Land Use Change Model Research Articles

Urbanization Process: A Simulation Method of Urban Expansion Based on RF-SNSCNN-CA Model

One of the focal points in Geographic Information Science (GIS) is to uncover the spatial distribution patterns of geographical phenomena. In response to the insufficient spatial feature learning concerning neighborhoods in traditional machine learning-based Cellular Automata (CA) models for land use change, this study couples the Random Forest (RF) model and the Spatially Non-Stationary Convolutional Neural Network (SNSCNN) model to the CA model. The resulting RF-SNSCNN-CA model considers the issue of spatial non-stationarity by incorporating attention mechanisms. Using observed urban land change data from 2010 to 2017 in the 21 districts of Chongqing’s main city as an example, two sets of experiments comprising eight scenarios were designed to verify the neighborhood effects. The results demonstrate that the proposed RF-SNSCNN-CA model achieves an Overall Accuracy (OA) of 97.82%, Kappa of 0.7683, and Figure of Merit (FoM) of 0.3836. The study reveals the following findings. Firstly, the RF-SNSCNN-CA model integrates the dual advantages of traditional machine learning and deep learning models, in which SNSCNN improves by the combined effect of channel and spatial attention mechanisms improves the learning of neighborhood features; secondly, the machine learning-like urban sprawl CA modeling process, regardless of the approach taken to obtain development suitability, cannot completely replace the learning of the neighborhood part; lastly, the use of traditional neighborhood modeling methods may produce suppression of simulation results and make the model inadequately learn spatial features.

Area-based scenario development in land-use change modeling: A system dynamics-assisted approach for mixed agricultural-residential landscapes

This study aimed to enhance land use and land cover (LULC) change models by addressing their main limitations, which include the lack of accountability and temporal stability of driving forces. Additionally, the study aimed to create area-based scenarios to forecast future LULCs, rather than solely relying on distribution-based scenarios. To accomplish this goal, the study developed a coupled System Dynamics (SD) and Cellular Automata (CA) modeling system to simulate possible LULC changes in the Gavkhooni Basin, central Iran. The study utilized LULC maps from Landsat images in 2001, 2011, and 2021 to analyze spatio-temporal land use changes in the region. Agricultural and residential transition suitability layers were produced using a spatial Multi-Criteria Evaluation procedure and applied to inform the CA model in the proper allocation of LULC changes. Three interconnected water supply, agricultural, and residential area projection subsystems were developed using system dynamics method to determine land requirements for LULC conversions from 2020 to 2041, taking into account factors such as water availability, land suitability, agricultural labor force, and economic development. Ten scenarios were developed based on changes in the key variables affecting the limiting factors, such as climatic conditions and water management policies, to project agricultural and residential areas in the future. The CA's spatial allocation informed by transition suitability layers was found to be satisfactory with a Kappa-location value of 0.85. The subsystems were competent in projecting water supply with Mean Absolute Error (MAE) values of 6.57% and the dynamics of agricultural and residential areas with MAE values of 2.94%, whereas those of the Markovian Chain model were found to be 23.02% and 7.5% for agricultural and residential areas, respectively. The study found that available agricultural areas varied significantly between 86.53 and 1480 sq.km under different climatic conditions, irrigation efficiency, and agricultural water assignment coefficients between 2024 and 2033. Residential area demand was found to be increasing with different rates under the scenarios between 47.40 and 73.01 sq.km. The SD-CA coupled framework presented in this research can be viewed as a decision support system to develop compensatory strategies for better management and planning of agricultural and residential lands.

Current policies are insufficient to protect or restore Brazil’s cost-effective conservation priority zones

In Brazil, conservation priority zones, in spite of their key role in preserving natural vegetation and its environmental resources are frequently located outside the country’s public network of protected areas (PAs). Here we present the first study on land-use impacts inside Brazil’s unprotected (i.e. outside PAs) Cost-Effective conservation priority Zones (CEZs), for the period 2020–2050. CEZs are conservation priority zones that had experienced low levels of human impact in 2020. In this study, we consider various governance scenarios, including different deforestation control and native vegetation restoration policies. To this end, a land-use change model is combined with a downscaling method to generate natural vegetation cover projections at a 0.01 resolution. Results, which include the effects of climate change on the expansion of the Brazilian agriculture, project native vegetation losses (through deforestation) or gains (through restoration) inside unprotected CEZs. If the current pattern of disregard for the environment persists, our results indicate that a large share of the native vegetation inside Brazil’s CEZs is likely to disappear, with negative impacts on biodiversity preservation, green-house gas emissions and ecosystem services in general. Moreover, even if fully implemented and enforced, Brazil’s current Forest Code is insufficient to adequately protect CEZs from anthropization, especially in the Cerrado biome. We expect that this study can help improving the conservation and restoration of CEZs in Brazil.

Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest

We used the Cellular Automata Markov (CA-Markov) integrated technique to study land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab, Pakistan. We plotted the distribution of the LULC throughout the desert terrain for the years 1990, 2006 and 2022. The Random Forest methodology was utilized to classify the data obtained from Landsat 5 (TM), Landsat 7 (ETM+) and Landsat 8 (OLI/TIRS), as well as ancillary data. The LULC maps generated using this method have an overall accuracy of more than 87%. CA-Markov was utilized to forecast changes in land usage in 2022, and changes were projected for 2038 by extending the patterns seen in 2022. A CA-Markov-Chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2038. Analysis of urban sprawl was carried out by using the Random Forest (RF). Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 8.12 to 12.26 km2 and from 18.10 to 28.45 km2 in 2022 and 2038, as inferred from the changes occurred from 1990 to 2022. The LULC projected for 2038 showed that there would be increased urbanization of the terrain, with probable development in the croplands westward and northward, as well as growth in residential centers. The findings can potentially assist management operations geared towards the conservation of wildlife and the eco-system in the region. This study can also be a reference for other studies that try to project changes in arid are as undergoing land-use changes comparable to those in this study.

Modeling of land use change under the recent climate projections of CMIP6: a case study of Indian river basin.

The aim of the study was to investigate the land use change dynamics under CMIP6 projections using Land Change Modeler(LCM). The Global Sensitivity Analysis (GSA) techniques was applied to quantify the sensitivity of single parameter and combinationof parameters. Land use and land cover (LULC) transitions of the baseline period (2006-2016) was assessed with a model performance accuracy of 80%. Receiver operating characteristic (ROC) analysis shows that the model has performed well for all the LULC classes except builtup land. Prediction under the SSP245 scenario depicts that areal extent of agricultural, forest, and snow, and glacier will decrease by the mid-century (2045). However, the grassland and barren land areawill increase from the baseline period. A similar change pattern with a higher magnitude has also been predicted under SSP585 scenario. The CMIP6 forcing index considers socio-economic effects and LCMpredicted an expansion in barren land which may be attributed to changes in cryosphere in the studied area.

Estimating Carbon Stock Change Caused by Multi-Scenario Land-Use Structure in Urban Agglomeration

Land-use changes remain a major source of terrestrial ecosystem carbon stock (TECS) change. However, existing studies estimating TECS change caused by land-use changes, considering the mixed land-use structure and its continuous structural changes in the land-use change model, are still absent. Our study first analyzed land-use changes during the years 2000–2020 in the Changsha-Zhuzhou-Xiangtan (CZX) urban agglomeration, and then simulated the multi-scenario mixed land-use structure pattern in 2030, and finally estimated its effects on TECS change. Results show that: (1) The TECS change in CZX urban agglomeration during the years 2000–2020 are trending downward, decreasing 5.62 Tg. (2) Over the past 20 years, land-use changes in CZX urban agglomeration were dominated by the increase of artificial surfaces, and the decrease of forest, farmland, and grassland, which are major sources of TECS reduction. (3) The total TECS in inertial development, cultivated land protection, and ecological priority scenarios, are 95.82 Tg, 95.97 Tg, and 97.31 Tg, respectively. Compared to TECS in 2020, the decrease is 2.61 Tg, 2.46 Tg, and 1.12 Tg, respectively. While the spatial pattern of losses in TECS is trending toward sustained expansion in the inertial development and cultivated land protection scenario, the ecological priority scenario is more concentrated. The results can provide decision support for regional carbon accounting, for the assessment of terrestrial carbon stock, and for the planning of the “dual-carbon” (refer to peak carbon and carbon neutrality in dioxide emissions) goal.

History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model

Spatio-temporal land-use change (LUC) modeling provides vital information about land development dynamics. However, accounting for such dynamics faces methodological challenges. This research introduces a Dynamic Spatial Panel Data (DSPD) modeling framework for LUC, incorporating spatial and temporal dependencies. A continuous response variable is introduced to take advantage of traditional spatial regression models. The DSPD model is applied to balanced spatial panel data at the block-group level covering Florida between 2010 and 2019 and incorporating both new and previously used proxy variables. The urban growth impacts of site-specific, proximity, neighborhood, socio-economic, and transportation factors are investigated. This study contributes to the literature by providing extensive insights into spatial autocorrelation, spillover, heterogeneity, and temporal lag effects in urban growth. Also, the study reveals the importance of mobility and mortgage financing in land development. The proposed modeling framework achieves high accuracy. The dynamic structure of this model provides an opportunity to predict future urban growth without the need for a land development scenario. Such predictions provide insights about future land development to practitioners and policymakers.

Assessing farmers’ income vulnerability to vanilla and clove export economies in northeastern Madagascar using land-use change modelling

ABSTRACT Using a participatory Bayesian network-based land-use decision model, we simulate future land-use patterns under various scenarios, including changes in vanilla and clove market prices as well as changes in irrigation water availability and potential harvest failures. Findings indicate that specifically the vanilla value chain (compared to the clove value chain) has a major influence on farmers income vulnerability. Abandoning vanilla cultivation may lead to poverty once income from vanilla reaches a certain threshold. By comparing farmer’s income gains from cash crop production with costs for buying rice to cover basic needs, we show that while focusing on cash crop production is more lucrative, it is, however, highly risky with regard to climate change, price volatility and possible crop thefts. Such lock-in-effects in cash crops, like the vanilla ones in northeastern Madagascar, are essential to be considered, when designing policies for a more sustainable development of resource-rich but poverty-prone regions.

Spatio-Temporal Evolution and Prediction of Carbon Storage in Guilin Based on FLUS and InVEST Models

In the context of sustainable development and dual-carbon construction, to quantify the carbon storage and its spatial-temporal distribution characteristics of Guilin City and predict the carbon storage of Guilin City in 2035 under different future scenarios, this study set four future scenarios based on SDGs and the sustainable development plan of Guilin City: natural development, economic priority, ecological priority, and sustainable development. At the same time, FLUS and InVEST models and GeoDa 1.20and ArcGIS software were used to establish a coupling model of land use change and ecosystem carbon storage to simulate and predict the distribution and change of ecosystem carbon storage based on land use change in the future. The results showed that: (1) From 2005 to 2020, forest land was the main type of land use in Guilin, and cropland and impervious continued to expand. In 2035, the forest land under four different future scenarios will be an important transformation type; (2) From 2005 to 2020, the carbon storage in the northwest of Guilin was relatively high, and the carbon loss area was larger than the carbon increase area. The carbon storage in the ecological priority scenario in 2035 is the highest, reaching 874.76 × 106 t. The aboveground carbon storage (ACG) is the main carbon pool in Guilin. Most of the regions with high carbon storage are located in the northwest and northeast of Guilin. No matter what scenario, the carbon storage in the main urban area is maintained at a low level; (3) In 2035, the distribution of carbon storage in Guilin has a strong spatial positive correlation, with more hot spots than cold spots. The high-value areas of carbon storage are concentrated in the northwest and east, whereas the low-value areas are concentrated in the urban area of Guilin.

Land-use change modeling with cellular automata using land natural evolution unit

Cellular automata (CA) is an important model to simulate land-use change, and the cellular unit can influence the organizational structure, scale selection, and evolutionary ability of CA through its size and shape. Therefore, determining a reasonable cellular unit is essential for land-use change modeling using CA models. However, existing methods used to define the cellular unit are inadequate in their description of the characteristics of the natural evolution of land use, leading to a mismatch between the unit used for simulation and the unit of actual land-use change. To address this issue, in this study, we propose a method for determining the cellular unit based on land natural evolutionary unit (LNU). First, the geographic similarity principle was used as the theoretical basis for this new method. Under this similarity basis, a CA modeling method based on coupling the land natural evolution unit with evolution behavior was then designed to improve the natural evolution of land use. Finally, a case study of the Chenggong District of Kunming City, Yunnan Province, China, was conducted to evaluate the effects of using LNU-defined cellular units on simulating land-use change. The experimental results from the case study show that, simulation of land-use changes using the CA model based on the land natural evolution unit (LNU-CA) exhibits better spatial distribution consistency, higher simulation accuracy with the Kappa coefficient increased by 5.11% and the FOM (Figure of Merit) increased by 159.36%, and a lower distortion of simulation results than those of regular grid CA (Grid-CA). These results prove that LNU-CA can simulate land-use change better than traditional Grid-CA, and LNU is a better choice to determine the cellular unit when using CA to simulate land-use change.

A series of spatio-temporal analyses and predicting modeling of land use and land cover changes using an integrated Markov chain and cellular automata models.

For sustainable land cover planning, spatial land cover models are essential. Deforestation, loss of agriculture, and conversion of pasture land to urban and industrial uses are only some of the negative consequences of human kind's insatiable need for more land. Using remote sensing multi-temporal data, spatial criteria, and prediction models can effectively monitor these changes and plan for sustainable land use. This research aims to predict the land use and land cover (LULC) with cellular automata (CA) and Markov chain models. Landsat TM, ETM + , and OLI/TIRS data were used for mapping LULC distributions for the years 1990, 2006, and 2022. A CA-Markov chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2054. Analysis of urban sprawl was carried out by using the support vector machine (SVM). Through the CA-Markov chain analysis, we expect that built-up area will grow from 285.68 km2 (22.59%) to 383.54 km2 (30.34%) in 2022 and 2054, as inferred from the changes that occurred from 1990 to 2022. Therefore, substantial deforestation area reduction will result if existing tendencies in change continue despite sustainable development efforts. The findings of this research can inform land cover management strategies and assist local authorities in preparing for the present and the future. They can balance expanding the city and preserving its natural resources.

Evaluation of Spatial Landscape Changes for the Period from 1998 to 2021 Caused by Extreme Flood Events in the Hornád Basin in Eastern Slovakia

Tracking changes in the structure of landscape dynamics as a result of flood activity is a complex process. This study presents a model for determining changes to landscapes caused by flood events by evaluating a specific territory in Eastern Slovakia, which has been affected by repeated large-scale flood events in the past. The area has not been subject to a comprehensive monitoring of changes in the landscape structure. Based on the observation of several sets of data, a combination of statistical methods and GIS spatial analysis tools (visualizing tools for compare categories, mapping, and modelling techniques, spatial analysis models for land use change and flood modelling) were used to identify changes in the landscape structure in the period from 1998 to 2021. The results point to the significance of the year 2010, with the precipitation totals for this year showing a level significantly higher than the rolling average and confirming the occurrence of an extreme flood event. The dynamics of landscape structure changes were evaluated based on changes in the representation of selected types of land cover classes. The results of a spatial evaluation of the Corine Land Cover demonstrate that the most-significant area changes were recorded in 2012 in the pasture class, with a decrease of 31% or approximately 96.5 ha. The identified difference in the frequency of representation of individual values of the normalized differential vegetation index confirms the loss of landscape diversity and the emergence of a more homogeneous type of landscape. An assessment of the state of pastures in the study area shows that this class has completely disappeared from the site near the watercourse.

The meso scale as a frontier in interdisciplinary modeling of sustainability from local to global scales

Achieving sustainable development requires understanding how human behavior and the environment interact across spatial scales. In particular, knowing how to manage tradeoffs between the environment and the economy, or between one spatial scale and another, necessitates a modeling approach that allows these different components to interact. Existing integrated local and global analyses provide key insights, but often fail to capture ‘meso-scale’ phenomena that operate at scales between the local and the global, leading to erroneous predictions and a constrained scope of analysis. Meso-scale phenomena are difficult to model because of their complexity and computational challenges, where adding additional scales can increase model run-time exponentially. These additions, however, are necessary to make models that include sufficient detail for policy-makers to assess tradeoffs. Here, we synthesize research that explicitly includes meso-scale phenomena and assess where further efforts might be fruitful in improving our predictions and expanding the scope of questions that sustainability science can answer. We emphasize five categories of models relevant to sustainability science, including biophysical models, integrated assessment models, land-use change models, earth-economy models and spatial downscaling models. We outline the technical and methodological challenges present in these areas of research and discuss seven directions for future research that will improve coverage of meso-scale effects. Additionally, we provide a specific worked example that shows the challenges present, and possible solutions, for modeling meso-scale phenomena in integrated earth-economy models.

Modeling urban land-use changes using a landscape-driven patch-based cellular automaton (LP-CA)

Cellular automaton (CA) is a widely-used tool for modeling land-use changes that can enhance our understanding of past and future urban development. Previous studies have demonstrated that patch-based CA models are superior to cell-based CA because the spatial homogeneity of urban growth at local scales can be considered. However, there still exists a major limitation that traditional patch-based CA did not incorporate any information about landscape pattern into land-use change modeling. To alleviate this issue, we present a novel landscape-driven patch-based CA (LP-CA) model that can simultaneously consider landscape similarity and cell-by-cell agreement. We have examined this new model by simulating and predicting the urban expansion in a fast-growing city. Results indicate that our method performs better than stand-alone traditional patch-based CA and landscape-driven cell-based CA in terms of the combined error. The capability to characterize and replicate landscape pattern is greatly important for urban planners to explore the potential influences of urban expansion under different land-use planning scenarios. Therefore, this proposed model has the potential to provide valuable support for urban land-use planning and policy-making processes.

MODEL OF LAND USE CHANGE TO THE HYDROLOGY RESPONSE

This research intends to build a model of land use change to the watershed hydrology response and was conducted in the area of Indonesia’s new country capital (IKN Nusantara). The total area of IKN Nusantara (KIKN) is 56,181 ha that includes 7 watersheds that are Sanggai, Trunen, Semuntai, Baruangin, Sepaku, Semoi, and Pemaluan. However, this study will only focus on the flood model in the Government Centre Area (KIPP) with the area is about 6,671 ha and only includes 3 watersheds such as Sanggai, Trunen, and Semuntai. The methodology consists of collecting the hydrology data that are land use, watershed boundary, IKN Nusantara area boundary, soil type, spatial planning (RTRW), observed rainfall station, GPM satellite rainfall. After that, there is carried out the data evaluation to correct the satellite rainfall data to the observed data. There was carried out the analysis about ability to give the recommendation on the boundary of country capital area development based on the consideration of run-off hydrology response that is still suitable with the environmental carrying capacity. The simulation results of the land use model and the rainfall pattern change model are expected to become the boundary in the development design of a city, mainly in the relation to the environmental carrying capacity toward flood, especially to provide a recommendation of new country capital area boundary based on the recommendation of CN value as the run-off hydrology response mainly related to the zone regulation of built up and protected area.

Integrating multi-criteria analysis and spherical cellular automata approach for modelling global urban land-use change

Existing geosimulation land-use change models are predominantly designed to operate at local or regional spatial scales. When these models are applied on data at the global level, they do not consider the effects of spatial distortions caused by the curvature of the Earth’s surface and often lack some refinements related to land suitability analysis. Therefore, the main objective of this study is to integrate multi-criteria evaluation with spherical cellular automata, to develop a novel modelling approach for simulating global urban land-use change. The world region is clustered into sub-regions to improve the suitability analysis. The obtained results reveal differences in urban growth rate and the size of the extent across the four clusters. The 64% of the total global urbanization are occurring in urban region clusters characterized by high gross domestic product and population density, while urban regions in isolated locations have the lowest urban growth rate.

Land use change and baseflow recession modelling in Wuryantoro Watershed, Wonogiri Regency, Central Java Province, Indonesia

Hydrological phenomena on the scale of a watershed are complex and may never be understood holistically. One of the innovations in baseflow hydrological modelling is the analysis of the baseflow recession curve, generally expressed as the natural storage of river flows and containing valuable information about the properties and characteristics of natural aquifer storage. This study aims to model land use change and baseflow recession in the Wuryantoro watershed, Wonogiri Regency, Central Java Province. The research method uses an exponential model in which changes in the characteristics of a baseflow recession are a function of land use changes over a certain period. The calibration of the seven graphical models of land use change against the characteristics of the baseflow recession shows that the seven curves of the land use change graphic model have model coefficients and curve slopes that vary from gentle to steep. The slope of the gentle and steep curve describes the bottom flow deposits' condition over time. The state of water storage in the seven better graphical models is that the change of forest remains forest followed by the change of agriculture into the forest, forest into the settlement, change of agricultural land into the settlement, change of forest to agricultural land, settlement remains settlement and change of agricultural land remains agrarian land.

The impacts of LULC and climate change scenarios on the hydrology and sediment yield of Rib watershed, Ethiopia.

Watershed-scale hydrology and soil erosion are the main environmental components that are greatly affected by environmental perturbations such as climate and land use and land cover (LULC) changes. The purpose of this study is to assess the impacts of scenario-based LULC change and climate change on hydrology and sediment at the watershed scale in Rib watershed, Ethiopia, using the empirical land-use change model, dynamic conversion of land use and its effects (Dyna-CLUE), and soil and water assessment tool (SWAT). Regional climate model (RCM) with Special Report on Emission Scenarios (SRES) and Representative Concentration Pathway (RCP) outputs were bias-corrected and future climate from 2025 to 2099 was analyzed to assess climate changes. Analysis of the LULC change indicated that there has been a high increase in cultivated land at the expense of mixed forest and shrublands and a low and gradual increase in plantation and urban lands in the historical periods (1984-2016) and in the predictions (2016-2049). In general, the predicted climate change indicated that there will be a decrease in precipitation in all of the SRES and RCP scenarios except in the Bega (dry) season and an increase in temperature in all of the scenarios. The impact analysis indicated that there might be an increase in runoff, evapotranspiration (ET), sediment yield, and a decrease in lateral flow, groundwater flow, and water yield. The changing climate and LULC result in an increase in soil erosion and changes in surface and groundwater flow, which might have an impact on reducing crop yield, the main source of livelihood in the area. Therefore, short- and long-term watershed-scale resource management activities have to be designed and implemented to minimize erosion and increase groundwater recharge.

Using ABM to Study the Potential of Land Use Change for Mitigation of Food Deserts

Land-use transition is one of the most profound human-induced alterations of the Earth’s system. It can support better land management and decision-making for increasing the yield of food production to fulfill the food needs in a specific area. However, modeling land-use change involves the complexity of human drivers and natural or environmental constraints. This study develops an agent-based model (ABM) for land use transitions using critical indicators that contribute to food deserts. The model’s performance was evaluated using Guilford County, North Carolina, as a case study. The modeling inputs include land covers, climate variability (rainfall and temperature), soil quality, land-use-related policies, and population growth. Studying the interrelationships between these factors can improve the development of effective land-use policies and help responsible agencies and policymakers plan accordingly to improve food security. The agent-based model illustrates how and when individuals or communities could make specific land-cover transitions to fulfill the community’s food needs. The results indicate that the agent-based model could effectively monitor land use and environmental changes to visualize potential risks over time and help the affected communities plan accordingly.

MODELING OF FOREST LANDSCAPE EVOLUTION AT REGIONAL LEVEL: A FOSS4G APPROACH

Abstract. In the last decades the Alpine landscape has dramatically changed due to social and economic factors. The most visible impact has been the reduction of the population for mid and high altitude villages and the shrinking of the part of the land used for agriculture and grazing, with a progressive reduction of pastures and meadows and the expansion of the forested areas. For these reasons, a dataset describing the forest, meadows and pasture coverage for the Trentino region, in the eastern Italian Alps, has been created. A set of heterogeneous sources has been selected so that maps and images cover the longest possible time span on the whole Trentino region with comparable quality, creating a Land Use/Land Cover (LULC) map based on historical maps from 1859 to 1936 and aerial images from 1954 to 2015. The achieved accuracy ranges from 98% for historical maps to 94% for aereal imagery. The analysis of selected landscape metrics provided preliminary results about the forest distribution and patterns of recolonization during the last 155 years. It has been possible to create future scenarios for the forest evolution for the next 85 years. Given the large number of maps involved, the great flexibility provided by FOSS for spatial analysis, such as GRASS, R, QGIS and GAMA and the possibility of scripting all the operations have played a pivotal role in the success both in the creation of the dataset and in the extraction and modeling of land use changes.