Remote Sensing Applications-Society and Environment最新文献

{"title":"Securing water for arid regions: Rainwater harvesting and sustainable groundwater management using remote sensing and GIS techniques","authors":"Mohamed Abdelkareem,&nbsp;Abbas M. Mansour,&nbsp;Ahmed Akawy","doi":"10.1016/j.rsase.2024.101300","DOIUrl":"10.1016/j.rsase.2024.101300","url":null,"abstract":"<div><p>Arid regions experience climatic stress under climate change: increased drought frequency coupled with intensified storm events. This disruption and lack of precipitation patterns leads to water scarcity and hinders the achievement of sustainable development goals. Egypt drainage basin exhibiting the greatest suitability for the implementation of rainwater harvesting (RWH) strategies. To facilitate the development of sustainable water resource management practices in the region, this study uses a multi-criteria methodology to delineate optimal zones for RWH within the Wadi Safaga. Integration of radar and optical remote sensing data obtained from Sentinel-1&amp;2, Landsat-8, ALOS/PALSAR, and Sentinel-1 Interferometric SAR with climatic Tropical Rainfall Measuring Mission (TRMM), hydrological, and geological datasets emphasizes the hydrologic characteristics of the catchments. Additionally, the analysis of rainfall intensity patterns within the basin was undertaken. Thirteen factors are used in the predicted model including elevation, slope, curvature, depression, lithology, radar, InSAR CCD, drainage density (Dd), distance to river (DR), vegetation, topographic wetness index (TWI), rainfall, and lineament density. A knowledge-driven Geographic Information System (GIS) methodology, including weighted factors based on the Analytical Hierarchy Process (AHP), was implemented to delineate plausible areas for RWH and groundwater potential zones (GWPZs). The resultant map categorized the basin into five GWPZ classes: very low (14%), low (28%), moderate (27%), high (21%), and very high (10%). Furthermore, the study identified optimal locations for constructing reservoirs to store harvested rainwater and provide protection for downstream mining, industrial, and tourism activities. In conclusion, the obtained information is crucial for planners and decision-makers to implement sustainable water resource management strategies within the Wadi Safaga basin.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101300"},"PeriodicalIF":3.8,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141701781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Faster R–CNN, RetinaNet and Single Shot Detector in different ResNet backbones for marine vessel detection using cross polarization C-band SAR imagery","authors":"Richard Dein Altarez","doi":"10.1016/j.rsase.2024.101297","DOIUrl":"https://doi.org/10.1016/j.rsase.2024.101297","url":null,"abstract":"<div><p>Detection of marine vessels plays an important role in monitoring, managing and securing seas and oceans, and forms the foundation of Maritime Domain Awareness (MDA). Although marine vessel detection has remained an active area of research for many years, unlike other object detectors, techniques of detection have been left far behind and lack systematic robustness. Hence, this study compared the performance of Faster R–CNN, RetinaNet and Single Shot Detector (SSD) across different epochs and complexities of ResNet architectures using Sentinel-1 VH polarization in one of the busiest ports in the Philippines. In particular, the models were created from the training samples dataset derived from Sentinel-1 VH imagery captured on January 12, 2024 in ResNet-34, -50, and −101 backbones, and 20 and 100 epochs. In this study, a total of 18 different object detector models were created for the comparative analysis. The models were tested with respect to different dates but having the same imagery type to determine their applicability across other base maps. Faster R–CNN with the highest F1 score of 0.85 outperformed RetinaNet with a highest F1 score of 0.74 and SSD with the highest F1 score of 0.38. The fastest model created was SSD, with an average speed of 9 to 44 minutes, followed by RetinaNet with an average speed of 8 to 58 minutes; the slowest is Faster R–CNN with an average speed of 25 minutes to 1 hour and 3 minutes. The use of Sentinel-1 VH imagery for marine vessel detection is a viable alternative, but the choice of object detectors should be carefully considered. The presence of geospatial software with advance deep learning tools improves remote sensing applications and allows non-programmers to optimize their competence. This study highlights the potential utilization of other imagery with higher spatial resolution, testing of other deep learning algorithms, finetuning of parameters, and utilization of higher computing infrastructure. The findings of this study can be applied in other areas for MDA, particularly in regions where advanced remote sensing applications have yet to be extensively explored.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101297"},"PeriodicalIF":3.8,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Devanur and Manamedu Ophiolite Complexes in SGT, India: A detailed examination employing remote sensing techniques and Laboratory Spectral Signature investigations","authors":"M. Monisha ,&nbsp;M. Muthukumar ,&nbsp;V.J. Rajesh","doi":"10.1016/j.rsase.2024.101294","DOIUrl":"https://doi.org/10.1016/j.rsase.2024.101294","url":null,"abstract":"<div><p>This study employs advanced satellite imagery from ASTER and Sentinel-2A to conduct detailed lithological mapping of the Devanur and Manamedu ophiolite complexes in the southern Central Shear Zone (CSZ). The primary focus is on the Manamedu Ophiolite Complex (MOC) and the Devanur Ophiolitic Complex (DOC). Image enhancement techniques such as Color composites, Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) were utilized to differentiate various rock types. RGB band combinations derived from PCA and MNF outputs demonstrated effective discrimination of rock units. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods were employed on ASTER and Sentinel-2A images, yielding classified lithologies that closely matched existing maps from the Geological Survey of India (GSI) and other studies, validating the accuracy of the findings. Additionally, Laboratory Spectral Signature Studies were conducted on 10 rock samples using an ASD FieldSpec Pro® spectroradiometer, providing reflectance spectra from 350 nm to 2500 nm. These spectra, particularly the continuum-removed reflectance, revealed diagnostic absorption features that were corroborated by geochemical analyses. A detailed analysis investigated how elemental compositions and key minerals influenced absorption bands. Major oxide geochemical compositions of DOC and MOC samples were identified using XRF methods. The aim of this research is to characterize DOC and MOC through remote sensing and spectral signature analysis. Sentinel-2A data proved more effective in lithological discrimination compared to ASTER, with spectral signatures indicating the presence of iron (Fe) and magnesium (Mg) contents. Notably, SVM classification of Sentinel-2A MNF + DEM data achieved an overall accuracy of more than 90% when compared with field investigations. This study underscores the efficacy of processing VNIR and SWIR bands from ASTER and Sentinel-2A satellite imagery alongside DEM data and ground surveys for mapping mafic-ultramafic rocks in the DOC and MOC regions of the CSZ.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101294"},"PeriodicalIF":3.8,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RDC-UNet++: An end-to-end network for multispectral satellite image enhancement","authors":"Shilpa Suresh ,&nbsp;Ragesh Rajan M. ,&nbsp;Asha C.S. ,&nbsp;Fabio Dell’Acqua","doi":"10.1016/j.rsase.2024.101293","DOIUrl":"10.1016/j.rsase.2024.101293","url":null,"abstract":"<div><p>Multi-spectral satellite imagery is an ideal data source for comprehensive, real-time Earth observation (EO) due to its extensive coverage of Earth and regular updates. It has a wide range of applications in environment monitoring, disaster management, urban planning, weather forecasting etc. Yet, the visual aspect of these images and thus the possibility to extract useful information using image processing techniques is often degraded due to fog, rain, dust, cloud, etc. Satellite image enhancement denotes a set of techniques designed to improve the quality of a satellite image such that the result is more useful for image analysis. The image enhancement aims to improve the quality of an image such that the enhanced image is more useful for image analysis than the original image for a particular remote sensing application. This study introduces a multi-spectral satellite image enhancement architecture called Residual Dense Connection-based UNet++ (RDC-UNet++). The unique design can improve multi-spectral images by enhancing their color and texture details. Extensive experimental studies on multi-spectral image datasets containing more than 150 images prove that the proposed architecture performs better than recent state-of-the-art satellite image enhancement algorithms.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101293"},"PeriodicalIF":3.8,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352938524001575/pdfft?md5=252297efb6b71ee5ace801a56f2e2120&pid=1-s2.0-S2352938524001575-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote Sensing Assessment of Water Resources, Vegetation, and Land Surface Temperature in Eastern Saudi Arabia: Identification, Variability, and Trends","authors":"Mazen E. Assiri ,&nbsp;Md Arfan Ali ,&nbsp;Muhammad Haroon Siddiqui ,&nbsp;Albandari AlZahrani ,&nbsp;Lama Alamri ,&nbsp;Abdullah Masoud Alqahtani ,&nbsp;Ayman S. Ghulam","doi":"10.1016/j.rsase.2024.101296","DOIUrl":"https://doi.org/10.1016/j.rsase.2024.101296","url":null,"abstract":"<div><p>Saudi Arabia has one of the biggest water shortages and the least vegetation in the world, which is presumed to provoke this problem further due to climate change. Therefore, the present study investigates the water, vegetation, and temperature over Al-Asfar Lake region, Al Ahsa, Eastern province of Saudi Arabia using the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST: °C) from Landsat-8 based operational land imager (OLI) measurements for the period 2013 to 2023. This study presented annual and seasonal (dry months: June–September and wet months: December–April) spatiotemporal distribution and variations, calculated their absolute change and trends, and examined their relationship. Results showed positive NDWI values over Al-Asfar Lake, indicating waterbodies; while positive NDVI values on the lake's bank, signifying vegetation. Notably, there were significant temporal variations in water and vegetation observed on annual, seasonal, and monthly scales. The study also found an overall decrease in vegetation areas of 5.36 km² in 2023 compared to 2013, while waterbodies increased by 8.83 km². The trend analysis using area-averaged data demonstrated that NDWI increased on annual (0.0075/year) and seasonal (dry: 0.0083/year and wet: 0.0049/year) scales, while NDVI decreased (annual: 0.0066/year, dry: 0.0083/year, and wet: 0.0009/year). Moreover, LST was recorded least amount over waterbodies (28.23 °C) and vegetation (32.45 °C) covered areas compared to the entire lake region (38.43 °C), respectively. Remark, LST displayed decreasing trends over waterbodies (−0.05/year), followed by vegetation (−0.17/year), and the entire lake region (−0.0001/year), signifying that water and vegetation are vital components to controlling land surface temperature in this region. Finally, the LST showed a positive correlation with NDVI and negative correlation with NDWI. There may be a direct and indirect impact of climate change upon NDVI, LST, and NDWI as shown by the decreases in NDVI and LST and an increase in NDWI. This study can be considered as a base document to monitor waterbodies, vegetation cover, and temperature changes using remote sensing measurements of NDWI, NDVI, and LST, which will assist policymakers in developing water resource management, irrigation planning, and environmental monitoring strategies.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101296"},"PeriodicalIF":3.8,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352938524001605/pdfft?md5=b8cf11e394623a0f6144a1393294398b&pid=1-s2.0-S2352938524001605-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating machine learning approaches for aboveground biomass prediction in fragmented high-elevated forests using multi-sensor satellite data","authors":"Asim Qadeer ,&nbsp;Muhammad Shakir ,&nbsp;Li Wang ,&nbsp;Syed Muhammad Talha","doi":"10.1016/j.rsase.2024.101291","DOIUrl":"https://doi.org/10.1016/j.rsase.2024.101291","url":null,"abstract":"<div><p>Accurate aboveground biomass (AGB) estimations over large areas are essential for assessing carbon stocks and forest resources. This study evaluated machine learning approaches for AGB modeling in Pakistan's mountainous region of Diamir district using freely available Sentinel-1 and Sentinel-2 data and 171 field-measured AGB training points. Random Forest, Gradient Tree Boosting, CatBoost, LightGBM, and XGBoost algorithms were implemented and optimized. Models were developed using individual and combined datasets. Sentinel-2 optical data outperformed Sentinel-1 radar data, but the fusion of both sensors achieved the highest accuracy (R2 &gt; 0.7, RMSE = 105.64 Mg/ha, MAE = 85.34 Mg/ha). Tree canopy height was the most informative predictor for this data, besides terrain variables and radar textures. The machine learning models significantly improved AGB estimates compared to traditional regression techniques, and gradient boosters outperformed Random Forest. This research demonstrates the potential of multi-sensor remote sensing data and advanced algorithms for forest biomass mapping in complex terrain, with modeling accuracies reaching root mean squared errors below 90 Mg/ha. The framework provides an effective solution for monitoring biomass using freely available satellite data. Further refinements include integrating higher-resolution optical data and additional field samples for better validation. This study contributes to remote sensing capabilities for assessing vegetation carbon stocks and dynamics.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101291"},"PeriodicalIF":3.8,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring and assessing the effectiveness of the biological control implemented to address the invasion of water hyacinth (Eichhornia crassipes) in Hartbeespoort Dam, South Africa","authors":"Pawu Mqingwana ,&nbsp;Cletah Shoko ,&nbsp;Siyamthanda Gxokwe ,&nbsp;Timothy Dube","doi":"10.1016/j.rsase.2024.101295","DOIUrl":"https://doi.org/10.1016/j.rsase.2024.101295","url":null,"abstract":"<div><p>Water hyacinth is one of the most aggressive alien invasive plants, which invades freshwater resources and destroys native biodiversity. The plant proliferates rapidly over a short space of time, forming thick dense layer on the surface of freshwater bodies. Monitoring and management of water hyacinth is essential to protect water resources affected by the presence of this plant. The study assessed the effectiveness of biological agent (<em>Megamelus scutellaris</em>) applied in the Hartbeespoort Dam from pre (2016–2017) and post (2018–2023) biological control to manage water hyacinth spread and proliferation. In achieving this main goal, the study used advanced cloud-computing machine learning techniques and multi date Sentinel-2 Multispectral Instrument (MSI) data to monitor the effectiveness of such biological control. During this analysis, remote sensing data was acquired for two time periods namely: pre-intervention (2016–2017) and post intervention (2018–2023) to establish variation in the spatio-temporal distribution of water hyacinth in the Hartbeespoort Dam using various machine learning techniques (Support Vector Machine (SVM), Classification and Regression Tree (CART), Random Forest (RF) and Naïve Bayes (NB)) in Google Earth Engine cloud computing platform, and assessed the spectral separability of water hyacinth from numerous land cover types, within and around the Hartbeespoort Dam using the Sentinel-2 derived spectral reflectance curves. The results indicated that the extent of water hyacinth area coverage decreased from 15% to below 6% between the period of 2018 and 2021, however, a significant increase was noted between November 2022 and April 2023, after the biological control was introduced. The significant increase observed during the time period of November 2022 and April 2023 can be attributed to nutrient rich water discharging into the dam from the Crocodile River during the time of flooding reported in November 2022. The result further indicate that RF produced the highest overall accuracies ranging between 93.42% and 98.70%. While NB produced the lowest accuracies ranging between 87.76% and 92.08%. These findings underscore the relevance of new generation satellite dataset and machine learning algorithms in monitoring the effectiveness of the biological controls of alien invasive spread provide information regarding alien plant invasion. Therefore, aligning with Sustainable Development Goals (SDG 6) emphasizing on the importance of implementing effective control measures to control invasive species and their impact on water resources thus ensuring the sustainability of freshwater ecosystems and the availability of clean water resources.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101295"},"PeriodicalIF":3.8,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal characterization of the subsidence and change detection in Tehran plain (Iran) using InSAR observations and Landsat 8 satellite imagery","authors":"Sasan Babaee ,&nbsp;Mohammad Amin Khalili ,&nbsp;Rita Chirico ,&nbsp;Anna Sorrentino ,&nbsp;Diego Di Martire","doi":"10.1016/j.rsase.2024.101290","DOIUrl":"https://doi.org/10.1016/j.rsase.2024.101290","url":null,"abstract":"<div><p>Urban areas worldwide are increasingly facing challenges related to land subsidence, a phenomenon exacerbated by uncontrolled groundwater extraction and urban expansion. This research focuses on the Tehran plain, Iran's capital city, where significant subsidence has been observed due to uncontrolled migrations influenced by various economic and political factors. This expansion has increased demand for energy, notably water, leading to irregular water withdrawals from underground sources and, consequently, land subsidence. Monitoring this subsidence, particularly its effects on urban infrastructure, has become a critical challenge. This research first reviewed the existing body of knowledge related to subsidence measurement in the Tehran plain with an emphasis on their findings and limitations and then used radar images to study the subsidence patterns in the Tehran plain from 2016 to the end of 2020. Finally, the results collaborated by optical imagery analysis to find the relationship between surface change detection and spatiotemporal distribution of subsidence. As a result, through processing Sentinel-1A SAR images, consistent vertical displacements (subsidence) were observed, especially in areas heavily reliant on groundwater from wells, with some areas experiencing a rate of more than −20 mm/year. Horizontal displacement, however, was approximately about ±8 mm/year. Also, our results show that the subsidence rate in this plain has decreased in recent years. Therefore, the study integrated multispectral satellite data to clarify this issue and compensate for missing groundwater level data, specifically the Normalized-Difference Vegetation Index (NDVI) and Normalized-Difference Moisture Index (NDMI). These datasets were used to monitor changes in vegetation cover distribution and moisture in response to the variations of groundwater depth over time. The results of this research can be beneficial in adequately managing groundwater resource utilization to reduce the potential damage to infrastructure and the environment.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101290"},"PeriodicalIF":3.8,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235293852400154X/pdfft?md5=fb8ae3fdee64f027619985f3a9af77a0&pid=1-s2.0-S235293852400154X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Changes in Land Cover, NDVI, and LST in the Sundarbans Mangrove Forest in Bangladesh and India: A GIS and Remote Sensing Approach","authors":"Kingsley Kanjin,&nbsp;Bhuiyan Monwar Alam","doi":"10.1016/j.rsase.2024.101289","DOIUrl":"https://doi.org/10.1016/j.rsase.2024.101289","url":null,"abstract":"<div><p>Mangrove ecosystems, although limited in diversity and area compared to tropical forests, provide essential ecological and economic services, such as carbon sequestration and coastal protection. The Sundarbans mangrove forest, shared by Bangladesh and India, is one of the largest mangrove ecosystems in the world and is crucial for biodiversity, economy, and climate regulation. Unfortunately, this ecosystem has been under severe stress over the years, with alarming rates of deforestation leading to habitat loss and a decline in ecosystem services. This study analyzes the spatiotemporal changes in the Sundarbans mangrove forest coverage from 1973 to 2023 using supervised image classification on Landsat images. It also assesses the relationship between the Normalized Difference Vegetation Index and Land Surface Temperature in the Sundarbans using MODIS data which were extracted in Google Earth Engine. It finds that, despite the loss of denser mangrove areas, an improvement in overall vegetation health is visible, which suggests a natural resilience within the Sundarbans mangrove forest. The Land Surface Temperature result shows a weak but statistically significant negative correlation with the Normalized Difference Vegetation Index, indicating that the depletion of the Sundarbans mangrove forest could have an impact on the area’s surface temperature. As such, the study regressed the Normalized Difference Vegetation Index on Land Surface Temperature. The results confirm that although the Normalized Difference Vegetation Index has a statistically significant negative impact on Land Surface Temperature, the Coefficient of Determination is low. This suggests that other factors such as water bodies that intersect with the mangrove forest in the area may play an important role in influencing Land Surface Temperature. The paper reveals a nuanced picture of the Sundarbans’ ecological state, with both declining mangrove densities and signs of vegetation recovery. It highlights the need for comprehensive conservation strategies to mitigate further ecosystem degradation.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101289"},"PeriodicalIF":3.8,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352938524001538/pdfft?md5=72f71d063ef3ad7c929b702ac878e92f&pid=1-s2.0-S2352938524001538-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface deformation monitoring and forecasting of sinabung volcano using interferometry synthetic aperture radar and forest-based algorithm","authors":"Muhammad Hanif ,&nbsp;Sarun Apichontrakul ,&nbsp;Pakhrur Razi","doi":"10.1016/j.rsase.2024.101288","DOIUrl":"https://doi.org/10.1016/j.rsase.2024.101288","url":null,"abstract":"<div><p>The Sinabung volcano on Sumatra Island stands out as one of the most active volcanos, having recorded the highest number of eruptions since it resumed activity in 2010. The eruptive activities have caused significant deformations on the volcano's surface. This research aimed to analyze, cluster, and forecast its deformation patterns based on Sentinel-1 A time series data from 2016 to 2023. The differential interferometry synthetic aperture radar (DInSAR) technique was used to monitor monthly deformations and to create time series data. A forest-based forecast (FBF) model was used to predict the rate of changes in volcano surface inflation from January 2024 to December 2027. The deformation times series patterns were also analyzed and clustered into three regions to reveal areas with similar deformation behaviors. The results indicated that Mount Sinabung's deformation is an overall continuous sporadic phenomenon where random ground inflation and deflation were recorded throughout the area with an average deformation rate ranging from 0.06 to 0.32 cm/month and an overall average of 0.197 cm/month with a standard deviation of 0.96 cm, confirming that the volcano is inflating. The highest single-pixel monthly inflation of 4.62 cm was recorded in 2023, while the highest deflation occurred in 2018 at −4.58 cm. The FBF model predicted a gradual and increasing inflationary pattern at the rate of 0.54 cm/month for 2024–2027, higher than the average of the observed data. The deformation within the lava dome and caldera poses a significant risk and could lead to wall collapses and landslides in the crater dome, potentially triggering explosive eruptions. The outcomes of this research serve as valuable supporting information and offer an early warning of potential volcanic disasters in the future.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101288"},"PeriodicalIF":3.8,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}