Remote Sensing Applications-Society and Environment最新文献

{"title":"The influence of different building height and density data on local climate zone classification","authors":"Tianyue Ao ,&nbsp;Mengmeng Wang ,&nbsp;Renfeng Wang ,&nbsp;Zhengjia Zhang ,&nbsp;Wei Gao ,&nbsp;Xiuguo Liu","doi":"10.1016/j.rsase.2024.101429","DOIUrl":"10.1016/j.rsase.2024.101429","url":null,"abstract":"<div><div>The selection of classification features is significant for local climate zone (LCZ) classification using machine learning methods. Although the sequential utilization of various feature datasets for LCZ classification, the impact of different spatial datasets on the classification outcomes of LCZ remains unclear. This study systematically analyzes the impact of four building height datasets and two building density datasets, combined with spectral data, on LCZ classification using the random forest method. The comparative analyses are performed in three aspects: different building height data, different building density data, and various combinations of the two. The results show that various types and sources of spatial data have distinct impacts on improving the accuracy of LCZ classification. Generally, building density datasets prove more effective in enhancing LCZ classification compared to building height datasets. Among four building height datasets, the digital surface model (DSM) exhibits the most significant improvement in LCZ classification. Additionally, building density extracted from CNBH-10 m (BD1) demonstrates superior improvement in LCZ classification compared to that attained from roof vector data (BD2). Notably, the synergy of DSM and BD2 exhibits the most substantial enhancement, achieving an OA (Over Accuracy) of 89.60% and a Kappa coefficient of 87.70%. This combination of building height and density data simultaneously enhances the classification accuracy of both building area and natural surface types. The result of this study can not only enhance our understanding of the influences of spatial information data on the LCZ classification, but also provide a useful reference to improve LCZ classification.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101429"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125467","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 the spatiotemporal dynamics and seasonal trends in NO₂ concentrations across India using advanced statistical analysis","authors":"Aman Srivastava,&nbsp;Aditya Kumar Thakur,&nbsp;Rahul Dev Garg","doi":"10.1016/j.rsase.2025.101490","DOIUrl":"10.1016/j.rsase.2025.101490","url":null,"abstract":"<div><div>Increasing industrialization and urbanization pose significant risks to air quality and public health. It increases the necessity of a comprehensive understanding of air pollution dynamics. Further, prior research fails to explain detailed variability with levels of the seasonal trends of the pollutants over the years. This study aims to analyze the spatiotemporal variation of NO₂ concentrations across India from 2019 to 2023, focusing on understanding spatial continuity, seasonal trends, and dominant temporal patterns. The processed Sentinel-5P satellite data, which is spatially averaged over grids, was used in the study to enhance map accuracy. Various statistical parameters were used for spatiotemporal assessment. The consistency of NO₂ pollutants distribution over time was analyzed using Global Moran's Index. Further, moving average and cubic spline smoothing techniques were applied to assess the seasonality behaviour. Fast Fourier Transformation (FFT) was used to determine the dominant frequency cycles within the time series data of NO₂ pollutants. The result shows a significant increase in NO₂ levels from 0.7053∗10⁻⁴ mol/sq.m. in 2019 to 0.9634∗10⁻⁴ mol/sq.m. in 2023. Correspondence analysis showed consistent and shifting pollution patterns with spatial associations ranging from 0.6401 to 0.7072. The seasonal fluctuations in NO₂ concentrations represent peaks during pre-monsoon and fall in winter. Further, FFT analysis shows dominant seasonal patterns, a frequency of 0.0833, with an amplitude of 0.3209, depicting strong yearly variations. However, smaller semi-annual and quarterly fluctuations are also observed with amplitude of 0.1402 and 0.0530, respectively. Overall, this paper provides valuable insights into the spatiotemporal distribution of NO₂ pollutants that can be utilized in effective planning of air quality management and targeted pollution control strategies for mitigating the health impacts of pollution.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101490"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421208","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":"Warming trends in the Nile Delta: A high-resolution Spatial statistical approach","authors":"Faten Nahas ,&nbsp;Islam Hamdi ,&nbsp;Mohamed Hereher ,&nbsp;Martina Zelenakova ,&nbsp;Ahmed M. El Kenawy","doi":"10.1016/j.rsase.2024.101408","DOIUrl":"10.1016/j.rsase.2024.101408","url":null,"abstract":"<div><div>The Nile Delta, a region of historical significance, is facing significant environmental changes driven by climate change. This study employs a novel pixel-level spatial statistical analysis to assess the intensity and trends in the daytime and nighttime urban heat island (UHI) from 2003 to 2021. We employed high-resolution data from the Global Artificial Impermeable Area (GAIA) dataset (30 m), land surface temperature (LST) from the MODIS Aqua satellite 1000 m, and the MOD13A3 Normalized Difference Vegetation Index (NDVI) (1000 m). Bivariate choropleth maps were used to illustrate the spatial relationships between daytime and nighttime LST and NDVI. Ordinary least squares (OLS) regression method was used to calculate the trend for each pixel and the Mann-Kendall test was used to assess the statistical significance of the trend at 95% confidence level (p &lt; 0.05). The central and southern regions of the delta experienced significant LST increases, highlighting the risk of warming due to vegetation degradation. Specifically, the diurnal LST trend ranged from −0.46 °C to 0.34 °C/year, while the nocturnal trend ranged from −0.12 °C to 0.26 °C/year. Spatially, the study also indicates cooling trends in coastal cities such as Port Said, New Damietta and Alexandria due to the moderate influence of the Mediterranean Sea. In contrast, the inland and southern Delta cities are warming rapidly. The relationship between diurnal UHI average and the NDVI showed a modest negative correlation (R = −0.31, p &lt; 0.0001). This association was much stronger at night, with a negative correlation of (R = −0.71, P &lt; 0.0001) A strong negative correlation between diurnal UHI trend and NDVI (R = −0.68, p &lt; 0.0001). The relationship between nocturnal UHI trend and NDVI is negative (R = −0.61, p &lt; 0.0001). The analysis reveals that 13 cities exhibited significant warming during the daytime, compared to 35 cities at night. The results highlight the importance of pixel-level data to accurately assess environmental changes and inform urban planning strategies to mitigate the effects of warming on the Nile Delta.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101408"},"PeriodicalIF":3.8,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748002","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":"Improving the estimation approach of percentage of impervious area for the storm water management model — A case study of the Zengwen reservoir watershed, Taiwan","authors":"Chih-Wei Chuang,&nbsp;Ming-Huei Chen,&nbsp;Wen-Yan Zhang","doi":"10.1016/j.rsase.2024.101409","DOIUrl":"10.1016/j.rsase.2024.101409","url":null,"abstract":"<div><div>The Zengwen Reservoir, located within the Zengwen River watershed, is a crucial water supply source in southern Taiwan. Water resources can be estimated using the rainfall-runoff model of the Storm Water Management Model (SWMM). However, the percentage of impervious area (PIA) is one of the significant factors influencing the SWMM. The purpose of this study is to utilize remote sensing imagery to rapidly and accurately estimate land use as PIA for the SWMM rainfall-runoff model. The rainfall-runoff model of SWMM was calibrated and validated based on observed discharge data in 2005 and 2017. The results of goodness-of-fit indicators of NSE value and R² value showed in the acceptable range of 0.745, 0.764 in 2005, 0.715, and 0.883 in 2007, respectively. The modified composite of the built-up index and PIA (MCBI-PIA) was used for rainfall-runoff simulation in 2005, 2009, 2014, 2017, and 2021. The simulation results revealed the NSE value varied from 0.484 to 0.851, and the R² value between 0.519 and 0.894 which represented a statistically acceptable performance of the simulation model. It indicates that the proposed method can be applied to estimate the PIA for land use patterns during different periods and utilized as the actual PIA for rainfall-runoff simulation with the SWMM.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101409"},"PeriodicalIF":3.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702974","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":"Multilayer optimized deep learning model to analyze spectral indices for predicting the condition of rice blast disease","authors":"Shubhajyoti Das ,&nbsp;Pritam Bikram ,&nbsp;Arindam Biswas ,&nbsp;Vimalkumar C. ,&nbsp;Parimal Sinha","doi":"10.1016/j.rsase.2024.101394","DOIUrl":"10.1016/j.rsase.2024.101394","url":null,"abstract":"<div><div>Rice blast disease is one of the most destructive infectious diseases that affects world food security. Proper monitoring and an accurate decision-making process can assist in disease management strategy. Ground surveys and sampling are the less accurate, expensive, and time-consuming processes that are ineffective to check epidemic. Satellite data-driven approach might be an ideal cost and time-efficient technique that can provide an accurate result due to its revisit across farmland. Temperature variation is a salient feature of this disease trajectory. Hence, land surface temperature can be a cardinal property for disease risk estimation. Spectral indices-based analysis can be more efficient for tracking the disease density. In this study, the MODIS satellite-based Land Surface Temperature (LST) parameter is used to indicate the disease in the field. The indicated risk estimation is also examined using ground truth observation to provide less erroneous labeling. Various spectral combination based remote sensing indices were accumulated to audit the disease states. Remote sensing indices such as Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and Moisture Stress were obtained from the Sentinel-2 archive. These images, depicting the various indices, are processed through a novel optimized deep learning model to predict the disease condition of farmland. The model is developed using various residual networks with <span><math><mrow><mi>L</mi><mn>2</mn></mrow></math></span> regularization and batch normalization to enhance the performance of the model. A combination of convolution layers is used to extract crucial spectral information from the remote sensing images and processed through fully connected layers to prognosticate the state of the disease. The model can predict with 89.67% accuracy using the EVI parameters for different geographical positions compared with other remote sensing parameters and has less chance of erroneous possibilities. The proposed system will lead to improved agricultural monitoring management for the incidence of leaf blast disease in real-time.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101394"},"PeriodicalIF":3.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702975","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 analysis of atmospheric methane concentrations and key influencing factors using machine learning in the Middle East (2010–2021)","authors":"Seyed Mohsen Mousavi","doi":"10.1016/j.rsase.2024.101406","DOIUrl":"10.1016/j.rsase.2024.101406","url":null,"abstract":"<div><div>Methane (CH₄) is a potent greenhouse gas that significantly impacts climate change due to its rising atmospheric concentrations. Hence, it is crucial to comprehend the spatial and temporal fluctuations in atmospheric CH₄ concentration (XCH₄) at both national and international levels. This study investigates the correlation between atmospheric XCH₄ concentrations (XCH₄) and key influencing factors to identify the primary sources and sinks of CH₄ across the Middle East (ME). Initially, XCH₄ data from the GOSAT satellite, covering the period from 2010 to 2021, were employed to generate spatiotemporal distribution maps of XCH₄ across the ME region. Subsequently, the study investigated the single and simultaneous relationship between XCH₄ and relevant environmental factors, such as vegetation, temperature, precipitation, and others, across different months using correlation analysis and the Permutation Feature Importance (PFI) method to identify the key factors influencing XCH₄ variations. The results reveal significant spatial and temporal variations in XCH₄ concentrations, with higher levels detected in the central and southern regions of the ME during the summer months. The results also highlight the presence of both peak positive and negative correlations with temperature and moisture during winter months. Additionally, both precipitation and vegetation demonstrated negative correlations with XCH₄, especially during the winter and plant-growing seasons. According to the PFI results, temperature emerged as the most significant factor, accounting for over 40% of the variance in XCH₄ concentrations during summer. At the same time, anthropogenic activities exerted minimal influence on these patterns. This comprehensive spatiotemporal analysis provides crucial insights into the variation of CH₄ and its primary drivers in this climatically vulnerable region. Identifying emission patterns can support the development of targeted mitigation policies to curb the future rise of CH₄.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101406"},"PeriodicalIF":3.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720418","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":"Examining the nexus of social vulnerability, land cover dynamics, and heat exposure in Reno, Nevada, USA","authors":"Consolata Wangechi Macharia,&nbsp;Lawrence Kiage","doi":"10.1016/j.rsase.2024.101400","DOIUrl":"10.1016/j.rsase.2024.101400","url":null,"abstract":"<div><div>Intense heat is a persistent urban challenge whose impacts are detrimental to human health. Heat-related effects disproportionately impact underserved populations. Modification of urban landscapes through increased imperviousness intensifies surface temperatures, leading to heightened heat exposure risks. While climate adaptation efforts have advanced, they are inadequate in addressing the uncertainties of climate change and the long-term risks of climate-related hazards. In addition, despite the numerous heat vulnerability studies across U.S. cities, the City of Reno is largely understudied. To address these gaps, the research examined the relationship between the spatiotemporal patterns of social vulnerability, changes in biophysical properties, and the heat hazard in Reno, Nevada. We utilized CDC census data to map the Social Vulnerability Index (SVI) and Landsat satellite data from 1990 to 2023 to analyze Land Surface Temperature (LST) trends for a temporal comparative study of heat patterns. Additionally, we employed the Normalized Difference Vegetation Index (NDVI) for vegetation extent. The zonal statistics tool helped assess the influence of different land use features on surface temperatures. The results showed that regions identified as social vulnerability hotspots often coincided with areas highly exposed to extreme temperatures and vice versa. Our findings also revealed an extension of heat vulnerability hotspots from the urban core to suburban regions. We observed a decline in mean LST values in regions covered by vegetation and a rise in mean surface temperatures in regions encompassed with imperviousness. These findings underscore the need for increased vegetation for heat mitigation.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101400"},"PeriodicalIF":3.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720417","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":"Integrated multi-satellite data and machine learning approach in mapping the successional stages of forest types in a tropical montane forest","authors":"Richard Dein D. Altarez ,&nbsp;Armando Apan ,&nbsp;Tek Maraseni","doi":"10.1016/j.rsase.2024.101407","DOIUrl":"10.1016/j.rsase.2024.101407","url":null,"abstract":"<div><div>Understanding the successional stages in tropical montane forests (TMF) is crucial for its conservation and management. This study integrated Sentinel-1, Sentinel-2, InSAR, GEDI, and machine learning to map the categorical successional stages of different forest types in a Philippines’ TMF. Field data collected from December 2022 to January 2023 were used to create and validate successional stages models. Sentinel-1 interferogram, unwrapped interferogram, and coherence exhibited moderate positive correlations with canopy height (r = 0.43). Incorporating GEDI with InSAR to predict canopy height yielded less accurate predictions (r = −0.20 to 0.04; RMSE = 12–13 m). Results show that canopy height, a widely accepted attribute for forest structure, appears secondary to other biophysical variables. Integrating optical, radar, and auxiliary variables achieved an overall accuracy of 79.56% and a kappa value of 75.74%. Feature importance analysis using Random Forest enhanced the overall accuracy (84.22%) and kappa value (81.19%). The integration of multi-satellite data with machine learning has proven effective for studying TMFs successional stages. Elevation emerged as the most significant predictor of forest type distribution, with mature and young pine forests dominating lower elevation (700–1,400m) and mossy forests dominating above 1,400m. Given the observed disturbances, the study underscores the need for robust conservation strategies and sustainable TMF management. Future research should focus on time-series analyses of successional stages, further optimization of machine learning models, and integrating additional data sources, such as LiDAR, to enhance canopy height predictions and forest monitoring efforts. The findings also provide valuable knowledge applicable to TMFs globally, supporting informed conservation and policies intended to protect biodiversity.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101407"},"PeriodicalIF":3.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748003","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":"A review of the global operational geostationary meteorological satellites","authors":"Ram Kumar Giri ,&nbsp;Satya Prakash ,&nbsp;Ramashray Yadav ,&nbsp;Nitesh Kaushik ,&nbsp;Munn Vinayak Shukla ,&nbsp;P.K. Thapliyal ,&nbsp;K.C. Saikrishnan","doi":"10.1016/j.rsase.2024.101403","DOIUrl":"10.1016/j.rsase.2024.101403","url":null,"abstract":"<div><div>Geostationary meteorological satellite data and products are proven to be indispensable in operational weather monitoring and forecasting for various sectorial applications and disaster risk reduction due to their large spatial coverage and spatio-temporally consistent availability. The meteorological instruments such as imager or radiometer and atmospheric sounder onboard these satellites have gone through incremental advancement in terms of accuracy, stability, and resolutions. In addition, new meteorological instruments such as lightning detection and ocean monitoring payloads have been developed in the recent decades. This paper reviews brief history of the global operational geostationary meteorological satellites and onboard meteorological instruments. The capability of currently available operational geostationary meteorological satellites is also highlighted. In order to prepare a global climate data record of geostationary satellite observations, well-calibrated data are essentially required from each operational satellite. The calibration exercises taken up by several satellite agencies under the Global Space-based Inter-Calibration System, and development of global and regional long-term inter-calibrated geostationary climate data records are briefly discussed. Moreover, expected meteorological instruments onboard the proposed next-generation geostationary satellites from different satellite agencies across the globe are summarized.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101403"},"PeriodicalIF":3.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703690","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 spatiotemporal surface water variability and drought conditions using remote sensing indices in the Kagera River Sub-Basin, Tanzania","authors":"Nickson Tibangayuka ,&nbsp;Deogratias M.M. Mulungu ,&nbsp;Fides Izdori","doi":"10.1016/j.rsase.2024.101405","DOIUrl":"10.1016/j.rsase.2024.101405","url":null,"abstract":"<div><div>Drought is one of the major challenges affecting water resources, agriculture, and ecosystem resilience in the sub-Saharan region. This study analyzed the spatial and temporal variation of surface water and drought conditions in the Kagera sub-basin using remote sensing indices: the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI). The analysis covered the period from 1985 to 2020 at 5-year intervals. The Standardized Precipitation Index (SPI) was utilized to assess rainfall anomalies, which were then compared with surface water variability and drought intensity indicated by remote-sensing indices. The SPI revealed multiple instances of extreme and severe drought, with higher frequencies observed in the 3-month and 6-month SPI compared to the 12-month SPI. The NDWI revealed significant spatial and temporal variations in surface water area in the Kagera sub-basin. In general, surface water area showed a mixed trend, decreasing from 660 km² in 1985 to 632 km² in 2000, and then gradually increasing to 698 km² in 2020. Additionally, the NDWI exhibited a strong correlation with 3-month and 6-month SPI but a weaker correlation with 12-month SPI. On the other hand, the NDVI indicated significant variations in drought conditions, with areas experiencing severe drought ranging between 446 km² and 1892 km². These severe drought events were prevalent from 1990 to 2000. The results also indicated a strong correlation between drought extent and intensity extracted from NDVI and rainfall anomalies, with SPI-3 and SPI-6 showing stronger correlations compared to SPI-12. Moreover, the SAVI results were consistent with those of NDVI, suggesting that the soil brightness effect on the NDVI is not significant in the sub-basin. In contrast, NDMI indicated that severe drought areas generally increased over the analyzed years and exhibited a weak correlation with SPI for all time scales. These findings contribute valuable insights that are important for decision-makers in managing surface water resources and implementing proactive and targeted environmental conservation measures to enhance ecosystem resilience in the Kagera sub-basin.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101405"},"PeriodicalIF":3.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703692","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}