Land Cover Change Analysis Research Articles

An automated approach for updating land cover change maps using satellite imagery

Land cover change is a critical aspect of global environmental dynamics, influencing ecosystems, biodiversity, and climate change. This study presents an automated approach for updating land cover maps across Europe, combining Sentinel-1 and Sentinel-2 satellite imagery within the Copernicus framework. The application utilises machine learning algorithms to categorise land cover changes into classes such as no change, retained/reclassified, deurbanisation, afforestation, and urbanisation. Case studies in Poland, Greece, and Italy demonstrate the application's effectiveness, revealing the impact of motorway construction, afforestation efforts, and rapid urbanisation. Overall accuracy rates ranged from 68% to 95%, emphasising the reliability of the methodology. The open-source application, implemented in Python Jupyter and Voila, provides a user-friendly platform for researchers and stakeholders to monitor and analyse land cover changes, supporting informed decision-making for sustainable land management and conservation efforts. This study contributes valuable insights to understanding and addressing the environmental consequences of land cover changes in diverse geographical contexts.

Monitoring Wise Use of Wetlands During Land Conversion for the Ramsar Convention on Wetlands: A Case Study of the Contiguous United States of America (USA)

Wetlands provide the world with important ecosystem services (ES) including carbon (C) storage. The Ramsar Convention (RC) is the only global treaty on wetlands outside of the United Nations (UN) with 172 contracting parties across the world as of 2025. The goals of the convention are to promote the wise use and conservation of wetlands, designation of suitable wetlands as wetlands of international importance, and international cooperation. The problem is that there is no consensus for standard global analysis, which is needed to ensure wetlands conservation. The novelty of this study is the use of methodology that combines satellite-based land cover change analysis with high-resolution spatial databases to help understand the change in wetlands area over time and identify potential hotspots for C loss. Greenhouse gas (GHG) emissions from wetland conversions represent “transboundary” damages. Therefore, C loss from wetlands conversions can be expressed through the “realized” social cost of C (SC-CO2) which is a conservative estimate of the damages caused by carbon dioxide (CO2) release. A case study of the contiguous United States of America (USA) using raster analysis within ArcGIS Pro showed key findings that almost 53% of the wetlands area was lost between 1780 and 1980, starting with 894,880.7 km2 in 1780 and falling to 422,388.2 km2 in 1980. This net loss generated damages including midpoint total soil C loss (6.7 × 1013 kg of C) with associated midpoint “realized” social costs of C (SC-CO2) value of $11.4T (where T = trillion = 1012, $ = United States dollars, USD). Recent analysis of the contiguous USA (2001–2021) revealed wetlands area losses and damages in all states. The newly demonstrated method for rapid monitoring of wetlands changes over time can be integrated into systems for worldwide monitoring to support the RC wise use concept.

Dynamics and impacts of monsoon-induced geological hazards: a 2022 flood study along the Swat River in Pakistan

Abstract. This study examines the impacts of the unprecedented 2022 monsoon season in Pakistan's Swat River basin, where rainfall exceeded historical averages by 7 %–8 %. This extreme weather led to catastrophic debris flows and floods, worsening challenges for low-income communities. The resulting financial instability affected millions, causing significant damage to homes, crops, and transportation. The study employs a multidisciplinary approach, combining field investigations, remote sensing data interpretation, and numerical simulations to identify the factors contributing to debris flow incidents. Analysis of land cover changes reveals a decrease in grasslands and an increase in barren land, indicating the adverse effects of deforestation on the region. Topography and gully morphology are crucial in initiating debris flows, with steep gradients and shallow-slope failures predominant. Numerical simulations show that debris flows reached high velocities of 18 m s−1 and depths of 40 m within 45 min. Two debris flows resulted in the formation of dams along the Swat River, intensifying subsequent floods. The study emphasizes the interplay of extreme rainfall and deforestation during the rainy season, rendering the region susceptible to debris flows and hindering restoration efforts. Recommendations include climate change mitigation, reforestation initiatives, and discouraging construction activities in flood-prone and debris-flow-prone regions. The study advocates for enhanced early warning systems and rigorous land use planning to protect the environment and local communities, highlighting the imperative of proactive measures in the face of escalating climate challenges. Additionally, the study investigates the spatial distribution of various events and their consequences, including potential hydrometeorological triggers, and how such events initiate processes that change mountain landscapes. It also assesses the extent to which the 2022 monsoon can be classified as abnormal. The combination of empirical evidence and practical insights presented in this study highlights research gaps and proposes routes toward a deeper understanding of monsoon-triggered geological hazards and their consequences.

NDVI-Based Geospatial Analysis of Forest Cover Alterations in Daldali Reserve Forest, Assam, India

Geospatial analytics integrates spatial as well as temporal data with conventional datasets, enabling for the blossoming of visual representations of the data. A Geospatial tool serves a key function in analysing the shifts in land use and land cover (LULC). The study conducts a comprehensive comparative and analytical analysis of Land Use and Land Cover (LULC) changes in Daldali Reserve Forest (RF) from 1994 to 2024 using the Normalized Difference Vegetation Index (NDVI) method. The investigation focuses on the transformation of vegetation cover over three decades, with NDVI maps from 1994, 2009, and 2024 serving as the primary data sources. The NDVI range was classified into five threshold values: below 0 for water bodies, 0 to 0.2 for bare soil, rock, sand, and cloud, 0.2 to 0.3 for shrubs and grassland, 0.3 to 0.5 for sparse vegetation, and above 0.5 for dense vegetation. The analysis reveals significant trends, including a marked decline in dense vegetation, an increase in sparse vegetation and shrub/grassland, and a troubling expansion of bare soil and degraded land. The study identifies both anthropogenic and natural factors as drivers of these LULC changes. Deforestation and land use change, forest fires, climate change, and soil erosion are highlighted as key contributors to the observed degradation. The ecological implications are profound, with potential consequences including biodiversity loss, disruption of ecosystem services, and reduced resilience to climate change. In response to these findings, the study recommends a multifaceted approach to conservation and restoration, including sustainable forest management, reforestation, fire prevention, soil conservation, and the restoration of hydrological balance. Strengthening legal and institutional frameworks, promoting alternative livelihoods for villagers, and enhancing biodiversity conservation efforts are also critical. The study underscores the urgency of collective action by government agencies, non-governmental organizations, research institutions, and local communities to restore the health and vitality of Daldali RF, ensuring its long-term sustainability and continued provision of ecological, economic, and cultural benefits.

Spatiotemporal analysis of land surface temperature and land cover change: Assessing the impact of urbanization and vegetation dynamics

Background: Land cover change refers to changes in the surface cover of an area over time due to natural and human factors. The urbanization of Cibadak District, near the toll exit, contrasts with the rural Cikidang District, resulting in different dynamics of land surface temperature (LST) and land cover change. This study focuses on the observed temperature increase in both districts from 2013 to 2023, aiming to analyze the relationship between land cover change and LST variation. Methods: This study used a spatiotemporal analysis method, with land cover as the independent variable and LST as the dependent variable. Clustered purposive sampling was used. Land cover was validated using Google Earth imagery, while LST was validated with air temperature data from BMKG. Landsat 8 imagery was processed using the Google Earth Engine (GEE) platform to create spatiotemporal maps of land cover and LST. The relationship between the two variables was analyzed through cross-sectional spatial analysis and statistical calculations, including Spearman correlation and multiple linear regression. Findings: From 2013 to 2023, the average increase in LST in land cover was 7.76°C. The analysis showed that vegetated land cover (forest and garden) showed temperatures between 24-32°C, while bare land had temperatures between 32-36°C, with bare land exceeding 40°C in 2023. The statistical results showed a strong positive correlation between land cover changes and LST increases. The correlation coefficient between 2013-2018 was 0.8117 (R² = 0.6588), and between 2018-2023, it was 0.7925 (R² = 0.6560). Conclusions: This study revealed a significant increase in LST in both study sites from 2013 to 2023, with land cover changes playing a key role in this trend. Urban areas with less vegetation contribute to higher temperatures, while vegetated areas help mitigate temperature increases. Novelty/Originality of this article: This study uniquely combines spatiotemporal analysis and statistical methods to assess the impact of land cover change on LST dynamics.

Assessment of Chemical Pollution Load in Surface Waters of the Turkestan Region and Its Indirect Impact on Landscapes: A Comprehensive Study

This study is aimed at a comprehensive assessment of the chemical composition of surface waters in the Turkestan region and their impact on regional landscapes. The primary objective of the research is to systematically evaluate the level of chemical pollution in the region’s water resources and determine its indirect effects on landscape-ecological stability. In August 2024, water samples from eight sampling points (S1–S8) were analyzed for 24 physicochemical parameters, including total hardness (mg*eq/L), pH, dry residue (mg/L), electrical conductivity (µS/cm), total salinity (mg/L), Al, As, B, Ca, Cd, Co, Cr, Ti, Fe, Pb, Cu, Mg, K, Mn, Na, Ni, Zn, SO₄²⁻, and C₆H₅OH. To determine the degree of pollution, variational-statistical analysis, principal component analysis (PCA), as well as the calculation of the OIP, NPI, and HPI indices were performed. For land use and land cover change (LULC) analysis, LULC classification was carried out based on Landsat data from 2000 to 2020, forming the basis for land resource management and planning. The research results showed a deterioration in the ecological condition of water resources and an increasing anthropogenic impact. Specifically, at point S8, the concentration of Al was found to be 56 times higher than the maximum allowable limit, while the concentration of Fe was 42 times higher. High levels of pollution were also recorded at points S1, S4, S5, and S6, where the increase in Al and Na concentrations caused a sharp rise in the OIP value. The main factors influencing water pollution include industrial effluents, agricultural waste, and irrigation drainage waters. The pollution’s negative impact on regional landscapes has led to issues related to the distribution of vegetation, soil fertility, and landscape stability. To improve the current ecological situation and restore natural balance, the phytoremediation method is proposed. The research results will serve as the foundation for developing water resource management strategies for the Turkestan region and making informed decisions aimed at ensuring ecological sustainability.

A Methodology for the Multitemporal Analysis of Land Cover Changes and Urban Expansion Using Synthetic Aperture Radar (SAR) Imagery: A Case Study of the Aburrá Valley in Colombia

The Aburrá Valley, located in the northwestern region of Colombia, has undergone significant land cover changes and urban expansion in recent decades, driven by rapid population growth and infrastructure development. This region, known for its steep topography and dense urbanization, faces considerable environmental challenges. Monitoring these transformations is essential for informed territorial planning and sustainable development. This study leverages Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission, covering 2017–2024, to propose a methodology for the multitemporal analysis of land cover dynamics and urban expansion in the valley. The novel proposed methodology comprises several steps: first, monthly SAR images were acquired for every year under study from 2017 to 2024, ensuring the capture of surface changes. These images were properly calibrated, rescaled, and co-registered. Then, various multitemporal fusions using statistics operations were proposed to detect and find different phenomena related to land cover and urban expansion. The methodology also involved statistical fusion techniques—median, mean, and standard deviation—to capture urbanization dynamics. The kurtosis calculations highlighted areas where infrequent but significant changes occurred, such as large-scale construction projects or sudden shifts in land use, providing a statistical measure of surface variability throughout the study period. An advanced clustering technique segmented images into distinctive classes, utilizing fuzzy logic and a kernel-based method, enhancing the analysis of changes. Additionally, Pearson correlation coefficients were calculated to explore the relationships between identified land cover change classes and their spatial distribution across nine distinct geographic zones in the Aburrá Valley. The results highlight a marked increase in urbanization, particularly along the valley’s periphery, where previously vegetated areas have been replaced by built environments. Additionally, the visual inspection analysis revealed areas of high variability near river courses and industrial zones, indicating ongoing infrastructure and construction projects. These findings emphasize the rapid and often unplanned nature of urban growth in the region, posing challenges to both natural resource management and environmental conservation efforts. The study underscores the need for the continuous monitoring of land cover changes using advanced remote sensing techniques like SAR, which can overcome the limitations posed by cloud cover and rugged terrain. The conclusions drawn suggest that SAR-based multitemporal analysis is a robust tool for detecting and understanding urbanization’s spatial and temporal dynamics in regions like the Aburrá Valley, providing vital data for policymakers and planners to promote sustainable urban development and mitigate environmental degradation.

Analysis of Ecosystem Service Value Trends and Drivers in the Yellow River Delta, China

Ecosystem service value (ESV) reflects ecosystem functions and benefits; however, the factors influencing ESV and the mechanisms driving it in wetlands and non-wetlands are not yet fully understood. The Yellow River Delta (YRD) is distinguished by the presence of numerous wetland areas that are both Reserve and non-Reserve and thus it was selected as the designated study area. In this study, the spatiotemporal structures of ESV in the YRD between 2000 and 2020 were studied using land cover change analysis and the equivalent factor methodology. In addition, we analyzed the drivers behind the geographical variability in ESV by applying the Geographical Detector method. The results showed that the land structure of the YRD National Nature Reserve was relatively stable, whereas the non-Reserve area exhibited greater fluctuations; that is, wetlands in the YRD non-Reserve area decreased by 11.43% compared with the more stable land structure in Reserve areas, where wetland decreased by 4.93%. Furthermore, disparities in the distribution of land use types gave rise to a discernible spatial distribution of overall ESV, with the northeast exhibiting significantly higher ESV levels compared to the southwest. Additionally, in the past two decades, the center of gravity of the ESV in both regions has shifted towards urban centers, and wetlands have migrated towards the coastline. The Normalized Difference Vegetation Index was identified as the main driver of ESV heterogeneity. The findings of this study are highly relevant to regional ecological conservation and the promotion of economic and social development.

Land cover analysis of two university campuses: Examination over satellite images by Chat GPT

Land Use and Land Cover Analysis are important in detecting the changes in urban areas, rural areas, and focused lands like university campuses. The availability of high-quality satellite images from diverse time sequences makes evaluations for changes by time possible. The analysis methods include insights from remote sensing fields to Artificial intelligence (AI) tools. AI has been significantly developed in the last decades in various fields, and applications of AI on satellite imagery analysis are being influenced. This study explores the capability of Chat GPT, which is one of the leading Language Models and can generate prompts and analysis due to inputs for Land Cover and Use Analysis. Firstly, an unstructured conversation with Chat GPT was held, and then, considering this experience, a land cover change analysis was executed for two university campuses. Besides, the analysis was also re-executed in Colab with codes generated by Chat GPT to seek differences. Two university campuses, Erzurumm Technical University and Adıyaman University, founded in the last two decades, were utilized as case studies. Chat GPT explained the steps and procedure of the analysis in detail generated codes in a defined framework. The analysis results have problems in classifying the land cover; however, the imperviousness change analysis shows most of the construction improvement. The experiment and findings have important implications for future research in Land Cover analysis implementing AI tools.

Integrating google earth engine and random forest for land use and land cover change detection and analysis in the upper Tekeze Basin

Integrating google earth engine and random forest for land use and land cover change detection and analysis in the upper Tekeze Basin

Multi-temporal analysis of land cover changes due to hurricanes Iota and Eta on the islands of San Andres, Providencia and Santa Catalina

Multi-temporal analysis of land cover changes due to hurricanes Iota and Eta on the islands of San Andres, Providencia and Santa Catalina

Multitemporal Analysis of Land Cover Changes in the Rabanal Páramo Ecosystem (Colombia) From 2000 to 2020 and Its Planning Regulations

ABSTRACTThe páramos, with their high biodiversity and benefits to humanity, are crucial ecosystems in Colombia. Despite their importance, there is a significant knowledge gap regarding the extent of land cover changes and the identification of planning regulatory milestones on these ecosystems. The Rabanal Páramo Regional District, a water supplier for the capital city of Tunja, is under threat. The protected area faces land use conflicts, and updated information needs to be provided on how natural covers have been affected by grazing or the expansion of the agricultural frontier. Considering this, the main goal of this research was to recognize land cover changes in the Rabanal Páramo from 2000 to 2020. To achieve this, we conducted a multi‐temporal analysis using the Corine Land Cover methodology adapted for Colombia. Additionally, we identified the various planning regulations that have contributed to this ecosystem's conservation and restoration efforts. Results showed that the bush cover has significantly decreased, from 27.9% of the total area in 2000 to 20.5% in 2020. In contrast, the mosaic of crops, pastures, and natural spaces has expanded from 2.5% of the total area in 2000 to 8.1% in 2020. The primary strategy in the páramo management plan involves using zoning and land type regulations. Urgent action is needed to balance the demands of agricultural development with the preservation of the páramo ecosystem to ensure a sustainable existence between human activities and the environment.

Comparative analysis of land cover changes on outdoor thermal comfort in Doha, Dubai, Kuwait City, Manama, Muscat, and Riyadh

ABSTRACT Rapid urbanization in Gulf cities has driven significant land cover changes, influencing outdoor thermal comfort and land surface temperatures (LST). This study investigates land cover dynamics from 1998 to 2023 across six cities – Doha, Dubai, Kuwait City, Manama, Muscat, and Riyadh – using Landsat imagery to assess LST, Normalized Difference Vegetation Index (NDVI) and approximated wet-bulb globe temperature (AWGBT). Results reveal an increase in urban areas, with Manama and Kuwait City experiencing the largest expansions (47.50% and 47.02%). Vegetation patterns varied, with cities like Dubai and Riyadh showing consistent increases, while Doha stagnated from 2013 to 2023. LST ranged from 42°C to 55°C, with desert areas showing the highest temperatures. Built-up areas had LST comparable to desert land, highlighting a reverse urban heat island effect. Dubai’s LST decreased between 2013 and 2023 due to successful green initiatives, contrasting with rising temperatures in other cities. The mean LST difference between the desert and urban areas was 2.5°C, and vegetation displayed a cooling effect, with a 3.5°C difference between vegetated and desert areas. Thermal comfort maps aligned with LST data, showing increasing heat stress, particularly in Doha and Kuwait City, while Dubai maintained stable comfort levels. This study underscores the critical role of vegetation and sustainable urban planning in mitigating heat stress and enhancing outdoor thermal comfort across Gulf cities.

Flood Hazard Prediction and Land Cover Change Analysis in Tegal Regency for 2045: A Geospatial Approach Using Geographic Information System

Purpose: The purpose of this research is to analyze the prediction of flood hazard in 2045 from the flood hazard map and compare it with the 2023 data geospatially using Geographic Information Systems. Methodology: The purpose of this research is to analyze the prediction of flood hazard in 2045 from the flood hazard map and compare it with the 2023 data geospatially using Geographic Information Systems. Results: The results showed that Tegal's highly flood-prone areas would increase by 1.7% in 2045, decreased by 0.59% in prone areas, and decreased by 0.77% in moderately prone areas by 2023. Applications/Originality/Value: One of the causes was a very high land cover change caused by an increase in built-up land and a decrease in vegetation.

Land use and cover changes and sand fly (Diptera: Psychodidae) assemblages in an emerging focus of leishmaniasis.

Land use and cover changes lead to fragmentation of the natural habitats of sand flies and modify the epidemiological profile of leishmaniasis. This process contributes to the infestation of adjacent rural settlements by vector sand fly species with different degrees of adaptation, promoting leishmaniasis outbreaks. This study aimed to assess land use and cover changes over a 12-year period and investigate the diversity and abundance of sand fly assemblages in the rural area of Codó, Maranhão State, Brazil. Temporal analysis of land use and cover changes was conducted using Sentinel-2 satellite imagery treated in QGIS software (free version 3.10) and classified using Orfeo Toolbox. Sand flies were sampled in alternate months between August 2022 and June 2023 using Centers for Disease Control and Prevention (CDC) light traps and white and black Shannon traps installed in peridomestic and extradomestic environments. Map images showed that the predominant land covers in 2012 and 2014 were dense and sparse vegetation, with few buildings. In 2021 and 2023, areas of sparse and dense vegetation were fragmented as new settlements were established. The entomological survey resulted in the capture of 3375 sand flies (CDC = 856, white Shannon = 650, black Shannon = 1969) belonging to 20 species. The most abundant species were Psychodopygus wellcomei Fraiha, Shaw & Lainson, 1971 (78.19%), followed by Nyssomyia whitmani (Antunes & Coutinho, 1939) (7.53%). Additionally, Ny. whitmani was the most abundant species (84.97%) in peridomestic environments, whereas Ps. wellcomei was the most abundant species (96.51%) in extradomestic environments. The sand fly assemblage was highly diverse, with a high abundance of competent vectors of Leishmania spp. These findings can promote community participation in surveillance and control efforts to prevent leishmaniasis cases.

Traditional Dual Tank Water Management: A Study of Kolakanatham in Perambalur District, Tamil Nadu

Societies make rainwater harvesting structures to collect and store rain water to cater for their immediate and future needs. In a remote village in the Perambalur district, the people use the dual temple tank, nearly 350 years old, for their drinking purposes. This study tries to understand the traditional knowledge embedded in the dual pond structures, cascade aerators, and an open well connected with the ground aquifer inside the tank. This study delves into projected rainfall trends using NASA POWER data for 2050, revealing a noticeable increase and an assessment of pond water quality parameters demonstrating adherence to acceptable limits set by the Bureau of Indian Standards (BIS). However, despite meeting these standards, the water quality index deems the water unsuitable for direct human consumption, though it remains suitable for domestic household use. Moreover, analysis of land cover and land use changes by Sentinel2A data over the past 22 years underscores significant growth in settlement areas, indicative of a rising demand for water resources. The study proposes the restoration and utilization of the dual tank system as a potential solution to address this burgeoning water demand, offering a reliable water source and preserving traditional practices within the local community.

Analisis Perubahan Penutupan Lahan di Taman Hutan Raya Pocut Meurah Intan (Tahura-PMI) Aceh

The study focuses on the analysis of land cover changes in the PMI Forest Park (Tahura PMI) area from 2012 to 2018. It aims to understand the reasons for these changes by utilizing the Geographic Information System (GIS) method and comparing three land use interpretation maps derived from Landsat ETM+ images taken in 2012, 2015, and 2018. The results of the classification show that TAHURA PMI has eight land cover classes, with 2 categorized as forest and 6 as non-forest. Forest management in this area is crucial to prevent further forest degradation and is therefore a key focus of the study.

Spatiotemporal analysis of land surface temperature and land cover changes in Prešov city using downscaling approach and machine learning algorithms

In recent decades, global climate change and rapid urbanization have aggravated the urban heat island (UHI) effect, affecting the well-being of urban citizens. Although this significant phenomenon is more pronounced in larger metropolitan areas due to extensive impervious surfaces, small- and medium-sized cities also experience UHI effects, yet research on UHI in these cities is rare, emphasizing the importance of land surface temperature (LST) as a key parameter for studying UHI dynamics. Therefore, this paper focuses on the evaluation of LST and land cover (LC) changes in the city of Prešov, Slovakia, a typical medium-sized European city that has recently undergone significant LC changes. In this study, we use the relationship between Landsat-8/Landsat-9-derived LST and spectral indices Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) derived from Landsat-8/Landsat-9 and Sentinel-2 to downscale LST to 10 m. Two machine learning (ML) algorithms, support vector machine (SVM) and random forest (RF), are used to assess image classification and identify how different types and LC changes in selected years 2017, 2019, and 2023 affect the pattern of LST. The results show that several decisions made during the last decade, such as the construction of new urban fabrics and roads, caused the increase in LST. The LC change evaluation, based on the RF classification algorithm, achieved overall accuracies of 93.2% in 2017, 89.6% in 2019, and 91.5% in 2023, outperforming SVM by 0.8% in 2017 and 4.3% in 2023. This approach identifies UHI-prone areas with higher spatial resolution, helping urban planning mitigate the negative effects of increasing urban LSTs.

ANALISIS PREDIKSI PERUBAHAN TUTUPAN LAHAN TAHUN 2033 MENGGUNAKAN METODE CELLULAR AUTOMATA DAN LOGISTIC REGRESSION

Land cover change is an important phenomenon that illustrates the interaction between human activities and land resources. Kemiling District, experienced an increase in land needs due to significant population growth from 2013 to 2023, which was 23,147 people or 36.64%, which prompted the need for research to predict land cover changes until 2033. Remote sensing technology and Geographic Information Systems (GIS) allow analysis of land cover changes by considering driving factors such as roads, settlements, soil types, slopes, and population density. The Cellular Automata (CA) and Logistic Regression (LR) methods were chosen because of their accuracy in spatial-temporal simulations. This study uses Landsat 8 imagery data for 2013, 2018, and 2023. The data is processed using a supervised classification method with the Support Vector Machine (SVM) algorithm. The results of the study showed significant changes in land cover, especially an increase in residential areas by 47.24% in 2023. Predictions for 2033 indicate that built-up land will continue to increase, while agricultural and open land will decrease. The CA and LR methods proved effective with kappa values in the good category. This study provides knowledge for decision-making on spatial planning and sustainable development in Kemiling District.

Drivers and Dynamics of Forest and Grassland Ecosystems in the Altai Mountains: A Framework for National Park Conservation

The Altai Mountains region, characterized by its unique biodiversity and significant ecological value, is increasingly under pressure from anthropogenic activities and climate change. This study investigates the spatial and temporal dynamics of forest and grassland ecosystems in the Altai Mountains National Park Candidate Area from 2000 to 2020, and proposes a comprehensive framework for the conservation and management of national parks. Through a detailed analysis of land cover changes, we observed significant forest expansion of 13.65% and grassland degradation of 11.69%. Rapid forest expansion occurred before 2010, followed by accelerated grassland degradation after that, with 2010 identified as the critical turning point. Our analysis highlights the role of key drivers, such as soil type, elevation, cropland expansion, and human activities, in shaping these ecosystems. Using Geodetector and propensity score matching methods, we evaluated the effectiveness of existing protected areas in mitigating forest and grassland loss. While protected areas effectively contributed to forest restoration, they were less successful in preventing grassland decline, underscoring the need for integrated management approaches. The findings from this study provide critical insights into ecosystem dynamics and conservation effectiveness, offering valuable guidance for the establishment of national-park-type protected areas and broader regional conservation efforts.