 38 . For example, the surface temperature over western India is found to be warming by ~0.13 °C/decade due to the combined effect of greenhouse gases and LULC change of which ~50% was attributed to LULC change 27 . Also, in 2001 an area covering 26.4% of New Delhi had a diurnal temperature range (DTR) below 11 °C whereas in 2011 65.3% of New Delhi had a DTR below 11 °C which was attributed to the increase in built up area by 53% 24 , 29 , 31 , 39 , 40 . Furthermore, the LULC has also been linked to Indian monsoon rainfall changes 18 , 25 . Studies linking LULC to surface temperature changes are limited over Eastern India though this region is among the most rapidly changing landscape over the entire Indian region 41 . The region is also rich in mineral deposits and its continued exploitation for mineral wealth has accelerated LULC change in the past few decades. In addition, Odisha being one of the most natural disaster prone regions of India, a very few studies have investigated the relationship between LULC change and surface temperature, heat waves, extreme rainfall etc.

In this paper, we investigate the surface temperature changes over the state of Odisha using long term ground, satellite and reanalysis datasets and explore its relation to LULC changes. We investigate whether the surface temperature has increased and, if so, whether this is in response to changes in land cover and/or changes in climate. Then multiple line of evidences are used to link LULC to observed spatial and temporal pattern of temperature. This would help in establishing changes associated with local activities such as LULC and those due to regional and global climate change.

Results and Discussion

Trends in surface temperature.

The Indian subcontinent is characterized by large spatio-temporal variability in meteorological parameters displaying large annual, inter-annual, seasonal and decadal variability in surface temperature. In this section, the observed trends in temperatures both on an annual and inter-decadal basis are discussed. It is found that the state of Odisha had undergone a warming of ~0.3 °C during 1981 to 2010 and that the trend in surface temperature is positive irrespective of the meteorological station location (coastal or non-coastal) and altitude (high altitude or the plains). These trends were found to be statistically significant at 95% confidence level in most of the cases (Fig.  1 and Supplementary Information, Table  S1 ). The temperatures were also found to have large inter-decadal variability. A separation into three decades (starting 1981, 1991 and 2001) shows that during the first decade (1981 to 1990) the mean temperature for sites below 500 m above mean sea level (amsl) decreased by ~−0.7 °C whereas, in the subsequent decades (1991 to 2000 and 2001 to 2010) an increase in temperature of ~1 °C and ~0.8 °C respectively was found (Fig.  2 and Table  S1 ). Low values in the mean temperature trend are partly a consequence of differences in the maximum and minimum temperatures. For example, though maximum temperature was steadily increasing over the region, minimum temperature was found to be decreasing during 1981 to 1990. However, within these sub-periods, both of the recent decades show that minimum temperatures are increasing at different rates. It is evident that the recent two decades show a clear increase in both minimum (~1.2 °C) and maximum (~0.13 °C) temperatures which is reflected by the increasing mean temperature. However, the most interesting aspect is that the diurnal temperature range (DTR), which is observed to be increasing during the 1980’s by ~1.16 °C has decreased by ~−0.46 °C and ~−1.19 °C between the second (1991–2000) and third (2001–2010) decades respectively in areas below 500 m amsl (Table  S1 , Fig.  1b–e ). These indicate that the overall trends are larger for low altitude stations in comparison to those at higher altitudes. In order to determine whether these findings are spatially consistent across the entire state or result from urbanization/LULC signatures we have used gridded climate datasets from IMD, University of Delaware (UDel) and NCEP/NCAR Reanalysis–1. Also, to assess the impact of El-Nino/Southern Oscillation (ENSO) events, years with extreme ENSO events (1982, 1983, 1997 and 1998) were removed from the analysis. It may be mentioned that the results with and without ENSO years are consistent (Supplementary Information, Table  S5 ). Overall, our analysis reveals that the surface temperatures over the state has been increasing since 1981 (Fig.  1b–e ).

( a ) The climatological annual mean surface temperature (°C) over Odisha during the period 1981 to 2010 (Source: University of Delaware). The inset map of the study area shows Odisha state with locations of all IMD observation sites (black) along with district headquarters (blue). The striped region represents areas that lie 500 m above mean sea level (Source: NGDC, NOAA). The numbers on the spatial plot represent serial numbers of the stations. (details are given in Supplementary Table  S3 ) ( b – e ) shows the time series (10 point running mean) of the maximum, minimum, mean temperature and diurnal temperature range anomaly for all stations (averaged) within the plains (Source: IMD station datasets. See Table  S3 ). The grey shaded region in ( b – e ) represent standard deviation. The map was generated using MATLAB 2015b, www.mathworks.com .

Decadal temperature trends over Odisha ( a ) Annual ( b ) December-January-February (DJF) ( c ) March-April-May (MAM) ( d ) June-July-August (JJA) ( e ) September-October-November (SON) (Source: IMD station datasets below 500 m amsl). Black dots in the plot represent statistical significance at 95% confidence level. The map was generated using MATLAB 2015b, www.mathworks.com .

Seasonality of trends in surface temperature

We find that the observations and inferences made based on annual and decadal timescales are also applicable to seasonal timescales and indicate that the changes in temperature are forced on large spatial and temporal scales. Figure  2 indicates that between 1981 and 1990 the temperature reduced by 0.3 to 0.9 °C depending on season and this is evident both on annual and seasonal scales.

However, the region has warmed up since 1991 (~0.4 to 0.9 °C). It may be noted that analysis based on both gridded and station datasets show similar trends on cooling and warming during the first and subsequent two decades respectively. However the magnitude of the trends differ. The largest rate of increase in temperature is observed in the June to August (JJA) and September to November (SON) periods whilst the least is observed for December to February (DJF) period. Similar characteristics are also evident in the trends in maximum and minimum temperatures and that, irrespective of season, the minimum temperature is increasing at a much faster rate than the maximum temperature since 1991 (Fig.  2 ). Studies have shown that the rapid rate of change in minimum temperature over more than 70% of the global land surface could be linked to climate change 40 . However, those changes arising out of LULC are expected to be more localized in space and we therefore explore these temperature changes and their spatial patterns to assess the potential influence of land use.

Spatial pattern of trends

Using the IMD gridded datasets, we find that the spatial pattern of the trends adhere to the inferences made using meteorological station datasets (Fig.  3 ).

Decade-wise temperature trend of: ( a – c )- Mean (University of Delaware) ( d – f )- Mean (IMD) ( g –i)- Maximum (IMD) ( j – l )- Minimum (IMD) ( m – o )- DTR (IMD). White circles in the plot represent statistical significance at 95% confidence level. The map was generated using MATLAB 2015b, www.mathworks.com .

Overall, the major finding is that between 1981 and 1990 a cooling trend is evident whereas the subsequent decades show the inverse to this. The spatial patterns and their temporal variability within different decades are similar for the average mean, minimum and maximum temperatures. In contrast, the diurnal temperature range (DTR) shows opposing trends with an increase during the decade starting 1981 and a decrease in the subsequent decades. In addition, the increase in minimum temperature is also more widespread spatially. This may indicate that the changes to surface temperature could be driven by climate change, overwhelming the LULC impacts. However, a warming trend is observed in the most recent decade over Odisha state and to determine whether this is linked to LULC change we utilize the widely used OMR technique which is detailed in the methodology section.

The role of LULC in the observed warming

The OMR was calculated at a spatial resolution of 0.5° for the whole state of Odisha which is shown in Fig.  4 for the different decades. The first decade since 1981 displays a declining trend in OMR of ~−0.04 °C/year over the whole state. Though positive OMR are related to either urbanization or LULC linked changes, negative values are not directly related to these changes and could result from issues associated with the development of the reanalysis datasets that assimilate observations from surface and satellite measurements. The advent of satellite measurements and its assimilation in reanalysis or changes to the instrumentation, or both these factors combined could alter reanalysis results thereby impacting the OMR calculations and hence the negative values 42 . We therefore do not explore this decade further which is cooling since 1981 in our analysis. However, the past two decades since 1991 show a clear increasing trend of OMR over Odisha especially over West which shifts to the East during the most recent decade (since 2001). An interesting aspect is that the highest increasing trend of OMR (~0.04 °C/year) coincides with the location of cities such as Bhubaneswar and Cuttack (which are the densely populated cities) and are also to the East of the state (Figs  1a and 4c ). The city of Bhubaneswar is among the fastest growing tier 2 cities in India and suggests that the OMR trend indicates the impact of LULC/urbanization. The OMR has been shown to be a robust method to detect urbanization/LULC impacts on surface temperatures 10 , 43 , 44 , 45 . To further strengthen this finding, we explore whether the highest OMR trends are coincident with the largest LULC changes. It may be noted here that further analysis are mostly carried out for the last decade due to availability of validated LULC dataset over the Indian region. In addition, even other supplementary datasets are expected to be better from various sources due to the availability/assimilation of data of highest quality since year 2000 from earth observing system (EOS) satellites.

OMR trends over Odisha during the period ( a ) 1981–1990 ( b ) 1991–2000 ( c ) 2001–2010. White circles in the plot represent statistical significance at 95% confidence level. The map was generated using MATLAB 2015b, www.mathworks.com .

The LULC change analysis

Our analysis reveals that the largest LULC changes occur over the NE part of the state (Fig.  5c ) which shows the number of pixels that have undergone a change from the earlier classification (Fig.  5c ) at a spatial resolution of 10 km. This was necessary due to heterogeneous land use and land cover change in the region and also to highlight the spatial extent of these changes better. We have therefore not specifically targeted any land use/cover type, but only investigated the land cover change during the study period. Therefore, the change analysis refers to those areas (number of pixels) where land has undergone change over the period 2004 to 2010. Overall, we find that the LULC change map matches well with the OMR trends shown in Fig.  4c during the recent decade since 2001. This provides us an independent confirmation that the OMR and its spatial pattern is due to temperature changes associated with LULC change. Now, the question is what caused these LULC changes? We therefore quantified individual LULC classes and their changes. It is found that there is a decrease in green vegetation over the state of Odisha (Supplementary Information, Fig.  S1 ). We also carried out a detailed analysis to understand changes of different land use categories using the Advanced Wide Field Sensor (AWiFS) datasets between the periods 2004 to 2010 which coincides with the latest decade discussed in previous sections. Our analysis reveals (Supplementary Information, Fig.  S1 , Table  S2 ) that Rabi Crop (October to March) cultivation has increased (~97%) over the state of Odisha during 2004 to 2010. This has occurred at the cost of decrease in the Kharif Crops (July to October) showing agricultural diversification and changing cropping patterns 41 , 46 , 47 , 48 . The largest changes are associated with vegetation (Kharif, Rabi crops, Fallow lands, Grasslands, Plantations etc.). Table  S2 details the individual changes (in terms of area & percentages) to each LULC class. Several studies have also revealed the role of agriculture in changing the vegetation pattern thereby altering the characteristics of local meteorological parameters 19 , 49 , 50 . Our analysis using the NDVI for the period (2001 to 2010) further reveals consistent patterns, with large decrease in vegetation (Fig.  6 ) especially over the eastern part of the state.

Land Use and Land Cover during ( a ) 2004 ( b ) 2010 ( c ) # LULC change (2010–2004) using AWiFS. The map was generated using MATLAB 2015b, www.mathworks.com . Classes: A-Urban/Built Up, B-Kharif Crop, C-Rabi Crop, D-Zaid Crop, E-Double Crop, F-Current Fallow, G-Plantation, H-Evergreen, I-Deciduous, J-Shurbland, K-Swamp, L-Grassland, M-Other Wasteland, N-Gullies, O-Scrubland, P-Water Bodies, Q-Snow, R-Shifting Cultivation. (Source: AWiFS LULC product of 1 km spatial resolution).

Trend of Normal Difference Vegetation Index (NDVI) during 2001 to 2010 ( a ) Annual ( b ) Winter (Dec to February, DJF) ( c ) Pre-monsoon (March to May, MAM) ( d ) Monsoon (June to August, JJA) and ( e ) Post-monsoon (September to November, SON). Source: MODIS –Terra (5.6 × 5.6 km). The map was generated using MATLAB 2015b, www.mathworks.com .

This further confirms that the change in surface temperature are mostly a consequence of LULC change which is also evidenced by our change analysis using satellite based land cover classification. It may be mentioned that the change in NDVI pattern also coincides with the OMR pattern and LULC change pattern. These multiple line of evidences support the notion that the LULC change is associated with the changes to green cover and is related to vegetation or cropping patterns.

Quantification of LULC linked temperature changes and urbanization

In the previous sections we found that the surface temperature increased due to land surface changes during the period 2001–2010 and is maximum over the eastern part of Odisha. The pattern of OMR, LULC and NDVI trends are all spatially coincident suggesting that the land use changes associated with green vegetation cover have led to the observed warming. However, urban growth may also alter temperature locally and to quantify relationship between LULC induced changes to temperature trends from the overall warming and to explore the signatures of urbanization, we calculated the percentages of the OMR’s LULC induced warming trends in relation to the total observed warming for all district headquarters over Odisha. The results indicate that the percentage of temperature rise due to OMR with respect to observations is highest over the urban centres. For example, Cuttack and Bhubaneswar being the most populous cities of Odisha experience temperature increase of ~40% and ~50% respectively during the period 2001–2010 (Fig.  7b ) followed by Angul, Dhenkanal, Jajapur. The smaller rate of increase in the NNR dataset as compared to observation dataset in the past two decades has clearly signified that the surface temperature has increased mostly because of the LULC change. The largest rise for larger cities rather than smaller towns (Fig.  7a,b ) highlights the additional impact of urbanization in the OMR analysis.

( a ) Temperature rise during 2001–2010 due to LULC changes over Odisha (°C). ( b ) Percentage of temperature rise during 2001–2010 due to LULC changes over Odisha. (OMR in terms of percentage with respect to station observations). The map was generated using MATLAB 2015b, www.mathworks.com .

Physical mechanism

The important parameters modulating LST are surface level soil moisture content and vegetation cover. Changes to these can alter the soil thermal properties and evapotranspiration. It is known that rise in the soil moisture leads to rise in the soil thermal capacity, conductivity and inertia thereby slowing the rise in the LST. In addition, surface heat fluxes such as the Latent Heat Flux (LHF) and Sensible Heat Flux (SHF) get modified with changes to land use. LHF (SHF) increases (decreases) with increasing vegetation leading to a decrease in LST 3 , 4 , 5 , 7 , 8 , 17 , 23 , 25 , 51 , 52 , 53 , 54 , 55 , 56 .

We therefore also explored changes to LHF and SHF. Our analysis reveals that the changes to both LHF (decreasing) and SHF (increasing) favors warming over Eastern part of Odisha (Fig.  8 ). Thus it can be confidently stated that the OMR patterns for the period 2001 to 2010, are consistent with those of the LULC, NDVI, SHF and LHF. Therefore, the spatial pattern of temperature changes during the most recent decade are primarily driven by LULC changes over the Eastern part of the state. However, the largest observed LULC linked changes are over the cities where urbanization further enhances the LULC signatures thereby showing the largest percentage wise increase in temperatures (Fig.  7 ).

Change in ( a ) Latent Heat Flux and ( b ) Sensible Heat Flux during the period 2001–2010 (in Wm −2 ). Source: NCEP CFSv2 forecast product (30 km × 30 km). The map was generated using MATLAB 2015b, www.mathworks.com .

Summary and Conclusion

The LULC/urbanization induced surface temperature rise has become a common phenomenon all across the globe though the rate of change depends upon several external factors such as latitude, forest cover, soil type, mitigation practices etc. It may be noted that LULC change has been attributed to increased surface temperatures in Eastern China, USA, Europe, and India 24 , 27 , 28 , 31 , 36 , 39 , 43 , 44 , 45 , 57 , 58 . Despite varied locations, it is observed that the rate of increase in most of these places is ~0.1 °C/decade which is comparable to that of our study. Though the LULC induced warming has emerged over this region only in the past couple of decades, we find that in terms of LULC induced temperature rise, Eastern India is no less than any other developed regions in the world. This shows that more detailed investigations are urgently required to understand land use related changes to local and regional climate as several regions are undergoing rapid transformation as a result of developmental activities exacerbating the effects of modern climate change. Thus, for the first time over Eastern India this study has integrated surface, satellite and reanalysis datasets to reveal that,

The state of Odisha has warmed by about ~0.3 °C during the period 1981 to 2010 with accelerated warming of ~0.9 °C during the recent decade 2001 to 2010.

The minimum temperature is increasing at a rate higher than that of mean and maximum temperature since 2001 irrespective of location or altitude. It is also observed that there is a corresponding decrease in DTR during the recent decade.

A quarter of this warming is associated with LULC. However, over urban centers such as Bhubaneswar and Cuttack, this fraction is as high as half of the total warming.

There has been a general decreasing trend in NDVI over the Eastern part of the state during the period 2001 to 2010 associated with increasing SHF and decreasing LHF.

Overall the LULC induced warming is a result of changing vegetation cover. The changing cropping patterns (decreased Kharif and increased Rabi crops) appears to be the leading cause for these LULC changes which exacerbates the warming trend.

Data and Methods

Study domain.

The state of Odisha with a population of ~42 million 59 lies in the Eastern part of India, extending approximately from 81°E to 88°E and 17°N to 23°N (Fig.  1a ) and is surrounded by the Bay of Bengal to the East and the Indian peninsula to the west. The region has a tropical climate resulting in high surface temperature during the months of April and May even leading to heat waves. On a climatological basis, most of the state has a mean temperature >26 °C annually with lower temperatures observed over high altitude locations (Fig.  1a ). Odisha experiences an annual average rainfall of ~1500 mm primarily from the south-west monsoon during June to September 60 . In addition, it is also influenced by the monsoon depressions and tropical cyclones that makes landfall from the Bay of Bengal both during pre-monsoon and post-monsoon seasons.

A combination of station, satellite and reanalysis datasets over the past 30 years (1981 to 2010) are used to identify changes in surface temperature and its relationship to land use and land cover (LULC). The identification of LULC forced changes is based on the widely used observation minus reanalysis (OMR) technique. To characterize the change in temperature, we have used measurements of daily mean, maximum and minimum temperature at 29 stations (Fig.  1a and Table  S3 ). In addition, we have also used daily gridded datasets from IMD 61 , 62 and University of Delaware 63 to explore the spatial patterns of temperature changes. The University of Delaware gridded mean temperature datasets were developed using surface measurements such as those from Global Historical Climate Network 64 , 65 (GHCN). We have used NCEP-NCAR Reanalysis-1 (NNR) surface temperature datasets 66 primarily for the calculation of OMR.

The land use classification and their change is inferred from the Indian Remote Sensing Satellite (IRS) satellite Resourcesat-1 (P6), Advanced Wide Field Sensor (AWiFS) derived gridded datasets (from ISRO’s Bhuvan data portal, https://bhuvan.nrsc.gov.in/ ) for the period 2004 and 2010. This is a gridded product developed and validated for use with mesoscale models 67 for regional climate applications specifically over India. In addition, the topography dataset was obtained from National Geophysical Data Center (NGDC), NOAA to identify stations suitable for OMR analysis. This dataset was generated from the best available datasets which were further evaluated and edited before Digital Elevation Model (DEM) generation 68 .

The sensible and latent heat flux datasets from NCEP Climate Forecast System Version 2 (CFS v2) were used to detect changes in surface energy exchange. The NCEP CFS v2 is a consistent and stably calibrated forecast product. It provides a continuity of the climate data record with predictability of seasonal and sub-seasonal scale features 69 . The NDVI dataset were obtained from MODIS–Terra (MOD13C2) to determine changes in green vegetation cover. The MOD13C2 is derived using atmospherically corrected cloud free surface reflectance observations. Additional details about these datasets, their spatial and temporal resolution and period of observation are provided in Table  S4 (Supplementary Information).

Calculation of OMR and trends

To quantify the increase in temperature due to changes in LULC we have used Observation minus Reanalysis (OMR) technique developed by Kalnay and Cai (2003) 45 . This technique has been widely used to discern signatures related to land use changes and urbanization on surface temperature 10 , 17 , 27 , 43 , 44 , 45 from observations. OMR relates the change in the temperature trend due to LULC by subtracting NCEP/NCAR Reanalysis-1 (NNR) from the observation. The NNR product 66 was developed without assimilating surface parameters viz. surface temperature, moisture and winds 13 , 43 , 44 , 45 effectively making it insensitive to local surface changes. Therefore, any trend in OMR may be attributed to the impact of urbanization or change in LULC 27 , 35 , 37 , 45 , 53 , 70 , 71 . The premise here is that observational trends are modulated by all processes including large scale (modern climate change) and local forcing such as LULC change, but the NNR product includes large scale forcing but not LULC change. Therefore, the difference in their trends will highlight the impact of LULC change.

The decadal trends in all parameters were calculated using a simple linear fitting tested against parametric student t-test for statistical significance. The observed changes/trends in station datasets are compared with both gridded and satellite retrieved products depending on availability and accessibility of the datasets during the study period for consistency.

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Acknowledgements

The authors would like to acknowledge the India Meteorological Department (IMD), Giovanni-NASA, Bhuvan-ISRO, NGDC (NOAA) and NCEP-NCAR for providing all the datasets free of cost. The authors acknowledge UDel_AirT_Precip data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at https://www.esrl.noaa.gov/psd/ . The authors also thank IIT Bhubaneswar for providing all the necessary infrastructure for carrying out this study. Lastly, the authors would like to acknowledge DST-UKIERI (UKIERI-DST-2014-15-046) and ISRO-ARFI projects for providing financial support to carry out some of the analysis used in the study. P.P.G. would like to thank MHRD, Government of India for providing fellowship for his graduate research. The authors also gratefully acknowledge the constructive reviews by Prof. Dev Niyogi and the anonymous reviewers whose comments and suggestions have greatly improved the manuscript.

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Land use and land cover as a conditioning factor in landslide susceptibility: a literature review

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Landslide occurrence has become increasingly influenced by human activities. Accordingly, changing land use and land cover (LULC) is an important conditioning factor in landslide susceptibility models. We present a bibliometric analysis and review of how LULC was explored in the context of landslide susceptibility in 536 scientific articles from 2001 to 2020. The pattern of publications and citations reveals that most articles hardly focus on the relationship between LULC and landslides despite a growing interest in this topic. Most research outputs came from Asian countries (some of which are frequently affected by landslides), and mostly with prominent international collaboration. We recognised three major research themes regarding the characteristics of LULC data, different simulated scenarios of LULC changes, and the role of future scenarios for both LULC and landslide susceptibility. The most frequently studied LULC classes included roads, soils (in the broadest sense), and forests, often to approximate the negative impacts of expanding infrastructure, deforestation, or major land use changes involving agricultural practice. We highlight several articles concerned primarily with current practice and future scenarios of changing land use in the context of landslides. The relevance of LULC in landslide susceptibility analysis is growing slowly, though with much potential to be explored for future LULC scenario analysis and to close gaps in many study areas.

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Introduction

Landslides are natural and potentially hazardous phenomena that move rock, debris, or earth downslope under the influence of gravity (Cruden and Varnes 1996 ). Landslides arise from interactions between slope geometry, soil and rock properties, as well as surface and groundwater dynamics (Bogaard and Greco 2016 ). These preconditioning characteristics influence weathering processes and may contribute to a decrease in shear strength (Skilodimou et al. 2018 ). This decrease, combined with triggering factors such as precipitation, earthquakes, snow melt, or human activities, can lead to potentially destructive landslides (Petley 2012 ; Haque et al. 2019 ). For example, Froude and Petley ( 2018 ) reported that 4862 landslides caused 55,997 fatalities between 2004 and 2016. Nevertheless, the impact of landslides on society remains underestimated because much of the damage attributed to earthquakes and storms is tied instead to resulting landslides (Varnes 1984 ; Aleotti and Chowdhury 1999 ). In this context, landslide susceptibility analysis has become essential for disaster risk reduction, aiming to prevent damages and casualties (Bragagnolo et al. 2020 ).

Landslide susceptibility maps identify terrain locations likely to be most prone to slope failure by analysing the characteristics of reported landslides (Guzzetti et al. 2005 ). For this purpose, it is necessary to identify the factors influencing landslide occurrence. Numerous studies have explored the influence of geology (Henriques et al. 2015 ; Kim and Song 2015 ), rainfall (Guzzetti et al. 2007 ; Zêzere et al. 2015 ), and geomorphometric characteristics (Vorpahl et al. 2012 ; Nugraha et al. 2015 ) in this respect. For example, Reichenbach et al. ( 2018 ) identified as many as 596 different conditioning factors used in 565 articles published between 1983 and 2016; geomorphometric variables made up 37% of all these conditioning factors, followed by those linked to land cover (18%).

Land cover refers to the biological and physical materials on the Earth’s surface (Herold et al. 2006 ). It comprises natural elements, such as water bodies, forests, exposed rock or soil, and surfaces modified by humans, such as roads, buildings, and agriculture. In contrast, land use alludes to the socio-economic appropriation of the land (Herold et al. 2006 ), i.e., the purpose humans give to the terrain, to safeguard occupation or production. Land cover impacts soil mechanical behaviour and moisture in many ways. For example, vegetation may protect soil from erosion and improve slope stability through mechanical anchoring and soil suction by roots (Löbmann et al. 2020 ; Parra et al. 2021 ; Masi et al. 2021 ). On the contrary, deforestation, road construction, slope cutting, or building construction on hillslopes often reduce slope stability (Chen et al. 2019 ).

Therefore, land use and land cover (LULC) are important conditioning factors that influence rainfall-triggered landslides (Glade 2003 ), and many studies have argued that land use/cover changes (LUCC) might increase landslide susceptibility (Chen and Huang 2013 ). For example, Lehmann et al. ( 2019 ) explored the relationship between deforestation and landslide occurrence by looking at root reinforcement in four distinct climatic environments with different forest management practices. Since deforested lands had negligible root strength, the authors simulated forest alteration scenarios in which areas without forest regrowth showed higher landslide occurrence and impacts. Similarly, Mugagga et al. ( 2012 ) reported a spatial relationship between landslide occurrence and slopes deforested for cultivation. Apart from areas where crops have replaced forests, abandoned cultivated lands may also be highly susceptible to landslides (Galve et al. 2015 ; Persichillo et al. 2017 ). Like deforestation, most construction works compromise slope stability (Karsli et al. 2009 ; Meneses et al. 2019 ) by altering infiltration, surface runoff, and groundwater flows (Vuillez et al. 2018 ). Furthermore, excavation and blasting commonly used for construction may change the natural stress state and force equilibrium in a given hillslope (Liu et al. 2021 ).

Many studies argued that LUCC might alter landslide susceptibility (Chen and Huang 2013 ; Liu et al. 2021 ). In this sense, Pisano et al. ( 2017 ) analysed the influence of LUCC on landslide susceptibility through future scenarios. One of the future scenarios considered past trends, with increases in forest and cultivated areas, and another presented a decrease in forest area and agricultural activity. The authors concluded that reducing forest areas and abandoning farming lands might increase the erosional processes. Promper et al. ( 2014 ) analysed LUCC over 138 years in Austria to simulate the evolution of landslide risk. The authors considered two main future trends; the first adopted the LULC trends previously verified in the region, and the second took into account no newly built areas. The result was that the expansion of housing construction was predicted in landslide susceptibility areas. Both studies showed that LULC scenarios might aid landslide susceptibility studies for reducing disaster risk.

While an increasing number of studies have been considering LULC in landslide susceptibility (Quevedo et al. 2021 ) or inventory analysis (Uehara et al. 2022 ), most review articles separately focus on LULC (Montalván-Burbano et al. 2021 ), landslide susceptibility modelling (Budimir et al. 2015 ; Huang and Zhao 2018 ; Pourghasemi et al. 2018 ; Reichenbach et al. 2018 ; Merghadi et al. 2020 ; König et al. 2022 ; Lima et al. 2022 ), landslide classification system (Hungr et al. 2014 ; Li and Mo 2019 ), and landslide study area (Dikshit et al. 2020 ; Dias et al. 2021 ; Valdés Carrera et al. 2021 ). Here, we offer a systematic review of how landslide susceptibility studies include the multi-faceted aspects of LULC and bibliometric analysis of the use of LULC data in landslide susceptibility. Finally, we discuss major research themes, focusing on which and how LULC types were parameterised to arrive at a possible ranking.

Materials and methods

Research on natural hazards has seen an increasing number of bibliometric studies analysing the scientific output on landslides (Wu et al. 2015 ; Briones-Bitar et al. 2020 ; Carrión-Mero et al. 2021 ). However, none of the studies explored the use of LULC in susceptibility analyses. Below, we outline our database search criteria; filtering criteria; data pre-processing; bibliometric analysis; and strategies for reviewing (Fig.  1 ).

Search criteria, filters, and database

This study considered the Web of Science™ (WoS), since it is the oldest scholarly database (Birkle et al. 2020 ), maintained by Clarivate Analytics™, with more than 74.8 million records and 1.5 billion references in 254 subject disciplines (Singh et al. 2021 ). Furthermore, we follow the choice of many previous bibliometric studies of landslides (Gokceoglu and Sezer 2009 ; Reichenbach et al. 2018 ; Merghadi et al. 2020 ; Dias et al. 2021 ). We focused on search terms that express LULC to capture the effect of human activities on landslide susceptibility (Meneses et al. 2019 ; Knevels et al. 2020 ) and LUCC. We considered all fields ( topic option) and applied the search argument: ((“landslide susceptibility” OR “mass movement susceptibility”) AND (“land cover” OR “land use” OR “land use cover change” OR “LULC” OR “LUCC”)). This first selection, carried out in July 2021, resulted in 1071 articles.

The search was limited to peer-reviewed articles and excluded books, book chapters, conference proceedings, and reports, as well as “grey literature”, theses, and dissertations (Reichenbach et al. 2018 ). All articles post-dating 2020 were also excluded from this collection, resulting in 814 articles. Next, we checked whether the remaining papers included mention of landslide susceptibility, as many articles focused on other natural hazards, such as soil erosion, gully occurrence, and soil subsidence, though with cursory reference to landslides. Nearly a fifth of the papers was irrelevant to the analysis, leaving us with 645 articles.

All abstracts were screened to ensure that the analysis considered only articles that used LULC as a conditioning factor in the susceptibility analysis. Where abstracts did not clearly indicate whether the article was suitable, the entire publication was screened. For example, some papers mentioned that landslide susceptibility might support land use planning but disclosed little about how LULC may affect landslide susceptibility instead. Hence, we manually removed articles focused on landslide inventory or susceptibility modelling without LULC as a conditioning factor, resulting in 536 articles in our final database.

Bibliometric analysis and review

The bibliometric analysis was conducted in three stages: (i) assessment of productivity and impact, based on publication and citation counts; (ii) network mapping to visualise collaborations among authors and countries; and (iii) most frequent keywords and research areas to portray major research themes.

The first stage explored trends in scientific output, considering publications, citations, cited references in each article, and the focus of the most cited papers. Next, we analysed co-authorship using bibliometric maps of collaboration networks generated through VOSviewer software (van Eck and Waltman 2010 ). Subsequently, the publication counts were mapped according to countries and study areas to depict the geographical focus of the studies, to explore which countries have been most studied at which scale, and to check whether the most studied countries also suffered major landslide disasters in the past.

The most frequent keywords approximate the personal and collective choice of technical jargon, the structure consistency (Herrera-Franco et al. 2021 ), and trends in the subject areas (Leung et al. 2017 ). The most frequent keywords in the abstracts and the most common authors’ keywords were computed using the R programming language and VOSviewer software, respectively. For the abstract analysis, a cleaning step was performed to remove stop words and the terms used to select the articles (i.e., landslide, landslide susceptibility, mass movement susceptibility, land cover, land use, land use cover change, LULC, LUCC). Then, only stem words were considered to avoid over-presenting slight variations of the same term in the word cloud, e.g., map, maps, or mapping. Finally, we analysed the authors’ keywords with a co-occurrence network map (van Eck and Waltman 2010 ) to identify prominent groupings.

Lastly, we highlighted articles that specifically explored the relationship between LULC and landslide susceptibility, following the more traditional line of literature review. While the massively rising publication numbers in the general field of landslide research may encourage, if not even partly justify, the use of bibliometric exploration, we see this as an addition to, rather than a replacement of, conventional literature reviews. Therefore, we selected articles that contained terms related to LULC in their titles, such as land use and land cover. This selection allowed us to thoroughly explore the contribution of these specific articles and highlight trends in the use of LULC in landslide studies.

Publication trends

Among all the 536 articles, 533 were written in English, and the other three were in Korean, Malay, and Portuguese. The database comprises articles published between 2001 and 2020, divided into four equal intervals: (i) 2001 – 2005; (ii) 2006 – 2010; (iii) 2011 – 2015; and (iv) 2016 – 2020. In these phases, the annual average number of publications grew nearly 15-fold between the first and the last phase (Fig.  2 ).

Methodological flowchart

Phase I had the highest proportion of individual publications (19%) and the least international collaboration (24%), with most articles addressing landslide susceptibility with statistical models, such as discriminant function (Dai and Lee 2001 ) and logistic regression (Lee and Min 2001 ; Baeza and Corominas 2001 ). These papers analysed LULC according to landslide occurrence (Baeza and Corominas 2001 ). In phase II, articles began to focus on model comparison, with increasing use of artificial neural networks for susceptibility modelling (Merghadi et al. 2020 ) and some studies about the influence of LULC characteristics on landslide susceptibility (Yesilnacar and Süzen 2006 ). In phase III, models such as logistic regression and frequency ratio remained popular, with several studies recognising the effects of LULC on landslide susceptibility (Reichenbach et al. 2014 ). Finally, phase IV amassed the highest fraction of publications concerned with the relationship between LULC and landslide susceptibility.

The rapid increase in publication numbers since 2012 reached a peak in 2019 when 80 articles were published. This peak may be partly related to the International Consortium on Landslides (ICL) strategies to understand and reduce landslide disaster risk (Sassa 2015 ). In 2012, for example, the 10th Anniversary Conference of ICL created a strategic plan for the period between 2012 and 2022 (Sassa 2012 ). Additional boosts for this rapid rise in research output are the substantial increase in freely available satellite imagery and topographic and LULC data (Wulder and Coops 2014 ; Jun et al. 2014 ; Gómez et al. 2016 ).

The number of references per article has also increased. The average reference list has roughly tripled in length over the past two decades, being the shortest in 2002, with 25 references per article, and surpassing 80 references per article in 2020. This increase in the number of current publications and references per article improves the probability of a published article being cited at least once. For example, the total number of citations per year increased considerably in 2016, reaching a maximum of 5860 citations in 2020 (Fig.  2 ). This increase in citations may be related to the “Sendai Partnerships 2015–2025 for global promotion of understanding and reducing landslide disaster risk”, which was adopted for 33 countries aiming to improve landslide research (Sassa 2015 ).

Articles published in 2012 were the most cited: eight of the 39 papers received more than 150 citations each. The main contributions of these eight articles concerned the comparison of model performance, using an average of ten conditioning factors, focusing on study areas in Iran, Vietnam, Malaysia, and South Korea. In some of these studies, LULC was the second (Pourghasemi et al. 2012a , b ) or the third (Tien Bui et al. 2012 ; Mohammady et al. 2012 ) most important landslide conditioning factor. Some authors found that settlements or residential land were mainly located in susceptible areas (Pourghasemi et al. 2012b ; Althuwaynee et al. 2012 ), while many authors pointed out the higher susceptibility close to roads (Tien Bui et al. 2012 ; Althuwaynee et al. 2012 ; Mohammady et al. 2012 ).

The 15 most-cited articles represented 3% of all analysed papers and concentrated 18% of all citations between 2001 and 2017, most of them published during phases I (33%) and III (33%). These papers featured landslide susceptibility models based on two to four different algorithms. For example, the most cited article (Lee and Min 2001 ) exemplified how to model susceptibility with logistic regression and analysed the relationship between conditioning factors and landslide occurrence. In addition, the Annual Citation Index (ACI) represents the average citation according to the article publication year. Most articles (79%) received between one and 20 citations per year, while only 1% received more than 60 citations per year on average (Fig.  3 ). Among the 15 most cited articles, the paper with the highest ACI (Chen et al. 2017 ) pioneered the use of the logistic model tree for landslide susceptibility, showing that the normalised difference vegetation index (NDVI) was among the most relevant proxies of land cover in the study area.

Trends in publication numbers on LULC and landslide susceptibility, considering (i) publications: the number of publications per year; (ii) citations per year (left y -axis): citations registered in each year; (iii) times cited: how many times articles published in each year were cited ; (iv) references (right y -axis): the average number of references cited in each article per year

Co-authorship and geographic spread

The 536 articles comprised 1305 authors; out of these, 1008 contributed only to a single paper. The articles had between one (3%) and 15 (1%) authors; most contributions had reached three to four authors. In depicting collaborations between co-authors, we considered a minimum publication threshold of five joint papers with the largest set of connected items. The resulting map of 34 authors shows that 12% collaborated with ten or more authors; 27 articles were single-authored (Fig.  4 ).

The distribution of articles over time according to ACI ( A ) and their total percentage ( B )

The most collaborative authors work from Asia. For example, cluster 1 (red) has nine authors from six institutions in China, Iran, and Malaysia; cluster 2 (green) binds eight researchers from institutions in Australia, Belgium, Iran, Nepal, and Turkey; cluster 3 (blue) includes five authors associated with three institutions from Austria, India, and Iran (Fig.  4 ). The remaining clusters have three authors, each with fewer variations in institutions: all researchers in clusters 4 (olive-yellow) and 5 (purple) are from South Korea and China, respectively; clusters 6 (light blue) and 7 (orange) include authors from Norway and India, respectively, with one collaborator from Vietnam.

Regarding the top contributing countries (Table 1 ), 53% are in Asia, 33% in Europe, and 13% in America and Oceania. Authors from the People’s Republic of China were the most numerous, being involved in 102 publications in collaboration with 28 countries, mainly Iran and Malaysia. These articles focused on landslide susceptibility modelling performance, drawing on various machine learning algorithms. Based on the performance of each country, the average citation (AC) values exceeded 100 for Malaysia, Turkey, Norway, and South Korea.

The data on landslide disasters was compiled to check if these most-contributing countries were also among the most impacted by landslides. We used the Emergency Events Database (EM-DAT) (Guha-Sapir et al. 2009 ) (Fig.  5 ), which has been recording disasters since 1903, for selecting the 20 most-affected countries by landslide disasters. These 20 countries had 66% of all recorded landslide disasters and 64% of all fatalities. Only five of the most affected countries between 2001 and 2020 had commensurately high numbers of publications: China, India, Japan, Turkey, and Italy (Table 1 ). However, we stress that this representation is only a rough indication and may hardly capture the international mobility of landslide researchers working abroad or targeted international research programmes.

Co-authorship network map showing seven main collaboration clusters in studies on landslide susceptibility and LULC. Circle sizes represent the publication counts of each author, i.e., the higher the dot size, the higher the publication count. The line thickness shows the collaboration among the authors, in which thicker connecting lines represent more articles jointly co-authored

The study areas of each analysed article and the national research output support the observation that almost all the most affected countries featured as study areas (Fig.  6 ). Also, the most studied and productive countries, such as China, India, Iran, and Italy, have LULC mapping initiatives at the national scale (Congedo et al. 2016 ; Moulds et al. 2018 ; Zhang et al. 2019 ; Ghorbanian et al. 2020 ). However, not all studied regions were represented by authors in these countries, which means that not necessarily the most studied and affected areas are the most contributing. For example, there are studies about Afghanistan, Colombia, Costa Rica, Ecuador, and El Salvador, carried out by researchers whose affiliation is associated with countries such as China and Nepal, the Czech Republic, USA, Germany and Canada, and Spain, respectively.

Countries most affected by landslide disasters, according to EM-DAT ( www.emdat.be ) records from 1903 to 2020 (blue) and the deaths caused by these events (red); and the records of landslide disaster events (dark blue) and the number of fatal victims (orange) in the timespan covered by this literature review (2001–2020)

Keywords and research areas

The most common term in the abstracts of all 536 articles was “map” (Fig.  7 ), which appeared more than 1700 times, describing the characteristic research product of landslide susceptibility studies. Other common words were “factor”, “slope”, and the stem form of distance (“distanc”). About two dozen terms were related to landslide conditioning factors and eight to LULC. Among the 15 most used LULC keywords, “road” was the top ranking, followed by “soil” and “forest” (Fig.  8 ). References to the proximity of roads appeared mainly as a landslide conditioning factor, while only two abstracts informed about the presence of roads in unstable areas. Rawat and Joshi ( 2012 ) and Sujatha et al. ( 2012 ), for example, highlighted, respectively, that up to 9% of the roads in the Igo river basin, and intense anthropogenic activities, such as busy roads, in Tevankarai Ar sub-watershed, both in India, were located in high landslide susceptible areas. Other common, though similarly generic, LULC terms such as “settlement” or “agriculture” remain comparably scarce in the publication record.

Comparison of most studied countries and national research output on landslide susceptibility and LULC: (i) the study area (green dots) shows the percentage of articles that studied a given country; (ii) the publications (gradient colour) are the number of articles published by each country, according to the authors’ affiliation

Word cloud of the most frequent terms in the 536 abstracts of studies on landslide susceptibility and LULC

Regarding keywords provided by the authors, a total of 542 different entries were identified and corrected for minor inconsistencies. We focused on those 50 keywords that appeared at least five times: “landslide susceptibility” occurred in 45% of all articles, followed by “GIS” (Geographic Information System) (43%), “landslide” (35%), and “frequency ratio” (15%). The keywords “land use” and “land cover” appeared only in seven and five articles, respectively. The authors’ keywords were analysed with a co-occurrence network map, considering seven words as the minimum cluster size (Fig.  9 ). All four clusters have keywords related mainly to algorithms used for landslide susceptibility modelling, underscoring the strong methodological focus in this field, and many less frequently mentioned keywords refer to specific statistical methods. The central core of the network has three nodes (relevant topics) with similar importance and many connections among the clusters.

The 15 most frequently mentioned LULC keywords in the abstracts. The percentage refers to the number of abstracts in which each keyword was mentioned

The research covered 27 subject areas, reflecting the interdisciplinary nature of landslide and LULC studies. The most frequent research areas (Fig.  10 ) were Geology (30%), followed by Water Resources (15%), and Environmental Sciences and Ecology (14%). Despite the predominance of areas focused on science, technology, engineering, and mathematics (STEM), few of them are also related to social analysis and public management, i.e., fields closely studying LULC from various perspectives. Furthermore, the analysed articles were published in 128 different journals, of which the top 15 most frequent journals covered 57% of all publications and 74% of all citations. The greatest number of articles was published in the “Environmental Earth Sciences” journal. On the other hand, considering the citations, the highlighted journal was “Environmental Geology”, which presented the highest average citation by article. According to the database, the journal that showed the longest activity was “Natural Hazards”, with publications between 2004 and 2020, followed by “Landslides” and the “International Journal of Remote Sensing”, both since 2005. Both research areas and journals may confirm the pattern of interdisciplinarity shown by the keyword analysis.

Co-occurrence network map of keywords provided by authors in publications on landslide susceptibility and LULC. The circles are the keywords, while lines connect the words supplied together. Circle sizes are scaled to word frequencies; the thicker the connecting lines, the more related the terms

The ten most-frequent WoS research areas

Landslide susceptibility focused on LULC

Most of the studied papers sought better models for landslide susceptibility analysis. Some identified LULC as one of the most influential landslide conditioning factors for susceptibility modelling. For example, Arabameri and Rezaei ( 2019 ) found that LULC, NDVI, and distance to road were the most important landslide conditioning factors in the Sangtarashan watershed, Iran. All these factors are related to LULC. Similarly, LULC was the most important factor in the susceptibility modelling in the Sarkhoun catchment, Iran (Shirani et al. 2018 ), and in Mezam Division, Cameroon (Afungang et al. 2017 ). The former study highlighted that poor rangeland, agriculture, and rock outcrops were the most related to landslide susceptibility; the latter pointed out that predicted landslides are concentrated in a hilly area with an expanding urban population. Austin et al. ( 2013 ) analysed the effects of the Three Gorges Dam on local urban areas and landslide susceptibility in Yichang City, China. With the flooding of the reservoir area, numerous settlements were moved to higher-elevation areas. In this sense, the authors assessed the landslide occurrence likelihood in these new urban areas, concluding that the new constructions are below steep slopes, which could lead to high susceptibility. The influence of LULC on landslide susceptibility was also explored by Galve et al. ( 2015 ) in Cinque Terre, Italy. The authors generated scenarios by changing LULC classes, i.e., they used maps with abandoned lands and simulated scenarios in which vineyards, forests, or structural measurements replaced the abandonment. They found that vineyards may slightly reduce landslide susceptibility; on the other hand, forests might be more effective in reducing susceptibility. With a focus on vegetation class, Miller ( 2013 ) proposed to use the Surface Cover Index in landslide susceptibility analysis in Dominical, Costa Rica. This index represents vegetation vigour and degradation and improved the model performance when incorporated into the landslide susceptibility analysis. While these studies focused on model performance improvement or urban land cover changes and landslide susceptibility, they hardly explored why LULC was the most important factor for a given study area or how it could alter susceptibility.

Indeed, few publications explored the influences of LULC in detail. Yesilnacar and Süzen ( 2006 ) pioneered LULC mapping, considering multispectral satellite images, vegetation indices, topographic indices, and transformation components (principal component analysis). In studying the Asarsuyu basin, Turkey, the authors showed that including multiple indices and principal component analysis improved the overall classification accuracy while estimating the influence of LULC classes using the logistic regression algorithm. Although the logistic regression overall accuracy rose by only 2%, the landslide locations estimation improved by up to 20%. Moreover, young forests (forests removed by fires or deforestation and regrown after the 1980s) and a moist mixed group (rocks, grassland, and agriculture) were the most frequent LULC classes in landslide bodies and high susceptibility areas. Meneses et al. ( 2019 ) evaluated the influence of different LULC datasets on landslide susceptibility in the Zêzere watershed, Portugal, to identify road networks that could be more predisposed to future blockages caused by landslides. For that, the authors included the same predisposing factors in all models; the only change in modelling processes corresponded to variation in LULC spatial resolution. The study results demonstrated that more detailed LULC data improved the landslide susceptibility mapping, though not necessarily their transferability to similar catchments elsewhere. The authors emphasised the lack of studies that compare LULC maps with different properties (scale and spatial resolution) since LULC is usually taken from pre-existing cadastres or mapped from satellite imagery. Both studies exemplify that including LULC data in landslide susceptibility analysis may provide more insights about LULC and landslides; however, the approaches remained static since they considered a single slice.

Given that human activities can modify vast areas quickly (Glade 2003 ), LULC may need more dynamic scenarios. For example, Chen et al. ( 2019 ) tracked LULC over 21 years (1992, 2002, and 2013) to quantify their relevance for landslides in Xuan’en County, China, an area marked by a substantial increase in anthropic activities such as clearing forests for grass and arable lands. The authors reported that these conversions compromised slope stability, though less so in recent years, commensurate with lower deforestation rates. The study suggested that including LUCC in landslide assessment and proper land use planning in urbanisation may decrease landslide susceptibility. A similar temporal analysis was also done by Persichillo et al. ( 2017 ), who studied landslide-prone terrain in rural areas, mainly agriculture fields and vineyards, in three different catchments in Altrepò Pavese, Italy. The study built on 58 years of data with five pre-existing LULC maps and found that LULC was one of the most important conditioning factors on slope stability, especially in abandoned lands. Maintaining cultivated areas seemed crucial to support land conservation and reduce shallow landslide activity. Similarly, Reichenbach et al. ( 2014 ) classified the LULC for 2 years (1954 and 2009) before and after reported landslides in the Briga catchment, Italy. The study contemplated various scenarios to explore the relationship between forest and landslide susceptibility and pointed to more stable slopes in 1954, likely because of the increase in forested areas. Comparing 1954 and 2009 through different land use scenarios, the authors demonstrated an increase in landslide susceptibility with decreasing forest areas and expanding patches of bare soil. The scenarios of reforestation resulted in more stable slopes in the model. This study used only DEM-derived variables and LULC classification, offering high reproducibility.

The relationship between forest and landslide susceptibility was also explored by Malek et al. ( 2015 ) for Buzau County, Romania. The authors studied past (1989, 2000, and 2010) and future scenarios (2040) for three LUCC classes: persistent forest, forest expansion, and deforestation. The study points out forest cover changes in different landslide susceptibility classes and how these modifications can be considered for risk management. For example, areas with higher susceptibility had more non-forest but were also more likely to expand forests in the future; hence, landslide susceptibility is prone to change accordingly. Shu et al. ( 2019 ) explored how LULC changed along with landslide susceptibility over 150 years (1946 – 2097) in the Val d’Aran region, Spain. Again, outcomes showed an increase in areas with low susceptibility and a decrease in high susceptibility zones, possibly related to the 163% increase in forest cover areas from 1946 to 2097 in one of the scenarios. The authors excluded other future impacts, such as climate changes or rainfall regimes. Finally, Pisano et al. ( 2017 ) considered past land cover (1954, 1981, and 2007) trends for simulating LULC scenarios for the Rivo basin, Italy, for 2030 and 2050. Then, more cultivated areas decreased landslide susceptibility, likely because cultivation replaced areas without prior maintenance, resulting in better land and water management practices. The authors argued that good management practices would lower landslide susceptibility in the future. Hence, simulating different LULC scenarios allows to identify how LUCC may alter landslide susceptibility, thus aiding decision makers in territorial planning for disaster risk reduction.

Landslides are a widespread phenomenon that causes disasters around the world. In this sense, many researchers have been seeking the most influential landslide conditioning factors to improve landslide susceptibility mapping. Some of these factors are physical, such as geology and geomorphometry; others are related to human activity (Skilodimou et al. 2018 ) that rapidly transforms the landscape (Guzzetti et al. 2005 ). In this sense, considering the impact of anthropic activities on slopes, researchers have increasingly been trying to relate LULC changes and slope instability (Karsli et al. 2009 ; Mugagga et al. 2012 ; Chen and Huang 2013 ; Austin et al. 2013 ). In the following, we discuss how the influence of LULC on landslide susceptibility modelling has been assessed.

Landslides and LULC studies

Landslides have been studied for a long time (Radbruch-Hall and Varnes 1976 ) due to their impacts on society (Haque et al. 2019 ). However, much landslide research became more structured as an independent discipline during and following the International Decade for Risk Reduction of the United Nations in 2000. In this process, the core study was defined, aiming to standardise and review terminologies (Sassa 2007 ) and further quantitative susceptibility assessment studies (Cruden 1997 ). This mission may partly explain the focus on model performance that most reviewed articles focused on. We recognise a trend from landslide susceptibility studies to a stronger focus on statistical (Dai and Lee 2001 ), deep learning (Pradhan and Lee 2010 ), machine learning (Tien Bui et al. 2012 ), and hybrid models (Shirzadi et al. 2017 ; Roy et al. 2019 ) in the past two decades.

Furthermore, LULC classifications have been done since the 1970s (Phiri and Morgenroth 2017 ) but have rapidly improved in coverage and resolution thanks to the increase in available satellite imagery (Wulder and Coops 2014 ; Gómez et al. 2016 ). He et al. ( 2022 ) highlighted that the LUCC research focused on modelling until 2004; between 2005 and 2013, eco-environmental impacts were emphasised, while the current phase focused on improving global sustainability. These different focuses on LULC and LUCC research are reflected broadly in our literature database. For example, there was an increasing trend in the number of publications on landslide susceptibility that used LULC data as a conditioning factor since about 2004, and more studies that explored LULC and LUCC impacts on hillslopes to assess landslide susceptibility (Persichillo et al. 2017 ).

The importance of considering LULC or LUCC on landslide assessment relies on the impacts of human activities on slopes, mainly agricultural and forestry activities, which are also affected by global warming and call for efficient management strategies to reduce landslide susceptibility (Gariano and Guzzetti 2016 ). However, considering that landslides will occur under the same conditions as past landslides might represent a limited vision, as hillslope conditions change drastically in response to human activities (Guzzetti et al. 2005 ). Hence, considering LULC’s future scenarios in landslide susceptibility analysis would provide more practical results to aid public administrators in long-term land use management and landslide disaster risk reduction.

LULC as a landslide conditioning factor

One confounding issue is that LULC encompasses various impacts on soil structure according to each cover class (Chen et al. 2019 ; Löbmann et al. 2020 ; Masi et al. 2021 ). For example, a developed forest presents more significant root reinforcement than undergrowth or cultivation areas (Lehmann et al. 2019 ). Also, changes in LULC likely modify soil shear strength, causing slope instability, and in some cases, rapid changes can become a landslide trigger (Davies 2015 ).

Our analysis found that the most common LULC classes were road, soil, and forest (Fig. 8 ), most likely because the relevant data are easy to obtain most objectively. Generally, roads enter susceptibility models regarding the distance from mapped landslides (Yan et al. 2019 ). Road construction and maintenance directly or indirectly impact the slope through slope cuts or changes in surface water runoff (Vuillez et al. 2018 ). Other road impacts include the construction of paths for forestry logging (Jaafari et al. 2015 ), which generally follow different regulations than those for official roads. “Soil” is also frequently reduced to patches of bare land or poorly vegetated ground instead of distinct soil types. Many studies reported that deforestation or logging exposes soil to erosion processes and slope instability (Reichenbach et al. 2014 ; Cohen and Schwarz 2017 ; Persichillo et al. 2017 ). Again, forests are commonly associated with an increase in slope stability (Cohen and Schwarz 2017 ). While many studies concur that deforestation raises landslide susceptibility (Dai et al. 2002 ), few mention the roles of forest type, structure, health, or natural disturbances (Parra et al. 2021 ).

Hence, while the different classes of LULC can be analysed separately, they are also often interconnected, which may compromise some statistical models in terms of collinearity. Furthermore, when analysing LUCC, not only will the change be a determining factor of landslide occurrences, but also the relative gains and losses for a given LULC class (Liu et al. 2021 ). For example, replacing forests for agriculture may promote more slope instability (Lehmann et al. 2019 ), whereas cultivating previously abandoned areas may decrease landslide susceptibility (Pisano et al. 2017 ). Scenario-based approaches to LUCC (Promper et al. 2014 ; Malek et al. 2015 ; Pisano et al. 2017 ; Shu et al. 2019 ) can guide future actions for reducing landslide disaster risk.

Countries’ contribution and landslide disasters

The pattern of countries with more publications in landslide research differs from other reviews (Carrión-Mero et al. 2021 ; Huang et al. 2022 ) because only articles that included LULC data in landslide susceptibility analysis were considered. Nonetheless, many studies agree that the countries with the most publications about landslides tend to be from Asia (Pourghasemi et al. 2018 ; Reichenbach et al. 2018 ). For example, South Korea has the highest number of researchers per million inhabitants (R/MI), whereas China and the USA have the highest total number of researchers (TR) (Table 1 ). On the other hand, Greece, Norway, and Austria had the lowest TR, probably because of their small populations.

While the susceptibility to landslides may be a strong incentive for landslide research, it is also the government investment in research and science (Habib et al. 2019 ). For example, India has the second-highest population in the world and presents low TR, possibly related to the low investment in research and development (R&D) (UNESCO Institute for Statistics 2021 ). Furthermore, some countries that suffered disasters, such as Afghanistan, Colombia, and Ecuador, hardly published, at least internationally (Fig.  6 ). For example, Colombia and Ecuador presented low investments in R&D (UNESCO Institute for Statistics 2021 ) and few researchers (there is no available data for Afghanistan). On the other hand, the authors’ affiliations that studied the aforementioned countries are related to China, the Czech Republic, Germany, and Canada, which corroborate the importance of investment in R&D to encourage more studies on the landslide topic.

Some of the most productive countries, in terms of publication numbers (Table 1 ), suffered from recent landslide disasters (Fig.  5 ). The landslide disaster occurrence may be related to the difficulty of establishing official regulations and strategies for landslide risk reduction due to budgetary constraints or cultural factors, among other causes (Winter and Bromhead 2012 ). For example, Mateos et al. ( 2020 ) indicated that even in Europe, there are no general regulations for landslide risk reduction, and not all European countries have official landslide guidelines for territorial planning or methodological guides.

Limitations and future trends

The selection of the scholarly database and the search keywords might add some limitations to this study. The focus on WoS may render a one-sided perspective. For example, Valdés Carrera et al. ( 2021 ) used multiple database platforms (WoS, Scopus, SciELO, REDIB, and Redalyc) of Latindex to analyse landslide studies in Latin America. Hence, the low number of studies from South America, Central America, and the Caribbean might be an artefact of the database choice.

Moreover, our analysis focused on articles that considered LULC as a conditioning factor in landslide susceptibility assessment; yet, LULC classes are diverse and multiple and sometimes non-exclusive. Then, limitations on the keywords used in the first search criteria might result in a limited articles database. As we considered only words directly related to LULC (land cover, land use, land use cover change, LULC, and LUCC) on the first search, it may result in not including relevant articles in our database (Guns and Vanacker 2013 ). For example, the road was not taken into account as a keyword in the first search, which generally appears as a specific conditioning factor (Jaafari et al. 2015 ), such as distance or proximity to roads (Brenning et al. 2015 ).

Some publications were excluded from the database because of keyword choices (e.g., landslide susceptibility vs. mass movement susceptibility). Preliminary searches did not include studies with terms such as “landslide occurrence” (Van Beek and Van Asch 2004 ; Wasowski et al. 2010 ; Promper et al. 2014 ; Cohen and Schwarz 2017 ; Gariano et al. 2018 ; Vuillez et al. 2018 ; Knevels et al. 2021 ), “debris flow” (Rogelis and Werner 2014 ; He et al. 2018 ), or “rockfall” (Lopez-Saez et al. 2016 ; Farvacque et al. 2019 ) or articles published after 2020 (Knevels et al. 2021 ; Rabby et al. 2022 ).

Furthermore, it is worth mentioning that the analysis of the influence of LULC on landslide susceptibility is a theme of expanding interest and interdisciplinary relevance and is still in need of studies. We identify several challenges for the future:

Bibliometric studies: executing a comprehensive search, including specific LULC classes and landslide occurrence, may provide a complete database. For example, including the LULC keywords “roads” and “forest” may provide specific articles which analyse the relationship between landslides and these land cover classes;

Review articles: since reviews tend to be applied to a small number of papers, the limitation of the preliminary search to the article title may generate a more restricted database focused on LULC in landslide analysis;

Research articles—modelling performance: there is an increasing interest in studies that apply hybrid models in landslide susceptibility analysis, which may focus more on LULC effects on landslide occurrence;

Research articles—LULC influence: simulating LULC future scenarios and evaluating how it modifies the landslide susceptibility seems to be a potential hotspot in landslide topics. This mapping is interesting to show for public managers which changes in LULC increase and which of them decrease the landslide susceptibility;

Research articles—other topics: the inclusion of possible climate change impacts; the consideration of variations in rainfall conditions; the root reinforcement according to different plant species and LULC classes.

Conclusions

This study explored the use of LULC data on landslide susceptibility assessment through bibliometric and review approaches. The literature database was composed of 536 articles, which revealed that most publications focused on landslide susceptibility modelling using LULC data as a conditioning factor. The lion’s share of scientific research was on model performance, varying from statistical and index-based models to deep learning and machine learning algorithms. In addition, we found that countries most affected by landslides were not necessarily the most productive in terms of international authorship quantities, likely reflecting national imparity in investment in research and development. Notably, South American and African nations seem strikingly absent from the international community of authors. Our analysis emphasises how LULC and LUCC influence on landslide susceptibility has become more common only gradually since 2016 onwards. The first approach focused on how LULC mapping influences landslide susceptibility, especially classification method, scale, and spatial resolution. More recent works consider the simulation of future LULC scenarios and how this changed landslide susceptibility. The future landslide susceptibility scenarios may provide helpful information for landslide risk management and land use planning. In conclusion, the analysis of LULC influence on landslide susceptibility is an expanding theme, which has the potential to be explored for future scenario analysis and to close gaps in study areas.

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Acknowledgements

The authors are thankful to Dr. Evlyn Marcia Leão de Moraes Novo for the insights and motivation to carry out a review study.

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brazil (CAPES)-Finance Code 001. The APC was funded by ESPOL Polytechnic University.

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Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José Dos Campos 12227010, São Paulo, Brazil

Renata Pacheco Quevedo & Camilo Daleles Rennó

Centro de Investigación y Proyectos Aplicados a las Ciencias de la Tierra (CIPAT), ESPOL Polytechnic University, P.O. Box 09-01-5863, Guayaquil, Ecuador

Andrés Velastegui-Montoya, Néstor Montalván-Burbano & Fernando Morante-Carballo

Facultad de Ingeniería en Ciencias de la Tierra, ESPOL Polytechnic University, P.O. Box 09-01-5863, Guayaquil, Ecuador

Andrés Velastegui-Montoya

Geoscience Institute, Federal University of Pará, 66075-110, Belém, Brazil

Department of Business and Economics, University of Almería, 04120, Almería, Spain

Néstor Montalván-Burbano

Facultad de Ciencias Naturales y Matemáticas (FCNM), ESPOL Polytechnic University, P.O. Box 09-01-5863, Guayaquil, Ecuador

Fernando Morante-Carballo

Geo-Recursos y Aplicaciones GIGA, ESPOL Polytechnic University, P.O. Box 09-01-5863, Guayaquil, Ecuador

Institute of Environmental Science and Geography, University of Potsdam, 14476, Potsdam, Germany

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Conceptualisation, R.P.Q., A.V.M., and N.M.B.; methodology, N.M.B., F.M.C.; software, N.M.B., O.K.; validation, R.P.Q., A.V.M., N.M.B., and F.M.C.; formal analysis, A.V.M., N.M.B., and F.M.C.; investigation, R.P.Q.; resources, R.P.Q., A.V.M., N.M.B., and F.M.C.; data curation, R.P.Q., A.V.M., and N.M.B.; writing—original draft preparation, R.P.Q.; writing—review and editing, A.V.M., N.M.B., F.M.C., O.K., and C.D.R.; visualisation, R.P.Q. and A.V.M.; supervision, C.D.R.; project administration, R.P.Q.; funding acquisition, A.V.M. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Andrés Velastegui-Montoya .

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Pacheco Quevedo, R., Velastegui-Montoya, A., Montalván-Burbano, N. et al. Land use and land cover as a conditioning factor in landslide susceptibility: a literature review. Landslides 20 , 967–982 (2023). https://doi.org/10.1007/s10346-022-02020-4

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Received : 26 July 2022

Accepted : 30 December 2022

Published : 13 February 2023

Issue Date : May 2023

DOI : https://doi.org/10.1007/s10346-022-02020-4

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