GIS Techniques Research Articles

Assessment of Soil-site Suitability for Major Horticulture Crops in Chikkumbi-3 Micro-watershed (4D7C5O2f), Karnataka Using Remote Sensing and GIS Techniques

Detailed soil resource inventorization was carried out at 1:7,920 scale in Chikkumbi-3 micro watershed, savadatti taluk, Belagavi district with an objective to assess the land suitability for various horticultural crops in the watershed. The soils of Chikkumbi-3 micro-watershed are characterized and classified into 3 soil series (CLK, CKB and HNL) comprising of 5 mapping units. The soil site characteristics and climatic factors were evaluated for its suitability for major horticultural crops viz., tomato, cabbage, chilli, rose, jasmine and coconut. The analysis revealed that the soil series CLK (Chulki), CKB (Chikkumbi), and HNL (Hanchinal) are presently not suitable for growing coconut due to significant limitations in physical conditions, rooting, and moisture. However, the region is deemed moderately to marginally suitable for the cultivation of tomato, cabbage, chilli, rose and jasmine. The main limitations in all the soil series found to be texture, CaCO3 content and climatic factors. These findings serve as foundational data for identifying specific soil constraints, aiding in the development of sustainable strategies for crop production in the study area.

Impact Analysis of Sanjay Gandhi Thermal Power Station (SGTPS) over the Surrounding Forest Using Remote Sensing and GIS Techniques

Impact Analysis of Sanjay Gandhi Thermal Power Station (SGTPS) over the Surrounding Forest Using Remote Sensing and GIS Techniques

Assessment of soil erosion risk in a hilly zone sub-watershed of Karnataka using geospatial technologies and the RUSLE model

ABSTRACT Human activities and climate change have sped up soil erosion, leading to annual losses of fertile soil and declining crop yields. The biggest concerns in land and water conservation are modeling and monitoring soil loss, as well as measuring the vulnerability and danger of active erosional processes. In the present study, a comprehensive approach used to evaluate the risk of soil erosion in the Ganjigatti sub-watershed of Karnataka, India, using the RUSLE model and GIS techniques. The annual average soil loss estimated using the RUSLE model ranged from 0 to 122.73 t ha−1 yr−1, with an average of 3.48 t ha−1 yr−1 in the Ganjigatti sub-watershed. The quantity of erosion was found to be greatly influenced by topography (LS factor) and land use and land cover (C factor). The results showed that a larger part of the study area, 98.34%, accounted for slight erosion (0–5 t ha−1 yr−1). The spatial pattern of erosion revealed high to very severe risk zones in the main stream of sub-watershed. The total soil loss was calculated to be 15,046.96 t yr−1 in the sub-watershed. The study’s results aid decision-makers, land administrators, environmentalists, and others in implementing effective land and water conservation measures in the Ganjigatti sub-watershed to control soil erosion.

Integrating Spatiotemporal Analysis of Land Transformation and Urban Growth in Peshawar Valley and Its Implications on Temperature in Response to Climate Change

Examining the interconnected dynamics of urbanization and climate change is crucial due to their implications for environmental, social, and public health systems. This study provides a comprehensive analysis of these dynamics in the Peshawar Valley, a rapidly urbanizing region in Khyber Pakhtunkhwa, Pakistan, over a 30-year period (1990–2020). A novel methodological framework integrating remote sensing, GIS techniques, and Google Earth Engine (GEE) was developed to analyze land use/land cover (LULC) changes, particularly the expansion of the built-up environment, along with the land surface temperature (LST) and heat index (HI). This framework intricately links these elements, providing a unique perspective on the environmental transformations occurring in the Peshawar Valley. Unlike previous studies that focused on individual aspects, this research offers a holistic understanding of the complex interplay between urbanization, land use changes, temperature dynamics, and heat index variations. Over three decades, urbanization expanded significantly, with built-up areas increasing from 6.35% to 14.13%. The population surged from 5.3 million to 12.6 million, coupled with significant increases in registered vehicles (from 0.171 million to 1.364 million) and operational industries (from 327 to 1155). These transitions influenced air quality and temperature dynamics, as evidenced by a highest mean LST of 30.30 °C and a maximum HI of 55.48 °C, marking a notable increase from 50.54 °C. These changes show strong positive correlations with built-up areas, population size, registered vehicles, and industrial activity. The findings highlight the urgent need for adaptive strategies, public health interventions, and sustainable practices to mitigate the environmental impacts of urbanization and climate change in the Peshawar Valley. Sustainable urban development strategies and climate change mitigation measures are crucial for ensuring a livable and resilient future for the region. This long-term analysis provides a robust foundation for future projections and policy recommendations.

An Improved Method to Identify Built-Up Areas of Urban Agglomerations in Eastern and Western China Based on Multi-Source Data Fusion

The rapid urbanization in China has significantly contributed to the vast expansion of urban built-up areas. Precisely extracting and monitoring these areas is crucial for understanding and optimizing the developmental process and spatial attributes of smart, compact cities. However, most existing studies tend to focus narrowly on a single city or on global scale with a single dimension, often ignoring mesoscale analysis across multiple urban agglomerations. In contrast, our study employs GIS and image-processing techniques to integrate multi-source data for the identification of built-up areas. We specifically compare and analyze two representative urban agglomerations in China: the Yangtze River Delta (YRD) in the east, and the Chengdu–Chongqing (CC) region in the west. We use different methods to extract built-up areas from socio-economic factors, natural surfaces, and traffic network dimensions. Additionally, we utilize a high-precision built-up area dataset of China as a reference for verification and comparison. Our findings reveal several significant insights: (1) The multi-source data fusion approach effectively enhances the extraction of built-up areas within urban agglomerations, achieving higher accuracy than previously employed methods. (2) Our research methodology performs particularly well in the CC urban agglomeration. The average precision rate in CC is 96.03%, while the average precision rate in YRD is lower, at 80.33%. This study provides an objective and accurate assessment of the distribution characteristics and internal spatial structure of built-up areas within urban agglomerations. This method offers a new perspective for identifying and monitoring built-up areas in Chinese urban agglomerations.

A method based on GIS techniques to assess renewable energy self-consumption capacity. A case study

The exploitation of renewables through energy self-consumption installations is one of the key strategies for the decarbonisation of energy systems and the promotion of distributed electricity generation. Wind and solar photovoltaic energy are two of the renewable sources commonly used to generate electricity for self-consumption. One of the key limitations in the exploitation of medium-large scale wind and solar photovoltaic installations is their compatibility with the geographical characteristics of the territory. In this paper, a method has been developed using GIS techniques to precisely identify the optimal locations that maximise the energy yield of such installations for electrical energy self-consumption. In the implementation of the method, criteria related to the topographical characteristics of the terrain (slope, orientation and shading, etc.), to the energy potential (capacity factor, etc.), and to logistical questions are jointly considered. The method is applied to a case study on the island of Gran Canaria (Spain). The results derived from the application of the method show the importance of detailed consideration of the topographical characteristics. This aspect is even more important in regions/countries with limited availability of useful territory for these types of installations, and is therefore key in the elaboration of energy planning strategies.

Advancements in groundwater potential mapping through remote sensing and GIS techniques

Advancements in groundwater potential mapping through remote sensing and GIS techniques

Agroforestry area mapping using medium resolution satellite data and object-based image analysis

Agroforestry is gaining increased attention within global policy processes and has been promoted as a strategy for working towards better food security, climate change adaptation and mitigation and livelihood resilience. With this gaining importance of agroforestry, site-specific studies are required to delineate different agroforestry systems and estimate the exact area under agroforestry. In this study remote sensing and GIS techniques were used for the estimation of area under agroforestry using medium-resolution satellite (sentinel 2A and 2B) data. Agroforestry area classification and estimation were done in e-Cognition developer software (10.0 version) through multiresolution segmentation and object-based image analysis (OBIA). The agroforestry area estimated in the erstwhile Warangal district was 3753 ha with an overall accuracy of 86% and a kappa coefficient of 0.84. The major agroforestry systems observed in the erstwhile Warangal district were eucalyptus, subabul, malabar neem, teak, sandalwood and red sanders.

Modeling air pollution around major dumpsites in Lagos State using geospatial methods with solutions

Air pollution is gradually becoming a major health concern globally and Nigeria is especially susceptible to air pollution Available data indicates that vehicular traffics, industries, wildfire and biomass burning are significant sources of air pollutants such as Particulate Matter PM, Carbon Monoxide CO, Nitrogen Dioxide NO2, Sulphur Dioxide SO2 and Volatile Organic Compounds VOCs in the air in Lagos State. The aim of this study is to perform geospatial modelling of air pollution around Olusosun and Solous dumpsites in Lagos State. The study analyses, assesses, and predicts air pollution emission in these dumpsites and examines their possible health impact on residents. Using both ground and satellite-based data, different GIS techniques were used to model this pollution concentration. These methods include Kriging, Regression, and Landfill Gas Emissions Model (LANDGEM) which helps to analyse, model and predict the air pollutant levels around these dumpsite. The average pollution level of Particulate Matter with a diameter that is 2.5 micrometers (PM2.5) in Olusosun was 94.29 μg/m3 while those in Solous was 94.12 μg/m3 over a 24 h period. This is way above the Air Quality Guideline recommended by the WHO which is 15 μg/m3 over a 24 h period. This is similar to the high pollution level of NO2 which was 237.64 μg/m3 in Olusosun and 255.84 μg/m3 in Solous as against the WHO Air Quality Guideline of 25 μg/m3 over a 24 h period. SO2 level is also varyingly high in Olusosun and Solous. The reading of 109.27 μg/m3 in Olusosun is considerably high as compared to the reading of 43.82 μg/m3 in Solous. However, both readings are relatively higher than the Air Quality Guideline recommended by the WHO which was 40 μg/m3. The CO air quality level of 0.55 mg/m3 in Olusosun and 0.52 mg/m3 in Solous is within the accepted level of the Air Quality Guideline recommended by the WHO. This shows that CO is not a pollution threat in the major dumpsites in Lagos. This is similar to the readings of PM5 and PM10 which are within the WHO Air Quality Guidelines over a 24 h period. The Moderate Resolution Imaging Spectroradiometer (MODIS) data shows a trend of increasing concentration of PM2.5 over the 5 study months due to an increase in the quantity of waste disposal, which shows that high waste disposal is directly proportional to high level of PM2.5. It is recommended that living farther (more than 15 km) away from the dumpsites can reduce the high risk of air pollution and its related diseases for residents. Also, there is need for proper incineration and maintenance of waste to reduce the level of air and other pollutants in and around the dumpsites.

Understanding Dynamics of Land Cover Changes Using GIS Technique in a River Catchment with High Altitude Differences – an Analysis from Kosovo

This article analyses Land Cover Changes in the Lumbardhi of Prizreni (LP) River catchment in southern Kosovo. As a landlocked country, Kosovo has highly diverse landform features. LP River catchment is located in Sharri's transboundary high mountain range and the southern part of Dukagjini Plain. In the last two decades, the landscape has experienced a transformation in land cover changes, especially in the increasing of artificial surfaces in the plains. Using GIS techniques, data were calculated for the selected years, and their dynamics were visualised for the last two decades. The analysis shows a decrease in agricultural land and complex cultivation patterns (-806 ha), an increase in artificial surface, especially discontinuous urban fabric (+354 ha), and small changes in forest and seminatural areas, which are primarily included in "Sharri" National Park.

Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques

Assessing water quality in arid regions is vital due to scarce resources, impacting health and sustainable management.This study examines groundwater quality in Assuit Governorate, Egypt, using Principal Component Analysis, GIS, and Machine Learning Techniques. Data from 217 wells across 12 parameters were analyzed, including TDS, EC, Cl−, Fe++, Ca++, Mg++, Na+, SO4−−, Mn++, HCO3−, K+, and pH. The Water Quality Index (WQI) was calculated, and ArcGIS mapped its spatial distribution. Machine learning algorithms, including Ridge Regression, XGBoost, Decision Tree, Random Forest, and K-Nearest Neighbors, were used for predictive analysis. Higher concentrations of Na, K, Ca, Mg, Mn, and Fe were correlated with industrial and densely populated areas. Most samples exhibited excellent or good quality, with a small percentage unsuitable for consumption. Ridge Regression showed the lowest MAPE rates (0.22 % training, 0.26 % in testing). This research highlights the importance of advanced machine learning for sustainable groundwater management in arid regions. Thus, our results could provide valuable assistance to both national and local authorities involved in water management decisions, particularly for water resource managers and decision-makers. This information can aid in the development of regulations aimed at safeguarding and sustainably managing groundwater resources, which are essential for the overall prosperity of the country.

Integration of the AHP Method and GIS Techniques for Mapping Areas Susceptible to Forest Fires in the Southern Amazon Region (Peru)

Integration of the AHP Method and GIS Techniques for Mapping Areas Susceptible to Forest Fires in the Southern Amazon Region (Peru)

Knowing the lay of the land: changes to land use and cover and landscape pattern in village tank cascade systems of Sri Lanka

Social-ecological systems (SESs) possess a great diversity of land use and land cover (LULC) types with unique assemblages of biodiversity and ecosystem services. However, LULC changes due to landscape fragmentation are emerging as major threats to the system productivity of SESs around the world. This study examined changes to LULC extent and landscape patterns in the Village Tank Cascade Systems (VTCSs) of Sri Lanka using satellite imagery and GIS techniques between 1994 and 2021. Multispectral Landsat images (5 TM and 8 OLI/TIRS) obtained from Google Earth Engine were classified using machine learning algorithms. Overall accuracies obtained were 85.9% (1994) and 88.6% (2021). The LULC change matrix and spatial pattern metrics were used to examine LULC and landscape pattern change dynamics over the VTCS landscapes. LULC change matrix results revealed that forest, which is the dominant LULC class covering 73.7% of the total land area was reduced by 206,725 ha due to transformation into agricultural (70.43%) and scrub (24.33%) lands between 1994 and 2021. Over this time landscape pattern of the VTCS has gradually changed from forest to agricultural land-dominated landscape, with forest and agricultural land types showing a significant negative correlation (p < 0.001; R2 > 0.868), particularly in the southeastern region. Landscape patterns were analysed based on eight spatial metrics calculated at both the landscape and class levels using FRAGSTATS spatial pattern analysis software. At the landscape level, the structure became more dispersed and complex in shape. Heterogeneity was noted to have gradually increased with weakening connectivity, whereas the fragmentation process had gradually accelerated. At the class level, the dominance of forest patches decreased, fragmentation and isolation increased, and connectivity and shape complexity reduced leading to the loss of fragmented forest habitats. The number of patches within the agricultural class increased and became more aggregated and complex in shape. Landscape performance indicators show that VTCSs have experienced a gradual loss of environmental sustainability. Assessment of LULC along with fragmentation can help to monitor the spatial pattern impacts that determine ecological integrity. Thus, the study provides scientific guidance for ecological restoration in degraded VTCSs to effectively improve ecological productivity.

GIS-based MCDM approach for landslide hazard zonation mapping in east Gojjam zone, central Ethiopia

Landslides are prevalent in the Ethiopian highlands, particularly in the east Gojjam zone, which is highly affected by landslide problems. This research was carried out in the east Gojjam zone, northwestern Ethiopia. The study area is part of an economically important area in the country, and it is the main source of water for the Grand Ethiopian Renaissance Dam (GERD). The main objective of this work was to undertake a detailed inventory of past landslide locations and prediction of present and future landslide hazards, as well as the preparation of a landslide zonation map in the East Gojjam zone by using the Analytical Hierarchy Process (AHP) with the GIS technique. The parameters used for this study were slope degree, slope aspect, land use and land cover, road proximity, rainfall, lithology, altitude, and river proximity. The various causative parameters were collected from the field, and suitable modifications were made to the thematic maps. Finally, the ratings for various parameters were used as the basis to prepare the LHZ map in GIS windows. The landslide susceptibility and inventory mapping were produced in the GIS environment. The results of the study show that the main driving factors for the landslide hazards in the area were river proximity, rainfall, and manmade activities. Validation of this LHZ map revealed that more than 80% of past landslides match within the "high hazard zone" and reasonably accepted the rationality of the adopted methodology. The considered parameters, as well as their evaluation of the production of LHZ-Map, were confirmed. The produced landslide inventory map is very important for urban planners, agricultural studies, environmentalists, and future landslide hazardous prevention and mitigation strategies.

Remote sensing insights for sustainable development: Water quality and landscape dynamics in Mirik Lake, Darjeeling District, West Bengal, India

The study employs remote sensing and GIS techniques to assess the water quality dynamics of Mirik Lake, located in the Darjeeling Himalayas, West Bengal, India. To analyse the impact of land use and land cover (LULC) changes on the water quality of Mirik Lake from 1993 to 2023. Landsat imagery spanning from 1993 to 2023 was used to detect significant alterations in LULC patterns. Remote sensing and GIS techniques were utilised to analyse the data, focusing on changes in LULC and their implications for water quality. The results indicate a steady increase in total phosphorus (TP), total nitrogen (TN), and Biological Oxygen Demand (BOD) levels, attributed to anthropogenic activities such as urbanisation and tourism development. LULC change analysis highlights the expanding built-up areas and agricultural lands surrounding the lake, contributing to nutrient loading and organic pollution. The spatial distribution of pollution categories underscores the influence of tourist infrastructure on water quality degradation. Integrated watershed management and sustainable development strategies are recommended to mitigate anthropogenic impacts and preserve the ecological integrity of Mirik Lake.

Assessment of the extent of desertification in Gamawa LGA of Bauchi State using remote sensing and GIS techniques

Assessment of the extent of desertification in Gamawa LGA of Bauchi State using remote sensing and GIS techniques

Analysis of morphometric characteristics and prioritization of micro watersheds of Karamnasa River Basin using remote sensing & GIS technique

ABSTRACT The Karamnasa River, originating in Bihar’s Kaimur district, serves as a vital water resource in the region. This research, conducted at coordinates 25°30’54’‘N latitude and 83°52’30’’ E longitude, aimed to identify optimal sites for water conservation structures within the Karmanasa River Basin (KRS) using Remote Sensing and GIS techniques. By analysing morphometric parameters (MP) of the basin, including slope, stream length, and drainage density, the study delineated the basin into seven sub-watersheds and five stream orders. The comprehensive analysis revealed SW2 as the most critical sub-watershed, necessitating immediate conservation efforts. Assigning ranks to parameters like bifurcation ratio and circulatory ratio, SW2 emerged as the priority sub-watershed. Fifteen potential conservation sites were identified, comprising 9 farm ponds, 3 percolation tanks, and 3 check dams. The research underscores the significance of prioritizing sub-watersheds based on morphometric characteristics, with lower parameter values indicating higher priority. The study’s drainage network analysis, conducted through remote sensing and GIS, enhances understanding of the KRS hydrological features. This research highlights sustainable development through effective water resource use and targeted conservation in the Karamnasa River Basin, emphasizing community engagement and participatory methods for enduring, resilient environmental stewardship and successful land management.

Identifying human elephant conflict hotspots through satellite remote sensing and GIS to support conflict mitigation

Human-elephant conflict (HEC) is a significant issue in Sri Lanka and many parts of the world where elephants and humans coexist. To address HEC, this study integrates remote sensing and GIS analysis, focusing on monitoring changes in greenery. The study prepared the latest land cover and land use (LCLU) maps with Sentinel-2 satellite data, correlating them with reported HEC incidents reported in 2021 and 2022 to identify HEC hotspots in two forest-dominated regions of Southeast Sri Lanka. High-resolution sentinel-2 satellite imagery were used to detect areas of human activities and elephant habitats in proximity to each other. Random Forest (RF) and Support Vector Machine (SVM) classification methods were used for LCLU classification. The overall accuracy of the classification was 97.31 and 94.62, and kappa was 0.95 and 0.90 for RF and SVM, respectively. Multi-temporal normalised difference vegetation index (NDVI) analysis provided insights into vegetation health and coverage, offering a clear picture of greenery changes. Monthly changes in vegetation cover readings were quantified using NDVI values derived from MODIS data, identifying suitable regions for elephants to forage frequently. Furthermore, Kernel density estimation identified high-density areas for reported incidents of human and elephant deaths. This process involved assigning weight to conflict incidents within a 5 km radius, considering the proximity to the forest, and evaluating greenery changes using NDVI values, revealing varying levels of HEC risk, ranging from very high to low. The LCLU map, created using the RF classifier, indicates that all potential HEC hotspots for very high and high HEC risks are closely aligned with forest boundaries. The findings support HEC mitigation strategies through community awareness, HEC hotspots mapping and restoration practices to ensure a sustainable human-elephant coexistence. This method will help policymakers in wildlife conservation to identify high risk HEC zones to support HEC mitigation. In conclusion, this study highlights the potential of integrating remote sensing and GIS techniques in demarcating HEC hotspots in Sri Lanka to support conflict mitigation efforts.

Assessment of Deposited Red Clay Soil in Kirkuk City Using Remote Sensing Data and GIS Techniques

This study investigates the physical characteristics of red clay using the IDW approach and linear regression modeling in an area of 268.12 km2, focusing on Kirkuk, Bor, and Jambor structures. Through the analysis of 52 soil samples and the integration of laboratory data with IDW and regression results, several significant findings have emerged. The IDW method combined with linear regression proves to be a cost-effective and efficient approach for obtaining soil property data and generating accurate digital maps of red clay’s physical features. The Silt concentration exhibits a wide range, while the gravel content remains relatively low, indicating the predominance of silt in the soil composition. Analysis of Atterberg limits reveals the soil’s behavior and consistency in response to moisture, with the plasticity index generally falling within the low to medium range due to the considerable silt content in most soil samples. The linear regression model highlights positive correlations between the liquid limit, plastic limit, and plasticity index. Moderately positive relationships exist between the liquid limit and clay content, as well as a weak positive association between the liquid limit and specific gravity. Dry density, on the other hand, shows no significant correlation with other physical variables, suggesting its independence from the measured parameters. The plastic limit demonstrates a stronger relationship with the clay content compared to the liquid limit. Additionally, weak positive correlations are found between the liquid limit, plastic limit, and specific gravity and water content, indicating the influence of moisture on these parameters. Furthermore, gravel exhibits a moderate positive correlation with sand and silt concentrations, while a strong positive correlation is observed between sand and silt contents, underscoring their close association with the soil composition.

Restoration of degraded waterlogged saline soil through subsurface drainage: Spatiotemporal soil salinity assessment using electromagnetic induction (EMI) and geographic information system (GIS) techniques

AbstractReduction in soil salinity in the entire project area is a key indicator of proper functioning of the implemented subsurface drainage (SSD) projects. Electromagnetic induction (EMI) surveys were carried out to facilitate spatiotemporal assessment of soil salinity of a SSD project site in Kahni, Haryana (India). The surveys were conducted in two time frames: immediately after the start of SSD project in 2019 and after 2 years of successful operation in 2021. The collected data of 2 surveys was used to derive a depth‐specific calibrated correlation between the observed salinity (ECe) of collected soil samples and surveyed apparent electrical conductivity (ECa) observations in vertical (ECav) and horizontal (ECah) modes. Spatiotemporal analysis of soil salinity revealed notable changes in areas under different salinity classes over a period of 2 years, indicating the impact of SSD on soil desalinization. During the course of 2 years (2019–2021) of SSD operation, area under moderate salinity (4–8 dS m−1) in the top 0–0.15 m soil increased by 54.5% while it decreased by 40% and 6.6% under high (8–12 dS m−1) and very high (12–16 dS m−1) salinity classes. The rice and wheat yields were also enhanced by 24% and 7% respectively after 2 years operation of SSD in hitherto severally waterlogged saline soils. Overall findings of this study reinforce the scope of using EMI approach to quickly and effectively assess the efficacy of subsurface drainage. Creation of measurable soil salinity maps by proposed EMI and GIS techniques can help in planning and implementing site‐specific management strategies in saline soil reclamation projects.