Land Change Dynamics Research Articles

The interplay of future solar energy, land cover change, and their projected impacts on natural lands and croplands in the US

Projections for deep decarbonization require large amounts of solar energy, which may compete with other land uses such as agriculture, urbanization, and conservation of natural lands. Existing capacity expansion models do not integrate land use land cover change (LULC) dynamics into projections. We explored the interaction between projected LULC, solar photovoltaic (PV) deployment, and solar impacts on natural lands and croplands by integrating projections of LULC with a model that can project future deployment of solar PV with high spatial resolution for the conterminous United States. We used scenarios of LULC projections from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios from 2010 to 2050 and two electricity grid scenarios to model future PV deployment and compared those results against a baseline that held 2010 land cover constant through 2050. Though solar PV's overall technical potential was minimally impacted by LULC scenarios, deployed PV varied by −16.5 to 11.6 % in 2050 from the baseline scenario. Total land requirements for projected PV were similar to other studies, but measures of PV impacts on natural systems depended on the underlying land change dynamics occurring in a scenario. The solar PV deployed through 2050 resulted in 1.1 %–2.4 % of croplands and 0.3 %–0.7 % of natural lands being converted to PV. However, the deepest understanding of PV impacts and interactions with land cover emerged when the complete net gains and losses from all land cover change dynamics, including PV, were integrated. For example, one of the four LULC projections allows for high solar development and a net gain in natural lands, even though PV drives a larger percentage of natural land conversion. This paper shows that integrating land cover change dynamics with energy expansion models generates new insights into trade offs between decarbonization, impacts of renewables, and ongoing land cover change.

Quantifying land change dynamics, resilience and feedback: A comparative analysis of the lake Chad basin in Africa and Aral Sea basin in Central Asia

The intricate interaction of natural and anthropogenic factors drives changes in land and water in response to societal demands and climate change. However, there has been insufficient information on the feedback effects in dryland hotspots altered by land change dynamics. This research compared two transboundary inland lakes, the Lake Chad basin (LCB) in Africa and the Aral Sea basin (ASB) in Central Asia, using remote sensing and geographic information system techniques to analyze and quantify present and future land cover dynamics, resilience, and their feedback effects. The study integrated Cellular Automata, Markov Chain, and Multilayer Perceptron models to predict LULC changes up to 2030. Descriptive statistics, ordinary least squares regression, hotspot Gi-Bin, trend analysis, and advanced geostatistical methods were utilized to identify relationships, patterns, magnitudes, and directions of observed changes in the feedback effects. From 2000 to 2030, the analysis unveils intriguing trends, including an increase in cropland from 48% to 51% and a decrease in shrubland from 18% to 15% in the LCB. The grassland increased from 21% to 22%, and the settlement expanded from 0.10 to 0.60% in the ASB. Water bodies remained stable at 1.60 % in LCB, while in ASB, it declined from 3% to 2%. These changes were significantly influenced by population, elevation, and temperature in both basins, with irrigation also playing a significant role in the ASB and slope in LCB. The study further revealed discernible shifts in normalized difference vegetation index, temperature, and precipitation linked to specific land cover conversions, suggesting alterations in surface properties and vegetation health. This study underscores the complex interplay between land cover dynamics, resilience, climate variability, and feedback mechanisms in LCB and ASB.

Commercial land use change and growth processes – An assessment of retail location in Lisbon, Portugal, 1995–2020

Land use modelling is essential to understanding future land use and change dynamics. Analyses of land use and change using Cellular Automata (CA) have been frequent in the literature since the latter were introduced to geography in the 1970s. In this paper, the statistical subsections – city blocks – of a previous analysis of Lisbon's commercial structure are used as the cells of a CA. A multi-layer perceptron (MLP) models the transition potential of those cells between 1995 and 2020, considering the impact of street centrality, subway development and other proxies of urban growth. The results confirm that commercial growth and change can be simulated as a function of street centrality and that using Local integration and Choice as drivers of a CA may help improve predictions of commercial growth and change. Moreover, the CA results reinforce that street centrality positively affects street commerce, especially when combined with access to transit and amenities. While these results can already inform planning practice and policymaking, the framework presented in this paper may be replicated to simulate ​future commercial land use, thus helping planners and policymakers prepare for change.

The kinetic Ising model encapsulates essential dynamics of land pattern change.

A land pattern change represents a globally significant trend with implications for the environment, climate and societal well-being. While various methods have been developed to predict land change, our understanding of the underlying change processes remains inadequate. To address this issue, we investigate the suitability of the two-dimensional kinetic Ising model (IM), an idealized model from statistical mechanics, for simulating land change dynamics. We test the IM on a variety of patterns, each with different focus land type. Specifically, we investigate four sites characterized by distinct patterns, presumably driven by different physical processes. Each site is observed on eight occasions between 2001 and 2019. Given the observed pattern at the time ti we find two parameters of the IM such that the model-evolved land pattern at ti+1 resembles the observed land pattern at that time. The data support simulating seven such transitions per site. Our findings indicate that the IM produces approximate matches to the observed patterns in terms of layout, composition, texture and patch size distributions. Notably, the IM simulations even achieve a high degree of cell-scale pattern accuracy in two of the sites. Nevertheless, the IM has certain limitations, including its inability to model linear features, account for the formation of new large patches and handle pattern shifts.

Assessment of public and private land cover change in the United States from 1985–2018

An assessment of annual land cover on publicly and privately managed lands across the conterminous United States (CONUS) from 1985–2018 was performed, including land cover conversions within their management category, to inform future policy and land-use decision-making in natural resource management. Synthesizing land cover data with land management delineations aids our ability to address effects of land management decisions by public or private entities. The U.S. Geological Survey (USGS) Protected Areas Database of the United States (PAD-US) version 2.1 data delineate land management categories and enable examination of land cover composition and change using the USGS Land Change Monitoring, Assessment, and Projection (LCMAP) reference data. Average composition of our delineated CONUS results using LCMAP land cover classes is 40% Grass/Shrub (GS), 29% Tree Cover (TC), 18% Cropland (CP), 5% Developed (DV), 5% Wetland (WL), 1.8% Water (WR), and 0.9% Barren (BN). Private (public) land is composed of 35% (52%) GS, 27% (36%) TC, 25% (1%) CP, 7% (1%) DV, 5% (5%) WL, 2% (2%) WR, and less than 1% (3%) BN. Land cover change averaged less than 1% per year. The largest net percentage gains across CONUS were in DV land and GS, and the greatest net losses were in CP and TC. Approximately 73% of CONUS is private land and, thus, land cover change across CONUS is largely a reflection of private land change dynamics. Private compositional changes show net gains from 1985–2018 in DV (2.3%), WR (0.2%), and GS (0.1%) classes, while net losses occurred in CP (−1.9%), TC (−0.6%), WL (−0.1%), and BN (−0.01%). Public land cover changes show net gains in GS (1%), DV (0.2%), WR (0.01%), WL (0.05%), and BN (0.1%) classes, and net losses in CP (−0.3%) and TC (−1%). Our study reveals connections between land cover conversion and various policy and socioeconomic decisions through time.

A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models

Abstract. The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992–2020) of annual land cover maps at 300 m resolution, allowing for a detailed study of land change dynamics over the contemporary era. Because models need two-dimensional parameters rather than two-dimensional land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first-generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but that lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300 m resolution for 1992–2020, created by fusing the 300 m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method. In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300 m resolution indicate the percentage cover (0 %–100 %) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e.g. re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models – Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and the Joint UK Land Environment Simulator (JULES) – to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare-soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks. The dataset is accessible at https://doi.org/10.5285/26a0f46c95ee4c29b5c650b129aab788 (Harper et al., 2023).

Comparison of a Sentinel-2 land cover map obtained through multi-temporal analysis with the official forest cartography. the case of Galicia (Spain)

In numerous countries, official forest cartography is obtained through photointerpretation of aerial images which hinders having up-to-date information. This study explores the usefulness of a land cover map produced automatically using Sentinel-2 images as a complement of the official Spanish forest map in Galicia. It was obtained a map with an Overall Accuracy of 86%. Both maps were compared. Net area covered by each forest class differed among maps, the differences were higher in areas managed with shorter rotation cycles. Main differences were due to the capabilities of Sentinel-2 to identify harvestings or disturbances and to the minimum mapping units of each map. The Sentinel-2 map had a higher ability to map trees outside the forest, and the official cartography hides small parcels and incipient land change dynamics. Sentinel-2 based maps could be a powerful tool to reduce the information gap considering the official cartography updating frames.

An ensemble method for monitoring land cover changes in urban areas using dense Landsat time series data

This study proposes a new method for monitoring land cover change in urban areas using all available Landsat time series data, named the Ensemble of Bidirectional Time Series Analysis (EBTSA). In this method, the bidirectional Continuous Change Detection and Classification (CCDC) and the Chow Test are combined to improve the robustness against data scarcity in earlier times and reduce break detection errors and refine classification results. There are three key stages in this method: break detection using bidirectional CCDs, break refinement using the Chow Test, and bidirectional model integration and classification. The EBTSA method was evaluated over the Tianjin metropolitan area in China using Landsat data from 1989 to 2018. Results show that the proposed method improved spatial and temporal accuracies of both land cover classification and change detection, by reducing the influence of sparser Landsat data in the earlier years and the break detection errors. Using the land cover change results in the Tianjin area obtained using the EBTSA method, we analyzed the spatio-temporal distribution of land cover classification and change detection. It is found from the results that the Tianjin area experienced dramatic urban land changes, characterized by rapid urban expansion and noticeable transition from vegetation to urban land, with diverse changes from urban land to various nonurban land cover types. Results of this study showcase the effectiveness of the proposed method in land cover change monitoring in urban areas, which facilitates a more comprehensive understanding of urban land change dynamics and diversity.

Study on forest land change dynamics based on CA-Markov and FORECAST models

In order to study the dynamics of forest land change in Sichuan province, this paper developed a CA-Markov model to estimate the total amount of CO2 stored in the region in the future 100 years. Then, the visualization results of forest land change are combined with forest comprehensive value to provide suggestions for forest managers in the region. Finally, this prediction model was used to study the impact of different rotation periods on forest carbon sinks of specific tree species in the study area.

Linking Pattern to Process: Intensity Analysis of Land-Change Dynamics in Ghana as Correlated to Past Socioeconomic and Policy Contexts

Spatio-temporal analysis of transitions in land cover is critical to understanding many ecological challenges, especially in environmentally vulnerable regions. For instance, in Sub-Saharan Africa, large-scale cropland expansion is expected due to the increasing demand for fuel, food, and fibre. Clearing land for cropland expansion is a driving factor in the degradation of natural ecosystems. We present a spatio-temporal analysis of land-cover change in Ghana’s Northern, Upper East, and Upper West provinces using Intensity Analysis on the periods from 1992 to 2003 and 2003 to 2015. The objectives of this study were to determine whether the intensity of land-use and land-cover (LULC) change is consistent between the two periods and to investigate the direction and extent of change for different LULC categories in northern Ghana. The methodology measures land-cover changes at the interval, category, and transition levels. The results suggest that the annual rate of land change was higher between 1992 and 2003 compared to that between 2003 and 2015. Furthermore, the category-level analysis reveals that the gains in the arable land and tree/forest-cover classes during both time intervals were higher than the uniform intensity. The transition-level analysis results indicate that most of the gains in arable land and tree/forest-cover came at the cost of semi-arid shrublands during both periods. There is also evidence of local increases in forest-cover, likely linked to afforestation policies established by the Ghanian government; however, overall, there has been a loss of natural habitat. The study provides data to improve our understanding of the magnitude and direction of land-cover change, essential for the development of policies designed to mitigate the impact of land-cover change on the livelihoods of local people and the environment at the national and sub-national levels.

Spatiotemporal Changes in Land Use and Ecosystem Service Values Under the Influence of Glacier Retreat in a High-Andean Environment

Glaciers supply multiple ecosystem services that are threatened by climate change. The retreat and disappearance of tropical glaciers is an important dynamic that affects ecosystems and local communities. The knowledge of the impacts of this land-change dynamics on the supply of ecosystem services is lacking. In that sense, the assessment developed can provide evidence about the costs and benefits of promoting conservation and human well-being at the same time. Then, the main objective of this research is to determine the spatial–temporal changes and their effects on the economic value of ecosystem services in a glacial retreat environment. We selected the Marangani district as a study area. It comprises the La Raya Mountain range in the Andes. The assessments were carried out across two scales of observation: the municipality and the watershed level. Here, we process spectral information from Landsat Sensor using the Random Forest algorithm in the Google Earth Engine platform to classify 10 biomes. It was carried out over more than 30 years (from 1986 to 2019). After that, ecosystem services provided by the biomes were valued using the transfer method. This research shows that at the municipality level, almost all the LULCs faced variations over time, and the glaciers had the highest change, accumulating a ratio of –85.51%, whereas at the watershed level, a higher tendency of land changes is observed in the areas without glaciers, and those with glacier areas count on permanent larger bofedales. At the municipality level, the economic value of ecosystems shows that bofedales and water surfaces are the LULCs that supply the highest ecosystem services (∼33,000 USD ha−1 yr−1 each). In addition, without the inflation adjustment, the total ESV is on a trajectory of losing ESV (–$9.67 × 106). In the watersheds with glacier retreat, significant quantity of bofedales and natural grasslands controls the fluctuations of ESV. These high-mountain watersheds play an essential role in providing benefits and value to local communities. In general, the municipality level indicates the trajectory of changes in the district, whereas the watershed scale shows the urgency for implementing spatial conservation actions.

Dynamics of Land Use and Land Cover Change in Jammu and Kashmir

Abstract: Agriculture is the main occupation for the people of Jammu & Kashmir. About 65 per cent of the people are directly or indirectly dependent on agriculture and allied activities for their livelihood. Agriculture and its allied activities are the predominant sectors of the economy of Jammu & Kashmir. The cropping land use in the state also underwent drastic changes which led to a decrease in area under food crops and an increase in area under cash crops/plantation agriculture. The study of this shifting land use was necessary for the sustainable agriculture of the state. Knowledge of cropping land use helps in maximization of productivity and conservation of land. The study tried to assess the cropping pattern of land use and land change dynamics, the land use and land cover change among different land-use classes and to study the extent of productive and unproductive land-use patterns in Jammu & Kashmir. The study has revealed a major shift of land from the desirable to undesirable land-use classes. The area under the three major food crops (Paddy, Maize and wheat) grown in the state has decreased at the state level though with regional variations, it has increased in some districts as well. But the area under paddy recorded a decline in all the districts and the area under orchards has registered highly positive growth in all the districts of Jammu & Kashmir. Keywords: Agriculture, Land Use, Land Cover Change, Cropping Pattern, Jammu & Kashmir

Dynamics of Land Use and Land Cover Change in Jammu & Kashmir

Agriculture is the main occupation for the people of Jammu & Kashmir. About 65 per cent of the people are directly or indirectly dependent on agriculture and allied activities for their livelihood. Agriculture and its allied activities are the predominant sectors of the economy of Jammu & Kashmir. The cropping land use in the state also underwent drastic changes which led to a decrease in area under food crops and an increase in area under cash crops/plantation agriculture. The study of this shifting land use was necessary for the sustainable agriculture of the state. Knowledge of cropping land use helps in maximization of productivity and conservation of land. The study tried to assess the cropping pattern of land use and land change dynamics, the land use and land cover change among different land-use classes and to study the extent of productive and unproductive landuse patterns In Jammu & Kashmir. The study has revealed a major shift of land from the desirable to undesirable land-use classes. The area under the three major food crops (Paddy, Maize, and wheat) grown in the state has decreased at the state level though with regional variations, it has increased in some districts as well. But the area under paddy recorded a decline in all the districts and the area under orchards have registered highly positive growth in all the districts of Jammu & Kashmir

Place-Based Analysis of Satellite Time Series Shows Opposing Land Change Patterns in the Copperbelt Region of Zambia

The process of land degradation needs to be understood at various spatial and temporal scales in order to protect ecosystem services and communities directly dependent on it. This is especially true for regions in sub-Saharan Africa, where socio economic and political factors exacerbate ecological degradation. This study identifies spatially explicit land change dynamics in the Copperbelt province of Zambia in a local context using satellite vegetation index time series derived from the MODIS sensor. Three sets of parameters, namely, monthly series, annual peaking magnitude, and annual mean growing season were developed for the period 2000 to 2019. Trend was estimated by applying harmonic regression on monthly series and linear least square regression on annually aggregated series. Estimated spatial trends were further used as a basis to map endemic land change processes. Our observations were as follows: (a) 15% of the study area dominant in the east showed positive trends, (b) 3% of the study area dominant in the west showed negative trends, (c) natural regeneration in mosaic landscapes (post shifting cultivation) and land management in forest reserves were chiefly responsible for positive trends, and (d) degradation over intact miombo woodland and cultivation areas contributed to negative trends. Additionally, lower productivity over areas with semi-permanent agriculture and shift of new encroachment into woodlands from east to west of Copperbelt was observed. Pivot agriculture was not a main driver in land change. Although overall greening trends prevailed across the study site, the risk of intact woodlands being exposed to various disturbances remains high. The outcome of this study can provide insights about natural and assisted landscape restoration specifically addressing the miombo ecoregion.

Reforestation reversals and forest transitions

Forest transitions in Latin America have long been presumed to be characterized by progressively stable, expansive reforestation. Numerous studies note a dynamism surrounding reforestation, however, including associations with forest loss. Such apparent contradictions were recently underscored by descriptions of Latin American ‘reforestation reversals’ – marked declines in forest cover following net reforestation. Reversals arguably challenge classical perspectives on forest transitions while predominating within regional forest-transition epicenters. Potential reversal dynamics, still subject to speculation, bring into focus the shortcoming of recent forest-transition scholarship, particularly its reliance on generic satellite observation and neglect of land-use transitions shaping, and sustaining, reforestation. To catalyze research in this domain, I explore reversals in relation to land-change dynamics commonly linked to reforestation. I further frame reversals as inherent features of emergent tropical forest transitions, advancing a ‘pulsed’ forest-transition model and emphasizing that apparent reversals may not entail waning reforestation trajectories. A greater embrace of systematic, historical case studies parsing reforestation between land use and disuse is key to a fuller understanding of both forest transitions and reversals.

Dam-triggered Land Use Land Cover change detection and comparison (transition matrix method) of Urmodi River Watershed of Maharashtra, India: a Remote Sensing and GIS approach

ABSTRACT The present study evaluates the impact of reservoir on changing the landscape pattern in the Urmodi River watershed of Satara district, Maharashtra, India, by employing Remote Sensing (RS)-based change detection technique. To map the change in surface cover due to the reservoir, we have used Landsat TM and Landsat 8 imagery which represents before-dam and after-dam status of land cover in the year 1996 and 2016. The supervised classification used with the maximum likelihood algorithm was employed to generate the thematic map. Further, to detect the change, SAGA GIS software was used which has an inbuilt tool of the confusion matrix (grid-based) to represent changes in two grids. The tool has significant importance in showing the detection of the transformation of land cover in previous to next condition. The technique is found to be scientific and robust with more precision as compared to the traditional methods. The rate of change in land cover of agriculture land shows −2.50%, barren land 0.31%, settlement 6.64%, and forest 2.20% change per annum. The water body area has a sudden increment in the year 2016 with coverage of 11.86 km2 as compared to a previous area of 0.64 km2 in the year 1996, and is not considered under the rate of change as it was a one-time activity. The study has a potential role in analyzing the land change dynamics.

Using participatory system dynamics modelling to quantify indirect land use changes of biofuel projects

ABSTRACT The use of biomass to produce biofuels can lead to both direct and indirect Land Use Change (LUC). While the causes underlying LUCs are complex their quantification is a scientific challenge that hinders decision-making. Here we demonstrate the application of participatory modelling in combination with System Dynamics techniques to the analysis of the land-change dynamics associated with biofuel supply chains. The ambition is to provide decision-makers with useful and credible knowledge of direct and indirect LUCs. We illustrate the application of the approach by applying it to a real‐world project for the production of advanced biofuels in Sardinia (Italy). The results show that the land use displacements vary in intensity and persistence depending on the crop management regime applied and the future development of the market of sheep cheese. The results were considered credible by actors with direct knowledge of the ‘real’ system and useful by decision makers .

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

Annual land use land cover (LULC) change information at medium spatial resolution (i.e. at 30 m) is required in numerous subjects, such as biophysical modelling, land management and global change studies. Annual LULC information, however, is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities. Here we integrate high temporal resolution and coarse spatial resolution satellite images (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)) with high spatial resolution datasets (China’s Land-Use/cover Datasets (CLUDs) derived from 30-meter Landsat TM/ETM+/OLI) to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015. We also test the performance of a statistical based change detection algorithm (Breaks for Additive Seasonal and Trend), originally designed for tracking forest change, in classifying all-type LULC change. As a result, a nominal 30 m annual land use/land cover datasets (CLUD-A) from 1980 to 2015 was developed for the whole China. The mapping results were assessed with a change sample dataset, a regional annual validation sample set and a three-year China sample set. Of the detected change years, 75.61% matched the exact time of conversion within ±1 year. Annual mapping results provided a detail process of urbanization, deforestation, afforestation, water and cropland dynamics over the past 36 years. The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.

Estimating urban growth in peri-urban areas and its interrelationships with built-up density using earth observation datasets

Understanding the complex nature of urban dynamics, especially in the fast-growing cities of developing countries, has become crucial to the urban planners and researchers. It is also relevant from the viewpoint of smart city projects as the foundation of successful smart city lies in proper planning and urban growth analysis. In this context, the present study attempts to assess the urban expansion and land-change dynamics in and around Delhi. Multi-temporal Landsat data of 1977, 2003 and 2014 were used for analyzing the spatio-temporal pattern of built-up density, urban expansion, spatial change and their interrelationships. The annual urban expansion index of the study area reveals that it was comparatively high in Delhi National Capital Territory (NCT) during 1977–2003, but the expansion was much higher in peri-urban centers in the later period. However, the growth was not happened homogeneously across the peri-urban zones; instead, it occurred around few urban centers of peri-urban area. The annual urban expansion of Delhi NCT (2.52) was significantly less than peri-urban centers like Gurgaon (6.19) in the period, 2003–2014. Since the areas with high built-up density have little or no space for new settlement, the expansion of built-up area took place in the less dense areas. The correlation between urban expansion index and annual rate of change in built-up area shows that there is a good agreement and significant positive relationship (r ≥ 0.62) present between them. A negative correlation (r ≥ 0.92) between built-up density and urban expansion index indicates that areas with high built-up density have less potentiality to expand and vice versa.

Understanding the role of illicit transactions in land-change dynamics

Anthropogenic land use has irrevocably transformed the natural systems on which humankind relies. Advances in remote sensing have led to an improved understanding of where, why and how social and economic processes drive globally important land-use changes, from deforestation to urbanization. The role of illicit activities, however, is often absent in land change analysis. The paucity of data on unrecorded, intentionally hidden transactions makes them difficult to incorporate into spatially specific analyses of land change. We present a conceptual framework of illicit land transactions and a two-pronged approach using remotely sensed data to spatially link illicit activities to land uses. Advances in remote sensing have helped to understand the human drivers of land-use change globally, but have neglected the role of illicit transactions. This Perspective presents a framework to identify illicit land transactions, and an approach to link them to land uses using remotely sensed data.