Land Cover Analysis Research Articles

Indigenous land of Brazilian Cerrado and their changes in land cover

This research compares the evolution of land cover change inside the Indigenous Lands (ILs) of the Brazilian Cerrado with changes occurring in their respective buffer zones delimited by 10km. For this, data from the MapBiomas platform are used, which provides the annual series of land cover data, and FUNAI's (National Foundation of Indigenous Peoples) mapping of indigenous lands. For land cover analysis, the annual period of 1985 and 2020 were considered and compared, using the map with the 2022 IL layout. In this research, three Indigenous Lands were discussed, which are Avá Canoeiro, Menka and Morro Branco, located in the Brazilian Cerrado biome. The results point to changes in land cover from 1985 to 2020 within the Indigenous Lands as well as in their buffer, therefore, it is considered that the delimitation of ILs contributes to the conservation of biodiversity in addition to serving as a space to protect the forms of relations of the original peoples. Finally, possible future paths for research in this area are highlighted.

Effects of land use/land cover change on soil physicochemical properties and soil carbon stock in Kochore district, southern Ethiopia

Land use changes from year to year due to natural and human-made factors are a serious cause of the degradation of soil properties. Therefore, this study aimed to investigate the impact of land use land cover changes on selected soil physicochemical properties and soil organic carbon stocks in Kochore district, Southern Ethiopia. Satellite images (2000, 2010, and 2020) were used as sources of information for land use and land cover analysis using ArcGIS 10.3 and ERDAS Imagine 2014 software. Three land use types (cultivated, agroforestry, and grazing land) were identified through field surveys and satellite image analysis. Soil samples were collected at different depths (0–20 cm and 20–40 cm), and selected soil properties were analyzed. The results indicate that geospatial data of the land cover of agroforestry continuously increased and a decrease in grazing and cultivated in the given periods of 2000, 2010, and 2020, and soil properties revealed such as sand, bulk density, organic carbon, total nitrogen, pH, cation exchange capacity, and exchangeable bases were significantly (P < 0.001) affected by land use and soil depth. The highest content of OC (2.37), Av.P. (3.36), and TN (0.13) was observed on agroforestry land, and the lowest 1.43 and 0.08 was OC and TN respectively. The highest value of soil organic carbon stock (60.2 mg ha−1) was observed in AFL. The study suggests the deterioration of soil properties under cultivated and grazing land, emphasizing the importance of sustainable integrated soil fertility management to optimize and maintain favorable soil physicochemical properties.

Impact of urbanization on groundwater recharge: altered recharge rates and water cycle dynamics for Arusha, Tanzania

Abstract The profound effects of urbanization on groundwater recharge rates are investigated by conducting a comprehensive land use and land cover analysis in Arusha, Tanzania, using the WetSpass model. Between 1995 and 2016, the urban area has expanded from 14 to 45% within the study area. This rapid urbanization has resulted in the conversion of forested areas, agricultural land, shrublands, and bare soil into urban zones. Results indicated that under preurban conditions, groundwater recharge from precipitation was ~116 mm/year, which increased to an average of 148 mm/year by 2016. When accounting for anthropogenic factors such as drinking water leakage and on-site sanitation, recharge further increased to 195 mm/year. These supplementary recharge sources, along with reduced evapotranspiration due to land-use changes, contributed to the increase, despite higher surface runoff. These findings underscore the significance of land use and leakage management in urban areas, as well as the spatial variability in groundwater recharge rates across different urban zones, emphasizing the importance of local factors. This study advances the understanding of the intricate relationship between urbanization and groundwater dynamics, and provides insights for future water resource management in rapidly growing urban regions.

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.

Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, Indonesia

The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of land-use policies. Yet, it may also confuse the end-users when it comes to identifying the most appropriate product to address their requirements. Data fusion methods can help to combine competing and/or complementary maps in order to capitalize on their strengths while overcoming their limitations. We assessed the potential of the Dempster–Shafer Theory (DST) to enhance oil palm mapping in Sumatra (Indonesia) by combining four land-cover maps, hereafter named DESCALS, IIASA, XU, and MAPBIOMAS, according to the first author’s name or the research group that published it. The application of DST relied on four steps: (1) a discernment framework, (2) the assignment of mass functions, (3) the DST fusion rule, and (4) the DST decision rule. Our results showed that the DST decision map achieved significantly higher accuracy (Kappa = 0.78) than the most accurate input product (Kappa = 0.724). The best result was reached by considering the probabilities of pixels to belong to the OP class associated with DESCALS map. In addition, the belief (i.e., confidence) and conflict (i.e., uncertainty) maps produced by DST evidenced that industrial plantations were detected with higher confidence than smallholder plantations. Consequently, Kappa values computed locally were lower in areas dominated by smallholder plantations. Combining land-use products with DST contributes to producing state-of-the-art maps and continuous information for enhanced land-cover analysis.

Evaluasi Pengelolaan Hutan Kemasyarakatan (HKm) dan Dampaknya Terhadap Kondisi Hutan di Kelompok Perhutanan Hkm Buli Uher, Desa Ilinmedo Kecamatan Waiblama Kabupaten Sikka

The aims of evaluation of the community forest management of HKm Buli Uher, Ilinmedo Village, Waiblama District, Sikka Regency to observe and assess the implementation of HKm. The method used mixed method between qualitative and quantitative methods. The results of the quantitative method were a scoring evaluation of HKm management, which is based on the weight of several variables, including institutional, community empowerment, HKm area governance, and HKm business governance, as well as data collection on land cover analysis and vegetation analysis. A simple random sample is the first step method in the vegetation analysis process, and a line transect is subsequently used. Meanwhile, the qualitative method is information or data from interviews, observations, and documents related to the management of Buli Uher HKm. The results showed that the management of the Buli Uher HKm in Ilinmedo Village received an assessment with good criteria. In the meantime, the Ilinmedo Protected Forest in HKm Buli Uher experienced a notable reduction in forest conditions in 2023, according to the results of a vegetation study and land cover changes, with vegetation that is changing after being damaged. It happened as a result of the community's logging and forest burning.

Holistic analysis of Ganga basin water quality: a statistical approach with WQI, HMCI, HMQI and HRI indices.

The Ganga river, one of the largest and most culturally significant rivers in India, supports millions of people living along its banks. However, extensive use and untreated wastewater discharge have led to significant contamination. This study utilizes land use and land cover (LULC) analysis, along with regular water sampling from 2021 to 2222, to assess variations in physical, chemical, and biological characteristics and evaluate health risks posed by heavy metals across eight monitoring sites in the Ganga and Yamuna rivers, Prayagraj, India. Results were compared with drinking water standards established by the Bureau of Indian Standards (BIS) and the World Health Organization (WHO). The Water Quality Index (WQI) indicated substantial water quality degradation at sites S2 (Ganga) and S8 (Yamuna). Although heavy metal levels (Cu, Fe, Cd, Pb, Mn, Cr) fluctuated across sites, Pb and Cd frequently exceeded permissible limits. Health Risk Assessment (HRI) findings pointed to potential health risks at sites S4 (Ganga) and S8 (Yamuna) due to elevated Pb and Cd levels. The Heavy Metal Contamination Index (HMCI) ranged from 733.78 to 981.33, classifying all samples as highly polluted, with Heavy Metal Quality Index (HMQI) values also indicating high risk, especially at sites S4 and S8. Further analysis using principal component analysis (PCA) and cluster analysis highlighted correlations among water quality parameters, while Pearson's correlation matrix and heat maps indicated positive relationships for DO, pH, alkalinity, and SO4, with most heavy metals (except Zn and Mg) showing strong inter-correlations. These findings underline the urgent need for pollution control measures to safeguard public health in the region.

Assessment of Water and Soil Contamination and Land Cover Changes in the Spring Creek Bayou Watershed in Houston, Texas

Stormwater runoff and nutrient pollution are significant sources of water contamination that continue to grow in rural and suburban watersheds. The goal of this research is to analyze and evaluate the impact of urbanization and industrialization on suburban watersheds in southeast Texas. The objectives are to: (1) determine nutrient and heavy metal concentrations in soil and water samples along Spring Creek Bayou (SC), (2) analyze land cover changes over the last 30 years and (3) assess and evaluate socio-economic data within the watershed. The soil and water samples were collected from upstream, midstream and downstream locations in triplicate during the spring and fall seasons along the bayou. The samples were analyzed to determine chemical concentrations and Landsat 5, and eight imageries were used to derive thematic land cover maps. The soil and water chemical concentrations were interpolated to spatial maps for distribution analysis. The chemical analysis of water samples collected from SC Bayou revealed that N and P concentrations were at elevated levels that can pose a threat to water quality and aquatic organisms. Heavy metal concentrations of Zn were at elevated levels in water samples from the SC Bayou watershed. Land cover change patterns showed that high-vegetation surfaces decreased while low-vegetation surfaces increased slightly over the past three decades. The watershed experienced an increase in total population from 129,629 residents in 1990 to 389,977 residents in 2020. This research is important in improving our understanding on the impact of natural and human activities on suburban watersheds in the Greater Houston metropolitan region.

Assessment of urban wetlands loss and fragmentation using land use and land cover change and guidostoolbox: A study of Chorrillos district, Lima.

The coastal landscape of the Chorrillos district is an ecosystem characterized by hosting the largest population of migratory and resident bird species in Lima, including the Protected Natural Area of the Pantanos de Villa Wildlife Refuge (RVSPV). In response to wetland loss, this study aims to explore the process of natural landscape fragmentation in the Chorrillos district, including the RVSPV, from 1985 to 2021 to identify the impact of anthropogenic agents on structural connectivity. The methodology involves (i) an analysis of land cover and land use (LULC) using the MapBiomas Peru Collection 1.0 Platform, (ii) a patch accounting analysis, (iii) an analysis of Area Density in the Foreground (FAD), and (iv) an analysis of Morphological Spatial Patterns (MSPA) using the GuidosToolbox (Graphic User Interface for Image Object Description and Shapes-GTB) Version 3.304. The results indicate a hierarchy of disturbed patches, with the largest patch (RVSPV) displaying specific spatial processes that contribute to landscape transformation and persistence. The ecological function of the patches and habitat links demonstrate the benefits of connectivity and environmental exchange between fragments, aiming to counteract landscape loss.

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.

Nationwide Landslide Prediction in India Using Neural Networks and Multi-Source Satellite Data.

Landslides are a prevalent and devastating natural hazard in mountainous regions, particularly in high-risk areas such as Uttarakhand and Meghalaya, India, where factors like intense rainfall, steep slopes, and changing land cover significantly contribute to landslide occurrences. Traditional ground-based monitoring methods provide limited coverage and scalability, underscoring the need for a more extensive and real-time approach to landslide prediction. This study addresses this gap by developing an advanced predictive model using neural network architectures and multi-source satellite data, including Synthetic Aperture Radar (SAR) from Sentinel-1 for ground deformation, optical imagery from Sentinel-2 for vegetation and land cover analysis, and Digital Elevation Models (DEM) for slope assessment. Additionally, rainfall data from the Global Precipitation Measurement (GPM) mission was integrated to evaluate precipitation as a potential landslide trigger. Through a comparative analysis of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model, we found that the CNN-LSTM hybrid model achieved superior performance, with an F1 score of 0.91. This model effectively captures both spatial and temporal patterns, enabling early detection of landslide-prone conditions and surpassing the predictive accuracy of traditional methods. The proposed approach provides a scalable solution for real-time, large-scale monitoring, with the potential to enhance disaster preparedness and resilience in vulnerable communities. By advancing landslide prediction capabilities, this model contributes a valuable tool for proactive risk management and offers significant potential for integration into regional early warning systems.

Urban expansion induced land use changes and its effect on ecosystem services in Addis Ababa, Ethiopia

The study examines the dynamics of urban expansion and changes in land use in Addis Ababa from 1993 to 2023 using satellite data and GIS analysis, as well as the benefit transfer approach for valuing ecosystem services. In addition, an expert survey was conducted using the analytical hierarchy process (AHP) method to determine the factors driving urban expansion. Results reveal oscillations in urban expansion rates and intensities over the three-decade period. While the 2003–2013 decade witnessed rapid urban growth, subsequent years showed a slowdown. The overall period (1993–2023) has an intensity index greater than 1, indicating that urban areas expanded faster than the city’s overall growth during the entire time frame. Land cover analysis revealed a significant decrease in green areas and croplands and a remarkable increase in built-up areas. In aggregate, the natural land cover was reduced by 128.6% whereas built-up increased by 224.7%. This shift signifies a transition from natural and agricultural land uses to urbanization and development. Moreover, the study evaluates changes in ecosystem service values (ESVs), indicating a consistent decline over time, particularly in forestlands and urban green areas. The significant alteration in land use that built up caused to ecosystems has resulted in an annual loss of 90.7 million USD from 1993 to 2023 in terms of ecosystem service value. The most significant impacts were observed in food production, climate regulation, and habitat ecosystem services. The encroachment of built-up areas on different land use categories leads to substantial losses in ecosystem service values. Socioeconomic factors, notably population growth and migration, emerge as primary drivers of urban expansion, emphasizing the interconnectedness of demographic changes and land use patterns. Overall, the findings underscore the complex interplay between urban expansion, land use dynamics, and ecosystem services in Addis Ababa, highlighting the importance of informed urban planning and policy interventions to mitigate adverse environmental impacts.

MMS-EF: A Multi-Scale Modular Extraction Framework for Enhancing Deep Learning Models in Remote Sensing

The analysis of land cover using deep learning techniques plays a pivotal role in understanding land use dynamics, which is crucial for land management, urban planning, and cartography. However, due to the complexity of remote sensing images, deep learning models face practical challenges in the preprocessing stage, such as incomplete extraction of large-scale geographic features, loss of fine details, and misalignment issues in image stitching. To address these issues, this paper introduces the Multi-Scale Modular Extraction Framework (MMS-EF) specifically designed to enhance deep learning models in remote sensing applications. The framework incorporates three key components: (1) a multiscale overlapping segmentation module that captures comprehensive geographical information through multi-channel and multiscale processing, ensuring the integrity of large-scale features; (2) a multiscale feature fusion module that integrates local and global features, facilitating seamless image stitching and improving classification accuracy; and (3) a detail enhancement module that refines the extraction of small-scale features, enriching the semantic information of the imagery. Extensive experiments were conducted across various deep learning models, and the framework was validated on two public datasets. The results demonstrate that the proposed approach effectively mitigates the limitations of traditional preprocessing methods, significantly improving feature extraction accuracy and exhibiting strong adaptability across different datasets.

ANALISIS KETERSEDIAAN DAN KEBUTUHAN AIR BAKU DISTRIK BONGGO KABUPATEN SARMI

The development of an area will cause the demand for water to continue to increase along with thepopulation growth rate. The fulfillment of food needs and the activities of the population are always closely relatedto the need for water. The demands are unavoidable but must be predicted and planned for optimal utilization.The tendency that often occurs is the imbalance between water availability and water demand. To achieve abalance between water demand and availability in the future, efforts are needed to assess the components of rawwater availability and demand as clean water sources. The source of clean water for the people of Bonggo District, Sarmi Regency, comes from borehole pumps,shallow wells (pumps), and rainwater. The analysis results on the need for domestic and non-domestic clean waterin the Bonggo District in 2023 show a total requirement of 210.38 liters/day or 0.0024 m3/second. The need forclean water is expected to increase by 2043, with the total requirement for domestic and non-domestic clean waterreaching 285,820 liters/day or 0.0033 m3/second. The availability and need for clean water sourced from rawwater that can be utilized as clean water comes from pump bore wells as a suitable raw water source. In terms ofquantity, it is available 24 hours a day and throughout the year. The investigation results of the pump bore wellraw water source meet the clean water quality requirements according to Government Regulation No. 82 of 2001,Level II. This is supported by groundwater, obtained through the analysis of geological conditions, land cover,morphology, and river flow, which have a high to very high groundwater potential score, located in the northernpart of Bonggo District. Economically, in terms of quantity, rainwater can be used as clean water due to its sufficientavailability, with high rainfall (104,775 mm/year) and year-round service (continuity), and in terms of quality, theresults of rainwater quality tests meet the clean water standards according to quality standards.

Quantitative Analysis of Land Use Land Cover (LULC) Changes on the Hydrological Behavior of the Jhelum River Basin: North-West Himalayas, Kashmir

Quantitative Analysis of Land Use Land Cover (LULC) Changes on the Hydrological Behavior of the Jhelum River Basin: North-West Himalayas, Kashmir

Spatio-Temporal Analysis and Prediction of Land Use and Land Cover in Jagdalpur Sub-Division of Bastar District in State of Chhattisgarh, India from 2012 to 2037

Spatio-Temporal Analysis and Prediction of Land Use and Land Cover in Jagdalpur Sub-Division of Bastar District in State of Chhattisgarh, India from 2012 to 2037

Modern Pollen Signatures and Vegetation Dynamics in Northwestern Himalaya, India

ABSTRACT Palynological studies have been conducted for the first time in the Renukaji Wildlife Sanctuary (RWS), a Sal-dominated region within the foothills of Northwestern (NW) Himalaya in Himachal Pradesh, India. This research enhances our understanding of the relationship between modern pollen rain and vegetation, as well as pollen representation in sediments. Furthermore, it contributes to palaeovegetation reconstruction based on paleoclimatic trends by improving our interpretation of pollen deposition patterns. The study area was delineated into three distinct zones based on biological niche characteristics: Zone 1 (Recreation Zone), Zone 2 (Buffer Zone), and Zone 3 (Core Zone). Pollen analysis conducted on 30 surface soil samples and moss cushions, collected from all three zones, reveals the dominance of dry deciduous taxa, followed by extra-terrestrial taxa and wet deciduous taxa. Land Use Land Cover (LULC) analysis performed using satellite data reveals that Shorea robusta (Sal) mixed forest covers 32% of land, followed by Pinus spp. (8%) and their associates (25%). Shorea robusta, a mixed wet deciduous taxon and high pollen producer, exhibits low pollen counts, possibly due to chemical and microbial decomposition or low pollen dispersal efficiency. This study utilises a comparative database correlating pollen data with contemporary vegetation patterns to establish a robust framework for assessing sedimentary pollen sequences. Through this approach, we aim to elucidate past vegetation dynamics and infer climate fluctuations within the NW Himalaya.

Enhancing land cover classification via deep ensemble network

The rapid adoption of drones has transformed industries such as agriculture, environmental monitoring, surveillance, and disaster management by enabling more efficient data collection and analysis. However, existing UAV-based image scene classification techniques face limitations, particularly in handling dynamic scenes, varying environmental conditions, and accurately identifying small or partially obscured objects. These challenges necessitate more advanced and robust methods for land cover classification. In response, this study explores ensemble learning (EL) as a powerful alternative to traditional machine learning approaches. By integrating predictions from multiple models, EL enhances accuracy, precision, and robustness in UAV-based land use and land cover classification. This research introduces a two-phase approach combining data preprocessing with feature extraction using three advanced ensemble models DenseNet201, EfficientNetV2S, and Xception employing transfer learning. These models were selected based on their higher performance during preliminary evaluations. Furthermore, a soft attention mechanism is incorporated into the ensembled network to optimize feature selection, resulting in improved classification outcomes. The proposed model achieved an accuracy of 97 %, precision of 96 %, recall of 96 %, and an F1-score of 97 % on UAV image datasets. Comparative analysis reveals a 4.2 % accuracy improvement with the ensembled models and a 1 % boost with the advanced hybrid models. This work significantly advances UAV image scene classification, offering a practical solution to enhance decision-making precision in various applications. The ensemble system demonstrates its effectiveness in remote sensing applications, especially in land cover analysis across diverse geographical and environmental settings.

Temporal Analysis of Riparian Buffer Zones and Demographic Trends in the Coastal Districts of Kastamonu

Aim of study: This study investigates the temporal changes of forest areas within and outside the Riparian Buffer Zones (RBZs) and examines their correlation with demographic trends in the coastal districts of Kastamonu from 1987, 2000, and 2016 to assess how population dynamics influence forest cover. Area of study: The study was conducted in four coastal districts in northern Kastamonu province, Türkiye, covering approximately 1000 square kilometers and focusing on 153 forest villages. Material and methods: Geographic Information Systems and remote sensing data delineated and analyzed land cover and population changes. The study used Landsat imagery and population data to explore spatial and temporal dynamics within the RBZs Main results: The study found a significant increase in forest area from 1987 to 2000 and a subsequent decrease from 2000 to 2016, with these changes showing a weak but significant negative correlation with the declining local population. Research highlihts: The findings suggest that declining rural populations may reduce anthropogenic pressures on forests and pose challenges to effective forest management and conservation. The research highlights the need for sustainable strategies integrating demographic change with forest conservation efforts to maintain ecosystem services and biodiversity.

Mapping anthropogenic impacts on natural resources in the northeastern Sahara region: A case study on the past 26 years over the Ziban region (Algeria)

The objective of this research was to map and analyze anthropogenic impacts on natural resources in the Ziban region of Algeria using remote sensing imagery from 1995 to 2021. Medium-resolution satellite images from Landsat TM and OLI were utilized, with image processing techniques such as colorful compositions and supervised classification to map land cover. Seven land use and land cover (LULC) classes were identified, distinguishing human-derived categories (palm, agriculture, urban, and greenhouses) from natural surfaces (nebka, natural vegetation, and bare soil). The results indicated significant increases in human-driven elements like palm, urban areas, agriculture, greenhouses, and nebka, and notable decreases in natural components like vegetation and bare soil. Specifically, the rate of change (Tc) showed increases of 0.9% for palm, 1.14% for urban, 1.55% for agriculture, 1.91% for greenhouses, and 9.37% for nebka, and decreases of -3.68% for vegetation and -11.19% for bare soil. These findings highlight the impact of agricultural policies, population growth, and natural and human-induced conditions on natural resources. Palm areas remained largely unchanged due to state policies, whereas agriculture saw significant conversions to bare soil and palm. Vegetation declined substantially due to adverse climatic conditions and agricultural expansion. Nebka and urban areas had moderate to high conversion rates, and bare soil saw notable changes due to sand movement and urban/agricultural development. The study highlights the role of remote sensing and land cover analysis in managing natural resources sustainably, considering factors like agricultural policies and population growth. It emphasizes developing data-driven strategies for effective land use and management.