Land Cover Research Articles

The Impact of Population Density, Land Cover, and Spatial Patterns on the Geoecological Evaluation of the Surabaya Coast

Abstract Coastal areas are vulnerable to the influence of geomorphological processes from activities in the waters, so that the condition of coastal ecosystems will always change according to the dynamics that occur on the coast, both changes could be occurred in coastlines and land cover. Geoecology is the spatial interaction between natural phenomena and their relationship to phenomena in a particular ecosystem. Surabaya is a coastal city that frequently experiences changes in land cover. The construction of new settlements and changes in land use have a significant impact on the coastal environment of Surabaya. This research aims to determine the influence of population density, land cover and spatial plan on the geoecological evaluation of Surabaya’s coast using remote sensing data. The data used in the research is Landsat satellite image data from 1994, 2003 and 2018 (maximum likelihood classification method). The dynamics of changes in coastlines on the coast of Surabaya are caused by three phenomena, namely erosion, accretion and anthropogenic. Each phenomenon has a different value for land cover class, population density and spatial patterns. The research results show that geoecological evaluation is closely related to phenomena that occur on the Surabaya coast (value range 1-3). The geoecological evaluation value due to the erosion phenomenon is low (30%) and medium (70%). The geoecological evaluation value due to the accretion phenomenon is low (100%). Meanwhile, the geoecological evaluation value due to anthropogenic phenomena is low (25%) and medium (75%). Geoecological evaluation of Surabaya’s coast shows that it is in a “safe” condition, but there is requirement for coastline management. Where the coastal area planning process is carried out by identifying the dynamics of coastal areas and potential risks and consequences related to decisions that produce different future scenarios.

Analysis and Mapping of the Distribution of Groundwater Recharge Areas Using the Scoring Method (Case Study: Singgahan and Montong District, Tuban).

Abstract Tuban Regency, located in the northern part of East Java, borders the Java Sea to the north and is intersected by several rivers. The presence of numerous rivers and limestone or karst hills in regions like Singgahan and Montong Districts suggests significant potential for these areas to function as groundwater recharge zones. A groundwater recharge area is characterized by the vertical movement of groundwater, driven by the flow of water along the gradient of aquifers. Identifying and preserving these recharge zones in Singgahan and Montong is crucial to maintaining the groundwater supply, ensuring its availability in sufficient quantity and quality to meet the ongoing needs of ecosystems and human consumption. This study utilizes a scoring method to identify potential recharge areas, incorporating parameters such as land cover, soil type, rainfall, and slope inclination. In this method, each parameter is assigned a specific weight: slope inclination (20%), soil type (35%), rainfall (15%), and land cover (30%). The analysis revealed that the land cover in these districts primarily consists of plantations, with 6,840.63 hectares in August and 6,761.88 hectares in October. The slope inclination in Singgahan District is predominantly flat, covering 8,417.68 hectares. Both Singgahan and Montong Districts experience low rainfall, with 57% of the total area, equivalent to 12,670.89 hectares, falling into this category. The most prevalent soil type is Cambisol, which constitutes 33% of the area, or 7,463.46 hectares. Using the scoring method, it was determined that in August, Singgahan and Montong Districts possess high potential for recharge areas, with high-level recharge zones covering 1,314.70 hectares and medium-level recharge zones spanning 18,498.78 hectares. By October, the extent of high-level recharge zones had increased to 3,499.20 hectares, while medium-level recharge zones covered 16,942.45 hectares. These findings underscore the importance of managing these recharge areas to sustain groundwater resources effectively.

Assessment of Water Poverty Index (WPI) Under Changing Land Use/Land Cover in a Riverine Ecosystem of Central India

Watershed Development is a very common phenomenon in the river basins in India due to its dynamic and continuously changing nature, which are interconnected via. Land use/land cover (LULC) change and water poverty scenario over time. In the present study, the samples were chosen from seven sampled villages for the Water Poverty Index (WPI) in the upper Tons River Basin. Among them, Ghunwara and Maihar Village exhibit the highest and lowest WPI, i.e., 98.1 and 62.91 out of 100, respectively. This indicates that villages with a high WPI face challenges in their water requirements, regardless of the seasonal river serving the basin area. Conversely, villages with a low WPI can satisfy their water needs solely from the basin. The present analysis of the Upper Tons River Basin suggests that Land Use and Land Cover (LULC) will undergo influences or adjustments at various stages, ultimately affecting agricultural land in the impact region. It also becomes evident that areas with limited land use and land cover (LULC) extensions exhibit lower Water Productivity Index (WPI), primarily due to their reliance on agricultural land. It is observed that alterations, reductions, or modifications in LULC lead to changes in multiple aspects of agricultural land, resulting in noticeable variations in various metrics. The present paper not only evaluates the land use in the Upper Tons River Basin spanning from 2001 to 2021 but also highlights the changing patterns that impact water resources and their utilization capacity. Furthermore, the study estimates the influence of reducing specific features on the distribution of WPI and other LULC parameters. The Upper Tons River Basin faces challenges such as unfavorable rainfall patterns and inadequate planning for irrigation at the fundamental and local levels. Additionally, its geographical location in a rainfed area negatively affects the WPI.

Analisis Perubahan Tutupan Lahan di Kecamatan Pangururan Kabupaten Samosir Menggunakan Sistem Informasi Geografis

Land cover changes in Pangururan District, Samosir Regency, are a significant phenomenon due to population growth and development. This study aims to analyze land cover changes over the period 2018-2022 using Geographic Information System (GIS) technology and Sentinel-2 satellite imagery. The analysis was conducted using the overlay method to compare land cover maps from two different time periods. The results indicate an increase in agricultural land and built-up areas, along with a decrease in shrubland. These changes have impacted the environment and local communities, including erosion potential, declining soil quality, and urbanization. This study is expected to provide insights for sustainable land management in Samosir Regency.

Spatial-Based Flood Susceptibility Mapping Using Analytical Hierarchy Process Techniques (Case Study : Gresik Regency)

Abstract Over the past four years, Gresik Regency has frequently been hit by floods. In 2019, there were 17 occurrences, followed by 13 occurrences in 2020, 19 occurrences in 2021, and a rise to 33 occurrences in 2022. To support the 11th Sustainable Development Goals (SDGs) on sustainable cities and communities, especially in the mitigation of water-related disasters, research on flood-prone areas in Gresik Regency is needed. This study analyzes flood-prone areas in the Gresik Regency using the Analytical Hierarchy Process (AHP) and weighted overlay methods to provide information to the community and agencies to be used as a reference in disaster mitigation. The data utilized include the Digital Elevation Model, river network data, Sentinel image data, soil type data, rainfall data, and questionnaire results. The criteria used in this research are land elevation, slope, land cover, river distance, soil type, rainfall, and flood extent. Results show that rainfall criteria have the highest weight at 25%, followed by elevation at 17%, flood extent at 17%, land cover at 15%, river distance at 10%, slope at 9%, and soil type at 7%. The area of Gresik Regency classified as low vulnerability is 8.79 km2 or 0.82% of the total area, the medium vulnerability category is 936.13 km2 or 87.60% of the total area, and the high vulnerability category is 123.66 km2 or 11.57% of the total area. The most flood-prone sub-districts are Driyorejo with 28.46 km2 of highly vulnerable flood-prone area, followed by Wringinanom with 13.17 km2 Benjeng with 12.50 km2.

Fine-scale associations between land cover composition and the oviposition activity of native and invasive Aedes (Diptera: Culicidae) vectors of La Crosse virus.

Land cover is recognized as an important determinant of mosquito community assemblages and pathogen transmission, but few studies have investigated the role of land cover in populations of La Crosse virus (LACV) vectors. La Crosse virus is a zoonotic disease primarily maintained by native Aedes triseriatus, with accessory transmission by invasive vectors Ae. albopictus and Ae. japonicus. The objective of this study was to investigate the association between land cover composition and the egg abundances of LACV vectors in a city with endemic LACV transmission. Oviposition cups were deployed at sites ranging from recreational forests to urban areas. Mixed-effects regression models were used to test the relationships between land cover composition and species abundances. Sites characterized by large percentages of impervious and built surfaces were dominated by Ae. albopictus, but Ae. triseriatus was the most abundant species in forested areas. Aedes japonicus was rare at all sites, but its presence was associated with higher percentages of forested land. These results indicate that forested areas may allow Ae. triseriatus to maintain large populations near urban areas that are dominated by Ae. albopictus. Further research should explore the potential for these land cover-dependent population dynamics to influence LACV transmission cycles.

Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach

The terrestrial ecosystem plays a vital role in regulating regional and global carbon budgets. Ecosystem models are extensively employed to estimate carbon fluxes across different spatial scales. However, there remains a need to reduce the uncertainties associated with model parameterization and input data. To address these limitations, we assessed a distributed-calibration and independent-verification (DCIV) approach that uses (1) remotely sensed net primary production (NPP) and evapotranspiration (ET) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), (2) multi-site eddy covariance net ecosystem exchange (NEE) data; and (3) field sampling of soil organic carbon (SOC) and aboveground biomass (ABG) data to improve the overall predictability of carbon fluxes for the different land use and land cover (LULC) types at a watershed scale. The DCIV approach was applied to an advanced version of the Soil and Water Assessment Tool (SWAT)-Carbon (or SWAT-C), equipped with Century-based SOC algorithms to simulate carbon dynamics for watersheds with heterogeneous vegetation. The objective of the modeling effort was to assess carbon stocks and fluxes under different land management scenarios for a 3000-acre experimental farm and forest preserve in the northeastern United States. Our study showed that a large SOC stock of at least 100 tons ha−1 is stored under mixed forest, deciduous, shrubland, and floodplain (grass). Our study also showed that converting floodplain (grass) to deciduous forest has the potential to increase CO2 uptake (-NEE) by an order of three magnitude and ABG by 77 %, leading to an increased SOC stock of 23 % after twenty years. Similarly, we found that converting ungrazed grassland to grazed pasture leads to a non-statistically decreasing trend of SOC, especially in the 0–30 cm soil layer. Thus, the methodology used in this study can be applied to improve carbon dynamic prediction from a heterogeneous watershed at a regional scale.

Developing an early warning land degradation indicator based on geostatistical analysis of Ecosystem Functional Types dynamics

Identifying and quantifying ecosystem degradation and recovery is of critical importance for ecosystem health, biodiversity, food security and the livelihoods of local communities. Remote sensing datasets and techniques, particularly land cover maps, provide crucial data for capturing spatial and temporal changes, supporting informed decision-making and targeted interventions. However, these maps often emphasize structural rather than functional attributes and, also due to their thematic and temporal resolution, may not detect early degradation signs. This study evaluates the effectiveness of Ecosystem Functional Attributes (EFAs) and Ecosystem Functional Types (EFTs) as early warning indicators of ecosystem degradation. Using Sentinel-2 derived Normalized Difference Vegetation Index (NDVI) data from 2016 to 2022, we conducted qualitative and quantitative EFA/EFT analysis on two fragile protected areas, Moyowosi Game Reserve (Kigoma region, Tanzania) and the Sheikh Jamal Inani National Park (Cox’s Bazar district, Bangladesh). Firstly, EFA analysis characterized vegetation productivity and seasonality, revealing temporal trends and spatial patterns of change. Secondly, EFTs served as indicators of change levels. Our findings showed significant insights into productivity shifts due to human activities and climate anomalies, identifying specific temporal events and turning points. Variogram-based geostatistical analysis highlighted changes in vegetation diversity’s spatial distribution. Integrating EFA/EFT analysis with geostatistical methods proved effective for early detection of land degradation, surpassing traditional land cover change analysis. Hence, the presented approach forms a robust framework for an early warning system, aimed at monitoring and evaluating environmentally fragile areas and aiding decision-makers in mitigating environmental degradation and promoting sustainable land management.

Human-induced landscape modification in the in the last two centuries in the Po delta plain (northern Italy)

Historical maps with high degree of accuracy permit quantitative reconstructions of past land use and land cover (LULC), crucial to assess the impact of human activities on landscape evolution. After georeferencing in a modern reference system, the Carta del Ferrarese commissioned by Napoleon Bonaparte and completed in 1814, has been used to quantify the changes in LULC, occurred in the last two centuries in the Po delta plain. The map depicts a palaeo-landscape dominated by wetlands (49% of the study area) and agricultural areas (41%), whereas forests appear already largely depleted (5%). The Piantata Padana, a traditional agroforestry system with live trees used to support grapevines, is dominant (62% of the agricultural areas). The comparison with the 2014 LULC map highlights a dramatic reduction (85%) of wetland areas and the replacement of the Piantata Padana with bare arable lands, with the consequent removal of 4-40 million trees. Soils of areas formerly occupied by wetlands show high organic-carbon content, highlighting the potential of humid areas in carbon sequestration. Land reclaiming, prompted by the introduction of steam pumps, favoured the economic development of the area, but concurred to CO2 emissions through the oxidation of soil organic substances, energy consumption from pumping stations, and the extensive use of hydrocarbon fuels in agriculture. Although urbanisation is limited in the Po delta plain, this area appears nowadays largely shaped by human activities, with the dominance of lands devoted to agriculture, dissected by a dense network of draining channels. The landscape changes recorded in the last two centuries in the Po coastal plain have been uniquely driven by human activities, like in several coastal plains worldwide.

Stream flow modeling using SWAT model and performance evaluation in Adhaim watershed

https://ejuow.uowasit.edu.iq Understanding the hydrological cycle and finding data crucial for water management require a foundational understanding of basin-scale simulation. To create the most accurate stream flow modeling possible, data from the weather stations was combined with input from other maps of the research area, such as soil, land cover, land use (LCLU), and digital elevation model (DEM). Regarding Adhaim Watershed, period 2000 to 2013SWAT model calibration was done using the Sequential Uncertainty Fitting (SUFI–2) technique and the Nash–Sutcliffe simulation efficiency (NSE) and coefficient of determination (R2). This included an initial calibration from 2000/1/1 to 31/9/2009 which was then validated for 1/10/2010 to 31/12/2013 using daily streamflow values. Adhaim Watershed's whole area was determined to be 11816.82 km2. Grasslands make up 41.01% of the total area. Additionally, 28% of LCLU came from croplands and desolate regions in equal measure. A hydrological type D clay soil was discovered, along with the watershed's primary slope (0–5). The mean streamflow of Adhaim was determined to be 22.m3/????????????, and upon calibration, it was found to be 30 ????3/????????????.

Identification of direct and indirect drivers of land use and land cover changes from agriculture to Eucalyptus plantation using the DPSIR framework in Sinan and Mecha Districts of Northwestern Ethiopia

Identification of direct and indirect drivers of land use and land cover changes from agriculture to Eucalyptus plantation using the DPSIR framework in Sinan and Mecha Districts of Northwestern Ethiopia

Groundwater Potential Mapping Using Random Forest and Extreme Gradient Boosting Algorithms

Abstract The availability of groundwater is a crucial solution to ensure the sustainability of water resources, including providing clean water. Therefore, efforts to map groundwater potential are required to enhance the efficiency of groundwater utilization and support achieving one of the Sustainable Development Goals (SDGs), particularly clean water and sanitation. This research aims to identify the distribution of the groundwater potential in Kediri Regency using the random forest (RF) and extreme gradient boosting (XGB) algorithms. This research utilizes 13 parameters, including elevation, slope, aspect, drainage density, river density, distance from rivers, lineament density, Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), land cover, soil type, lithology, and band 3 from Sentinel-2A satellite imagery. The coordinates of groundwater wells are used as training and testing data with ratios of 80:20, 70:30, and 60:40. Through the evaluation of each model’s performance using a confusion matrix, it is revealed that the best model is the RF 70:30 ratio model with Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), Positive Predictive Value (PPV) values of 0.978, Cohen’s Kappa (CK) and Matthew’s Correlation Coefficient (MCC) of 0.956, and Area Under Curve (AUC) of 0.994. In this model, the elevation has the highest influence on the model, with a significance level equal to 100.

Integration of Cloud Score+ with Sentinel-2 Harmonized for land use and land cover classification using machine learning algorithms

Abstract West Kalimantan, Indonesia, has exotic nature reserves, with Danau Sentarum National Park (Sentarum) as its icon. Considering this importance, observing the use of Land Use Land Cover (LULC) around the lake is very important, which can be done using a remote sensing approach. Being in the equatorial region often results in continuous cloud cover throughout the year, which results in inaccurate observations and analysis results. Almost all cloud cover filters are used, but the results are often less than satisfactory. This study shows how the Cloud Score+ (CS+) product with Sentinel-2 Harmonized (S2H) can monitor image clarity in Sentarum and process it using machine learning to produce reliable (LULC) maps. Finding reasonably eliminating clouds, and clear pixels, and cloud shadows from L2A (surface reflection) or L1C (top-of-atmosphere) photos are both possible with the help of CS+ output. Using the RF algorithm, we conducted LULC research in Sentarum and produced precise results for five different LULC classes, namely water bodies, agriculture, forests, barren land, and urban. Sentinel-2 data for 2022-2023 is the basis for mapping because of anomalies that occurred in both years. In 2022, there will be extreme rainfall in Sentarum, while in 2023, the start of El Nino will begin. To identify the study area, three satellite indices were used: Normalized Difference Vegetation Index, Modified Normalized Difference Water Index, and Normalized Difference Building Index. Overall, the final results of the research in 2022 and 2023 produced an overall accuracy of 94.52% and 93.97% and a kappa index of 92.22% and 91.79%.

Disentangling Spatio‐Temporal Impacts of Multiple Environmental Factors on the Global Discharge Regime

AbstractGlobal discharge regimes are strongly affected by multiple environmental drivers. It remains a challenge to relate their contributions and complex spatio‐temporal cause‐effect chains to a given time and location. To disentangle these relationships, we combined 12 environmental variables with changes in more than 25,000 discharge time series globally and identified the variable importance across climate zones using random forest analyses. The results show that (a) about two‐thirds of the global catchments experienced significant changes in discharge between 1980s and 1990s and (b) more than 80% of the basins with new dams built during the study period have experienced changes, twice as many as basins without dams. Furthermore, (c) most environmental variables were subject to significant changes, especially precipitation, temperature and urban land cover; (d) strong changes in the discharge regime were mostly associated with precipitation, followed by land cover changes, and (e) impacts of water infrastructure are dominant in basins with weak evidence of change in discharge. Our findings highlight the contribution of each individual environmental factor on global discharge regimes and can be used to inform water‐related studies and global modeling efforts in the future.

Morphological changes of river Bank Erosion and channel shifting assessment on Arial Khan River of Bangladesh using Landsat satellite time series images

The continuous processes of erosion and deposition in rivers are intricately linked to changes in land use and land cover (LULC), reflecting dynamic landscape transformations. This study investigates the rate of riverbank erosion, channel shifting, and the impact of erosion on LULC dynamics along the Arial Khan River. Utilizing GIS and remote sensing techniques, we analyzed time-series satellite images from 1993 to 2024 within a 2 km buffer zone of the floodplain. Supervised classification methods were applied for LULC analysis, with results validated using confusion matrices to assess classification accuracy. ArcGIS 10.8 was employed to analyze channel centerline migration, while NDVI analysis assessed vegetation health. The study reveals that between 1993 and 2024, 5142.60 ha were lost to erosion, while deposition affected 4756.52 ha, with erosion surpassing deposition overall. The river shifted an average of 0.66 km during this period, with higher migration rates in Muladi Upazila compared to Babuganj Upazila. Settlement areas expanded significantly from 137.82 ha in 1993 to 2768.68 ha in 2024, while agricultural land declined substantially. These findings provide valuable insights for policymakers to address erosion severity in confluence zones and lay the groundwork for sustainable land management practices.

Quantifying sustainable urbanization by predictive modeling for better agricultural management: A case study in the South Asiatic Region

Global population growth and uncontrolled are creating threats to agricultural land. To address urbanization, proactive planning is required. Land use and land cover (LULC) classification maps for 2002-2022 were analyzed using remote sensing (RS) and geographic information systems (GIS) in Sahiwal, Punjab, Pakistan. Idrisi's Cellular Automata (CA)–Markov model was used to predict future scenarios. The results showed that urbanization was rapidly accelerated in large LULC changes that were unpredictable. In particular, the urbanized area increased by 234.7 km2 (91%) from 22.83 km2 in 2002 to 257.53 km2 in 2022, with a reduction of 656.05 km2 (52%), from 1252.52 km2 in 2002 to 596.47 km2 in 2022, of agriculture land. About 17.05 km2 of land was lost to urbanization; however a large portion of CA 251.75 km2 was absorbed due to careless urban growth. The CA-Markov projection revealed that from 2022 to 2042, agriculture will experience the largest net change, losing about -226.09 km2 of land. However, the projected results showed that the urban class will be expanded up to 450.23 km2 and will gain approximately 192.7 km2 in 2042. The overall findings show that it is possible to manage outcomes in a quantitative way and control haphazard and unplanned urban sprawl by putting forward a comprehensive master plan.

Analysis of vegetation dynamics from 2001 to 2020 in China's Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning

Rare earth mining, essential for modern industries and economic growth, often leads to severe environmental degradation. Previous research has explored the ecological impacts of rare earth mining but has not fully investigated the intricate interplay and subdivisions of environmental and anthropogenic factors driving vegetation changes over extended periods. This study addresses this gap by employing time series remote sensing and SHAP-enhanced machine learning to analyze vegetation dynamics in China's Ganzhou rare earth mining area from 2001 to 2020. Using the kNDVI derived from Landsat data, we identified three distinct vegetation trajectory types: pro-environment, des-environment, and res-environment. An ensemble machine learning model combined with SHAP analysis revealed the cropland area proportion, PM10 levels, and shrubland area proportion as the most influential factors affecting vegetation across all mining types. Additionally, after 2012, the palmer drought severity index and downward surface shortwave radiation emerged as positive contributors to vegetation health, while population pressure had a more substantial negative influence in des-environment areas. Our findings highlight spatial heterogeneity in vegetation recovery patterns and highlight the complex interactions among land cover changes, air quality, climate factors, and human activities in shaping vegetation dynamics. This study provides valuable insights for developing targeted, context-specific restoration strategies in rare earth mining areas, contributing to more sustainable mining practices and global environmental management.

On soil districts

In 2023, the European Commission released a legislative proposal for a Directive on Soil Monitoring and Resilience which aims to define a legal framework to achieve healthy soils across the European Union (EU) by 2050. A key component of the initial Directive is the mandate for Member States to establish basic geographic soil governance units, referred to as soil districts, and appoint a district-specific authority to oversee the implementation of soil health assessments. This paper proposes an operational definition of the districts following the conditions outlined in the proposal for the Directive and discusses various attention points for their implementation. Tentative districts were developed for seven EU countries, considering soil type, climate, topography, and land cover factors, starting from the smallest existing administrative unit (i.e. municipalities). Experts were asked to report on the applicability of the proposed districts within well-known pedo-ecological regions and discuss the relevance of the districts for establishing an EU-wide monitoring network and reporting on soil health and degradation. The outcomes highlight the need for detailed soil maps to account for specific soil types when stratifying countries into soil districts. The soilscape approach allows for a consistent method to defining soil districts across Member States. This enables contrasting soils within a district to be managed in a similar manner, with soil degradation/health thresholds applied to each district based on land cover. However, it is unclear whether soil districts as currently formulated in the Directive are in fact the right tool to support local soil management and monitoring of soil health. Districts can help ensure that all soil conditions are covered in a monitoring system, but they may not provide support for soil management or monitoring at a local scale due to short-scale soil variability and threats affecting soil management within the same soilscape. Beyond the use of districts for designing a European/national scale monitoring system, the districts can help create animations and other educational tools to promote soil literacy and connectivity of users to soils locally.

Geomorphic characterization by integrating automated approaches for sustainable land use planning in the earthquake-prone Himalayan region of Uttarakhand, India

This study leverages two cutting-edge automated landform classification approaches—Topographic Position Index (LCTPI) and Geomorphon (LCGm)—to map and categorize landforms in Uttarakhand, a strategically vital region in the Himalayas frequently impacted by natural hazards. Situated at the subduction boundary of the Indian and Eurasian tectonic plates, Uttarakhand's complex landscape demands innovative approaches for sustainable land use planning. Our analysis links classified landforms to critical factors such as slope, geology, soil types, land use land cover (LULC) patterns, fault density, and regional seismicity. Results reveal that high ridges, deep valleys (LCTPI), and slopes (LCGm), predominantly covered by trees and rangelands, are associated with lithosols and dystric cambisols soil types from the Precambrian period. In contrast, flat areas, shoulders, footslopes, U-shaped valleys, open slopes, and mild slope ridges, mainly used for agriculture and urban development, exhibit lower seismic activity. Notably, shallow earthquakes are more frequent in deep valleys and high ridges, which have low to moderate fault density and are majorly occupied by trees and rangelands. This research offers a robust framework for geomorphic characterization that can be adapted to other regions, despite the inherent limitations of automated studies, such as DEM resolution and neighbourhood cell size. Our approach supports sustainable land use planning, biodiversity preservation, natural resource management, and disaster risk reduction, ultimately fostering resilient futures in the built environment. This study emphasizes the necessity of integrating geomorphic insights into the planning process to mitigate natural hazards and enhance environmental sustainability in the Himalayan region by providing actionable insights for land use planners and policymakers.

Assessing the impact of land use and land cover changes on soil properties and carbon sequestration in the upper Himalayan Region of Gilgit, Pakistan

Assessing the impact of land use and land cover changes on soil properties and carbon sequestration in the upper Himalayan Region of Gilgit, Pakistan