Geospatial Techniques Research Articles

Exploring the impact of land use/land cover changes on the dynamics of Deepor wetland (a Ramsar site) in Assam, India using geospatial techniques and machine learning models

Exploring the impact of land use/land cover changes on the dynamics of Deepor wetland (a Ramsar site) in Assam, India using geospatial techniques and machine learning models

Sensitivity analysis on stormwater management response to land cover dynamics and urban expansion of developing City in Lake Hawassa watershed, Ethiopia

AbstractHawassa is a rapidly developing city in Lake Hawassa watershed of Ethiopia. Analyzing the effect of land cover dynamics on surface runoff remains imperative to adaptive urban stormwater management. This study quantified spatial variation of land cover and sensitivity of stormwater management response. Historical 30 years of daily annual rainfall, three satellite imageries, DEM, and hydrological soil group data were analyzed. A statistical‐based combined approach of geospatial techniques and Soil Conservation Service‐Curve Number (SCS‐CN) model was employed. CN and surface runoff depth for the delineated urban watersheds were determined. The result revealed that the built‐up area increased by 30.9 km2, where the rate varies spatially. The variation of impervious land cover explains 58.6% of change in CN with coefficient of 0.352. While CN is inversely correlated with agricultural and vegetation land cover variations. The finding suggests CN explains 96.78% of the change in surface runoff with a significant correlation coefficient of 3.91. The proposed integrated model approach justifies the potential to reorganize the relationship between the spatial effect of land cover variation on surface runoff at the urban watersheds. Thus, suitable local‐specific solutions can be devised for effective management of flood risk and optimize the drainage system of urban areas.

An integration of geospatial and fuzzy-logic techniques for flood-hazard mapping

An integration of geospatial and fuzzy-logic techniques for flood-hazard mapping

Drinking water quality in Cooch Behar Municipality, West Bengal, India: Assessment using WQI and public perception

AbstractEffective assessment and management of groundwater quality are essential for safeguarding the integrity of freshwater resources. Cooch Behar Municipality of West Bengal has been chosen for the present study due to its reliance primarily on groundwater for drinking water. The study focuses on assessing groundwater quality in the study area, a planned city with a long history of urbanization. The primary objective is to analyze various parameters relevant to drinking water and compute the Water Quality Index (WQI) with the residents' perception. Forty water samples were collected from Cooch Behar Municipality's Public Health Engineering (PHE) department, encompassing all 20 wards. Each ward provided one sample from a Deep Tube Well (DTW) and a Shallow Tube Well (STW). EC, Fe, TH, Mn, pH, TDS, Turbidity, Ca, and Mg levels were determined using standard instruments and methods, and analyzed geospatial techniques and statistical methods were employed. Results revealed concerning levels of manganese (Mn) and total hardness (TH), exceeding permissible limits in 82.5% and 75% of samples, respectively. While some parameters remained within acceptable limits, computed WQI values ranged from 63.28 to 162.67, indicating significant overall water quality concerns. Alarmingly, 45% of samples were unsuitable for drinking, with an additional 40% classified as very poor. Notably, demographic factors such as household income and education level correlated with perceptions of poor water quality. Policy implications suggest the urgent need for a well‐structured management system to address these findings and ensure safe drinking water for residents.

Assessment of soil erosion and prioritization of conservation and restoration measures using RUSLE and Geospatial techniques: the case of upper Bilate watershed

Soil erosion is still a vector of environmental and economic concern affecting most parts of the world, especially in Sub-Saharan African countries. Nevertheless, recent human activities in the hills, coupled with poor conservation measures and practices, could have amplified the rate at which soil is lost in the southwestern highlands of Ethiopia. This study focuses on quantifying and prioritizing micro-watersheds that require conservation actions by piloting spatial modeling of soil loss in the upper Bilate watershed. Sentinel image, soil, DEM, rainfall, and support practice data were used. A Revised Universal Soil Loss Equation (RUSLE) using GIS and satellite images was applied. The estimated average annual soil loss rate was demonstrated to be 24.1 t ha−1yr−1 and varied between 0.05 and 498.24 t ha−1yr−1. About 51.2% of the total revealed has a high soil truncation trait, of which 40% of the cropland has exceeded the soil loss tolerances of Ethiopia and tropical regions. The most affected micro-watersheds are MWS 16, 8, 6, and 3, which contributed 39.4% of the average annual loss rate, indicating the hotspots of soil loss problems in the region. This will have far-reaching off-site impacts on food security, soil productivity, human lives, infrastructures, and ecosystem service provisions.

Agriculture-livestock-forestry nexus and household income diversification: Experiences from selected villages of West Bengal, India

CONTEXTThe COVID-19 pandemic has posed unprecedented challenges to the livelihoods of rural households, especially in developing countries. The reverse migrated workers faced the most economic crisis. They have created additional pressure on local resources and opportunities to maintain their livelihood. In this connection the agriculture-livestock-forestry nexus plays a significant role by providing them livelihood opportunities. OBJECTIVESThis paper attempts to understand the resource use patterns during the post-COVID period, and analyse the impact of agriculture-livestock-forest nexus on household income diversification and poverty reduction. METHODSThe present study uses a novel approach to fulfil the objectives of the study. A well-designed survey was conducted to collect the necessary primary data from sixteen sample villages of West Bengal, India. The scientific design of the survey and data collection procedure is maintained. Moreover, geospatial techniques are used to examine the land use and land cover patterns of the study areas. The econometric models are estimated to inspect the role of agriculture-livestock-forest nexus and household characteristics on household level income diversification and the reduction of poverty. RESULTS AND CONCLUSIONSOur analysis reveals that a significant number of the sample households depend on agriculture, livestock and forestry to maintain their livelihoods. The reverse migration of workforce has created additional pressure on these activities. This is mainly due to non-availability of other non-farm employment opportunities locally. The analysis of econometric models shows that while engagement in different activities of the primary sector makes income diversified, it is more so when the households possess necessary technologies, skills and financial supports. All of these are crucial given that income diversification helps households to overcome of poverty. SIGNIFICANCEThe intricate web of the agriculture-livestock-forestry nexus serves as a critical source of livelihood for a substantial number of households, yet the challenges and opportunities it presents necessitate targeted policy interventions. Enabling technology transfer, fostering skill development, and extending financial support emerge as key measures to diversify rural livelihoods, thereby fostering sustainable rural development and poverty reduction in the post-pandemic landscape. The integration of socioeconomic data and geospatial satellite information offers an invaluable basis for formulating effective policy strategies in this evolving era.

Assessing tropical cyclone risk for improving mitigation strategies in Coastal Odisha, India.

Tropical cyclone causes large-scale devastation and destruction in the coastal plains of India, particularly in Odisha, which is the most cyclone-affected state in the country. Tropical cyclones are projected to be more powerful and widespread due to changing climate. Hence, the risk assessment of tropical cyclone is necessary to identify cyclone-risk areas in coastal Odisha which may help in the mitigation of the damages caused by cyclones. Therefore, this study utilizes geospatial techniques to produce a comprehensive risk map posed by tropical cyclones and to estimate the degree of risk for coastal districts of Odisha. For this, we evaluated the district-level cyclone risk for coastal Odisha using multi-criteria decision-making (MCDM) technique by considering 21 parameters for each of the four components of risk, i.e., exposure, hazard, vulnerability, as well as mitigation capacity. For each criterion, thematic raster map layers were created and weighted using a fuzzy analytical hierarchy process (FAHP). We prepared individual risk component maps using weighted overlay techniques and finally integrated all indices to create the risk map. The study shows that 13% area of the study area comes under a very high-risk zone whereas, 25% area comes under a high-risk zone. The central (Cuttack, northern parts of Khordha, and south-western parts of Jajpur district) and the eastern part (most of the parts of Jagatsinghpur, Kendrapara, and northern parts of Puri district) of the study area come under high to very high tropical cyclone impact zone. Almost 67% of the total area is highly vulnerable to tropical cyclones and mainly concentrated near the shoreline. The applied approach and results can assist the local authorities in identifying vulnerable and hazardous locations and developing workable solutions for the mitigation of revised cyclone threats in the coastal districts of Odisha.

Analysis of the Post-Cyclonic Physical Flood Susceptibility and Changes of Mangrove Forest Area Using Multi-Criteria Decision-Making Process and Geospatial Analysis in Indian Sundarbans

Tropical cyclones, one of the most extreme and destructive meteorological incidents, cause extensive damage to lives and livelihoods worldwide. This study utilized remotely sensed data along with multi-criteria decision-making, geospatial techniques, and major cyclonic events Aila, Amphan, and Yaas to identify the changes in the vulnerability of cyclone-induced floods in the 19 community development blocks of Indian Sundarbans in the years 2009–2010, 2020–2021, and 2021–2022 (the post-cyclonic timespan). The Sundarbans are a distinctive bioclimatic region located in a characteristic geographical setting along the West Bengal and Bangladesh coasts. In this area, several cyclonic storms had an impact between 2009 and 2022. Using the variables NDVI, MNDWI, NDMI, NDBI, BSI, and NDTI, Landsat 8 Operational Land Imager, Thermal Infrared Sensor, Resourcesat LISS-III, and AWiFS data were primarily utilized to map the cyclonic flood-effective zones in the research area. The findings indicated that the coastline, which was most impacted by tropical storms, has significant physical susceptibility to floods, as determined by the AHP-weighted overlay analysis. Significant positive relationships (p < 0.05, n = 19 administrative units) were observed between mangrove damage, NDFI, and physical flood susceptibility indicators. Mangrove damage increased with an increase in the flood index, and vice versa. To mitigate the consequences and impacts of the vulnerability of cyclonic events, subsequent flood occurrences, and mangrove damage in the Sundarbans, a ground-level implementation of disaster management plans proposed by the associated state government, integrated measures of cyclone forecasting, mangrove plantation, coastal conservation, flood preparedness, mitigation, and management by the Sundarban Development Board are appreciably recommended.

Demarcation of suitable site for solar photovoltaic power plant installation in Bangladesh using geospatial techniques

The major goal of this research is to adopt analytical hierarchy process (AHP) based geospatial technique to select suitable zone for the solar photovoltaic (PV) power plants. Nine thematic layers altogether—slope, global horizontal irradiation (GHI), relative humidity, direct normal irradiation (DNI), elevation, distance from major roads, distance from protected areas, rainfall, and land use/land cover (LULC)—are combined through overlay analysis in ArcGIS to create the final map of suitability for the placement of solar photovoltaic (PV) power plants in Bangladesh. This map has been classified into five categories namely, restricted zone, less suitable zone, moderate suitable zone, good suitable zone, and excellent suitable zone. These categories are covered by 7.28%, 16.61%, 28.51%, 27.77%, 21.83% land of total area in Bangladesh respectively. The findings of this research have been presented that ‘the excellent suitable’ and ‘good suitable’ areas for the construction of solar power plants are in the western and northwestern part (Rajshahi, Pabna, Sirajganj, Natore, Naogaon, Chapainawabganj, Bogura, Faridpur, Jessore, Jehenaidha, Magura, Kushtia, Choudanga, Meherpur) of the study area which contain a high value of global horizontal irradiation, direct normal irradiation, elevation and low value of slope, rainfall, temperature, relative humidity. Besides the restricted and less suitable zone for installing solar photovoltaic (PV) power plants indicates a high value of rainfall, slope, temperature, relative humidity and low value of global horizontal irradiation, direct normal irradiation, and elevation. Bangladesh's currently operational solar plants were taken into consideration for this study's validation purposes. The proposed framework may potentially be used in different locales on a national and worldwide scale. This study offers a consistent GIS process for the accurate, inexpensive implementation of a solar energy plan to achieve environmentally friendly goals.

Spatio-Temporal Changes and Driving Forces Analysis of Urban Open Spaces in Shanghai between 1980 and 2020: An Integrated Geospatial Approach

Although there is extensive research demonstrating the significant loss and fragmentation of urban spaces caused by rapid urbanization, to date, no empirical research in Shanghai has investigated the spatiotemporal dynamics of urban open spaces using a comprehensive set of integrated geospatial techniques based on long-sequence time series. Based on the Google Earth Engine (GEE) platform and using the Random Forest (RF) classifier, multiple techniques, namely landscape metrics, trend analysis, open space ratio, transition matrix, Normalized Difference Vegetation Index (NDVI), and fractal dimension analysis, were applied to analyze the Landsat satellite data. Next, Geographic Detector (GeoDetector) methods were used to investigate the driving forces of such spatial variations. The results showed that (1) the RF classification algorithm, supported by the GEE, can accurately and quickly obtain a research object dataset, and that calculating the optimal spatial grain size for open space pattern was 70 m; (2) open spaces exhibited declining and contracting trends; and open spaces in the city experienced a decline from 91.83% in 1980 to 69.63% in 2020. Meanwhile, the degree of open spaces in each district increased to different extents, whilst connectivity markedly decreased. Furthermore, the open space of city center districts showed the lowest rate of decrease, with open space patterns fragmenting due to encroaching urbanization; (3) the contribution of socioeconomic factors to the spatial–temporal changes in open space continually has increased over the past 40 years, and were also higher than natural geographic factors to some extent. Apart from offering policy insights guiding the future spatial planning and development of the city, this paper has contributions from both methodological and empirical perspectives. Based on integrated remote sensing and geographic information science (GIS) techniques, this paper provides updated evidence and a clearer understanding of the spatiotemporal variations in urban spaces and their influencing mechanisms in Shanghai.

Post-wildfire neighborhood change: Evidence from the 2018 Camp Fire

As the number of highly destructive wildfires grows, it is increasingly important to understand the long-term changes that occur to fire-affected places. Integrating approaches from social and biophysical science, we document two forms of neighborhood change following the 2018 Camp Fire in the United States, examining the more than 17,000 residential structures within the burn footprint. We found that mobile or motor homes, lower-value residences, and absentee owner residences had a significantly higher probability of being destroyed, providing evidence that housing stock filtering facilitated socially stratified patterns of physical damage. While the relationship between building value and destruction probability could be explained by measures of building density and distance to nearby roads, building type remained an independent predictor of structure loss that we could not fully explain by adding environmental covariates to our models. Using a geospatial machine learning technique, we then identified buildings that had been reconstructed within the burn footprint 20 months after the fire. We found that reconstructed buildings were more likely to have been owner-occupied prior to the fire and had higher average pre-fire property value, suggesting an emerging pattern of cost-burden gentrification. Our findings illustrate the importance of examining the built environment as a driver of socially uneven disaster impacts. Wildfire mitigation strategies are needed for mobile and motor home residents, renters, low-income residents, and dense neighborhoods.

Mapping of Groundwater Potential Zones in the Kuzhithuraiyar Sub Basin of Kodayar River, Kanniyakumari District, Tamilnadu: Using Analytic Hierarchy Process (AHP) and GIS


 
 
 Groundwater plays a very important role for all living beings. It is decisive to have a scientific understanding of groundwater management since, with careful use and replenishment, groundwater may help solve issues. The distribution of groundwater tables, slope, land-form, drainage pattern, lithology, topography, geological structure, fracturing density, opening and connectivity of fractures, secondary porosity, and landuse landcover all affect the occurrence and efficiency of groundwater in an aquifer system. An essential tool for assessing, tracking and protecting groundwater resources is the integration of geospatial techniques such as Remote Sensing and Geographic Information System for the identification of groundwater potential zones. This study aims to find the groundwater potential zones using Analytical Hierarchy Process (AHP) and managing the resources by creating different thematic layers such as rainfall, geology, geomorphology, drainage density, soil, slope, lineament and landuse landcover (LULC) of the Kuzhithuraiyar sub basin of the Kodayar river in Tamilnadu using the application of geospatial technologies. The thematic maps for all the thematic layers have been prepared using tools such as Interpolation, contour lines, Classification in ArcGIS 10.8. Theme weight and class rank were assigned to different thematic layers in weighed overlay analysis. The results were validated through field work and groundwater potential map was created. The groundwater potential zonation mapping was done by the overlay analysis in ArcGIS 10.8 software. The obtained map was classified into four categories namely very high groundwater potential zone, high groundwater potential zone, medium groundwater potential zone and low groundwater potential zone. The North Eastern part of the basin is considered as low groundwater potential zone while the Southern portion has high groundwater potentiality. The low groundwater potential zone covers an area of 10.58 sq.km and high groundwater potential zone covers an area of about 388.37sq.km.This study will be helpful for useful groundwater management for different tenacities.
 
 

Methods for economic assessment of forest resource degradation: A systematic review

Forest degradation is one of the challenges facing the planet today. Several methods have been used to measure forest degradation, including spatiotemporal model analysis, satellite analysis, remote sensing, time series data, geospatial techniques, and most recently aerial drone imagery. However, few studies have used economic valuation methods to assess forest degradation. Therefore, this research aimed to identify the methods used the economic assessment of forest degradation. This systematic review was carried out using PRISMA guidelines. Research articles on the economic valuation of forest resource loss, published from 2015 to 2022, were electronically collected from three databases. Three independent reviewers, with the third acting as referee, inventoried articles, extracted data, and assessed the risk of bias in the articles included in the study. A total of 10,095 articles were identified, including one article from the grey literature. Only five articles met the eligibility criteria. A qualitative content analysis was performed on the extracted data. The selected articles used various methods. However, only a few articles used the contingent valuation method, even though this is indicated for estimating the highest economic value of forests. Based on forest functions, the articles evaluated erosion due to the absence of trees, wood loss, recreation areas and externalities due to forest loss, air quality, water regulation, food supply, and wildlife. The main limitation of this review was the small number of studies included, which may have affected the findings. The study protocol is registered in PROSPERO under the number CRD42021223242

Spatial analysis of malaria risk using geospatial techniques in Wabi Shebele river sub-basin, Southeastern Ethiopia

Spatial analysis of malaria risk using geospatial techniques in Wabi Shebele river sub-basin, Southeastern Ethiopia

Evaluating the relationship of vegetation dynamics with rainfall and land surface temperature using geospatial techniques in South Wollo zone, Ethiopia

This study was conducted in the South Wollo zone to assess vegetation dynamics and their relationship with rainfall and land surface temperature. To that end, 16-day 250-meter resolution Moderate Imaging Spectro-radiometer (MODIS) Normalized difference vegetation Index (NDVI), 5-kilometer resolution MODIS Land Surface Temperature (LST), 5-kilometer CHIRPS data were used. A Mann-Kendall (MK) trend test was used to determine the trend of each dataset using seasonal and annual time series. In addition, the Pearson correlation coefficient was used to estimate the relationship between NDVI and climatic elements. The results of the study revealed that there was no significant change in the annual and seasonal rainfall and LST during the period 2000 to 2021, except LST in the autumn season (p = 0.01) and NDVI was significant only in the mean annual (p = 0.027) and summer seasons (p = 0.03). The correlation between NDVI and rainfall was positive (r = 0.84, 0.78, 0.72 & 0.79) and obtained for the summer, autumn, winter, and spring season respectively. Similarly, the correlation between NDVI and LST was negative (r= -0.79, -0.75, -0.72, and -0.78) in the summer, autumn, winter, and spring seasons respectively. The outcome of the study indicates precipitation is the most significant factor in the distribution of vegetation and LST

Predicting fluid flow in fractured reservoirs by application of lacunarity analysis of natural fracture analogues

Researchers have used lacunarity, a parameter that quantifies scale-dependent clustering in patterns to distinguish fracture networks that belong to the same fractal system. Also, in a previous study, the authors showed that lacunarity is efficient in representing the connectivity and fluid flow in synthetic fractal-fracture models having a single fractal dimension. The objective of this research is to investigate if the concepts thus developed is applicable to outcrop analogues which are representative of sub-surface fractured reservoirs. A set of nested fracture networks belonging to a single fractal system but mapped at different scales and resolutions is considered in this study. Lacunarity and connectivity values of these maps are evaluated using geospatial data analysis techniques. Fracture continuum (FC) models are built from these fracture maps and a streamline simulator, TRACE3D is used to flow simulate these maps. Results show that, although, the fractal dimension of these maps is same, but there exist stubble differences in the values of lacunarity, percolation connectivity, and also the fluid recovery values. It is further noted that the clustering, connectivity, and fluid recovery values can be pairwise correlated very well for these natural fracture maps. Thus, the overall results indicate that connectivity in fracture maps and hence in turn their flow properties are controlled by lacunarity or scale-dependent clustering attributes. Therefore, there could be novel applicability of lacunarity parameter in calibrating discrete fracture network (DFN) models with respect to connectivity of natural fracture maps and prediction of flow behavior in fractured reservoirs. Thematic collection: This article is part of the Digitally enabled geoscience workflows: unlocking the power of our data collection available at: https://www.lyellcollection.org/topic/collections/digitally-enabled-geoscience-workflows

The large-scale expansion of rubber plantations in southern India: major impacts and the changing nature of drivers

This study investigates the major environmental and socio-economic impacts of an increase in the area of rubber plantations and the changing patterns of drivers of land use changes. Using a combination of geospatial techniques and socio-economic methods, we mainly analyzed the rate of increase in area under rubber plantations, the major impacts of land use changes, and the changing drivers of land use changes. Our results show that the area under rubber plantations has increased significantly within the study area, with the area under rubber plantations increasing from 30 to 74% of the total area within five decades. Impact assessment of land use changes based on household surveys showed significant improvement in the socio-economic conditions of the farmers, however, at the expense of severe environmental degradation. Our results also indicate that while areas under rubber plantations continue to increase, the drivers of land use changes have changed over time. Furthermore, it has been observed that in the past, many interventions prioritized social and economic development and placed less emphasis on the ecological stability of the region. Perceptions of farmers revealed that the effects of ecological fragility already affected the economic robustness of the whole area. Therefore, we conclude that government interventions to support additional rubber cultivation should also focus on ecosystem stabilization in order to minimize the risk of an ecological catastrophe that would significantly affect the economic prosperity of the region.

Finding changes within protected habitats: an assessment of landscape characteristics using geospatial techniques in the lower Shivalik landscape of the Western Himalayas

A GIS-based approach was used in this study to assess 10 years of land-use change in the lower Shivalik landscape of the Western Himalayas, India. The landscape encompasses two major protected areas which are home to a thriving population of large mammals. The main objective was to identify the changes in the land use pattern, specifically after forming a new tiger reserve in the landscape. Landsat 8 and 4 imagery were used for the time series analysis for the years 2018, 2013, and 2008, respectively. The change was calculated using nine land use classes, with mixed forest showing the highest change value, followed by agriculture, grassland, and scrub. The dense forest area increased immensely after the formation of the new tiger reserve. The results for dense and mixed forest classes also identified less fragmentation in the number of patches during the same time. Even though most of the landscape is within the boundaries of a protected area, the change in land use forms is noticeable. As development activities continue to expand, this region will face increased strain, which will impact the area’s natural biodiversity. To protect that, a long-term conservation management effort is required.

Integrating GEO-Spatial Techniques for Reflecting Micro-Nutrients and Sulfur Variations and Spatial Distribution to Support Farm Management Practices

To understand the spatial dependency of available sulphur and micronutrients in an experimental farm, 83 soil samples (surface and subsurface) were taken from Agricultural College, Killikulam to characterize the spatial variability of available Sulphur and micronutrients. The geostatistics and geographic information system (GIS) techniques were applied. With the help of geostatistical analyst of ArcGIS software kriged map of different soil parameters were prepared. Available S, Fe, Cu and Zn were fitted with a spherical model and that available Mn was fitted with an exponential model. Soil available Cu had strong spatial dependence with a range of 3.62 km. Available Fe, Zn and Mn had moderate spatial dependence with a range of 9.47 km, 5.61 km and 2.70 km, respectively. Available sulphur had weak spatial dependence with a range of 6.86 km. The spatial distribution of the available sulphur and micronutrients were significantly correlated to the soil formation factors. Agricultural practices such as application of fertilizers and pesticides also had significant effects on the spatial distributions of the available micro-nutrients and sulfur.

Temporal dynamics of urban gas pipeline risks

Urban gas pipelines pose significant risks to public safety and infrastructure integrity, necessitating thorough risk assessment methodologies to mitigate potential hazards. This study investigates the dynamics of population distribution, demographic characteristics, and building structures to assess the risk associated with gas pipelines. Using geospatial analysis techniques, we analyze population distribution patterns during both day and night periods. Additionally, we conduct an in-depth vulnerability assessment considering multiple criteria maps, highlighting areas of heightened vulnerability in proximity to gas pipelines and older buildings. This study incorporated the concept of individual risk and the intrinsic parameters of gas pipelines to develop a hazard map. Hazard analysis identifies areas with elevated risks, particularly around main pipeline intersections and high-pressure zones. Integrating hazard and vulnerability assessments, we generate risk maps for both day and night periods, providing valuable insights into spatial risk distribution dynamics. The findings underscore the importance of considering temporal variations in risk assessment and integrating demographic and structural factors into hazard analysis for informed decision-making in pipeline management and safety measures.