Journal of Geomatics

Scientific Mapping and Scientometric Evaluation of Geospatial Technologies in Landslide Research: Trends, Collaboration Networks, and Emerging Research Directions

Adarsh T — 2026-10-28

Landslides remain one of the most frequent and destructive natural hazards globally, significantly influenced by climate change, rapid urbanization, deforestation, and unplanned developmental activities. In response, geospatial technologies such as Remote Sensing (RS), Geographic Information Systems (GIS), Light Detection and Ranging (LiDAR), Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS) and machine learning techniques have increasingly been applied to improve landslide prediction, monitoring, susceptibility mapping, and disaster risk reduction. This study presents a bibliometric evaluation of global research trends related to geospatial technologies in landslide studies during the period 2000–2025. A total of 14,909 publications indexed in Web of Science and Scopus were analysed using the bibliometrix package in R. The analysis identified 2,385 publication sources and an annual scientific growth rate of 14.64%, indicating the rapid expansion of research activities in the field. The study examined publication trends, citation impact, thematic evolution, keyword structure, and international collaboration patterns. The results revealed substantial growth in scientific production, particularly after 2018, driven by advances in spatial data acquisition, artificial intelligence integration, and increasing global disaster risk awareness. China, Italy, India, and the United States emerged as the leading contributors to the research domain. Keyword and thematic analyses demonstrated a clear transition from conventional geomorphological and mapping-based approaches toward advanced machine learning-driven predictive modelling and geospatial monitoring frameworks. The study further identified two major interconnected research streams: remote sensing-based monitoring and GIS-based susceptibility modelling. The findings highlight the growing global significance, interdisciplinary nature, and technological advancement of geospatial applications in landslide research and disaster management.

Decadal High-Resolution Monitoring of Climate-Driven Phenological Shifts Across Diverse Vegetation Zones of the Great Himalayan National Park Conservation Area

Sanjo Jose Veliyathukudy — 2026-10-28

Vegetation phenology is a sensitive indicator of climate change, particularly in mountainous ecosystems where elevation-driven climatic gradients strongly influence growing season dynamics. This study examined the spatial and inter-annual variability of the Start of Season (SoS), End of Season (EoS), and Length of Season (LoS) across distinct vegetation zones of the Great Himalayan National Park Conservation Area (GHNPCA) from 2015 to 2025. To achieve a decadal timeline, an integrated multi-sensor Enhanced Vegetation Index (EVI) time series was developed on Google Earth Engine, utilizing Landsat 8 (2015–2017) and Sentinel-2 (2018–2025) surface reflectance data. Minimum temperature (Tmin) and precipitation data from ERA5-Land and CHIRPS were topographically downscaled to 30 m using orographic gradients and analyzed to quantify climatic controls. Results showed pronounced altitudinal gradients, with earlier SoS, later EoS, and longer LoS in low-elevation forests, and short, thermally constrained seasons in alpine zones. Pre-season Tmin increases strongly advanced SoS, while precipitation effects were spatially heterogeneous and topographically mediated. These findings highlight the need for elevation and plant functional type specific monitoring to improve phenological projections mitigate the risks of trophic mismatches and guide climate adaptation strategies in the Himalaya.

Assessing the Influence of Land Use Change Pattern and Soil Erosion on Sediment Delivery Ratio in the Upper Mahaweli River Catchment of Sri Lanka

Wijethunga Mudiyanselage Manomi Wathsala Dilrukshi — 2026-10-28

The Mahaweli River takes in the largest volume of water amounting to one seventh of Sri Lanka’s total runoff and has a catchment area that covers 15 % of the land in the country. A number of multipurpose reservoirs have been stored in the Upper Mahaweli River Catchment (UMRC) mainly to generate hydropower, which contributes to about 40-50% of the total hydropower production in the country. This study investigates the impacts of land use change patterns and soil erosion on the sediment delivery ratio (SDR) within the UMRC in Sri Lanka. Utilizing Geographic Information Systems (GIS) and Remote Sensing (RS) tools, the land use and land cover (LULC) changes were analyzed over a period from 2007 to 2023, focusing on soil erosion's contribution to sediment dynamics in the basin. By employing the Universal Soil Loss Equation (USLE) model, factors such as rainfall erosivity, soil erodibility, slope characteristics, crop cover, and land management practices were assessed to estimate soil loss and map erosion hotspots. The InVEST SDR model was then applied to quantify sediment transport and deposition patterns, helping to identify priority areas for soil conservation. Results revealed an average annual soil loss ranging from 0 to 95.015 t ha⁻¹ yr⁻¹ in the catchment and a mean annual sediment export of 5.56 t ha⁻¹ yr⁻¹. It shows mean sediment deposition rate as 2.4 t ha⁻¹ yr⁻¹. Erosion severity was classified into five hazard classes, very low (0 - 5 t ha^-1 yr^-1), low (5 - 10 t ha^-1 yr ^-1), moderate (10 - 20 t ha^-1 yr^-1), high (20 - 50 t ha^-1 yr^-1) and very high ( >50 t ha^-1 yr ^-1). A critical 1.5% area was identified as a considerable soil erosion hazard area, necessitating urgent measures for erosion control. Findings indicate significant LULC changes, driven primarily by deforestation, agricultural expansion, and urbanization, which have intensified mean soil erosion rate from 2.39 t ha^-1 yr^-1to 11.22 t ha^-1 yr^-1, highest mean sediment export rate as 2.29 t ha^-1 yr^-1 and highest mean sediment deposition rate as 5.36 t ha^-1 yr^-1 in different watersheds. Importantly, the identified erosion hotspots and sediment export zones indicate a substantial risk of accelerated sedimentation in downstream reservoirs, potentially reducing storage capacity and affecting hydropower efficiency in the Upper Mahaweli system. These findings support targeted watershed management interventions to mitigate long-term sedimentation impacts.

Assessing the Impact of MGNREGA on Cold Desert Agriculture Using Geospatial and Agro-Economic Indices: A Case Study of Saboo, Ladakh

Joshi Sai Prakash Chowdary Mothineni — 2026-10-28

The Union Territory of Ladakh, located in the Western Himalayan agro-climatic zone, represents an ecologically sensitive cold desert environment characterised by low precipitation, limited arable land, and a short growing season. Agricultural production in this region is highly dependent on efficient land and water resource management, primarily supported by glacial meltwater irrigation. In this context, the present study evaluates the role of the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) in improving land-use efficiency, water resource management, and agricultural sustainability in Saboo Gram Panchayat, Leh district, using 2017 as the base year and 2024 as the assessment year. To carry out this study, a mixed-methods approach was adopted, integrating remote sensing, GIS analysis, and field-based agro-economic metrics. High-resolution satellite datasets, including Cartosat-3 and Resourcesat-2 (LISS-IV), were used to generate Land Cover/Land Use (LCLU) maps, while Sentinel-2 imagery was used to derive spectral indices such as the Normalised Difference Vegetation Index (NDVI) and Normalised Difference Moisture Index (NDMI). These datasets were integrated with 317 geo-tagged MGNREGA interventions, including irrigation channels, farm ponds, and land development works. Agro-economic indicators such as the Cultivable Land Utilisation Index (CLUI) and Wheat Equivalent Yield (WEY), along with community survey data, were used to assess comprehensive spatio-temporal changes in the study area. The results show a significant improvement in the utilisation of land and water resources. Between 2017 and 2024, agricultural land increased by 15.22%, while water bodies expanded by 36.44%. Positive trends in NDVI and NDMI suggest improvements in vegetation health and soil moisture conditions in areas associated with MGNREGA interventions. The CLUI increased from 0.456 to 0.497, and WEY increased from 14.15 t/ha to 26.12 t/ha respectively, indicating enhanced cropping intensity and agricultural productivity by shifting towards high-value crops, like vegetables and hardwood species in horticulture produce. Community responses further support these findings, highlighting improved irrigation access, increased crop yields, and stronger participation in local planning processes; these reveal that MGNREGA benefits are both measurable and experienced at the community level. Overall, the study demonstrates that the integration of geospatial monitoring with community-driven rural developmental programmes such as MGNREGA can support sustainable agriculture in high-altitude, climate-sensitive environments. The findings provide a replicable framework for evidence-based rural development and natural resource management in similar ecosystems.

Machine Learning Algorithms for High-Resolution Gravity Prediction

Joseph O. Odumosu — 2026-10-28

Namibia’s reliance on the coarse-resolution EGM2008 global geopotential model results in height conversion errors often above one meter, especially due to the lack of high-resolution terrestrial gravity data. This research analyzes Gaussian Process Regression (GPR), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Universal Kriging for the creation of high-resolution Bouguer gravity anomaly grids in the Khomas Region of Namibia. The study utilized 125 terrestrial gravity observations from the Bureau Gravimétrique International (BGI), along with SRTM elevation data and lithological density values, to train and test the models at spatial resolutions of 1.86 km and 9.26 km. Spatially Distributed Hold-Out Validation (SDHV) technique was used to check how accurate the model performed. GPR returned the best indicative accuracy (MAE = 4.67 mGal; RMSE = 5.33 mGal) and strongest agreement with Universal Kriging (r = 0.9073), while ANN exhibited overfitting tendencies at fine spatial scales and SVM produced the most conservative resolution-invariant outputs. All four techniques consistently replicated the prevailing northwest–southeast regional gravity gradient, with model divergence limited to short-wavelength spatial detail. These results show that machine learning may be used to simulate regional gravity fields and give a methodological framework to help with geoid refining and updating vertical datums in Namibia.

Geospatial Quantification of Human–Elephant Conflict Hotspots in the Southern Western Ghats

G.M. Pavithra — 2026-10-28

Human–elephant conflict (HEC) in the Southern Western Ghats has significant socio-economic and ecological impacts, highlighting the need for an integrated mitigation approach. The present study leverages a geo-tagged database of 1,22,986 HEC incidents from 2019 to 2023 sourced from the e-Parihara web portal, Karnataka Forest Department (KFD), to analyse conflict patterns, damage types, and compensation trends across eight districts of Karnataka. Kodagu became the district with the highest number of cases and compensation, highlighting the need for targeted interventions. The study distinguishes between districts within the Western Ghats and those outside, such as Mandya and Ramanagara, to understand the geographical and ecological aspects of HEC dynamics. A significant increase in HEC cases and compensation was observed during the study period, with the number of cases rising from 8,184 in 2019 to 30,912 in 2023, while compensation payments increased from ₹53.7 million to ₹390.4 million over the same period. As a result, a total of ₹1,050.5 million was spent on compensation during the 2019–2023 period. The damage was categorised into six types: plantation damage, crop loss, property damage, human injury and death, livestock kills, and other damages. A total of 1,998 conflict-prone villages were identified and classified into five categories: Very High (26.1%), High (35.4%), New/Emerging Hotspots (9.4%), Sporadic Hotspots (19.2%), and Cold Spots (9.9%). This classification aids in prioritising mitigation efforts, particularly in high and emerging hotspots, to prevent further escalation. The study aligns with the Ministry of Environment, Forest and Climate Change (MoEFCC) 2023 HEC mitigation guidelines, emphasising the implementation of early warning and rapid response systems. The village-level hotspot database serves as a strategic tool for conflict mitigation, supporting targeted habitat management and policy interventions to reduce human–wildlife conflict in the Southern Western Ghats.

Spatio-temporal Analysis of Land Use and Land Cover Change in the Achanakmaar-Amarkantak Biosphere Reserve, Central India: Implications for Forest Degradation and Conservation Planning

Jiwan Lal — 2026-10-28

Land Use and Land Cover (LULC) Change plays a crucial role in influencing ecological stability, biodiversity, and climate dynamics. This study investigates the spatio-temporal dynamics of LULC in the Achanakmaar-Amarkantak Biosphere Reserve (AABR), a critical ecological zone in Central India using 2001 and 2021 Landsat imagery, geospatial tools- ArcGIS, and maximum likelihood classification approach. Effort was made to map and analyze changes in major land cover types, viz., dense mixed forest (DMF), sal mixed forest (SMF), bamboo brakes (BB), teak plantation (TP), open mixed forest (OMF), agricultural land (AG), human habitations (HH), and water bodies (WB). The results revealed a significant decline in forested area, particularly DMF (-43.93 km²), SMF (-50.77 km²), BB (-6.07 km²), TP (-40.63 km²), and WB (-3.20 km²), while OMF (+131.14 km²), AG (+23.65 km²), and habitations (+2.88 km²) showed notable expansion in two decades. These trends are consistent with anthropogenic pressures reported in similar tropical forest ecosystems. The findings of this study corroborate with the global studies that tropical forests are under increasing threat due to unsustainable land use transitions. Our findings underscore the urgent need for policy interventions and ecological restoration through community-based forest management, reforestation, and promotion of sustainable agroforestry practices. This study contributes novel insights into the regional forest degradation patterns in Central India and demonstrates the utility of remote sensing for long-term environmental monitoring.

Probabilistic Multi-Temporal Land Use and Land Cover Change Analysis Using Random Forest and Sentinel-2 Data in Haridwar, India

Shilpa Suman — 2026-10-28

Multitemporal satellite images are useful for analyzing Land Use Land cover (LULC). It captures temporal images which are used to analyze the gradual transition which are difficult to detect with single date observations. This study focused on detecting the changes in LULC using multi-temporal approach for sentinel-2 images from 2017 to 2025.The approach obtains statistical results and different maps like the hard LULC maps, class wise probability maps, persistence, class to class change detection maps. Random Forest (RF) classifier has been used for classifying the stack of multispectral bands and different indices (Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), and Land Surface Water Index (LSWI), Normalized Difference Red Edge Index (NDRE)). Seasonal composites (January-April) were created to assess phenological regularity. Annual classification accuracy was high, ranging from 93.18% to 96.25% with Kappa coefficients of 0.92 to 0.96. Temporal Persistence analysis using a threshold greater than equal to six years to identify the stable classes. Statistics output indicated class specific temporal changes like vegetation phenology, hydrological dynamics, and land management techniques. The results show effectiveness of probabilistic multi-temporal analysis in capturing the gradual LULC changes.

Remote Sensing - Based Estimation of Evapotranspiration Using the Triangle Method: Insights from Tropical Forests of Northeast India

Jintu Moni Bhuyan — 2026-10-28

Understanding the interactions between LST, vegetation dynamics, and ET is critical in the context of climate change and its impacts on terrestrial ecosystems and the hydrological cycle. This study investigates the seasonal variability of ET and its relationship with LST and NDVI over forested regions of Lakhimpur, Dhemaji, and Dibrugarh districts in Assam. The analysis is specifically restricted to forest areas using a Dynamic World land use/land cover mask to ensure ecological consistency and minimize land cover heterogeneity. Evapotranspiration was estimated using the NDVI–LST triangle method, which derives evaporative fraction (EF) from the LST–NDVI feature space through the identification of dry and wet edges. Landsat 8 data were used to generate high-resolution NDVI and LST, while net radiation (Rn) was obtained from ERA5-Land and resampled to match Landsat spatial resolution. The results were further compared with widely used datasets, including MOD16A2, GLDAS, and FLDAS, to assess consistency and variability. The findings reveal clear seasonal patterns, with ET peaking during the monsoon (up to ~3.17 mm/day) and decreasing during pre- and post-monsoon periods. Forested areas exhibit relatively stable NDVI and lower LST, contributing to consistent and spatially coherent ET patterns. MOD16A2 significantly overestimates ET, while GLDAS and FLDAS provide more stable and physically realistic estimates. The approach demonstrates strong potential for monitoring seasonal ET dynamics in data-scarce regions. Future work should focus on integrating in-situ observations, improving methodological frameworks, and adopting multi-source data fusion techniques to enhance the accuracy and reliability of ET estimation for climate and water resource applications.

Assessment and Mapping of Soil Erosion Hotspots in the Himalayan Region Using RUSLE and Machine Learning Technique

Justin George Kalambukattu — 2026-10-28

Soil erosion is a major driver of land degradation, particularly in fragile and ecologically sensitive mountainous regions such as the Himalayas. This study assessed soil erosion risk and mapped erosion hotspots in the Bhagirathi watershed, Uttarkashi District, Uttarakhand, by integrating the Revised Universal Soil Loss Equation (RUSLE), remote sensing, GIS, field observations, and Random Forest (RF) machine learning. The assessment utilized 80 spatially distributed surface soil samples for estimating soil erodibility, ALOS PALSAR DEM (12.5 m) for terrain characterization and derivation of topographic factors, and Sentinel-2A multispectral data (10 m) for land use/land cover and spectral information. Rainfall, soil, topographic, vegetation, and management-related factors were integrated within the RUSLE framework to estimate soil loss, while terrain and spectral variables were subsequently used to develop the RF regression model. Areas experiencing estimated soil loss exceeding 40 t ha⁻¹ yr⁻¹ were classified as erosion hotspots. The RUSLE-based erosion risk assessment showed good agreement with field observations, achieving an overall validation accuracy of 85%. The RF regression model demonstrated good predictive performance, with an R² of 0.72, RMSE of 0.31, and Nash–Sutcliffe Efficiency of 0.67. Slope, aspect and NDVI were found to be the most important predictors governing the model performance as well as spatial distribution of erosion hotspots. Erosion hotspots occupied approximately 9.34% of the watershed and were predominantly associated with steep slopes and areas with sparse or absent vegetation. These locations were also clearly identifiable in high-resolution satellite imagery. The findings demonstrate that integrating empirical erosion modelling with field observations, geospatial data, and machine learning can effectively assess erosion risk and identify priority hotspots in complex and inaccessible Himalayan terrain. The approach can support targeted soil and water conservation planning and sustainable land management in fragile mountain ecosystems.

Assessing Hydrological Impacts of Deforestation and Land Use Change in the Tel River Basin

Triparna Sett — 2026-10-28

Land use and land cover changes (LULCC) alter catchment hydrology by changing how water moves between the land surface and the atmosphere. This study examines how land use change between 1985 and 2015 affected the water balance of the Tel River basin, a sub-basin of the Mahanadi system in eastern India. Land cover maps for 1985, 1995, and 2005 were obtained from the national decadal LULC database, and a 2015 map was prepared from Landsat 8 imagery using a Random Forest classifier on Google Earth Engine (GEE) and later validated against NRSC data. The 2015 map reached an overall accuracy of 78.6 % (Kappa 0.68) against independent reference data. Forest and agriculture were mapped reliably, while the smaller classes of built-up land, bare land, and water body were combined into one less reliable category. Hydrological response was simulated with the Variable Infiltration Capacity (VIC) model for 1980 to 2018, using a one-factor-at-a-time design that holds climate constant so that only land use varies. Forest cover fell by 13.02% over the period, with most of the loss going to agriculture (about 1575 km²). With climate held fixed, lower evapotranspiration (about 16 mm/year, or 1.7%) was passed almost entirely into higher surface runoff (about 7 mm, 2.0%) and higher base flow (about 9 mm, 4.6%), while long-term soil water storage stayed essentially unchanged. A pixel-level analysis shows that runoff rises in some parts of the basin and falls in others, so the basin average understates the local change in flood and erosion risk. The results point to the value of catchment-scale water management that accounts for where land use is changing within the basin.