Land Use Map Research Articles

Land-Use Land-Cover Change Detection Using Geospatial Techniques in Zalingei, Central Darfur, Sudan

Using remote sensing for land use and land cover (LULC) is essential for systems that help people make decisions because it gives valuable information about space and time. A study was conducted in Zalingei, Sudan, to analyze the changes in LULC over 30 years from 1991 to 2021 using multi-temporal Landsat images. Thematic Mapper (TM) and Operational Land Imager (OLI) were classified using the supervised classification method. The pictures were divided into four groups based on how the land was used: residential areas, bodies of water, vegetation cover, and bare land. Results showed that the residential area increased by 20.74% while the water body increased by 2.32%. However, the vegetation cover decreased by 0.7%, and bare land decreased by 22.37%. The changes were caused by people, which shows how vital good land management practices and involvement from the local community are for reducing LULC change. So, to reduce LULC change in the study area, proper land management practices and active participation from the local community are needed. The study concluded that remote sensing technology is an effective tool for assessing and mapping land use and land cover changes and providing valuable information for decision support systems.

Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling

This research uses a Classification and Regression Tree (CART) model with Google Earth Engine (GEE) to assess the winter season’s land cover and change detection mapping impact on the evapotranspiration (crop water requirement) parameters. Winter seasons, crucial for agricultural planning, and irrigation water requirement challenges in accurately mapping land cover and detecting changes due to the dynamic nature of farming practices during this period. In this study, Landsat-8 OLI images have been combined to map Land use and Land cover (LULC) and other change detection mapping in Akola Block, Maharashtra, India, during the 2018–2022 winter season. As an discoverer researcher that found detailed information of LULC classes during last 2018 to 2022 winter seasons, the use of the CART model in combination with a cloud-computing GEE demonstrates to be a practical approach for accurate land cover classification and change detection maps to create a pixel-based winter seasons information of study area. The novelty of this study lies in its innovative use of GEE, a powerful platform for remote sensing and geospatial analysis, to create LULC maps with remarkable accuracy. Achieving a 100% training accuracy across the four years under consideration is an exceptional feat, highlighting the reliability and stability of the methodology. Furthermore, the validation accuracy values, ranging from 89 to 94% for the winter seasons of 2018 to 2022, underscore the robustness of this approach. Such consistently high accuracy in mapping LULC over time is a groundbreaking achievement and offers a new dimension to the field of hydrology. For the hydrological community, the implications of this study are profound. Accurate LULC mapping and change detection provide critical data for modeling and analyzing the effects of land use changes on water resources, watershed management, and water quality. The User, Kappa, and Producer accuracy metrics used in this research highlight the model’s performance and its suitability for hydrological applications. These accurate LULC maps can aid in the development of hydrological models, forecasting, and decision-making processes, ultimately contributing to more effective water resource management and environmental conservation. In summary, this study’s innovative use of GEE, its remarkable accuracy in LULC mapping, and its relevance to the hydrological community demonstrate the potential for advanced remote sensing and geospatial tools to significantly improve our understanding of land use changes and their implications for water resources and environmental management.

A comparative study of soil fertility and biota population under organic, semi-organic, and conventional farming system of rice fields in Giriwoyo District, Wonogiri Regency, Indonesia

Continuous use of chemical fertilizers and farming practices in rice fields would reduce nutrient availability and biota population in the soil. Because soil biological linkages are sensitive to changes in soil function, changes in fauna and microbial populations can affect soil fertility. This study aimed to identify the condition of soil fertility and soil biota under various farming systems, namely organic, semi-organic, and conventional, and find the indicators that determine soil fertility index (SFI) in the research area. This research is a survey with the sampling method was purposive on the Land Mapping Unit (LMU) overlaid with thematic maps (land use, soil type, slope, and rainfall). The method analysis of SFI using Principal Component Analysis (PCA) and scoring methods were used for the effect of farming systems using One-way, continued by DMRT, and determinant factor using Pearson’s. Correlation. The results illustrated that soil fertility was a moderate category. The highest SFI was in organic rice fields (0.69), then in semi-organic (0.62), and the lowest fertility was in conventional (0.59). Organic farming also showed the best soil biota conditions (0.27 individuals/liter of earthworms and 0.755 µ/g of microbial C biomass) compared to semi-organic (0.15 individuals/liter, 0.508 µ/g microbial C biomass) and conventional farming (0.11 individuals/liter, microbial biomass C 0.325 µ/g). Soil fertility and soil biota are positively correlated, meaning that the higher the soil fertility, the higher the density of soil biota. The soil indicators most determining fertility are total N, P-available, K-available, CEC, and organic C.

Forest/Nonforest Segmentation Using Sentinel-1 and -2 Data Fusion in the Bajo Cauca Subregion in Colombia

Remote sensing technologies have been successfully used for deforestation monitoring, and with the wide availability of satellite products from different platforms, forest monitoring applications have grown in recent years. The observed potential in these technologies motivates the development of forest mapping and monitoring tools that could also be used for neighboring applications like agriculture or land-use mapping. A literature review confirmed the research areas of interest in deforestation monitoring using synthetic aperture radar (SAR) and data fusion techniques, which guided the formulation of the method developed in this article consisting of a data preprocessing workflow for SAR (Sentinel-1) and multispectral (Sentinel-2) data and a procedure for the selection of a machine learning model for forest/nonforest segmentation evaluated in different combinations of Sentinel-1 and Sentinel-2 bands. The selected model is a random forest algorithm that uses C-band SAR dual-polarimetric bands, intensity features, and vegetation indices derived from optical/multispectral data. The selected random forest classifier’s balanced accuracies were 79–81%, and the f1-scores were 0.72–0.76 for the validation set. The results allow the obtention of yearly forest/nonforest and forest loss maps in the study area of Bajo Cauca in Colombia, a region with a documented high deforestation rate.

Understanding the linkages between land-use transitions and soil erosion/sediment deposition: A case study of the transboundary Sio-Malaba-Malakisi watershed in Kenya and Uganda

Soil erosion consequent of unsustainable land-use practices represents one of the world's most pressing environmental problems affecting many watersheds. An assessment of sediment sources and delivery paths is essential for the development of site-specific soil management strategies for curbing environmental degradation. The present study employed a physically-based lumped approach to estimate soil erosion and sediment deposition patterns across the transboundary Sio-Malaba-Malakisi (SMM) watershed. The Unit Stream Power-based Erosion Deposition (USPED) model was adopted for the study, and integrated with physically-based parameters derived in a Geographical Information System (GIS). A land-use map for 2017 was used for simulating the reference soil erosion and deposition status of the basin. Furthermore, the impacts of two land-use scenarios representing options for cropland expansion (Scenario-I), and land reclamation (Scenario-II), were evaluated for the period 2047, corresponding to a thirty-year period used in assessing catchment management planning for SMM. The annual soil erosion ranged between 0 and 460 t. ha−1yr−1, with a mean erosion rate of 160 t.ha−1yr−1 for the baseline condition. Deposition zones were mainly concentrated along stream banks and floodplain in low-lying sub-catchments. The mean soil erosion rate increased by 61 % and 16 % for scenarios I and II respectively. Overall, the modelled erosion/deposition values were correlated with available sediment data for the basin, realizing a coefficient determination (R2) value of 0.74. These results could serve as a benchmark against which future trends in land-use changes can be compared in the region and hence support the development of regional soil erosion conservation strategies.

Land Resource-Based Cocoa Development Potential in North Luwu District, South Sulawesi Province, Indonesia

Developing crops based on land resources is necessary for increasing cocoa production. This study aims to analyze the potential for developing cocoa based on information on land resources in the form of characteristics and land distribution in the North Luwu District. The distribution of cocoa was interpreted using onscreen satellite imagery. Land suitability data were compiled by matching land characteristics with the Cocoa Land Suitability Criteria. The potential availability of land and directions for crop development were determined based on the overlay results of land suitability classes with land-use maps and area status. Based on this study, the North Luwu District is part of an active tectonic region and has eight soil types: alluvial, regiosol, renzina, kambisol, gleisol, podsolik, mediteran, and oxisol. The land suitability class for cocoa plants was marginally suitable. The total land area that has the potential to develop cocoa plants is 68,804 ha, with the diversification and extensification of the two development patterns. North Luwu District has the potential to develop cocoa plantations with several appropriate agricultural technology efforts to support the increased production and quality of cocoa plants.

Predicting the future land use and land cover changes for Saroor Nagar Watershed, Telangana, India, using open-source GIS.

The dynamics of land use and land cover are profoundly affected by the growth, mobility, and demand of people. Thematic maps of land use and land cover (LULC) help planners account for conservation, concurrent uses, and land-use compressions by providing a reference for analysis, resource management, and prediction. The purpose of this research is to identify the transition of land-use changes in the Saroor Nagar Watershed between 2008 and 2014 using the Modules for Land Use Change Evaluation (MOLUSCE) plugin (MLP-ANN) model and to forecast and establish potential land-use changes for the years 2020 and 2026. To predict how these factors affected LULC from 2008 to 2014, MLP-ANN was trained with maps of DEM, slope, distance from the road, and distance to a waterbody. The projected and accurate LULC maps for 2020 have a Kappa value of 0.70 and a correctness percentage of 81.8%, indicating a high degree of accuracy. Changes in LULC are then predicted for the year 2026 using MLP-ANN, which shows a 17.4% increase in built-up area at the expense of vegetation and barren land. The results contribute to the development of sustainable plans for land use and resource management.

Environmental land use conflicts and ecosystem services: a paper review

The topic of potential conflicts related to land use involving human activities in a watershed is an important matter to be discussed. The background of this research is that conditions in mountainous watersheds, and agricultural land use cause changes in ecosystem services, with trade-offs between crop production and erosion regulation. Watershed management with an environmental concept often faces problems with different interests among stakeholders. Although several studies have initiated the mapping of land-use conflicts between human activities and conservation, the spatial assessment of land-use conflicts on environmental issues and trade-offs of ecosystem services in agricultural areas has not been fully considered. The purpose of this study is to map land use, with indicators of measuring conflicts in the value of conservation and agricultural development, through scenarios of the level of erosion hazard on agricultural land. This study provides input to decision-makers regarding watershed conservation efforts that still consider aspects of the economic needs of agricultural land. The objective of the reviewers is to understand how the concept of mapping the potential conflict of land use in the Haean watershed in South Korea can be applied in Indonesia. The methodology used is to apply the agricultural land suitability index based on various analytical criteria to estimate the spatial preferences of agricultural activities. To predict erosion, using the Revised Universal Soil Loss Equation (RUSLE) method and the classification of agricultural land in the watershed is divided into four levels of land use conflict (lowest, low, high, and highest).

Accuracy Assessment of different classifiers for Sustainable Development in Landuse and Landcover mapping using Sentinel SAR and Landsat-8 data

Sentinel satellites make use of Synthetic Aperture Radar (SAR) which produces images with backscattered signals at fine spatial resolution from 10 m to 50 m. This study is mainly focused on evaluating and assessing the accuracy of various supervised classifiers like Random Forest classifier, Minimum Distance to mean classifier, KDTree KNN classifier, and Maximum Likelihood classifier for landuse / landcover mapping in Maduranthakam Taluk, Kancheepuram district, Tamilnadu, India. These classifiers are widely used for classifying the Sentinel SAR images. The SAR images were processed using speckle and terrain correction and converted to backscattered energy. The training datasets for the landcover classes, such as vegetation, waterbodies, settlement, and barren land, were collected from Google Earth images in high-resolution mode. These collected training datasets were given as input for the various classifiers during the classification. The obtained classified output results of various classifiers were analyzed and compared using the overall classification accuracy. The overall accuracy achieved by the Random Forest classifier for the polarization VV and VH was 92.86%, whereas the classified accuracy of various classifiers such as KDTree KNN, Minimum distance to mean, and Maximum Likelihood are found to be 81.68%, 83.17%, and 85.64% respectively. The random forest classifier yields a higher classification accuracy value due to its greater stability in allocating the pixels to the right landuse class. In order to compare and validate the results with sentinel data, the random classifier is applied with optical Landsat-8 satellite data. The classification accuracy obtained for Landsat-8 data is 84.61%. It is clearly proved that the random forest classifier with sentinel data gives the best classification accuracy results due to its high spatial resolution and spectral sensitivity. Thus accurate landuse and landcover mapping promote sustainable development by supporting decision-making at local, regional, and national levels.

Global urban high-resolution land-use mapping: From benchmarks to multi-megacity applications

Timely and reliable land-use maps are of great importance in urban planning and environmental monitoring. With the rich spatial structure information, very high resolution (VHR) imagery is an important data source for identifying complex urban land use. However, the existing scene datasets and land-use mapping products based on VHR images have the following three problems: 1) accurate geographic boundaries of urban land parcels are lacking; 2) the category systems are inconsistent with the definitions in urban land use; and 3) it is difficult to achieve efficient and fully automated mapping in multiple cities. To tackle these problems, the GlobalUrbanNet-based automatic multi-city mapping and analysis (GAMMA) framework is proposed in this article. The GAMMA framework is made up of the GlobalUrbanNet (GUN) dataset, the multi-city fully automatic urban land-use mapping (AutoULUM) method, and the analysis of urban development patterns. Specifically, the large-scale 42-category fine-grained VHR urban land-use dataset—the GUN dataset—was constructed to deal with the above global urban land-use mapping problems, which contains 1,846,151 samples and 42 land-use categories covering six continents. The GUN dataset samples with land-use semantics and parcel boundaries were generated automatically based on the open-source area of interest (AOI) data from OpenStreetMap (OSM). In addition, the AutoULUM method is proposed to automate the process of OSM road network rectification and land parcel generation. On this basis, efficient and complete multi-city land-use maps can be produced using the GUN-pretrained scene classification models. To establish a benchmark for urban land-use classification, the representative urban land-use classification methods were evaluated on the GUN dataset. For further application, eight megacities from six continents were selected for automatic land-use mapping and analysis, i.e., Shanghai, Wuhan, and Chengdu in Asia, Helsinki in Europe, Nairobi in Africa, New York in North America, Rio de Janeiro in South America, and Sydney in Oceania. The results show that the models trained on the proposed GUN dataset have good generalizability in global urban areas, the AutoULUM method achieves efficient and fully automatic land-use mapping, and the GAMMA framework will help boost the coordinated development of multiple cities around the world.

METHODOLOGICAL PROPOSAL FOR EVALUATING THE TRANSFORMATION OF URBAN MICROCLIMATE IN MEDIUM-SIZED CITIES: A CASE STUDY IN THE URBAN MESH OF THE MUNICIPALITY OF PARACATU, MINAS GERAIS – BR

This work aims to propose a methodological proposal for areas evaluated with potential for the formation of Urban Heat Islands (UHI). Satellite images were used to control surface temperature (Land Surface Temperature - LST) and the Normalized Difference Vegetation Index (NDVI) of the study area. They were also employed in the Annual Mapping of Land Cover and Land Use in Brazil. The city of Paracatu, Minas Gerais (MG) was chosen for application and validation of the proposed method. Images from LANDSAT-5 and 8 satellites were used, which initially underwent resampling and pixel standardization. Subsequently, LST and NDVI calculations were applied, and these data were compared with use and occupation. In general, it was possible to notice that, in the urban area of Paracatu, there was an increase in the average, minimum and maximum LST. The same space-spectral behavior occurred for the NDVI index, which are related to Land Use and Occupancy Changes (LULC). In relation to areas with potential formation of UHI, it was noted that, in the interval of 14 years, they tripled in the city, making it possible to relate the situation with urban expansion and the presence of new subdivisions in peripheral areas. It was also noted that areas with mitigation potential for UHIs were not planned, especially in the most populous neighborhood of the municipality. The results obtained were satisfactory and the proposed methodology can serve as a basis for municipal decision makers in relation to measures to mitigate urban microclimate.

Evaluating the effects of reservoir outflow and land-use change on the Zarrineh River basin

ABSTRACT The rapid decline of Lake Urmia in northwest Iran, caused by both human and natural factors, motivated our utilization of the Soil and Water Assessment Tool (SWAT) model. The objective was to identify the most effective reservoir outflow simulation scheme and assess the influence of land use changes in the Zarrineh River basin on Lake Urmia. The target release approach for controlled reservoirs and the measured outflow schemes were evaluated. Despite improved calibration results in the latter method, the former demonstrated superior performance during the validation phase. Three land use scenarios – a model with the 2001 land use map, a model with the 2008 land use map, and a model with a gradually updating land use map – were considered. The gradually updating land use scenario exhibited a significant enhancement in validation results; it showed a 250% increase in water yield, while evapotranspiration declined by 3%.

Data, Tools and Services for Spatial Sustainability Science

The emerging research data centre (RDC) at the Leibniz Institute of Ecological Urban and Regional Development (IOER) constitutes an essential milestone towards promoting sustainable land transition and transformative urban and regional development. The IOER RDC leverages spatial data science and artificial intelligence to process and analyse diverse and complex data sources. It provides high-resolution indicator maps of land use, ecosystems, and settlement structures, as well as cross-scale and cross-disciplinary spatial analyses, modelling, and simulations. Moreover, the IOER RDC offers digital tools to support decision-making, policy planning, and sustainable transformations. Hence, the IOER RDC has the potential to foster a sustainable future by facilitating the transition towards sustainable land use and development in urban and regional areas. The IOER RDC's endeavours offer a path towards addressing pressing societal challenges, such as rapid urbanisation, environmental degradation, climate change, and social inequality.

Downscaling Global Land-Use Scenario Data to the National Level: A Case Study for Belgium

Land use change scenarios, and their projected impacts on biodiversity, are highly relevant at local scales but not adequately captured by the coarse spatial resolutions of global land use models. In this study, we used the land use allocation tool of the GLOBIO 4 model to downscale the Land Use Harmonization v2 (LUH2) data from their original spatial resolution (0.25°) to 100 m and 10 m resolutions, using the country of Belgium as an example. Inputs to the tool included: (1) a reference present-day land cover map at the high spatial resolution, (2) regional land demand projections for three future scenarios, Sustainability (SSP1xRCP2.6), Regional Rivalry (SSP3xRCP6.0), and Fossil-fuelled Development (SSP5xRCP8.5), and (3) raster layers representing the suitability of the grid cells for different land use types. We further investigated the impact of using different reference land cover maps (CORINE at 100 m resolution and ESA WorldCover at 100 m and 10 m resolutions) on the downscaling outcomes. Comparison of downscaled current and future land use maps with the original LUH2 dataset showed that the use of ESA WorldCover as a reference map provides better agreement (RSR: 0.11–0.24, overall accuracy: 0.94–0.98, Kappa: 0.91–0.97) than CORINE (RSR: 0.28–0.33, overall accuracy: 0.90–0.93, Kappa: 0.90–0.91). Additionally, the validation of the present-day downscaled maps showed a good agreement with the independent Copernicus Global Land Service dataset. Our findings suggest that the choice of reference land cover map influences the degree of agreement between the downscaled and the original coarse-grain land-use maps. Moreover, the land use maps produced using our downscaling approach can provide valuable insights into the potential impacts of land use change on biodiversity and can guide local decision-making processes for sustainable land management and conservation efforts.

Monitoring and Modeling of Spatio-Temporal Urban Expansion and Land Use/Land-Cover Change in Mountain Landscape: A Case Study of Dalat City, Vietnam

The lack of ability to control human activities led to changes of land use/land cover (LULC) in Dalat City where rapid urbanization and the demand to expand agricultural land have resulted in dramatic forest reductions. This study assessed the rate and extent of LULC changes over the past 12 years and simulated future scenarios in Dalat City, Lam Dong Province, Vietnam by using an integrated model of Markov chain and logistics regression. Three land-use maps used to analyze land-use change were extracted from satellite images in 2010, 2016, and 2022 by classification approach. The outcome of this process indicates a significant increase in agricultural and built-up land of 48.22 km2 and 9.36 km2, respectively; a decrease in forest land of 55.61 km2, and a minor change in water bodies and bare land in the 2010-2022 period. Prediction maps of land-use change in 2028 and 2034 are generated after the model is validated by comparing the actual map with the prediction map of LULC in 2022 using Kappa statistics. Transition of forest area to other land use types, especially land for expansion of built-up and agricultural land is the crucial trend of land-use change in the future according to the forecast model. Compared to 2022, forest area in 2034 will decrease by 60.65 km2 while built-up and agricultural land will increase by 14.07 km2 and 43.61 km2, respectively. The research results provide valuable information as a foundation for land-use policy planning and local urban development to ensure sustainable development objectives.

Multitemporal Analysis of Land Use/Cover Changes and Landscape Fragmentation in Murshidabad District of West Bengal, India

Rapid population growth, human migration, and commercial activities are changing land use and Land cover at a faster rate. The human being's need and greed to sustain themselves alter the earth's natural environment, and that change affected us. However, poor and unmanageable land conversion led to severe environmental effects. For planning and management purposes, precise information regarding land use and its characteristics is required to ensure the sustainability of the area. The current study uses multi-temporal satellite images to analyze the decadal change from 1991 to 2021. Supervised image classification is performed using the Maximum likelihood classifier. The main goal of this study is to compare post-classification results using change vector analysis and analyze human impact on the environment using FRAGSTAT. Fragstat is a widely used software program designed for analyzing spatial patterns in categorical maps. It is commonly employed in landscape ecology, conservation biology, and land management studies. The primary purpose of Fragstats is to quantify and assess the composition and configuration of patches or landscape elements within a given area. The built-up area increased from 2.57% to 8.41% over the past 30 years, while the agricultural land decreased from 83.51% to 70.05%. It was observed that the density of patches and percentage of landscape reduction over time, the rise in the number of patches for agricultural class from 3570 in 1991 to 10173 in 2021 indicates that spatial diversity is increasing in the class with higher levels of anthropogenic disturbances. Moreover, in landscape-level indices, the number of patch and landscape shape index increases, and a fall in the largest patch index indicate that the landscape is becoming more complicated and fragmented. To achieve the sustainable land-use planning and safeguard natural ecosystems and biodiversity from anthropogenic activities, land-use change maps are utilized as an early warning system.

ACCESSIBILITY AND CONNECTIVITY ANALYSIS IN THE GROWING RURAL AREA: A CASE STUDY OF REMBAU DISTRICT, NEGERI SEMBILAN

Rural areas are the ideal place to handle urban sprawl, whether for future administrative, college, manufacturing, residential, commercial, or for business growth. In the land development context, the shifting from rural into several areas such as agricultural into houses or subdividing real estate into commercial lots etc. were seen to be worth trying. However, this will succeed with the help of better road connectivity to the demanded area. This research is aimed to explore the road accessibility and connectivity and its function in supporting the growth of land development in Rembau, Negeri Sembilan. The research methodology produces maps of new potential growth development areas using GIS techniques. For data acquisition, there was a two-approach which is using the primary and secondary data. The primary data consist of the observation on the site, interview method with the experts and local people. Meanwhile, the secondary data is using the Landsat data from USGS to produce a land-use map for four consecutive years, base map and route connectivity data for GIS method, and statistical data from related agencies. The result of this research is to produce a new potential development map in Rembau, Negeri Sembilan. Remote sensing (RS) and Geographical Information System (GIS) allows the town planners to identify the potential rural spaces to cater the urban population.

Extracting Land-Cover Information from Landsat Satellite Images by the Decision-Tree Model to Generate a Land-Use Map in Ba Tri District, Ben Tre Province

Extracting Land-Cover Information from Landsat Satellite Images by the Decision-Tree Model to Generate a Land-Use Map in Ba Tri District, Ben Tre Province

PREDICTION OF EROSION IN HILLY AREAS OF KHILAU SUB-SUB WATERSHED USING THE RUSLE METHOD

Soil erosion poses an environmental threat to the sustainability and productivity of the land in the Khilau Sub-Sub Watershed, Bulok Sub-Watershed, Sekampung Watershed, Pesawaran Regency. The research aimed to predict soil erosion by implementing the RUSLE model on the Khilau Sub-Sub-Watershed, combined with remote sensing, GIS techniques, and field observation. This research is divided into several phases, i.e. soil data collection, getting satellite maps, obtaining rainfall data from 2011 to 2019, making the topographic maps of land slopes, land use, and soil types, whose overlay will be made, and data analysis. The results show that the areas with the highest risk of erosion were located on the hills and in the areas characterized by steep slopes of 1,395.48 (t/ha/year) with a slope level of >45% (very steep), which had the highest LS factor value and were characterized as areas with strong relief, i.e. hills (with a slope level of >20%). In conclusion, the erosion rate of the Khilau Sub-Sub Watershed is dominated by the erosion falling into the heavy category with an average erosion value of 405.17 (t/ha/year) by soil parameter, slope inclination, vegetation cover, and climate. The most influential factors in this study were the climate and slopes, where the lands used as mixed gardens and annuals had a high risk of erosion. So, it is of utmost importance to take soil protection and conservation measures for potential erosion protection in the area.

Mapping Land Use and Land Cover Classes in São Paulo State, Southeast of Brazil, Using Landsat-8 OLI Multispectral Data and the Derived Spectral Indices and Fraction Images

This work aims to develop a new method to map Land Use and Land Cover (LULC) classes in the São Paulo State, Brazil, using Landsat-8 Operational Land Imager (OLI) data. The novelty of the proposed method consists of selecting the images based on the spectral and temporal characteristics of the LULC classes. First, we defined the six classes to be mapped in the year 2020 as forest, forest plantation, water bodies, urban areas, agriculture, and pasture. Second, we visually analyzed their variability spectral characteristics over the year. Then, we pre-processed these images to highlight each LULC class. For the classification, the Random Forest algorithm available on the Google Earth Engine (GEE) platform was utilized individually for each LULC class. Afterward, we integrated the classified maps to create the final LULC map. The results revealed that forest areas are primarily concentrated in the eastern region of São Paulo, predominantly on steeper slopes, accounting for 19% of the study area. On the other hand, pasture and agriculture dominated 73% of all São Paulo’s landscape, reaching 39% and 34%, respectively. The overall accuracy of the classification achieved 89.10%, while producer and user accuracies were greater than 84.20% and 76.62%, respectively. To validate the results, we compared our findings with the MapBiomas Project classification, obtaining an overall accuracy of 85.47%. Therefore, our method demonstrates its potential to minimize classification errors and offers the advantage of facilitating post-classification editing for individual mapped classes.