Remote Sensing Applications-Society and Environment最新文献

{"title":"New inventory and dynamics of glacial lakes in Alaknanda basin, Uttarakhand, India from 1990 to 2020: A multi-temporal landsat analysis","authors":"Rekha Sahu ,&nbsp;Parvendra Kumar ,&nbsp;Rajnandini Gupta ,&nbsp;Santram Ahirwar ,&nbsp;Vikram Sharma","doi":"10.1016/j.rsase.2025.101470","DOIUrl":"10.1016/j.rsase.2025.101470","url":null,"abstract":"<div><div>Glacial lakes are critical components of high-altitude mountainous regions in the Himalayas. In recent years, glaciers have rapidly receded due to climate change, resulting in the formation of glacial lakes with substantial risks for downstream communities and infrastructure. The present study uses Landsat satellite data to create a comprehensive glacial lake inventory in the Alaknanda Basin, focusing on spatiotemporal changes between 1990 and 2020. The study has recorded 73 glacial lakes (≥0.003 km²) with a total surface area of 2.538 ± 0.037 km² in 2020. The mean depth and volume of glacial lakes were assessed as 7.17 m and 0.432 x 10⁶m³, respectively. During 1990–2020, the total glacial lake area has increased from 0.748 ± 0.020 km² to 2.538 ± 0.037 km² with a growth of ∼1.790 km² (239%; 7.97% a⁻¹). Additionally, 15 common glacial lakes have shown significant growth rates of 91.24% (3.04% a^-¹). Among all the glacial lakes, tiny lakes (&lt;0.02 km²) have shown the maximum growth in both numbers (+33) and area (477.92%; 15.93% a⁻¹). Moraine-dammed lakes have expanded more rapidly in terms of number (+27), while supraglacial lakes have exhibited a higher rate of areal (1771.71%; 59.06% a⁻¹) expansion. Based on the current inventory, flood hazard studies in the Alaknanda Basin can be carried out for a better understanding of glacial-climate related dynamics.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101470"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387341","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":"Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables","authors":"María Paula Alvarez ,&nbsp;Laura Marisa Bellis ,&nbsp;Julieta Rocío Arcamone ,&nbsp;Luna Emilce Silvetti ,&nbsp;Gregorio Gavier-Pizarro","doi":"10.1016/j.rsase.2025.101485","DOIUrl":"10.1016/j.rsase.2025.101485","url":null,"abstract":"<div><div>The ecological condition of forest ecosystems is degraded. Limited prior research in vegetation has focused on monitoring ecological condition levels in dry forest at fine scale. We proposed a novel approach to obtain accurate indicators of the ecological condition of the Chaco Serrano forest (Córdoba, Argentina) by estimating forest structure variables (canopy cover (<span><math><mrow><mi>C</mi><mi>C</mi></mrow></math></span>), diameter breast height (<span><math><mrow><mi>D</mi><mi>B</mi><mi>H</mi><mtext>_</mtext><mi>s</mi><mi>u</mi><mi>m</mi></mrow></math></span>), number of woody individuals (<span><math><mrow><mi>N</mi><mi>W</mi></mrow></math></span>) and two first axes of a principal component analysis (<span><math><mrow><mi>P</mi><mi>C</mi><mn>1</mn></mrow></math></span> and <span><math><mrow><mi>P</mi><mi>C</mi><mn>2</mn></mrow></math></span>)) as a measure of forest degradation. To achieve this, first the correlation with two complementary groups of remote sensing derived data (texture metrics over Normalised difference vegetation index and SAR-derived data) was explored. Then, General linear models (GLM) were constructed using the most correlated remote sensing derived variables with forest structure variables as predictor variables. The best estimation was obtained to <span><math><mrow><mi>C</mi><mi>C</mi></mrow></math></span> (<span><math><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>=0.58, rmse=14,5%), followed by <span><math><mrow><mi>D</mi><mi>B</mi><msub><mrow><mi>H</mi></mrow><mrow><mi>s</mi><mi>u</mi><mi>m</mi></mrow></msub></mrow></math></span> (<span><math><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>=0.37, rmse=156.6) and <span><math><mrow><mi>N</mi><mi>W</mi></mrow></math></span> (<span><math><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>=0.22, rmse=14.6), with an spatial arrangement consistent with field observations. Moreover, <span><math><mrow><mi>C</mi><mi>C</mi></mrow></math></span> estimation was more accurate than those at regional and global scale, and highlights the importance of developing local models in areas that exhibit high ecological, geological, and human heterogeneity. In addition, other forest variables could also be evaluated, like floristic composition or others associated with functioning. Results offer valuable insights for developing management strategies suitable for each condition, and for future studies regarding the relationship of the mentioned condition and associated natural and anthropic factors.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101485"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421166","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":"High-resolution maximum air temperature estimation over India from MODIS data using machine learning","authors":"Amal Joy,&nbsp;K. Satheesan,&nbsp;Avinash Paul","doi":"10.1016/j.rsase.2025.101463","DOIUrl":"10.1016/j.rsase.2025.101463","url":null,"abstract":"<div><div>Maximum air temperature is one of the crucial parameters required for climate change research, public health, agriculture, energy consumption, and urban planning. Observations of maximum temperature are limited spatially and temporally, and available observations are discontinuous due to operational constraints. With the advent of the satellite era, the land surface temperature is available continuously across the globe over space and time. This study explores the potential of using land surface temperature data from MODIS satellites to estimate maximum air temperature across India, particularly in areas with low cloud cover. The research employs advanced machine learning techniques to estimate the maximum temperature over India from MODIS land surface temperature and other inputs like NDVI, elevation, land use, geographical location, and the Julian day. We have assessed the capability of three machine learning techniques: XGBoost, Neural network, Generalized additive model and multiple linear regression in estimating maximum temperature over India using MODIS and Insitu data spanning from 2010 to 2022. Results indicate that XGBoost outperforms the other techniques, achieving the lowest RMSE and R² values of 1.79 °C and 0.90, respectively. Our findings reveal that land surface temperature is the most influential predictor of maximum air temperature, followed by Julian day, elevation, latitude, distance to coast, NDVI, and land cover type, in order of importance. This research demonstrates the potential of satellite-derived data and machine learning in addressing gaps in maxiumum temperature observations, which could significantly benefit various sectors reliant on accurate data.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101463"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101040","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":"Data-driven identification of high-nature value grasslands using Harmonized Landsat Sentinel-2 time series data","authors":"Kim-Cedric Gröschler ,&nbsp;Tjark Martens ,&nbsp;Joachim Schrautzer ,&nbsp;Natascha Oppelt","doi":"10.1016/j.rsase.2024.101427","DOIUrl":"10.1016/j.rsase.2024.101427","url":null,"abstract":"<div><div>Europe’s high-nature value (HNV) grasslands have significantly declined in recent decades. European conservation strategies are mainly confined to protected areas, and many national initiatives aiming for comprehensive coverage suffer from long and irregular monitoring intervals. Addressing this, we propose a data-driven approach to derive information about the location, extent and HNV status of grasslands to improve the efficiency of large-scale field mappings. Serving as a representative example of European and national grassland monitoring, we utilize the regional habitat map of Schleswig-Holstein, Germany, in conjunction with Harmonized Landsat Sentinel-2 time series data to train XGBoost models for the period of 2017-2022. Our models achieved high classification performance, distinguishing eight grassland classes with average F1-scores of 0.89 before and 0.86 after feature selection. We examined model decision-making patterns using an adapted version of SHapley Additive exPlanation values, finding that start-of-season, end-of-season, Red-Edge, and spectral change features significantly impacted predictions. We produced annual HNV grassland maps and, by aggregating yearly results, derived a robust estimate of the HNV status in our study area. Applying our HNV estimate to an independent dataset comprising 2363 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> of grassland plots with unknown HNV status, we identified 84 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> as HNV, highlighting the significance of our result. Overall, our study demonstrates how integrating remote sensing data enhances the efficiency and comprehensiveness of large-scale mapping initiatives.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101427"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092428","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":"Satellite remote sensing for environmental sustainable development goals: A review of applications for terrestrial and marine protected areas","authors":"Matthew J. McCarthy ,&nbsp;Hannah V. Herrero ,&nbsp;Stephanie A. Insalaco ,&nbsp;Melissa T. Hinten ,&nbsp;Assaf Anyamba","doi":"10.1016/j.rsase.2025.101450","DOIUrl":"10.1016/j.rsase.2025.101450","url":null,"abstract":"<div><div>With few years left to achieve the vital United Nations Sustainable Development Goals (SDGs), member nations must urgently leverage technological advancements in environmental monitoring to succeed. Remote sensing now provides decades of global observations at a variety of spatio-temporal scales and a litany of data products to guide comprehensive measures for climate action, and aquatic and terrestrial biota preservation. Protected areas, such as national parks and wildlife preserves, represent largely untapped resources for both applying robust conservation measures and testing ambitious new approaches to sustainable development that could jumpstart the much-needed adoption of strategies to efficiently pursue global sustainability. This review summarizes recent demonstrated utilities of remotely sensed data applied to protected areas for research related to SDG goals 13, 14, and 15: “Climate Action”, “Life below Water”, and “Life on Land”. We identify successful uses of such data for each SDG, identify areas for improvement, and provide recommendations from the literature on how to expand what others have done to achieve lofty goals with global impact. We demonstrate that remote sensing provides a valuable tool for achieving SDGs as it facilitates monitoring vegetation health, water quality and condition, and climate variables at large spatial and fine temporal scales, while also evaluating the effectiveness of management and conservation practices. Issues remain, however, in that there is currently no reference from which to relate goal progress to human livelihoods. The current relationship between remotely sensed indices and ecological services that determine sustainable development omit steps that would establish this connection.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101450"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092437","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":"Integration of magnetic and remote sensing methods for mapping geothermal signatures in the middle part of Benue Trough, Northeastern Nigeria","authors":"Babatunde Joseph Fagbohun ,&nbsp;Naheem Banji Salawu ,&nbsp;Amin Beiranvand Pour ,&nbsp;Suraju Adesina Adepoju","doi":"10.1016/j.rsase.2024.101434","DOIUrl":"10.1016/j.rsase.2024.101434","url":null,"abstract":"<div><div>The Middle part of Benue Trough is one of the notable regions with geothermal manifestations in Nigeria, which is evident in the form of warm springs and mud pots. In this study, aeromagnetic and remote sensing data were utilized to evaluate the deep-seated and surface signatures of the geothermal system in the middle part of the Benue Trough. The analysis of aeromagnetic data involves the application of the reduction-to-pole (RTP) technique on total magnetic intensity (TMI) anomaly data to reposition anomalies above their magnetic sources. Filters were subsequently applied on the RTP map to suppress artefacts and anomalies produced by shallow magnetic sources. Additionally, the day-time land surface temperature (LST) of the study area was derived using Landsat-8 data. ASTER day-time and night-time images were subsequently used for detailed thermal anomaly mapping of a subset of the study area with elevated temperature in the Landsat derived LST. The STcorr algorithm was employed for the correction of topographic influences and generation of thermal anomaly maps from day-time and night-time ASTER data. Analysis of aeromagnetic data revealed that some of the geothermal manifestations within the study area are linked to deep-seated structures while others are related with the intrusive rocks. Generally, Akiri, Awe, Azara, and Ribi thermal springs display noticeable thermal anomalies in the night-time thermal anomaly map with the Akiri thermal spring having the most prominent thermal anomalies. The Ribi, Azara, and Akiri thermal springs, which are linked with deep-seated structures exhibit higher temperatures than Keana and Awe thermal springs, which are thought to be associated with intrusive rocks in the day-time LST and thermal anomaly images. The integration of geophysical and remote sensing data for the exploration of geothermal resources adopted in this study offers a rapid and cost-effective approach that can be adapted for geothermal resource exploration in other areas suspected to have blind geothermal systems with minimal surface manifestation.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101434"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092528","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":"Temperate forest tree species classification with winter UAV images","authors":"Yunmei Huang ,&nbsp;Baijian Yang ,&nbsp;Joshua Carpenter ,&nbsp;Jinha Jung ,&nbsp;Songlin Fei","doi":"10.1016/j.rsase.2024.101422","DOIUrl":"10.1016/j.rsase.2024.101422","url":null,"abstract":"<div><div>Tree species classification using unmanned aerial vehicle (UAV) images has gained increasing attention due to recent advancements in deep learning algorithms and UAV technology. Recent studies have primarily focused on the use of UAV images captured during the growing seasons. Despite the fact that winter is a critical and convenient period for forest inventory, limited studies have explored the application of winter images for species classification. By training a deep learning model (ResNet18), we achieved an average F1-score of 0.9 for classification among eight species using winter UAV images in a temperate forest. To enhance model interpretability, we applied the Grad-CAM method, which generated feature maps identifying critical regions for species classification. To examine the impact of color on species classification, we converted RGB images to grayscale. Model accuracy on grayscale images decreased slightly (F1-score 0.86) but it effectively learned features from canopy images. This study contributes to the field by pioneering the use of winter images for tree species classification in temperate forests, which provides new opportunities for year-round UAV-based forest inventory. Given winter provides the opportunity to inventory other under-canopy features such as trunk diameter, adding the capability of species classification with winter images could greatly improve the capacity and efficiency of UAV-based forest inventory.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101422"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092531","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":"An assessment of different line-of-sight and ground velocity distributions for a comprehensive understanding of ground deformation patterns in East Jharia coalfield","authors":"Aditya Kumar Thakur,&nbsp;Rahul Dev Garg,&nbsp;Kamal Jain","doi":"10.1016/j.rsase.2024.101446","DOIUrl":"10.1016/j.rsase.2024.101446","url":null,"abstract":"<div><div>Jharia Coalfield is one of the oldest and most crucial mining regions. It faces ongoing challenges with land surface deformation due to mining operations and coal seam fires. Previous studies often overlooked the complex interplay of various LOS and ground velocities and focused mainly on vertical subsidence. This paper used the PS-InSAR for detailed ground deformation analysis of East Jharia. Ascending and descending pass time series LOS deformation datasets were obtained using advanced multi-image sparse point processing. Further, the study employed IDW interpolation in LOS velocity, followed by velocity decomposition to derive horizontal and vertical velocity components. Multi-sparse point processing and IDW interpolation enhance spatial continuity and reduce noise, ensuring the robustness of decomposed velocities. Interdependency and distribution similarity between different velocities were explored using Correlation analysis and the Global Moran's Index. Analysis revealed significant ground movement patterns with weak spatial association and underscored the necessity of both vertical and horizontal velocity for a comprehensive understanding of deformation. Subsidence smaller than −30 mm/year was observed in Sahana Pahari, northeast of Rajapur opencast mines, Jharia Main Road, and southeast of Jharia Gurudwara to Kujama Colliery at Tisra. Upliftment greater than 30 mm/year occurred in Jorapokhar, Karmik Nagar, and Kustai Basti near Ena Colliery, while lateral displacement of the same value was notable in CIMFR Colony Dhaiya, Koyla Nagar Saraidhela, Ghanoodih, Dobari, Kujama, and Barari Colliery over dumps. Correlation coefficients of 0.9165 (horizontal) and 0.7933 (vertical) revealed the dominant influence of horizontal movement on overall ground deformation. Overall, the study provided valuable insights into the spatial distribution of subsidence for 2023, highlighting the importance of different velocities in assessing and managing ground movement in mining-affected regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101446"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092544","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":"Accelerating Electric Vehicle (EV) adoption: A remote sensing data driven and deep learning-based approach for planning public car charging infrastructure","authors":"Prakash P.S.,&nbsp;Jenny Hanafin,&nbsp;Divyajyoti Sarkar,&nbsp;Marta Olszewska","doi":"10.1016/j.rsase.2024.101447","DOIUrl":"10.1016/j.rsase.2024.101447","url":null,"abstract":"<div><div>Car fleet electrification is critical for achieving ambitious climate action goals. Access to charging stations is a major barrier for widespread adoption of EV, especially impacting members of lower socio-economic groups who cannot easily install home chargers in their residences. This research aims to examine the demand for public EV charging stations in residential areas and their geographical distribution. By utilizing advanced deep learning models and high-resolution remote sensing imagery, the study aims to identify specific clusters of households that require access to the public infrastructure. The study uses high-resolution aerial images and property parcels as input to a deep learning model YOLOv8 to recognize properties that may require access to public charging stations. This study presents an innovative approach that addresses challenges pertaining to EV adoption using remote sensing data, machine learning, and geospatial analysis. The results of the study demonstrate spatial analysis using sociodemographic data and household parking data, generated through the innovative method developed in this work, to aid Irish towns in planning public EV charging facilities among residential neighbourhoods. The study's findings are expected to aid in the implementation of expansion strategies for the public EV charging network, which is vital for meeting ambitious EV fleet targets.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101447"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092545","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":"GEE-based environmental monitoring and phenology correlation investigation using Support Vector Regression","authors":"Fatemeh Parto Dezfooli ,&nbsp;Mohammad Javad Valadan Zoej ,&nbsp;Ali Mansourian ,&nbsp;Fahimeh Youssefi ,&nbsp;Saied Pirasteh","doi":"10.1016/j.rsase.2024.101445","DOIUrl":"10.1016/j.rsase.2024.101445","url":null,"abstract":"<div><div>Environmental changes over time and across different regions profoundly affect agriculture, forestry, water management, public health, and ecosystems. Therefore, monitoring these fluctuations is crucial for informing decision-making and developing strategies for long-term sustainability. While ground-based methods provide valuable insights into environmental dynamics, they are inherently limited in scope and coverage. Consequently, satellite-based techniques have become essential for comprehensive ecological monitoring over extensive spatial and temporal scales. This study investigates spatio-temporal patterns of environmental factors and their correlation with phenology in Ilam Province, Iran, from 2014 to 2021, utilizing remote sensing data and Google Earth Engine (GEE). Landsat 8 satellite data was used to generate time series maps and timelines for land cover, temperature, and soil moisture, using the Soil-Adjusted Vegetation Index (SAVI), Land Surface Temperature (LST) anomaly, and Soil Moisture Index (SMI). Subsequently, the Temporal Soil-Adjusted Vegetation Phenology Index (TSPI) was calculated to track annual vegetation variations and analyze its correlation with the specified parameters using Support Vector Regression (SVR). Our results revealed significant trends in environmental factors, highlighting robust correlations with the TSPI. Soil moisture peaked in late winter and early spring, declining during the summer, with the highest levels recorded in 2018. Vegetation reached its maximum density in mid-spring and its minimum in winter, with a notable greening surge observed in 2019. Temperatures were highest in summer and lowest in winter, showing minimal year-to-year variation. Spatial analysis indicated a consistent increase in land surface temperature from the northeast toward the southwest, corresponding to declines in vegetation and soil moisture levels. Regression analysis specified strong associations between the TSPI and environmental variables, with R-squared values of 0.83 for LST, 0.86 for SAVI, and 0.79 for SMI. These findings emphasize the effectiveness of remote sensing methods, such as time series satellite imagery and streamlined indices, for large-scale ecological analyses using the GEE platform and underscore the potential of TSPI as a proper indicator for future environmental management research.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101445"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092548","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}