Sensing Techniques Research Articles

Determination of Forest Health using Remote Sensing Techniques in Gashaka-Gumti National Park, Northeast, Nigeria

This study was conducted in order to determine the health status of forest vegetation in Gashaka-Gumti National Park. Landsat images were downloaded from the USGS website. The images were pre-processed using radiometric correction since the reflectance values were needed for computing spectral indices, the digital numbers were converted to radiance and reflectance, and analysis was carried out using a raster calculator. The range of NDVI, GNDVI, ARVI and MSI were used for health assessment. Utilizing NDVI, GNDVI, ARVI, and MSI as assessment tools revealed moderate to good health in most forest regions, with higher ARVI, GNDVI, and NDVI indicating healthier vegetation and elevated MSI values suggesting areas under moisture stress. the average values of NDVI, GNDVI, ARVI, and MSI over three decades indicate a potential decline in overall vegetation health, reduced green vegetation, changes in vegetation conditions, and a decrease in moisture stress, suggesting a potential increase in greening and photosynthetic activities in plants. These trends highlight the dynamic nature of the forest ecosystem over the studied period. Positive correlations between ARVI, GNDVI, and NDVI across years indicate a consistent vegetation pattern, while negative correlations with MSI suggest potential inverse relationships, providing valuable insights into forest health dynamics. Higher values of ARVI, GNDVI, and NDVI generally signify healthier vegetation, whereas elevated MSI values may indicate areas experiencing moisture stress, emphasizing the importance of monitoring these indices for sustainable forest management. The study recommends the sustained use of NDVI, GNDVI, ARVI, and MSI for forest health monitoring in the study area, implementing integrated pest management based on identified stress conditions, utilize spatial maps for strategic timber harvest planning, developing climate-resilient management considering moisture stress, and invest in research for enhanced assessment precision and understanding of ecosystem dynamics.

Monitoring Hazards in Dam Environments Using Remote Sensing Techniques: Case of Kulekhani-I Reservoir in Nepal

Maintaining the operability of a hydroelectric power station at a scale originally designed is being compromised by continuous reservoir sedimentation. The underlying factors include a complex mix of landscape alterations owing to natural and anthropogenic activities around dam areas, such as gully erosion, landslides, floods triggered by heavy rainfall, climate change, and construction activities. The hydropower projects in the low-to-mid mountain regions of Nepal are witnessing a combination of these phenomena, affecting their optimal performance in meeting long-term sustainable power supply targets. This paper presents a combination of geo-spatial analysis and field evaluations to identify the trends from Kulekhani-I, one of the oldest storage-type hydropower projects in Nepal, using long-term time series remote sensing satellite imagery from 1988 to 2020. Our analysis shows an expansion of the surface water content area over time, attributed mainly to high sedimentation deposition owing to multiple factors. This study has identified an urgent need for addressing the following two key contributory factors through an effective control mechanism to avoid rapid sedimentation in the reservoirs: natural—landslides and floods leading to mainly silt deposition during heavy rainfalls; and anthropogenic—road construction materials dumped directly in the reservoir. Effective implementation of a remote sensing monitoring scheme can safeguard future damages to dam environments of more recently built storage-type hydropower projects.

Shoreline Change Analysis in New Washington, Aklan using Digital Shoreline Analysis System (DSAS)

Abstract. Shoreline changes are a critical indicator of coastal vulnerability, impacted by natural processes and human-induced factors such as climate change. This study investigates shoreline changes in New Washington, Aklan, Philippines, focusing on erosion and accretion patterns since 1985. With the use of satellite imagery, coupled with Remote Sensing and Geographical Information System (GIS) techniques, this study examines the spatio-temporal variability in erosion and accretion patterns. Through the Digital Shoreline Analysis System (DSAS), a quantitative analysis of shoreline change rates was conducted. Using four (4) DSAS change metrics such as Endpoint rate (EPR), Linear Regression Rate (LRR), Shoreline Change Envelope (SCE), and Net Movement Shoreline (NMS), it was identified that the shoreline change in Bakhawan Park area is due to its location being at the mouth of Aklan River. Most of New Washington’s shoreline experiences erosion with 0-1.46 m/yr from LRR, eroding at most 40m of sediments in thirty years; evident along areas of Brgy. Tambac and Poblacion. The largest shoreline deviation can be observed in the areas located near the mouth of Batan Bay with 153 meters since 1985.

Characterization of artesian flow and heat transition in an ATES research wellbore using DTS monitoring and numerical modelling

Abstract. In the practice of Aquifer Thermal Energy Storage (ATES), the hydraulic connection to the wellbore of any other aquifers besides the planned thermal storage should be identified and prohibited in order to operate the ATES system in a sustainable way. The present study was aimed at locating an artesian aquifer other than the planned thermal storage in an ATES research wellbore (Berlin, Germany). Therefore, we analysed the wellbore temperature as monitored with a single fiber optic cable using the Distributed Temperature Sensing (DTS) technique in a series of artesian flow tests. The change in the wellbore temperature, the depth-dependent thermal gradients and the isotherms as derived from the DTS-monitored data helped positioning the artesian aquifer in the depth interval from 220 to 230 m. In addition, the transition from cooling to heating in the wellbore sections above the depth of 40 m was applied calculating the velocity of the artesian flow. A numerical model accounting for such artesian flow was built via matching the simulated volumetric flow rate to the wellhead measurements. The consistency of the simulated wellbore temperature with the DTS measurements validated this numerical model as well as the positioning of the artesian aquifer. These simulates extensively visualized the effect of the artesian flow on the near-wellbore temperature field.

Exploring the Geomorphological Nature and its Consequences in Agaie & Lapai L Gas of Niger State and Environment Using GIS & LandSat-8 Imagery

The activities of geomorphology has persisted over many years in the study area un-noticed, un-checked or controlled and un-abaited. That is why, this article is poised to evaluate the morphological consequences on the study area topography, particularly settlements and farm-land areas along the fringes of the river (coast) and suggest way-out. In order to assess the magnitude of morphological changes over-time, the of Geographic Information System (GIS), Remote Sensing (RS) techniques (that is, using LandSat Imagery-8) and Shuttle Radar Thematic Mapper (SRTM) were deployed and survey method (via a structured questionnaire) to extrapolate the socio-economic consequences on the inhabitants of the study area. The GIS, RS and SRTM revealed the morphological changes and its evolution such as the drainage pattern, topography (that is, relief), flooding and flood plains. It is expected that, policy makers, water resources engineers and environmental managers would find the out-come of this investigation use for planning and making informed decision to avert future environmental hazards of different types.

Geospatial Analysis of Erelu Reservoir using Remote Sensing and Bathymetric Techniques in Oyo, Oyo State, Nigeria.

Geospatial Analysis of Erelu Reservoir using Remote Sensing and Bathymetric Techniques in Oyo, Oyo State, Nigeria.

Reservoir Capacity and Sedimentation Assessment by Integrating Echo Sounding and Remote Sensing Techniques

Reservoir Capacity and Sedimentation Assessment by Integrating Echo Sounding and Remote Sensing Techniques

Eco-Environmental Quality Assessment of Two Seasons Based on Pressure-State-Response Framework by Using Remote Sensing and GIS Techniques

Determining the urbanization and sustainable development rate in Kirkuk City and Taza District (a town located in a suburb of Kirkuk) requires an evaluation of its ecological environment quality (EEQ). This study aims to determine the ecological index (El) and assess it using the popular pressure-state-response (PSR) method based on a set of statistical and remote sensing indicators. The indicators developed by remote sensing data were obtained using Sentinel-2 satellite images to measure the quality of the environment. As new and modern technical assistance for long-term, comprehensive mapping, monitoring through GIS techniques, and the comparison of the Ecological Index in 2023 of the spring and autumn seasons, these indicators provide a basis for decision-making related to sustainable development. It demonstrates the primary relationship between human activity and health under various weather circumstances and contrasts them. It also reveals that the ecological situation is healthier in spring than in autumn (these two seasons are considered transmission periods) due to meteorological conditions, social activities, and economic activity.

Predictive Modelling of Land Cover Changes in the Greater Amanzule Peatlands Using Multi-Source Remote Sensing and Machine Learning Techniques

The Greater Amanzule Peatlands (GAP) in Ghana is an important biodiversity hotspot facing increasing pressure from anthropogenic land-use activities driven by rapid agricultural plantation expansion, urbanisation, and the burgeoning oil and gas industry. Accurate measurement of how these pressures alter land cover over time, along with the projection of future changes, is crucial for sustainable management. This study aims to analyse these changes from 2010 to 2020 and predict future scenarios up to 2040 using multi-source remote sensing and machine learning techniques. Optical, radar, and topographical remote sensing data from Landsat-7, Landsat-8, ALOS/PALSAR, and Shuttle Radar Topography Mission derived digital elevation models (DEMs) were integrated to perform land cover change analysis using Random Forest (RF), while Cellular Automata Artificial Neural Networks (CA-ANNs) were employed for predictive modelling. The classification model achieved overall accuracies of 93% in 2010 and 94% in both 2015 and 2020, with weighted F1 scores of 80.0%, 75.8%, and 75.7%, respectively. Validation of the predictive model yielded a Kappa value of 0.70, with an overall accuracy rate of 80%, ensuring reliable spatial predictions of future land cover dynamics. Findings reveal a 12% expansion in peatland cover, equivalent to approximately 6570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses have increased, evidenced by an 85% rise in rubber plantations (from 30,530 ± 110.96 hectares to 56,617 ± 220.90 hectares) and a 6% reduction in natural forest cover (5965 ± 353.72 hectares). Sparse vegetation, including smallholder farms, decreased by 35% from 45,064 ± 163.79 hectares to 29,424 ± 114.81 hectares. Projections for 2030 and 2040 indicate minimal changes based on current trends; however, they do not consider potential impacts from climate change, large-scale development projects, and demographic shifts, necessitating cautious interpretation. The results highlight areas of stability and vulnerability within the understudied GAP region, offering critical insights for developing targeted conservation strategies. Additionally, the methodological framework, which combines optical, radar, and topographical data with machine learning, provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands that is applicable to other regions facing similar environmental challenges.

Urban Heat Island and Environmental Degradation Analysis Utilizing a Remote Sensing Technique in Rapidly Urbanizing South Asian Cities

Rapid urbanization in South Asian cities has triggered significant changes in land use and land cover (LULC), degrading natural biophysical components and intensifying urban heat islands (UHIs). This study investigated the impact of LULC changes on land surface temperature (LST) and the role of biophysical indicators in enhancing urban resilience to thermal extremes. We used Landsat satellite imageries from 1993 to 2023, conducted a comprehensive analysis of LULC changes, and estimated LST variations at 6-year intervals in the Dhaka, Gazipur, and Narayanganj districts in Bangladesh. Afterward, we performed statistical analysis upon employing correlation, regression, and principal component analysis (PCA) techniques to summarize information. The results reveal that 339.13 km2 worth of urban expansion has occurred in last 30 years, with an average annual growth rate of 3.5%, accompanied by a substantial reduction in water bodies (−139.17 km2) and vegetation cover. Consequently, summer temperatures exceeded approximately 36.52 °C in dense urban areas. Also, the results highlighted the strong influence of built-up areas (BSI and SAVI) on LST, while vegetation (NDVI) and water indices (NDWI) exhibited a negative association. The findings emphasize the urgency of integrating green infrastructure and deploying sustainable urban planning policies to mitigate the potential adverse impacts of scattered urbanization in the face of climate change.

Geo-spatial based cyclone shelter suitability assessment using analytical hierarchy process (AHP) in the coastal region of Bangladesh

The coastal district of Bangladesh is susceptible to cyclones, which cause significant damage to infrastructure, economy, and social structures every year. The importance of protecting lives and property in these vulnerable areas is a top priority, especially in times of cyclones and storm surges. Therefore, the identification of potentially suitable shelter locations is essential for disaster risk resilience planning and implementation in the coastal regions. In this context, our research focuses on Barguna, which has witnessed severe damage from previous cyclones over the last several decades. We aim to identify and map feasible cyclone shelter suitability by utilizing GIS, AHP, Hotspot Analysis, and Remote Sensing techniques. The use of advanced techniques enables a comprehensive assessment of multiple variables that influence shelter suitability, ensuring the selection of the most strategically significant locations. The goal is to enhance disaster preparedness and resilience in Barguna District, reducing the risk and vulnerability of its coastal communities to cyclones. Seven variables associated with cyclone hazards, such as elevation, slope, distance from roads and rivers, population density, land use, and cover, and proximity to healthcare facilities, are considered to identify the safest and most suitable cyclone shelter areas. The assessment of the appropriateness of shelter locations for the study area was facilitated by the collection of 181 GPS locations regarding existing cyclone shelters. The findings reveal that approximately 15.53 % (1956 ha) of the total land area is considered less suitable, 67.31 % (84763 ha) moderately suitable, 16.70 % (21024 ha) suitable, and 0.29 % (362 ha) highly suitable for optimal cyclone shelter establishment in the study area. This study has a major limitation due to the use of Landsat imagery, which may not always provide fully accurate image classification. To validate our methods, we collected existing cyclone shelter data and performed a pixel-based accuracy assessment, achieving high accuracy and confirming the reliability of our approach. This research addresses a critical gap in cyclone shelter planning, offering valuable insights for residents and decision-makers to mitigate cyclone risks not only in the Barguna district but also in similar coastal regions of Bangladesh. The framework developed in this study can aid non-government organizations and the government in determining the most appropriate locations to construct cyclone shelters to build a safer, more resilient future for the region, capable of withstanding the relentless threats posed by cyclones every year.

Hierarchical Analysis of Miombo Woodland Spatial Dynamics in Lualaba Province (Democratic Republic of the Congo), 1990–2024: Integrating Remote Sensing and Landscape Ecology Techniques

Lualaba Province, located in the southeastern Democratic Republic of the Congo (DRC), consists of five territories with varied dominant land uses: agriculture (Dilolo, Kapanga, and Musumba in the west) and mining (Mutshatsha and Lubudi in the east). The province also includes protected areas with significant governance challenges. The tropical dry forests that cover the unique Miombo woodland of Lualaba are threatened by deforestation, which poses risks to biodiversity and local livelihoods that depend on these forests for agriculture and forestry. To quantify the spatio-temporal dynamics of Lualaba’s landscape, we utilized Landsat images from 1990 to 2024, supported by a Random Forest Classifier. Landscape metrics were calculated at multiple hierarchical levels: province, territory, and protected areas. A key contribution of this work is its identification of pronounced deforestation trends in the unique Miombo woodlands, where the overall woodland cover has declined dramatically from 62.9% to less than 25%. This is coupled with a marked increase in landscape fragmentation, isolation of remaining woodland patches, and a shift toward more heterogeneous land use patterns, as evidenced by the Shannon diversity index. Unlike previous research, our study distinguishes between the dynamics in agricultural territories—which are particularly vulnerable to deforestation—and those in mining areas, where Miombo forest cover remains more intact but is still under threat. This nuanced distinction between land use types offers critical insights into the differential impacts of economic activities on the landscape. Our study also uncovers significant deforestation within protected areas, underscoring the failure of current governance structures to safeguard these critical ecosystems. This comprehensive analysis offers a novel contribution to the literature by linking the spatial patterns of deforestation to both agricultural and mining pressures while simultaneously highlighting the governance challenges that exacerbate landscape transformation. Lualaba’s Miombo woodlands are at a critical juncture, and without urgent, coordinated intervention from local and international stakeholders, the ecological and socio-economic foundations of the region will be irreversibly compromised. Urgent action is needed to implement land conservation policies, promote sustainable agricultural practices, strengthen Miombo woodland regulation enforcement, and actively support protected areas.

Integrated Morphometric and Hypsometric Analysis of Niragantipalli Micro Watershed Using Remote Sensing and Geographical Information System Techniques

Employing Remote sensing (RS) and geographic information system (GIS) for the morphometric parameters analysis are discovered to be tremendous usefulness in the prioritization of watersheds for soil, water conservation and natural resource management at micro watershed level. The analysis of morphometric parameters plays a crucial role for understanding and managing watersheds. In current study an, attempt was to determine the morphological parameters of Niragantipalli micro-watershed in Chikkaballapura District of Karnataka, India. For detail study, Google earth pro, GPS visualizer conversion toll and Arc GIS were used for preparation of DEM and delineation of the watershed boundary. GIS was used for evaluation of basic, linear, areal and relief aspects of morphometric parameters. The Niragantipalli micro-watershed occupies an area of 632 ha with third order stream (truck order) and dendritic drainage pattern. The mean bifurcation ratio of the micro-watershed is 2.5 it suggests that the drainage system formed on homogeneous rock when the influences of geologic structures on the network of streams were negligible and generate sharp peak flow. With a drainage density of 1.31 km/km2, the drainage network is extremely coarse. The watershed is elongated, as shown by the form factor of 0.27 and elongation ratio of 0.58. The results from the morphometric analysis of the watershed are very beneficial for developing and designing conservation structures of soil and watershed management measures. The hypsometric curves’ structure as well as estimated hypsometric integral results reflects the Niragantipalli Micro Watershed erosional stages. As a result, the study concludes that morphometric and hypsometric analysis findings may be useful to stakeholders participating in catchment development and management projects.

Evaluation of Potential Flood Areas in the Basin of Lake Ladik through AHP and GIS Integration, (Samsun, Türkiye)

Floods are a problem of many countries on a global scale. In Türkiye, especially in the summer months, large floods occur in the Black Sea Region coastal belt. The number and the destructive power of experienced floods are increasing day by day. It is not possible to prevent the occurrence of floods. But it is possible to take the necessary measures to prevent it from turning into a disaster. Recently, susceptibility analyses have been carried out for floods and similar natural disasters and the results help decision-makers. Within the scope of this study, flood susceptibility analysis was carried out by using multiple geographical factors together in the Basin of Lake Ladik of Samsun Province in the Black Sea Region. In the study, Analytical Hierarchy Process (AHP), Geographic Information Systems (GIS) and Remote Sensing (RS) techniques were used, which are multi-criteria decision-making methods. In this context, flood susceptibility analysis of the basin was carried out by using nine (9) different geographical factors (slope, aspect, lithology, soil, basin size, land use, landforms, precipitation, and drainage density). As a result of the study, for floods, there were identified four (4) different levels as low, medium, high, and very high; and 36.77% of the basin was found to be low, 30.03% was medium, 11.43% was high and 21.77% was found to be sensitive to possible floods at a very high level. The results of the study are also important for decision-makers to make flood risk planning.

Analysis of the Morphometric Characteristics of Wadi Rajil in the Azraq Basin using Geographic Information Systems

The study aimed to Analyse the Hydrometric Characteristics of Wadi Rajil basin and assess the flood hazards in the basin using Remote Sensing and Geographic Information Systems Techniques. The study used Models of Digital Visualizations of the basin and processed them using ARC GIS 10.5 and ARC HYDRO TOOLS. The result was that the Hypsometric Coefficient reached (21.4%) of the valley area that was exposed to erosion and (78.6%) of it is still waiting for its turn to erosion, indicating that the valley has reached the stage of youth. The analysis of Hot Spots in the GIS program resulted in negative values for internal Drainage Basins greater than -0.5 extending to the northern and northwestern parts of the basin. It also showed that the flow coefficient exceeds the internal Drainage Coefficient or Leakage. As for the positive values, they are distributed in the southern and southeastern parts, where the (Hot) values prevail and where the leakage coefficient exceeds the Flow Coefficient. We find that the Hot Spots are concentrated in the southeastern parts, which are mostly extended. The Rift and thus the most suitable areas for Groundwater Charging through it.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

Detecting Land-Use Changes in Greater Kandy Development Area

As urban growth became a threat to Kandy city, the Urban Development Authority (UDA) has introduced a new concept called the “Greater Kandy Development Area (GKDA)” to control and manage the rapid urban growth, as well as to minimize its’ impacts towards the environment. This study examines the land use/ land cover changes in GKDA, in selected time periods using GIS and Remote Sensing techniques. Findings demonstrate that dynamic patterns of urban expansion cause progressive growth in built-ups and lowering in non-built-ups. In 2005, there were only 16.9 percent of built-up areas in GKDA but, it has increased up to 40 percent in 2020. 81.7 percent of non-built-up areas in 2005, has reduced up to 58.8 percent. As population growth happened inside the city limits, land use changes also happened within. These changes create clustered as well as sprawl development patterns.

Drought Assessment Using Remote Sensing Techniques in the Great Rann of Kachchh and Adjoining Areas of Thar Desert

Drought Assessment Using Remote Sensing Techniques in the Great Rann of Kachchh and Adjoining Areas of Thar Desert

ML Approaches for the Study of Significant Heritage Contexts: An Application on Coastal Landscapes in Sardinia

Remote Sensing (RS) and Geographic Information Science (GIS) techniques are powerful tools for spatial data collection, analysis, management, and digitization within cultural heritage frameworks. Despite their capabilities, challenges remain in automating data semantic classification for conservation purposes. To address this, leveraging airborne Light Detection And Ranging (LiDAR) point clouds, complex spatial analyses, and automated data structuring is crucial for supporting heritage preservation and knowledge processes. In this context, the present contribution investigates the latest Artificial Intelligence (AI) technologies for automating existing LiDAR data structuring, focusing on the case study of Sardinia coastlines. Moreover, the study preliminary addresses automation challenges in the perspective of historical defensive landscapes mapping. Since historical defensive architectures and landscapes are characterized by several challenging complexities—including their association with dark periods in recent history and chronological stratification—their digitization and preservation are highly multidisciplinary issues. This research aims to improve data structuring automation in these large heritage contexts with a multiscale approach by applying Machine Learning (ML) techniques to low-scale 3D Airborne Laser Scanning (ALS) point clouds. The study thus develops a predictive Deep Learning Model (DLM) for the semantic segmentation of sparse point clouds (<10 pts/m2), adaptable to large landscape heritage contexts and heterogeneous data scales. Additionally, a preliminary investigation into object-detection methods has been conducted to map specific fortification artifacts efficiently.

Morphometric analysis using remote Sensing and GIS techniques of Pingalgarh watershed VNMKV, Parbhani, India

Morphometric analysis using remote Sensing and GIS techniques of Pingalgarh watershed VNMKV, Parbhani, India