Land Resource Management Research Articles

The transition towards carbon neutrality: Land use policy, resource and energy management modes, and spatial planning options

The transition towards carbon neutrality: Land use policy, resource and energy management modes, and spatial planning options

LEGAL MECHANISMS OF PUBLIC PARTICIPATION IN DECISION-MAKING IN THE FIELD OF LAND RESOURCES USE

The article examines mechanisms for public participation in decision-making regarding the use of land resources in Kazakhstan. The research is conducted as part of the program-targeted financing project of the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan. The main focus is on local self-governance, participatory budgets, and public oversight. Local self-governance in Kazakhstan is based on the Constitution and ensures citizens' rights to independently address local matters. However, the realization of this right is often limited by regulatory barriers and dependence on decisions made by governmental authorities. This hinders citizens' participation in governance and reduces the effectiveness of local self-governance. The participatory budget (PB) has become an important tool that allows residents to directly influence the allocation of local budget funds. The practice of PB, especially in large cities such as Almaty, demonstrates its potential for improving the urban environment. However, expanding this practice requires larger investments and the inclusion of a broader range of projects that can be proposed by citizens. Public oversight serves as a counterbalance to governmental activities, encouraging transparency and accountability. The 2023 Law "On Public Oversight" establishes the legal framework for citizens' participation in monitoring the actions of governmental bodies. A successful example of public oversight is the "Zher Amanaty" project, aimed at reclaiming unused agricultural lands. The article emphasizes the need to increase citizen engagement and trust in government bodies, which would make land resource management more transparent and effective. Improving the legal framework, developing local self-governance institutions, and adapting international experience can help address existing issues and improve the quality of land resource management in Kazakhstan.

State regulation of land relations abroad

The land is a fundamental natural asset, vital to existence, development and ecological balance of human civilization. Rational use and land protection are becoming a central prerequisite for spatial development, which is aimed at the social, environmental and economic sustainability of land policy. The increasing pace of development, digital transformation and the latest paradigms of sustainable development in the modern world have an impact on state land policy, which in turn regulates the sphere of land relations. In this context, special attention should be paid to the study of foreign experience in the field of state regulation of land relations for improving the current mechanism of the strategic management of land and other natural resources at all administrative levels.

Modeling Soil pH at regional scale using environmental covariates and machine learning algorithm.

Soil pH serves as a critical indicator of soil chemistry and fertility, and mapping its spatial distribution holds significant importance for effective crop management. Digital soil mapping (DSM) is a commonly employed method for making rapid and cost-effective quantitative predictions of soil properties and soil classes. In the present study, we mapped soil pH (0-15cm) on a regional scale in Karnataka using a combination of various environmental variables. Three distinct machine learning models, namely support vector machine (SVM), Cubist, and random forest (RF), were assessed using a dataset of 146,044 observations collected under various projects. The environmental covariates used for soil pH prediction encompassed terrain attributes, Landsat-8 data, vegetation indices, and climatic variables. Among these models, RF model exhibited the most acceptable results for predicting soil pH (R2val = 0.61, CCCval = 0.74, RMSEval = 0.66). On the other hand, the Cubist and SVM models displayed comparatively lower accuracy, explaining only about 46-49% of the variation. The inclusion of climatic variables and Landsat-8 data emerged as crucial factors for predicting soil pH. The study successfully produced high-resolution maps of soil pH for the entire state at a 90-m resolution, while also quantifying the associated uncertainty. These high-resolution maps have the potential to be valuable for decision-makers, stakeholders, and agricultural practitioners towards precision agriculture and land resource management.

Morphometric Characterization and Hydrological Dynamics of the Dzuza, Dhansiri and Khova Watersheds in Nagaland, India: Implications for Sustainable Management

Background: Morphometry is the quantitative assessment and mathematical analysis of landforms, essential for understanding watershed dynamics. The Dzuza, Dhansiri and Khova river basins in Nagaland, India, were analysed to comprehend their shape, drainage network and ecological importance. These watersheds are crucial for the sustainable management of water and land resources, particularly considering Nagaland’s diverse topography and hydrological circumstances. Methods: A field-laboratory study was performed utilising GIS and remote sensing technologies. Digital Elevation Model (DEM) data were acquired and analysed utilising ArcGIS 10.5 software to identify watersheds and calculate morphometric characteristics including drainage density, stream frequency, relief and slope. The data were examined to clarify the linear, relief and aerial characteristics of the three basins. Result: Dzuza Basin, a sixth-order system with 2815 m elevation and 6.44 ruggedness, is prone to erosion. Dhansiri, a seventh-order basin with a 4116.50-km stream network, has a complex drainage pattern, while the smaller Khova Basin also has distinct hydrological characteristics. High drainage densities (2.29-2.43 km/km2) suggest runoff potential, but north/northeast-facing slopes usually retain rainfall. Sustainable management requires customised watershed methods for erosion mitigation in Dzuza, water retention in Dhansiri and integrated soil-water conservation in Khova.

Land resources management under conditions of degradation, war-related threats and socio-economic instability

The article analyzes the current challenges of land resource management in Ukraine under conditions of soil degradation, full-scale war, and socio-economic instability. It emphasizes that soil degradation has become a systemic barrier to the sustainable development of rural areas and the restoration of the agri-industrial potential. Land is considered not only a foundation of agricultural production but also a critical asset for ensuring food security, economic stability, and ecological balance in the face of war-related threats. Methodologically, the study combines content analysis of scientific publications, regulatory documents, and analytical reports with comparative assessment of key risks in the field of land use. The paper systematizes the main types of soil degradation – erosion, salinization, compaction, and contamination – explores their causes, including anthropogenic pressure, climate change, and military operations, and outlines their multidimensional consequences: declining fertility, crop losses, increasing regional disparities, rural depopulation, investment withdrawal, and escalating ecological risks. The study identifies four major challenges that hinder effective land governance: spatial asymmetry of agricultural development, insufficient investment in land restoration, ineffective state control, and weak integration of modern agrotechnologies. It is shown that under martial law, the state has largely lost its regulatory mechanisms over land use, which has led to a rise in violations and initiated legal reforms. The paper substantiates that overcoming land degradation and ensuring the sustainability of the land fund require an integrated policy focused on ecological recovery, digitalization, support for agricultural entrepreneurship, and balanced spatial development. The conclusion highlights the need for cross-sectoral cooperation among public institutions, the scientific community, agribusiness, and international donors to establish a new model of land resource management in wartime and post-war reconstruction.

Prescribed Fire Smoke: A Review of Composition, Measurement Methods, and Analysis

Prescribed fire has become an increasingly important strategy for removing biomass from forests and mitigating the risk of severe wildfire. When considering where and to what extent prescribed fire should be applied, land resource managers must consider a host of concerns including biomass density, moisture content, and meteorological conditions. These variables will not only affect how effective the burn will be, but also what sort of smoke is produced by the prescribed fire and how that smoke impacts individuals and local communities. After briefly summarizing how prescribed fire practices have evolved, this review describes how the properties of prescribed fire smoke depend on prescribed fire conditions and the methods used to measure molecular and particulate species in prescribed fire smoke. The closing section of this review identifies areas where advances in smoke monitoring and characterization can continue to improve our understanding of prescribed fire behavior.

Analysis of characteristics of land use change and its ecological effects in the Datong River Basin

Based on the lack of understanding of the attribution of land use change and the resulting ecological effects in the Datong River Basin (DTRB), we analyzed the spatial-temporal characteristics of the land use change and explored the drivers of the changes, and calculated the effect of the land use change on the net primary productivity (NPP) from 1990 to 2020. The main conclusions are as follows: The predominant land use types comprised grassland, forest, cropland, bare land, ice/snow and other categories. The cropland, grassland and shrubs showed a decreased trend. However, forest, bare land and impervious surface were increased. The comprehensive index of the land use degree showed a significant downward trend, indicating that land use intensity will likely remain at a low level. Additionally, these conversions occurred among different land use types. Cropland and Grassland were dominated by transfer-out; Forest, bare land and impervious surface were dominated by transfer-in. Regarding the analysis of the driving factors of land use changes. The low elevation area was affected by human activities, and under the impact of “green gain” protection policy, the area of grassland increased; coupled with the impact of “returning farmland to forests,” the area of cropland decreased. Bare land was significantly and negatively correlated with GDP and population, while impervious surface was positively correlated. However, high-altitude areas were affected by natural factors, with grassland partial negatively correlated with precipitation. For bare land, it partial positively correlated with temperature. Snow/ice partial positively correlated with precipitation. Regarding the ecological effect of land use change, the multi-year average NPP was about 167.0 gC·m−2·a−1 and showed a decreased trend during the period of 1990–2020, and the NPP decreased in the northwest and increased in the southeast. This study provides scientific suggestions for the management of land resources and ecological environmental protection.

Determinants of Land Use Change in Phonthong County, Phonxay District, Luang Prabang Province, Lao PDR

Land use change is a key global issue with significant impacts on human livelihoods and the environment. This study aims to identify the factors influencing land use change practices among local communities in Phonxay District, Luang Prabang Province, in the Northern Uplands of Lao P.D.R. A binomial logistic regression analysis was conducted to examine the determinants of land use change, based on data collected through structured interviews with 252 households in two villages. The questionnaire focused on demographic and economic characteristics, land tenure, physical conditions, and institutional factors. The results indicate that significant factors influencing land use change in Phonthong County include landholding size, product prices, access to quality extension services, distance, and household income. These variables were found to strongly influence local land use decisions. The findings suggest that demographic-economic characteristics, land tenure systems, physical factors, and institutional support all play a crucial role in shaping land use practices. To promote more sustainable land management in the Northern Uplands, policy measures should be tailored to the local context, encouraging communities to adopt responsible and sustainable land-use practices. This includes empowering local people to manage their village resources effectively and implementing context-specific policies that enhance sustainable land resource management

Research on Land Use Change and the Contribution Degree of its Driving Force Factors: A Case Study of the Yangtze River Delta, China

In recent decades, global land use change has had a profound impact on ecosystems, economic development and environmental quality. The study of land use change plays an important role in promoting the optimization of land resource management and urban planning and achieving the United Nations Sustainable Development Goals (SDGs). Based on the random forest method, this study classifies and interprets seven periods of remote sensing images in the Yangtze River Delta from 2000 to 2020, and systematically analyzes the spatio-temporal characteristics of land use change in this region and the contribution rate of its driving factors. From 2000 to 2020, the developed land increased significantly by 54.60%, while the areas of plowland, woodland and grassland decreased by 8.89%, 1.25% and 2.95% respectively. The areas of unused land and water bodies increased by 5.80% and 403.62% respectively. The analysis of the contribution degree of driving force factors indicates that natural factors such as precipitation, temperature and slope, along with socio-economic factors such as shopping services, medical services and catering services, jointly drive land use change. Among them, what is particularly prominent is that the contribution of precipitation to the expansion of woodland is as high as 0.164, and the contribution of the shopping service industry to the expansion of urban land is 0.149. It reveals the dual driving mechanism of "natural basis - humanistic traction" of land use change, providing a scientific basis for regional land resource management and sustainable development.

Spatiotemporal Evolution of Cultivated Land Ecosystem Service Functions in the Yangtze River Delta and Its Driving Mechanism

It is of great significance to clarify the spatiotemporal evolution of cultivated land ecosystem services（CLESs） and their driving factors to achieve sustainable development. This paper described the spatiotemporal characteristics and the trade-off relationship of different types of CLESs and identified the driving factors of the spatiotemporal evolution of CLESs. The results show that： ① From 2000 to 2020， CLESs showed different temporal trends and had large spatial differences. ② The trade-off level of CLESs showed the evolutionary characteristics of agglomeration and improvement. ③ Slope， dem， per capita income of rural residents， and chemical fertilizer application had high driving forces on the spatiotemporal evolution of CLESs. The results of this study provide decision-making support for promoting the differentiated management of cultivated land resources and the mutual promotion of cultivated land multi-function.

Utilizing Remote Sensing Data for Species Distribution Modeling of Birds in Croatia

Accurate information on species distributions and population sizes is essential for effective biodiversity conservation, yet such data are often lacking at national scales. This study addresses this gap by assessing the distribution and abundance of 111 bird species across Croatia, including breeding, wintering, and migratory flyway populations. We combined Species Distribution Models (SDMs) with expert-based population estimates to generate spatially explicit predictions. The modeling framework incorporated high-resolution Earth observation (EO) data and advanced spatial analysis techniques. Environmental variables, such as land cover, were derived from satellite datasets, while climate variables were interpolated from ground measurements and refined using EO-based co-variates. Model calibration and validation were based on species occurrence records and EO-derived predictors. This integrative approach enabled both national-scale population estimates and fine-scale habitat assessments. The results identified critical habitats, population hotspots, and areas likely to experience distribution shifts under changing environmental conditions. By integrating EO data with expert knowledge, this study enhances the robustness of population estimates, particularly where species monitoring data are incomplete. The findings support conservation prioritization, inform land use and resource management, and contribute to long-term biodiversity monitoring. The methodology is scalable and transferable, offering a practical framework for ecological assessments in diverse regions. We integrated expert-based population estimates with species distribution models (SDMs) by applying expert-derived density values to areas of suitable habitat predicted by SDMs. This approach enables spatially explicit population estimates by combining ecological modeling with expert knowledge, which is particularly useful in systems with limited data. Experts provided species-specific density estimates stratified by habitat type, seasonality, behavior, and detectability, aligned with habitat suitability classes derived from SDM outputs.

Efficiency of drip irrigation in amaranth production using the HYDRUS-1D model

The negative impact of climate change is potentially damaging agroecosystem services that have constrained agricultural production and caused water scarcity in Central Asian countries, particularly in Uzbekistan. This study evaluates the efficiency of full (FDI) and deficit (DDI) drip irrigation regimes for amaranth (Amaranthus spp.) cultivation in the Tashkent region of Uzbekistan using the HYDRUS-1D simulation model. Field experiments were conducted over two growing seasons, accompanied by soil moisture monitoring, root zone analysis, and crop performance measurements while the accuracy of the obtained results was assessed against ground measured data. The results showed that compared to the FDI regime, amaranth under the DDI improved water productivity by 56.5% while exhibiting tolerance to water scarcity. The Pearson correlation analysis revealed a strong relationship between the simulated and observed SWC data for both irrigation regimes (R2 = 0.862 for FDI and R2 = 0.936 for DDI), indicating the model’s predictive reliability. Although FDI produced higher yield (2004 kg/ha) over the two-year period, which was 25% (2 t ha−1) higher than the DDI regime (1,604 kg/ha). However, DDI demonstrated significantly greater water productivity (56.5% higher), attributed to reduced unproductive evaporation and the C4 nature of amaranth. Root system analysis revealed deeper penetration under DDI, suggesting adaptive responses to water stress. The findings of this study suggest that implementing precise irrigation technology in amaranth cultivation combined with the use of the HYDRUS-1D model in the context of inevitable climate change, can ensure the long-term sustainable management of water and land resources in arid regions.

Spatiotemporal Dynamics of Soil Erosion, Sediment Yield and Their Driving Forces Since 1990 in the Xiliugou Basin, Upper Yellow River, China

ABSTRACTUnderstanding and quantifying the spatiotemporal variations in regional soil erosion and sediment yield, as well as identifying the driving factors, are crucial for managing land resources and addressing environmental issues induced by soil erosion. However, critical challenges persist in semi‐arid basins due to the interplay of wind‐water erosion processes and nonlinear responses to coupled climatic‐anthropogenic drivers. This study addresses these challenges by integrating the InVEST (v3.14.1) Sediment Delivery Ratio (SDR) model with the geographical detector method. We analysed spatial and temporal changes in soil erosion and sediment output in the Xiliugou basin from 1990 to 2020 and clarified the driving factors behind spatial variations in sediment yield and temporal changes in SDR. The results revealed significant annual sediment yield fluctuations from 1990 to 2020, ranging from 0.01 × 104 t in 2011 to 1480 × 104 t in 1998, with a mean of (191.9 ± 354.9) × 104 t. In 1990, sediment yield was predominantly distributed in upper slopes and channels, but specific sediment yield in upper slopes decreased substantially after 2000. Annual soil erosion rates for 1990, 2000, 2010 and 2020 were 99.98 × 104, 30.51 × 104, 44.62 × 104 and 45.30 × 104 t, respectively. Slight erosion dominated the soil erosion regime, accounting for 67.7%–97.4% of the watershed area, with post‐2000 values exceeding 90%. Slope‐driven spatial heterogeneity was the most pronounced factor influencing soil erosion and specific sediment yield distribution. Interactions between slope (a topographic factor) and climatic and anthropogenic drivers significantly amplified their impacts on erosion patterns. The decline in sediment load, primarily driven by reduced hyperconcentrated flows, vegetation cover changes, and sediment‐trapping dam constructions, was identified as the main contributor to decreased SDR in the Xiliugou basin.

Determination of land use and land cover change using multi-temporal PlanetScope images and deep learning CNN model

Abstract Land use and land cover (LULC), an important component of Earth observation technology, plays a crucial role in image classification. Detailed LULC information is of great importance for monitoring agriculture and wetlands, urban and rural planning, land resource management, and the monitoring and detection of climate change-induced changes. To create a detailed LULC, high-resolution satellite imagery, ground truth data, and a model that provides high accuracy are required. For this reason, 8-band PlanetScope images with 3 m spatial resolution, Land Parcel Identification System (LPIS) physical blocks (1:5000 scale), and a convolutional neural network (CNN) model were used as ground truth data. While using the CNN model, hyperparameter optimization was performed to determine the most suitable parameters for achieving high classification accuracy. As a result of the classification, the overall accuracy value was 93.14%, while the accuracy value for each class (arable land, artificial surface, forest, grassland, shrubland, tree crops, and water) was around 90%. Although this study focuses on the use of CNN for LULC mapping, one of its main objectives is to create a high-resolution LULC map to serve as a basis for updating LPIS, a reference system for the management and control of support payments, which is one of the main components of the Integrated Administration and Control System (IACS). LPIS is generally required to be updated every five years according to European Union legislation. As a result of this study, the LULC classification (2023) and LPIS physical blocks (2015) were compared, and areas of change as well as areas needing updates were identified. This study introduces an innovative approach by integrating high-resolution, multi-temporal PlanetScope imagery with LPIS data using a deep learning-based CNN model for LULC classification. Unlike previous studies that primarily rely on medium-resolution imagery or traditional machine learning methods, this study leverages the spectral and temporal advantages of PlanetScope’s 8-band imagery to enhance classification accuracy. Moreover, this research provides a novel methodology for LPIS updating, offering a scalable and automated framework that can be applied at a national scale. By combining CNN-based classification with LPIS change detection, this study establishes a systematic approach for identifying areas requiring updates, contributing to more efficient land resource management and agricultural policy implementation.

MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing images

ABSTRACT Land cover classification is vital for land resource management. However, challenges such as feature similarity among ground objects, blurred boundaries, and indistinct small objects persist. To address these challenges, we propose the Multi-Scale High-Resolution Network (MSHRNet) for classifying ground objects from high-resolution remote sensing images. MSHRNet is an encoder-decoder network that incorporates an attentional boundary refinement branch in the decoder to sharpen object boundaries. It features a multi-scale feature interaction module that integrates feature maps across different resolutions in the encoder and enhances the importance of these fused features using a coordinate attention module. Additionally, we introduce a Laplacian operator-based boundary loss function (LBLoss) to further improve segmentation performance. Evaluated on the GID and Huawei Ascend Cup AI + Remote Sensing Image Competition datasets, MSHRNet demonstrates robustness with a mean Intersection over Union (mIoU) of 82.45% and 72.26%, respectively, and surpasses nine recently published models by at least 1.52% and 1.01% mIoU. Moreover, when tested on the LoveDA dataset without additional training, MSHRNet exhibited strong transferability, achieving an mIoU of 18.53% and surpassing the second-best model by 2.33%. This framework represents a significant advancement in land cover classification, addressing challenges of high-resolution imagery and exhibiting generalization across diverse datasets.

Regenerative Agrivoltaics: Integrating Photovoltaics and Regenerative Agriculture for Sustainable Food and Energy Systems

Regenerative agriculture has emerged as an innovative approach to food production, offering the potential to achieve reduced or even positive environmental and social outcomes compared to the soil degradation and greenhouse gas emissions of conventional agriculture. Simultaneously, a sophisticated dual-use system combining solar energy generation from photovoltaics with agricultural production, called agrivoltaics, is rapidly expanding. Combining these approaches into regenerative agrivoltaics offers a promising solution to the challenges regarding food in a rapidly warming world. This review theoretically examines the compatibility and mutual benefits of combining agrivoltaics and regenerative agriculture while also identifying the challenges, opportunities, and pathways for implementing this system. A foundation for advancing regenerative agrivoltaics is made by identifying areas for research, which include the following: (1) carbon sequestration, (2) soil health and fertility, (3) soil moisture, (4) soil microbial activity, (5) soil nutrients, (6) crop performance, (7) water-use efficiency, and (8) economics. By addressing the intersection of agriculture, renewable energy, and sustainability, regenerative agrivoltaics emphasizes the transformative potential of integrated systems in reshaping land use and resource management. This evaluation underscores the importance of policy and industry collaboration in facilitating the adoption of regenerative agrivoltaics, advocating for tailored support mechanisms to enable widespread implementation of low-cost, zero-carbon, resilient food systems.

Assessment of the Intervention Design and Benefits of Sustainable Land Resource Management Approach (SLRMA) on Corn Farmers in Ilagan City, Isabela, Philippines

Sustainable farming systems have been acknowledged as an approach that balances the production of food and preservation of the environment. It involves practices that protect natural ecosystems such as soil conservation and water management, while also supporting the long-term economic viability for the farmers. To promote sustainable agriculture, various soil conservation technologies were introduced to corn farmers in Ilagan City, Isabela, Philippines through Sustainable Land Resource Management Approach (SLRMA). This study aimed to assess the intervention design and benefits of SLRMA to the 49 farmer-beneficiaries using the collected data on contour farming systems, crop diversification, training, and challenges encountered. A quantitative research design was utilized, incorporating descriptive and comparative approaches to examine and interpret the collected data. The analysis was focused on changes in production and profit before and after the implementation of SLRMA, using paired t-tests to compare these variables. Results revealed that SLRMA has had a positive and significant effect on farming practices and livelihoods, particularly for those with 2 to 5 years of participation. These beneficiaries experienced increased income, improved farming practices, and enhanced land productivity, including reduced soil erosion, restored cultivation areas, and higher crop yields. Beneficiaries with 5 years of involvement saw significant increases in ROI (from 16.54% to 147.81%), net income (from PhP5,504.45 to PhP45,724.45), and overall income (from PhP42,979.35 to PhP94,095.64). Similarly, 4-year, 3-year, and 2-year participants experienced notable improvements in ROI, net income, and overall income, with significant statistical results. On the other hand, only minimal changes with no significant impact on ROI and net income in the production were observed with 1-year participants. The study found that prolonged participation in the program was associated with higher income, but further analysis using more robust statistical methods is needed to determine the actual drivers of income changes. Based on the findings, recommendations include provision of additional support on manpower or financial assistance on the first year to ensure crop survival; evaluation of the suitability of the research design and methodologies employed for future researches; and formulation and implementation of strategic adaptation and upscaling plan for the SLRMA.

Sustainable Production Systems in the Brazilian Amazon: A Systematic Review

The integration of the Amazon into the global commodities market requires ensuring the rational use of resources to meet market and socio-political demands, such as the UN’s 2030 Agenda. Responsible production practices are essential to address the current demand for sustainable land use and resource management. This study reviewed the literature (2004–2024) on the opportunities and challenges of implementing and consolidating sustainable production systems in the Amazon. It found a low distribution of studies across Brazilian Amazon states and a surge in publications since 2015, focusing on agroforestry systems and forest management. Challenges include socio-political limitations that hinder public decision-making, leading to inefficient policies, as well as economic issues, lack of know-how, inadequate infrastructure, poor logistics, and cultural resistance. Nevertheless, these systems offer opportunities such as intensified and diversified production, carbon sequestration, and soil and forest conservation. Finally, future research should consider political, social, and economic aspects to facilitate the transition from traditional to sustainable models, supporting strategies for consolidating these systems in the Amazon.

Satellite and UAV surveying as tools for monitoring land resources: modern technologies and their application in Ukraine

The article analyzes innovative methods of remote sensing of land resources, in particular satellite imaging and the use of unmanned aerial vehicles (UAVs), which ensure increased efficiency of land resource management through operational monitoring of the state of territories, analysis of the dynamics of changes in land use and a comprehensive assessment of environmental indicators, which together contribute to the optimization of management decisions. It is established that the integration of satellite technologies, characterized by wide coverage and systematic data updating, with highly detailed UAV imaging, focused on local research, forms a comprehensive mechanism for collecting and processing geospatial information, which not only increases the accuracy of monitoring, but also provides for the prediction of structural changes in land use, taking into account environmental, social and economic factors. The study conducted a comparative analysis of the advantages and limitations of satellite and aerial photography, where the key parameters considered were spatial resolution, which determines the scale of the analysis, regularity of data acquisition, which ensures the relevance of information, dependence on atmospheric conditions, which limits the efficiency of the survey, and the cost of using technologies, which affects the accessibility of methods for different categories of users. Based on the results obtained, the critical need for the implementation of geographic information systems (GIS) for automated processing of remote sensing data sets was argued, which allows not only to identify areas of soil degradation, assess their moisture level and track the phenological cycles of agricultural crops, but also to integrate these data into climate change adaptation strategies. A model for the application of remote sensing is proposed, which is based on algorithms for collecting, processing and visualizing data obtained from satellite images and UAV aerial photography, which provides cost-effectiveness by reducing the cost of field research, increasing the accuracy of analysis through a combination of macro-scale and local data, and minimizing environmental risks associated with soil degradation and biodiversity loss.