Satellite Images Research Articles

The archaeological record of the Qaraçay River Basin along the northern piedmont of the Lesser Caucasus

Abstract This project documents the current archaeological record of the Qaraçay River Basin in western Azerbaijan. Integrating intensive pedestrian survey, satellite imagery analysis and topographic mapping, the study identified 85 kurgans, six necropolises and nine sites from the Chalcolithic or medieval periods. The authors believe this demonstrates the potential for further archaeological studies in the region.

Towards Nationwide Mapping of Seagrasses in the Philippines: Empowering Students through Remote Sensing Education

Abstract. Seagrasses are critical marine ecosystems facing numerous threats in the Philippines. This study aimed to develop a robust methodology for nationwide seagrass mapping using open-access satellite imagery and empower students to contribute through remote sensing education. We achieved this through a two-pronged approach: (1) developing a satellite-based mapping methodology and (2) training students to apply this method across the archipelago. Utilizing Sentinel-2 satellite images, we processed the data using SNAP and Google Earth Engine to identify seagrass beds. Before classification, the images underwent preprocessing steps including land masking, water column correction, and extraction of shallow water bathymetry. These processed layers were then stacked with Sentinel-2 bands 2, 3, 4, 8, and 11. Two classification methods, support vector machine, and random forest, were employed, and their results were compared with existing seagrass maps. Students achieved overall accuracies ranging from 70 to 90 percent. Prior to mapping activities, students received training on remote sensing techniques and seagrass ecology. This project demonstrates the potential of remote sensing for large-scale seagrass mapping and highlights the value of student engagement in marine conservation.

Geospatial Analysis of Malaria and Typhoid Prevalence Due to Waste Dumpsite Exposure in Kinshasa Districts with and without Waste Services: A Case Study of Bandalungwa and Bumbu, Democratic Republic of Congo

Municipal solid waste (MSW) management poses substantial challenges in rapidly urbanizing areas, with implications for both the environment and public health. This study focuses on the city of Kinshasa in the Democratic Republic of Congo, investigating whether the presence or absence of solid waste collection services results in varying health and economic impacts, and additionally, seeking to establish a correlation between residing in proximity to dumpsites and the prevalence of diseases like malaria and typhoid, thereby providing a comprehensive understanding of the health implications tied to waste exposure. Health data were collected through survey questionnaires, and the geospatial distribution of 19 dumpsites was analyzed using Google Earth Pro 7.3.1 for satellite imagery and GIS software 10.3.1 to map dumpsites and define 1 km buffer zones around the largest dumpsites for household sampling. Statistical analyses were conducted using R Version 4.2.3, employing Chi-square tests for disease prevalence and logistic regression to assess associations between waste management practices and health outcomes. A multivariate regression was used to evaluate correlations between discomfort symptoms (e.g., nasal and eye irritation) and waste activities. The geospatial analysis revealed significant variation in dumpsite size and location, with larger dumpsites near water bodies and flood-prone areas. The study contributes valuable insights into waste-related health risks, emphasizing the need for improved waste management policies in rapidly urbanizing areas like Kinshasa. The socio-demographic analysis reveals distinct traits within the surveyed populations of two communes, Bandalungwa (Bandal) and Bumbu. Bumbu, characterized by larger open dumpsites and limited waste collection services, exhibits a higher prevalence of certain diseases, particularly typhoid fever, and malaria. This discrepancy is statistically significant (p < 2.2 × 10−16), suggesting a potential link between waste exposure and disease prevalence. In Bandal, self-waste collection is a high risk of exposure to typhoid (OR = 4.834 and p = 0.00001), but the implementation of a waste collection service shows protective effect (OR = 0.206 and p = 0.00001). The lack of waste collection services in Bumbu increases the risk of exposure, although not significantly (OR = 2.268 and p = 0.08). Key findings indicate that waste disposal methods significantly differ between Bandal and Bumbu. Bumbu’s reliance on burning and dumping creates environments conducive to disease vectors, contributing to elevated disease transmission risks. However, an in-depth correlation analysis reveals that specific waste management practices, such as burning, burying, and open dumping, do not exhibit statistically significant associations with disease prevalence, underlining the complexity of disease dynamics. This study contributes valuable insights into the importance for urban public health, particularly in rapidly urbanizing cities like Kinshasa, where inadequate waste management exacerbates health risks. By investigating the correlation between proximity to unregulated dumpsites and the prevalence of diseases such as malaria and typhoid fever, the research underscores the urgent need for targeted waste management policies. The stark health disparities between Bandal, with better waste services, and Bumbu, where services are lacking, highlight the protective effect of organized waste collection. These findings suggest that expanding public waste services and enforcing stricter regulations on waste disposal could reduce disease prevalence in vulnerable areas. Additionally, the study supports integrating waste management into urban planning as a critical public health measure. Its evidence-based approach offers valuable insights for policymakers in Kinshasa and other cities facing similar challenges, emphasizing the broader health implications of environmental governance in urban settings.

Coral Bleaching Detection Using PRISMA Hyperspectral Satellite Imagery in Calatagan, Batangas

Abstract. Coral bleaching arises due to stress from warming seas and pollution. Several studies on bleaching detection utilize multispectral imagery, due to data availability. In this study, the usage of PRISMA hyperspectral data imagery was assessed for coral bleaching detection in Calatagan, Batangas, and sea surface temperature (SST) for confirming bleaching occurrence. In-situ measurements were gathered for spectral data of live and bleached corals, and a collaborative qualitative data interview with a coral bleach patroller and municipal office was conducted to gain further information about bleaching details in the area. Sentinel-2 L2A was also used for comparison during classification. Spectral Angle Mapper (SAM) was utilized for image classification, while using field data and data from related studies for spectral library. A total of 27.12% relative to the coral reef area was classified as bleached corals. T-test statistics were done to analyze the live and bleached corals, which exhibited that only two (2) bands in the green region were found to be not separable. Using box plots, bleached corals showed to have generally lower reflectance values than live corals for RGB region, as compared to Sentinel, with bleached having higher values, which implies that spatial resolution can affect the spectral response of different features. Using ground truth data, 60.6% user’s accuracy for bleached corals, and 63.4% overall accuracy is achieved. Hence, hyperspectral satellite imagery can be utilized in coral bleaching detection, despite its limitations. Depth variables and diverse sample points are recommended for the betterment of coral bleaching detection.

Geospatial Analysis of Climate Change Impacts on Vegetation Dynamics in Owerri West, Imo State

Climate change is a global phenomenon with profound effects on the environment, economies, and societies. It refers to significant changes in global temperatures and weather patterns over time. This study explores the impact of climate change on vegetation in Owerri West, Imo State, Nigeria, using geospatial techniques over a 20-year period (2003–2023). By analyzing satellite imagery and Normalized Difference Vegetation Index (NDVI) values alongside key climatic variables such as precipitation and land surface temperature (LST), the research seeks to quantify the extent of vegetation changes due to climate variability. The study uses Landsat 8 satellite data to evaluate spatiotemporal trends in vegetation health. NDVI values in 2003 ranged from a high of 0.45, indicating healthy and dense vegetation, to a low of -0.04, representing barren areas. By 2013, NDVI values had drastically declined, with a maximum of 0.12 and a minimum of -0.33, despite an increase in precipitation from 3300 mm (2003) to 3900 mm (2013). This decline suggests that other factors, such as extreme weather events or urbanization, contributed to vegetation stress. In 2023, NDVI values showed partial recovery, with a maximum of 0.28 and a minimum of 0.05, although precipitation levels dropped to a range of 600 mm to 540 mm. The broader temperature range in 2023, with a maximum of 32°C and a minimum of 17°C, likely reduced heat stress, allowing for vegetation recovery, though still below the 2003 levels, The findings highlight the complex interplay between climatic variables and vegetation health, where precipitation and temperature changes significantly influence vegetation dynamics. However, the NDVI decline in 2013, despite high precipitation, suggests that anthropogenic factors like urbanization and land use changes also play a critical role. This research emphasizes the importance of integrating remote sensing and GIS for monitoring vegetation responses to climate change. The study calls for sustainable land management practices and climate adaptation strategies to enhance vegetation resilience in the region.

Google Earth Engine-based Mangrove Mapping and Change Detections for Sustainable Development in Tien Yen District, Quang Ninh Province, Vietnam

Abstract. Vietnam secures a place among the top countries that possess the largest mangrove areas worldwide. The mangrove forests are mainly found in the Northern, Northeast, Central and Southern Delta, providing goods to habitants, and make significant help mitigate global climate change. Despite this, mangroves are severely threatened because of extensive deforestation in Vietnam. Recent advances have utilized remotely sensed imagery to present the spatial and temporal distribution of mangroves. Nevertheless, the approach is limited by imagery availability and computing resources, and difficult to share within the community. Therefore, a shareable Web-based tool that reinforces coastal managers to monitor the changes in mangroves is needed. Recently, Google Earth Engine (GEE) is a cloud-based geospatial analysis platform, which allows users to freely exploit the availability of satellite imagery and harness their computing capacity. This research aims to use GEE to detect mangrove changes, a case study in Tien Yen district, Quang Ninh, over a period from 2010 to 2020. Four supervised classification algorithms, including Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes classifier, and Classification and Regression Trees (CART) have been implemented on GEE platform to select the best algorithm to produce spatial-temporal mangrove maps, then change detection of mangroves is performed. The results showed that RF demonstrated the highest accuracy with overall accuracy and kappa values of 91.04% and 0.75 respectively. The mangrove areas statistically reported by the GEE-based platform are in line with the governmental statistic report annually, marking the capabilities of GEE in natural resource management.

Satellite Image–Based Ecosystem Monitoring with Sustainable Agriculture Analysis Using Machine Learning Model

Satellite Image–Based Ecosystem Monitoring with Sustainable Agriculture Analysis Using Machine Learning Model

The contribution of remote sensing and geographic information systems to the morphometric study of six watersheds in the tindouf region (extreme south-western Algeria)

Understanding the hydrological system and improving water resource management requires a thorough analysis of the morphometric characteristics of watersheds. This study seeks to examine the hydrological and morphometric features of the water drainage network in the Tindouf region's numerous watersheds. A digital elevation model (DEM), which is an essential digital asset in GIS, was created using a 30 m resolution SRTM satellite image. The DEM model was then used to produce a map of the drainage network for the basins in the study area. Additionally, various hydrological and geomorphological features were extracted. This research is significant in promoting economic development and improving the quality of life of residents by establishing better resource allocation and water management, determining appropriate areas for urban development, infrastructure planning, tourism, and entertainment.

The Seismic Surface Rupture Zone in the Western Segment of the Northern Margin Fault of the Hami Basin and Its Causal Interpretation, Eastern Tianshan

The Eastern Tianshan region, influenced by the far-field effect of northward compression and expansion of the Qinghai-Xizang block, features highly developed Late Quaternary active faults that exhibit significant neotectonic activity. Historically, the Barkol-Yiwu Basin, located to the north of the Eastern Tianshan, experienced two major earthquakes in 1842 and 1914, each with a magnitude of M71/2. In contrast, the Hami Basin on the southern margin of the Eastern Tianshan has no historical records of any major earthquakes, and its seismic potential, mechanisms, and future earthquake hazards remain unclear. Based on satellite image interpretation and field surveys, this study identified a relatively recent and well-preserved seismic surface rupture zone with good continuity in the Liushugou area of the western segment of the Northern Margin Fault of the Hami Basin (HMNF), which is the seismogenic structure responsible for the rupture. The surface rupture zone originates at Kekejin in the east, extends intermittently westward through Daipuseke Bulake and Liushugou, and terminates at Wuzun Bulake, with a total length of approximately 21 km. The rupture zone traverses the youngest geomorphic surface units, such as river beds or floodplains and first-order terraces (platforms), and is characterized by a series of single or multiple reverse fault scarps. The morphology of fault scarps is clear, presenting a light soil color with heights ranging from 0.15 m to 2.13 m and an average displacement of 0.56 m, suggesting that this surface rupture zone likely represents the most recent seismic event. Comparison with historical earthquake records in the Eastern Tianshan region suggests that the rupture zone may have been formed simultaneously with the Xiongkuer rupture zone by the 1842 M71/2 earthquake along the boundary faults on both sides of the Barkol Mountains, exhibiting a flower-like structural pattern. Alternatively, it might represent a separate, unrecorded seismic event occurring shortly after the 1842 earthquake. The estimated magnitude of the associated earthquake is about 6.6~6.9. Given that surface-rupturing earthquakes have already occurred in the western segment, the study indicates that the Erdaogou–Nanshankou section of the HMNF has surpassed the average recurrence interval for major earthquakes, indicating a potential future earthquake hazard.

Monitoring of Bardawil Lake, Egypt using Satellite images for Sustainable Management

Monitoring of Bardawil Lake, Egypt using Satellite images for Sustainable Management

Sandy soil spots in northwestern Paraná: approaches for identification and quantification

The predominance of sandy soils in the Northwest region of the state of Paraná, associated with the removal of natural vegetation, favored the development of patches of white sand on the surface without any aggregation. These spots are associated with the different types of land use existing in the region and the process of lateral soil transformation. As they constitute material without aggregation, they can be easily transported by wind and water and deposited in water bodies, causing severe environmental impacts. Thus, using thermal infrared satellite images, the objective of the present study was to map, determine the formation process of the spots and estimate the predominant particle size fractions. The model used and validated with field and laboratory information, allowed the identification of surfaces with patches of white sand present in the study region and estimating their percentage in different classes. After filtering the samples with vegetation cover during the product calculation period, an R² of 0.78 was obtained. The removal of natural vegetation has contributed to the formation and expansion of sandy patches and erosion in the northwest region of Paraná.

Analysis of the Wangping Brownfield Using a Two-Step Urban Brownfield Redevelopment Model

With societal progress, urban brownfields have become restrictive, and redevelopment studies have become an important part of urban renewal. In this work, we developed a two-step model for urban brownfield redevelopment, while considering the Wangping brownfield as the study area. Site suitability evaluation models for brownfield parks, agricultural picking gardens, and creative industrial centers were developed based on the elevation, slope, and surface runoff, and the evaluation results were categorized into five levels. The redevelopment plan was formulated based on these evaluation results. To study the effect of the plan, a transition matrix of land use was assessed using satellite images and the cellular automata (CA)–Markov model; based on the analysis, we predicted the land use situation of the Wangping brownfield, with respect to natural development, for 2030. A comparison of the redevelopment planning with the forecasted results revealed that the proportions of grassland, construction, and unused land decreased by 25.68, 3.12, and 2.38% and those of plowland and forest land increased by 6.61 and 24.57%. This confirms the advantages of redevelopment planning for restoring plowland and increasing biological carbon sinks. Notably, our two-step urban brownfield redevelopment model can enrich the current research on urban brownfields and guide similar urban renewal projects.

Thermal Footprint of the Urbanization Process: Analyzing the Heat Effects of the Urbanization Index (UI) on the Local Climate Zone (LCZ) and Land Surface Temperature (LST) over Two Decades in Seville

Urbanization is a multifaceted process characterized by changes in urban areas through various means, such as sprawl, ribbon development, or infill and compact growth. This phenomenon changes the pattern of the local climate zone (LCZ) and significantly affects the climate, vegetation dynamics, energy consumption, water resources, and public health. This study aims to discern the impacts of changes in urban growth on the LCZ and land surface temperature (LST) over a two-decade period. A comprehensive methodology that integrates statistical analysis, data visualization, machine learning, and advanced techniques, such as remote sensing technology and geospatial analysis systems, is employed. ENVI, GEE, and GIS tools are utilized to collect, process, and monitor satellite data and imagery of temporal and spatial variations in intensive or diffuse urbanization processes from 2003 to 2023 to analyze and simulate land use and land cover (LULC) changes, urbanization index (UI), LCZ patterns, and LST changes over the years and to make overlapping maps of changes to recognize the relation between LULC, LCZ, and LST. This study focuses on Seville’s urban area, which has experienced rapid urbanization and a significant increase in average temperature during the last few decades. The findings of this study will provide actionable recommendations into the interplay between urban growth and climate and highlight the pivotal role of urban growth in shaping resilience and vulnerable areas based on microclimate changes. Urban planners can leverage these insights to predict alternatives for the future development of urban areas and define practical climate mitigation strategies.

RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR

Object detection is a fundamental task in computer vision. Recently, deep-learning-based object detection has made significant progress. However, due to large variations in target scale, the predominance of small targets, and complex backgrounds in remote sensing imagery, remote sensing object detection still faces challenges, including low detection accuracy, poor real-time performance, high missed detection rates, and high false detection rates in practical applications. To enhance remote sensing target detection performance, this study proposes a new model, the remote sensing detection transformer (RS-DETR). First, we incorporate cascaded group attention (CGA) into the attention-driven feature interaction module. By capturing features at different levels, it enhances the interaction between features through cascading and improves computational efficiency. Additionally, we propose an enhanced bidirectional feature pyramid network (EBiFPN) to facilitate multi-scale feature fusion. By integrating features across multiple scales, it improves object detection accuracy and robustness. Finally, we propose a novel bounding box regression loss function, Focaler-GIoU, which makes the model focus more on difficult samples, improving detection performance for small and overlapping targets. Experimental results on the satellite imagery multi-vehicles dataset (SIMD) and the high-resolution remote sensing object detection (TGRS-HRRSD) dataset show that the improved algorithm achieved mean average precision (mAP) of 78.2% and 91.6% at an intersection over union threshold of 0.5, respectively, which is an improvement of 2.0% and 1.5% over the baseline model. This result demonstrates the effectiveness and robustness of our proposed method for remote sensing image object detection.

Analysis of Land Use and Land Cover Dynamics in the Highlands of Northern Ethiopia

In the highlands of northern Ethiopia, many land use and land cover changes (LULCC) have occurred during different activities of the populations. Despite its coverage and unwise use of natural resources, these land uses and land covers are facing increasing or decreasing their area coverage. Information derived from land use and land cover change detection is important to land conservation, sustainable development, and management of Natural resources. This purpose of this study is therefore concerned with identifying the change in land use and land cover detection of the Wejic watershed. To identify land cover changes detection; remote sensing data, satellite imagery and image processing techniques had done within two dates of 1998 and 2017 using Land sat TM 30 m resolution images using Arc GIS 10.1. Hence, a field survey was carried out in 2017 to identify the major land cover types in the watershed. Detection Analysis of land use and land cover dynamics were calculated from the selected watershed in Southern Tigray, Northern Ethiopia. These land use types were cultivated, forest, bush, shrub, and homestead lands. Furthermore, a normalized difference vegetation index (NDVI) was developed for both years. The land use and land cover change results indicated that forest, cultivated land, and bush land decreased by 4.4%, 2.2%, and 2.4%, respectively. However, shrub land and homestead increased by 5.5% and 3.6%, respectively. Therefore The change detection analysis using GIS and remote sensing could deliver useful information to understand the seasonal patterns of land use dynamics for planners and decision makers; consequently, sustainable land management planning is possible.

Analysis of Paddy Field Changes (1989–2021) Using Landsat Images and Flooding-Assisted MLC in an Urbanizing Tropical Watershed, Vientiane, Lao PDR

Paddy fields are essential for food security and sustaining global dietary needs, yet urban expansion often encroaches on agricultural lands. Analyzing paddy fields and land use/land cover changes over time using satellite images provides critical insights for sustainable food production and balanced urban growth. However, mapping the paddy fields in tropical monsoon areas presents challenges due to persistent weather interference, monsoon-submerged fields, and a lack of training data. To address these challenges, this study proposed a flooding-assisted maximum likelihood classification (F-MLC) method. This approach utilizes accurate training datasets from intersecting flooded paddy field maps from the rainy and dry seasons, combined with the Automated Water Extraction Index (AWEI) to distinguish natural water bodies. The F-MLC method offers a robust solution for accurately mapping paddy fields and land use changes in challenging tropical monsoon climates. The classified images for 1989, 2000, 2013, and 2021 were produced and categorized into the following five major classes: urban areas, vegetation, paddy fields, water bodies, and other lands. The paddy field class derived for each year was validated using samples from various sources, contributing to the overall accuracies ranging from 83.6% to 90.4%, with a Kappa coefficient of between 0.80 and 0.88. The study highlights a significant decrease in paddy fields, while urban areas rapidly increased, replacing 23% of paddy fields between 1989 and 2021 in the watershed. This study demonstrates the potential of the F-MLC method for analyzing paddy fields and other land use changes over time in the tropical watershed. These findings underscore the urgent need for robust policy measures to protect paddy fields by clearly defining urban expansion boundaries, prioritizing paddy field preservation, and integrating these green spaces into urban development plans. Such measures are vital for ensuring a sustainable local food supply, promoting balanced urban growth, and maintaining ecological balance within the watershed.

Anomalous scaling of branching tidal networks in global coastal wetlands and mudflats

Branching networks are key elements in natural landscapes and have attracted sustained research interest across the geosciences and numerous intersecting fields. The prevailing consensus has long held that branching networks are optimized and exhibit fractal properties adhering to power-law scaling relationships. However, tidal networks in coastal wetlands and mudflats exhibit scaling properties that defy conventional power-law descriptions, presenting a longstanding enigma. Here we show that the observed atypical scaling represents a universal deviation from an ideal fractal branching network capable of fully occupying the available space. Using satellite imagery of tidal networks from diverse global locations, we identified an inherent “laziness” in this deviation—where the increased ease of channel formation paradoxically decreases the space-filling efficiency of the network. We developed a theoretical model that reproduces the ideal fractal branching network and the laziness phenomenon. The model suggests that branching networks can emerge under a localized competition principle without adhering to conventionally assumed optimization-driven processes. These results reveal the dual nature of branching networks, where “laziness” complements the well-known optimization process. This property provides more flexible strategies for controlling tidal network morphogenesis, with implications for coastal management, wetland restoration, and studies in fluvial and planetary systems.

Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales

Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an ’18-tree’ stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards.

Dynamics of Shoreline Changes Along the Coast of Subarnarekha and Budhabalanga River Estuary, North Eastern Coast of India Using DSAS Technique: A Geospatial Technology Approach

ABSTRACTThe coastal regions represent one of the most significant environmental and economic resources, offering critical ecosystems that support biodiversity. Shoreline change analysis offers critical insights into coastal dynamics, providing trends in erosion and accretion, enabling effective coastal management and hazard mitigation. In the current study, shoreline change is assessed utilizing the Digital Shoreline Analysis System (DSAS) model for Subarnarekha and Budhabalanga river estuaries in Baleswar district, Odisha, India. The shoreline was extracted using multitemporal satellite images like Landsat‐5 and Landsat‐7 for the years 1991, 2000, and 2011. Similarly Sentinel‐2 and Landsat‐8 satellite imagery were used for the year 2022. The satellite data from 1991 to 2022 were processed using Envi, ArcGIS softwares and the shoreline is extracted for each year using band ratioing methods to demarcate shoreline. In the current research, the net accretion and erosion along the coast were analyzed using the GIS (geographic information systems) technique and the DSAS model. The four important statistical parameters of the DSAS model utilized in the study area are end point rate (EPR), net shoreline movement (NSM), linear regression rate (LRR), and least median of squares (LMS). The shoreline change analysis for the coast of Subarnarekha and Budhabalanga river estuary area from 1991 to 2022 reveals that 44% of the coast is under accretion, 7% is under stable coast, and 23% is under erosion. According to the findings, the coastal area of the recent study is both progressive and regressive in nature. For the study area, the average rates of shoreline accretion and erosion are 1.05 m and 0.45 m per year, respectively. The study gives information on erosion and accretion near the Subarnarekha and Budhabalanga river estuaries, which will aid in the development of an adaptive shoreline management strategy and coastal vulnerability assessment.

Landslide Rapid Impact Assessment at Hydro Dam via THyCAS and UAS

The TNB Hydro Catchment Area Surveillance (THyCAS) is a web-based geographic information system (GIS) used to monitor hydropower dam catchment areas in Peninsular Malaysia. It can also be used for landslide rapid impact assessment by monitoring the surrounding landslide area. However, during the rainy season, satellite images are unable to provide the information required by users due to full cloud cover. Therefore, an Unmanned Aircraft System (UAS) is used to provide the data required by THyCAS by mapping the surrounding area affected by landslides and uploading the processed data in THyCAS. Thus, the user can get the required information regarding the landslide incident. Furthermore, with UAS Full Motion Video (FMV) data, the location of the collapsed power transmission towers can be acquired. In addition, any information on land changes that could jeopardise the surrounding dam area is gathered. Therefore, with the information from the rapid impact assessment provided by THyCAS and UAS data, a preliminary risk assessment plan can be established and implemented by the relevant parties. The results obtained from the rapid assessment can also be used as additional information to find out the actual cause of landslides in the area.