Pub Date : 2026-01-01DOI: 10.1016/j.rsase.2025.101850
Saoussen Belhadj-aissa , Marc Simard , Adriana Parra Ruiz , Jordi Palacios , Sergio Fagherazzi
Coastal wetlands are highly vulnerable to climate change and sea level rise. Hydroperiod, defined as the duration of flooding, is a key indicator of salt marsh resilience, influencing vegetation zonation and health, sediment deposition, and overall ecosystem stability. This study uses Synthetic Aperture Radar (SAR) time series analysis to map hydroperiod in the salt marshes of Plum Island Sound, Massachusetts, USA. We integrate in situ water level measurements to overcome the limited temporal sampling of SAR observations. SAR-derived hydroperiod was evaluated against a ‘bathtub’ model that simulates flooding by filling a LiDAR-derived digital terrain model (DTM) including bathymetry and topography. The method shows strong agreement in hydroperiod estimates (, ). These findings demonstrate the capability of SAR time series to provide high-resolution, spatially extensive estimates of hydroperiod. We anticipate that this method will enable large-scale monitoring of seasonal and interannual variations in saltmarsh hydrology, supporting assessments of wetland vulnerability and resilience in the face of accelerating sea-level rise.
{"title":"Mapping salt marsh hydroperiod using Synthetic Aperture Radar time series","authors":"Saoussen Belhadj-aissa , Marc Simard , Adriana Parra Ruiz , Jordi Palacios , Sergio Fagherazzi","doi":"10.1016/j.rsase.2025.101850","DOIUrl":"10.1016/j.rsase.2025.101850","url":null,"abstract":"<div><div>Coastal wetlands are highly vulnerable to climate change and sea level rise. Hydroperiod, defined as the duration of flooding, is a key indicator of salt marsh resilience, influencing vegetation zonation and health, sediment deposition, and overall ecosystem stability. This study uses Synthetic Aperture Radar (SAR) time series analysis to map hydroperiod in the salt marshes of Plum Island Sound, Massachusetts, USA. We integrate in situ water level measurements to overcome the limited temporal sampling of SAR observations. SAR-derived hydroperiod was evaluated against a ‘bathtub’ model that simulates flooding by filling a LiDAR-derived digital terrain model (DTM) including bathymetry and topography. The method shows strong agreement in hydroperiod estimates (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>92</mn></mrow></math></span>, <span><math><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi><mo>≈</mo><mn>12</mn><mo>.</mo><mn>3</mn><mtext>%</mtext></mrow></math></span>). These findings demonstrate the capability of SAR time series to provide high-resolution, spatially extensive estimates of hydroperiod. We anticipate that this method will enable large-scale monitoring of seasonal and interannual variations in saltmarsh hydrology, supporting assessments of wetland vulnerability and resilience in the face of accelerating sea-level rise.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101850"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884537","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}
Mangrove forests are increasingly threatened by human activities such as aquaculture, agriculture, urban development, and illegal logging. Monitoring these dynamic changes requires accurate and efficient methods. However, traditional change detection approaches typically involve multi-step processes which can be time-consuming and prone to errors. Most existing deep learning models combined with remote sensing have shown great potential for environmental monitoring but are limited to binary classification (change and no change), making it difficult to capture specific land cover transitions such as mangrove gain or loss. To address these limitations, this study introduces DECDNet (Dual Encoder Change Detection Network), a novel deep learning model specifically designed for detecting and mapping mangrove gain and loss using Sentinel-2 imagery. The model utilizes a dual encoder-decoder structure that extracts spatial features from two time points and compares them using a subtraction layer. DECDNet was trained on Sentinel-2 data from 2015 to 2020, incorporating spectral indices to enhance discrimination. As a result, DECDNet achieved superior performance, with an IoU of 0.87, F1 score of 0.93, precision of 0.94, and recall of 0.92. In comparison, the standard deep learning models U-Net and FCN produced IoU values of 0.84 and 0.84, F1 scores of 0.91 and 0.91, precision values of 0.92 and 0.93, and recall values of 0.90 and 0.89, respectively. The generalization capability of DECDNet was further confirmed on a separate 2020–2023 dataset. The model detected 204.22 ha of mangrove loss and 747.09 ha of gain (2015–2020), and 463.48 ha of loss with 48.36 ha of gain (2020–2023) in the Wunbaik Reserved Mangrove Forest. These findings highlight practical implementation of DECDNet as a robust and scalable tool for mangrove monitoring and management.
{"title":"DECDNet: A dual encoder change detection network for monitoring mangrove gain and loss using Sentinel-2 data","authors":"Win Sithu Maung , Satoshi Tsuyuki , Takuya Hiroshima","doi":"10.1016/j.rsase.2025.101867","DOIUrl":"10.1016/j.rsase.2025.101867","url":null,"abstract":"<div><div>Mangrove forests are increasingly threatened by human activities such as aquaculture, agriculture, urban development, and illegal logging. Monitoring these dynamic changes requires accurate and efficient methods. However, traditional change detection approaches typically involve multi-step processes which can be time-consuming and prone to errors. Most existing deep learning models combined with remote sensing have shown great potential for environmental monitoring but are limited to binary classification (change and no change), making it difficult to capture specific land cover transitions such as mangrove gain or loss. To address these limitations, this study introduces DECDNet (Dual Encoder Change Detection Network), a novel deep learning model specifically designed for detecting and mapping mangrove gain and loss using Sentinel-2 imagery. The model utilizes a dual encoder-decoder structure that extracts spatial features from two time points and compares them using a subtraction layer. DECDNet was trained on Sentinel-2 data from 2015 to 2020, incorporating spectral indices to enhance discrimination. As a result, DECDNet achieved superior performance, with an IoU of 0.87, F1 score of 0.93, precision of 0.94, and recall of 0.92. In comparison, the standard deep learning models U-Net and FCN produced IoU values of 0.84 and 0.84, F1 scores of 0.91 and 0.91, precision values of 0.92 and 0.93, and recall values of 0.90 and 0.89, respectively. The generalization capability of DECDNet was further confirmed on a separate 2020–2023 dataset. The model detected 204.22 ha of mangrove loss and 747.09 ha of gain (2015–2020), and 463.48 ha of loss with 48.36 ha of gain (2020–2023) in the Wunbaik Reserved Mangrove Forest. These findings highlight practical implementation of DECDNet as a robust and scalable tool for mangrove monitoring and management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101867"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926025","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}
Pub Date : 2026-01-01DOI: 10.1016/j.rsase.2025.101860
Prabesh Khatiwada , Pragya Khatiwada , Him Lal Shrestha
Flooding is one of the most devastating natural disasters in Nepal, causing significant socioeconomic losses annually. However, existing studies on multi-temporal and regional-scale flood dynamics are scarce, limiting effective disaster management. In this study, we conducted one of the first long-term, district-level flood mapping studies in Nepal and analyzed the flood dynamics from 2019 to 2024 of three flood-prone districts, Parsa, Bara, and Rautahat. Using multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) and CHIRPS rainfall data in Google Earth Engine (GEE), we produced monthly flood maps and evaluated both flood dynamics and the relationship between the short-term cumulative rainfall and flood extent. Our results indicate that the densely populated southern region of the study area is frequently affected by flooding, with two extreme events exceeding 285 km2. Flood maps from 2019 to 2024 revealed both monthly and annual variations in flooding, with 5.94 % of the study area being inundated 3–4 times. A strong positive correlation between the 3-day cumulative rainfall and flooded area was observed, with >130 mm identified as a preliminary threshold for major events. The regional-scale SAR-based flood mapping in Nepal improves our understanding of the flood patterns, which is significant for developing data-driven mitigation measures and targeted flood risk management strategies to reduce socioeconomic impacts in data-scarce regions.
{"title":"Multi-temporal flood mapping and dynamics in Nepal's Terai (2019–2024) using Sentinel-1 SAR and change-detection approaches","authors":"Prabesh Khatiwada , Pragya Khatiwada , Him Lal Shrestha","doi":"10.1016/j.rsase.2025.101860","DOIUrl":"10.1016/j.rsase.2025.101860","url":null,"abstract":"<div><div>Flooding is one of the most devastating natural disasters in Nepal, causing significant socioeconomic losses annually. However, existing studies on multi-temporal and regional-scale flood dynamics are scarce, limiting effective disaster management. In this study, we conducted one of the first long-term, district-level flood mapping studies in Nepal and analyzed the flood dynamics from 2019 to 2024 of three flood-prone districts, Parsa, Bara, and Rautahat. Using multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) and CHIRPS rainfall data in Google Earth Engine (GEE), we produced monthly flood maps and evaluated both flood dynamics and the relationship between the short-term cumulative rainfall and flood extent. Our results indicate that the densely populated southern region of the study area is frequently affected by flooding, with two extreme events exceeding 285 km<sup>2</sup>. Flood maps from 2019 to 2024 revealed both monthly and annual variations in flooding, with 5.94 % of the study area being inundated 3–4 times. A strong positive correlation between the 3-day cumulative rainfall and flooded area was observed, with >130 mm identified as a preliminary threshold for major events. The regional-scale SAR-based flood mapping in Nepal improves our understanding of the flood patterns, which is significant for developing data-driven mitigation measures and targeted flood risk management strategies to reduce socioeconomic impacts in data-scarce regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101860"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926034","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}
Pub Date : 2026-01-01DOI: 10.1016/j.rsase.2025.101863
Adriana Bilar Chaquime dos Santos , Patricia Pedrozo Lamberti , Deimison Rodrigues Oliveira , Micaella Lima Nogueira , Cesar Ivan Alvarez , Reginaldo Brito da Costa
Accurate estimation of vegetation carbon stocks is essential for monitoring climate change impacts, assessing ecosystem services, and informing global mitigation strategies. In recent years, the integration of remote sensing techniques with cloud-based platforms—particularly Google Earth Engine (GEE)—has transformed how vegetation dynamics and carbon fluxes are analyzed, largely through the widespread use of the Normalized Difference Vegetation Index (NDVI). This study presents a comprehensive bibliometric and thematic review of global research trends in vegetation carbon stock monitoring using GEE and NDVI, covering 91 peer-reviewed articles published between 2017 and early 2024. Analyses were conducted using the Bibliometrix R package and included publication patterns, leading contributors, geographic distribution, keyword evolution, sensor usage, and collaborative networks. Results indicate a substantial increase in scientific output since 2017, with China, the United States, and Brazil emerging as leading contributors. Most studies relied on MODIS, Landsat, and Sentinel-2 imagery within GEE workflows, with a growing trend toward multi-sensor integration and machine learning applications. Despite technical advancements, the review identifies persistent gaps in policy integration, in-situ validation, and geographic representation—particularly in carbon-rich but underrepresented regions of the Global South. We conclude by recommending enhanced international collaboration, expanded ground-truth validation efforts, and stronger alignment with climate policy instruments such as REDD+ and the Sustainable Development Goals (SDGs). This review provides a structured synthesis of the current state of GEE-based carbon monitoring research and highlights key opportunities to increase its scientific impact and policy relevance.
{"title":"Global trends in vegetation carbon stock monitoring using Google Earth Engine and NDVI: A systematic review (2017–2024)","authors":"Adriana Bilar Chaquime dos Santos , Patricia Pedrozo Lamberti , Deimison Rodrigues Oliveira , Micaella Lima Nogueira , Cesar Ivan Alvarez , Reginaldo Brito da Costa","doi":"10.1016/j.rsase.2025.101863","DOIUrl":"10.1016/j.rsase.2025.101863","url":null,"abstract":"<div><div>Accurate estimation of vegetation carbon stocks is essential for monitoring climate change impacts, assessing ecosystem services, and informing global mitigation strategies. In recent years, the integration of remote sensing techniques with cloud-based platforms—particularly Google Earth Engine (GEE)—has transformed how vegetation dynamics and carbon fluxes are analyzed, largely through the widespread use of the Normalized Difference Vegetation Index (NDVI). This study presents a comprehensive bibliometric and thematic review of global research trends in vegetation carbon stock monitoring using GEE and NDVI, covering 91 peer-reviewed articles published between 2017 and early 2024. Analyses were conducted using the Bibliometrix R package and included publication patterns, leading contributors, geographic distribution, keyword evolution, sensor usage, and collaborative networks. Results indicate a substantial increase in scientific output since 2017, with China, the United States, and Brazil emerging as leading contributors. Most studies relied on MODIS, Landsat, and Sentinel-2 imagery within GEE workflows, with a growing trend toward multi-sensor integration and machine learning applications. Despite technical advancements, the review identifies persistent gaps in policy integration, in-situ validation, and geographic representation—particularly in carbon-rich but underrepresented regions of the Global South. We conclude by recommending enhanced international collaboration, expanded ground-truth validation efforts, and stronger alignment with climate policy instruments such as REDD+ and the Sustainable Development Goals (SDGs). This review provides a structured synthesis of the current state of GEE-based carbon monitoring research and highlights key opportunities to increase its scientific impact and policy relevance.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101863"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926095","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}
Pub Date : 2026-01-01DOI: 10.1016/j.rsase.2026.101868
Zifeng Liu, Qiang Zhang, Baomo Zhang, Jian Zhu
In recent years, wildfires have occurred frequently around the world, which not only greatly threaten the social security, but also cause serious pollution to the environment. Currently, remote sensing satellites can monitor wildfires with a large range and long time-series data. These satellites can be divided into two types: geostationary satellites (such as Himawari-8/9) and polar-orbiting satellites (such as MODIS/VIIRS). In this paper, MODIS and VIIRS polar-orbiting satellite wildfire products from 2015 to 2022 are used as the comparative data, to verify the accuracy and effectiveness of Himawari-8/9 10-minute near real-time wildfire products. Firstly, we utilize the polar orbit satellite data to analyze the wildfire detection accuracy as well as the fire radiative power (FRP) estimation ability for Himawari-8/9. Secondly, we compare the new (Himawari-9) with old (Himawari-8) satellite sensors on wildfire detection ability. The results show that, due to the advantage of high temporal resolution, Himawari-8/9 wildfire products have more fire hotspot numbers in the same range than MODIS and VIIRS. Due to the insufficient spatial resolution at 2-km level, the omission rate of Himawari-8/9 wildfire products comparison with VIIRS is higher than that of comparison with MODIS. Especially in spring and summer, there is a peak period of Himawari-8/9 omission rate. For large wildfires, Himawari-8/9 wildfire products show a lower omission rate and stronger detection capability. However, in terms of FRP retrieval, due to the difference in spatial resolution of different sensors, the Himawari-8/9 wildfire products contrastive with MODIS show a smaller difference than its contrastive with VIIRS. In addition, Himawari-9 unexpectedly shows slightly weaker fire hotspot detection capability than Himawari-8. Finally, based on above validation works, this paper provides the more effective algorithm design idea, for the near real-time wildfire detection of geostationary satellites.
{"title":"Validation of Himawari-8/9 10-minute wildfire products: Comparisons with MODIS and VIIRS from 2015 to 2023","authors":"Zifeng Liu, Qiang Zhang, Baomo Zhang, Jian Zhu","doi":"10.1016/j.rsase.2026.101868","DOIUrl":"10.1016/j.rsase.2026.101868","url":null,"abstract":"<div><div>In recent years, wildfires have occurred frequently around the world, which not only greatly threaten the social security, but also cause serious pollution to the environment. Currently, remote sensing satellites can monitor wildfires with a large range and long time-series data. These satellites can be divided into two types: geostationary satellites (such as Himawari-8/9) and polar-orbiting satellites (such as MODIS/VIIRS). In this paper, MODIS and VIIRS polar-orbiting satellite wildfire products from 2015 to 2022 are used as the comparative data, to verify the accuracy and effectiveness of Himawari-8/9 10-minute near real-time wildfire products. Firstly, we utilize the polar orbit satellite data to analyze the wildfire detection accuracy as well as the fire radiative power (FRP) estimation ability for Himawari-8/9. Secondly, we compare the new (Himawari-9) with old (Himawari-8) satellite sensors on wildfire detection ability. The results show that, due to the advantage of high temporal resolution, Himawari-8/9 wildfire products have more fire hotspot numbers in the same range than MODIS and VIIRS. Due to the insufficient spatial resolution at 2-km level, the omission rate of Himawari-8/9 wildfire products comparison with VIIRS is higher than that of comparison with MODIS. Especially in spring and summer, there is a peak period of Himawari-8/9 omission rate. For large wildfires, Himawari-8/9 wildfire products show a lower omission rate and stronger detection capability. However, in terms of FRP retrieval, due to the difference in spatial resolution of different sensors, the Himawari-8/9 wildfire products contrastive with MODIS show a smaller difference than its contrastive with VIIRS. In addition, Himawari-9 unexpectedly shows slightly weaker fire hotspot detection capability than Himawari-8. Finally, based on above validation works, this paper provides the more effective algorithm design idea, for the near real-time wildfire detection of geostationary satellites.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101868"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884539","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}
Pub Date : 2026-01-01DOI: 10.1016/j.rsase.2025.101859
Nilufa Akhtar , Shiro Tsuyuzaki
Tropical cyclones (TCs) pose a major disturbance to mangroves; however, the long-term effects of TCs on large-scale mangrove forests remain unclear. TC-induced disturbances in the Sundarbans mangrove forest of Bangladesh from 1988 to 2024 were quantified by remote sensing. Mangroves were mapped through the enhanced vegetation index (EVI) from Landsat reflectance data (30 m resolution) via Google Earth Engine. Spatiotemporal changes in EVI over 36 years were evaluated using the Mann-Kendall test and Theil-Sen median trend analysis. Forest disturbances were assessed by comparing pre- and post-EVI of 32 TCs. Forest disturbances were analyzed with wind speed and track distance of TCs, temperature, precipitation and elevation by structural equation modeling (SEM). Interannual changes in EVI indicated TC-induced forest disturbances, estimating a decline of 0.2 %–22 % of the total 5980 km2 forest area following TCs. SEM revealed wind speed as the highest predictor of forest disturbances, with direct positive relation with disturbed forest areas. TC track distance from the centroid and precipitation were related to forest disturbances, highlighting the sensitivity of mangroves vegetation to strong TCs making landfall with increased precipitation, likely decrease EVI afterward a TC strikes through defoliation, trunk and stem breakage, along with uprooted trees. While mangroves showed revegetation within a few years post-TC, strong and frequent TCs should hinder regeneration, threatening mangrove forests and their critical role in coastal bio-protection. These findings underscore the expected effects of TC wind speeds and increased exposure on mangroves, thereby encouraging sustainable conservation and restoration strategies to prioritize mangrove forest resilience.
{"title":"The effects of disturbances derived by tropical cyclones on mangrove forests in Bangladesh","authors":"Nilufa Akhtar , Shiro Tsuyuzaki","doi":"10.1016/j.rsase.2025.101859","DOIUrl":"10.1016/j.rsase.2025.101859","url":null,"abstract":"<div><div>Tropical cyclones (TCs) pose a major disturbance to mangroves; however, the long-term effects of TCs on large-scale mangrove forests remain unclear. TC-induced disturbances in the Sundarbans mangrove forest of Bangladesh from 1988 to 2024 were quantified by remote sensing. Mangroves were mapped through the enhanced vegetation index (EVI) from Landsat reflectance data (30 m resolution) via Google Earth Engine. Spatiotemporal changes in EVI over 36 years were evaluated using the Mann-Kendall test and Theil-Sen median trend analysis. Forest disturbances were assessed by comparing pre- and post-EVI of 32 TCs. Forest disturbances were analyzed with wind speed and track distance of TCs, temperature, precipitation and elevation by structural equation modeling (SEM). Interannual changes in EVI indicated TC-induced forest disturbances, estimating a decline of 0.2 %–22 % of the total 5980 km<sup>2</sup> forest area following TCs. SEM revealed wind speed as the highest predictor of forest disturbances, with direct positive relation with disturbed forest areas. TC track distance from the centroid and precipitation were related to forest disturbances, highlighting the sensitivity of mangroves vegetation to strong TCs making landfall with increased precipitation, likely decrease EVI afterward a TC strikes through defoliation, trunk and stem breakage, along with uprooted trees. While mangroves showed revegetation within a few years post-TC, strong and frequent TCs should hinder regeneration, threatening mangrove forests and their critical role in coastal bio-protection. These findings underscore the expected effects of TC wind speeds and increased exposure on mangroves, thereby encouraging sustainable conservation and restoration strategies to prioritize mangrove forest resilience.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101859"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926026","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}
Pub Date : 2026-01-01DOI: 10.1016/j.rsase.2025.101846
Bruno Lima da Silva , Antonio Henrique Cordeiro Ramalho , Nilton Cesar Fiedler , Lia de Oliveira Melo , Fernanda Dalfiôr Maffioletti , Leonardo Seibert Kuhn , Daiane de Moura Borges Maria , Evandro Ferreira da Silva , Vinícius Duarte Nader Mardeni , Duanne Karine dos Anjos Colares , Flávio Hebert da Silva Fonseca , Hana Saiumy Favacho dos Santos , Wesley Lopes Pinto , José Maria Franco Santos Júnior , João Gabriel Ferreira Colares
The spatial modeling of forest fire risk in tropical environments requires methods capable of integrating multiple variables and addressing the inherent uncertainties of environmental systems. This study presents an innovative approach that combines fuzzy logic with Geographic Information System layers to conduct seasonal forest fire risk zoning in the Tapajós National Forest, located in the state of Pará, within the Brazilian Amazon. Physical, socioeconomic, meteorological, and land use and land cover variables were integrated. The model employed the Fuzzy Gamma overlay method and was validated through a chi-square test and comparison with actual burned area data. The “high” and “very high” risk classes represented 66.4 % in the first quarter, 77.49 % in the second, 65.15 % in the third, and 59.77 % in the fourth. Temperature, water vapor pressure, precipitation, and proximity to anthropized areas were identified as the most influential variables. The results demonstrated high accuracy in predicting areas classified as “high” and “very high” risk, with values exceeding 90 % in three of the four analyzed quarters. The integration of fuzzy logic and GIS proved to be an effective and adaptable framework for analyzing the quantitative and seasonal variability of fire risk. Therefore, the proposed model serves as a strategic tool for environmental management and the development of integrated fire management plans. By providing early-warning capabilities and essential information to support decision-making in the control and mitigation of forest fires, it contributes to more proactive prevention strategies and promotes a safer and more sustainable future for the Amazon.
{"title":"Fuzzy modeling in a GIS environment for identifying the seasonality of forest fire risk in a protected area in the Brazilian Amazon","authors":"Bruno Lima da Silva , Antonio Henrique Cordeiro Ramalho , Nilton Cesar Fiedler , Lia de Oliveira Melo , Fernanda Dalfiôr Maffioletti , Leonardo Seibert Kuhn , Daiane de Moura Borges Maria , Evandro Ferreira da Silva , Vinícius Duarte Nader Mardeni , Duanne Karine dos Anjos Colares , Flávio Hebert da Silva Fonseca , Hana Saiumy Favacho dos Santos , Wesley Lopes Pinto , José Maria Franco Santos Júnior , João Gabriel Ferreira Colares","doi":"10.1016/j.rsase.2025.101846","DOIUrl":"10.1016/j.rsase.2025.101846","url":null,"abstract":"<div><div>The spatial modeling of forest fire risk in tropical environments requires methods capable of integrating multiple variables and addressing the inherent uncertainties of environmental systems. This study presents an innovative approach that combines fuzzy logic with Geographic Information System layers to conduct seasonal forest fire risk zoning in the Tapajós National Forest, located in the state of Pará, within the Brazilian Amazon. Physical, socioeconomic, meteorological, and land use and land cover variables were integrated. The model employed the Fuzzy Gamma overlay method and was validated through a chi-square test and comparison with actual burned area data. The “high” and “very high” risk classes represented 66.4 % in the first quarter, 77.49 % in the second, 65.15 % in the third, and 59.77 % in the fourth. Temperature, water vapor pressure, precipitation, and proximity to anthropized areas were identified as the most influential variables. The results demonstrated high accuracy in predicting areas classified as “high” and “very high” risk, with values exceeding 90 % in three of the four analyzed quarters. The integration of fuzzy logic and GIS proved to be an effective and adaptable framework for analyzing the quantitative and seasonal variability of fire risk. Therefore, the proposed model serves as a strategic tool for environmental management and the development of integrated fire management plans. By providing early-warning capabilities and essential information to support decision-making in the control and mitigation of forest fires, it contributes to more proactive prevention strategies and promotes a safer and more sustainable future for the Amazon.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101846"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926021","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}
Pub Date : 2026-01-01DOI: 10.1016/j.rsase.2025.101866
Wan-Hsin Jen, Yi-Chin Chen
Landslide and debris-flows are significant natural hazards worldwide, and their long-term geomorphic changes pose major challenges for hazard assessment. SfM-MVS photogrammetry applied to historical aerial photographs provides an opportunity to reconstruct multi-decadal topographic changes for better hazard assessment. In this study, we combine historical aerial and UAV imagery (1980–2023) with SfM-MVS photogrammetry and introduced a residual correction method to align DSMs to reconstruct four decades of topographic changes associated with a historical landslide and debris-flow hazard in Taiwan. The results show that correction reduced systematic DSM biases (up to ±10 m) to ∼2–3 m RMSE, substantially improving alignment between the DSMs. Multi-temporal analysis detects pre-failure slope deformation, revealing ∼6.6 m of escarpment subsidence prior to the event. The debris-flow mobilized 372 × 103 m3 of material, with 43.3 % derived from escarpment retreat and 56.7 % from colluvial deposits, resulting in 181 × 103 m3 of valley deposition. These deposits have remained largely stable, retaining 81.2 % of their volume over 37 years, although new crown cracks indicate ongoing gravitational deformation. This study highlights the application of historical photogrammetric reconstruction to provide early-warning indicators and quantitative hazard assessments in debris-flow-prone areas, offering valuable insights for disaster risk management.
{"title":"Photogrammetric reconstruction of multi-decadal topographic changes from historical aerial imagery for landslide and debris-flow hazard assessment","authors":"Wan-Hsin Jen, Yi-Chin Chen","doi":"10.1016/j.rsase.2025.101866","DOIUrl":"10.1016/j.rsase.2025.101866","url":null,"abstract":"<div><div>Landslide and debris-flows are significant natural hazards worldwide, and their long-term geomorphic changes pose major challenges for hazard assessment. SfM-MVS photogrammetry applied to historical aerial photographs provides an opportunity to reconstruct multi-decadal topographic changes for better hazard assessment. In this study, we combine historical aerial and UAV imagery (1980–2023) with SfM-MVS photogrammetry and introduced a residual correction method to align DSMs to reconstruct four decades of topographic changes associated with a historical landslide and debris-flow hazard in Taiwan. The results show that correction reduced systematic DSM biases (up to ±10 m) to ∼2–3 m RMSE, substantially improving alignment between the DSMs. Multi-temporal analysis detects pre-failure slope deformation, revealing ∼6.6 m of escarpment subsidence prior to the event. The debris-flow mobilized 372 × 10<sup>3</sup> m<sup>3</sup> of material, with 43.3 % derived from escarpment retreat and 56.7 % from colluvial deposits, resulting in 181 × 10<sup>3</sup> m<sup>3</sup> of valley deposition. These deposits have remained largely stable, retaining 81.2 % of their volume over 37 years, although new crown cracks indicate ongoing gravitational deformation. This study highlights the application of historical photogrammetric reconstruction to provide early-warning indicators and quantitative hazard assessments in debris-flow-prone areas, offering valuable insights for disaster risk management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101866"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926024","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}
Pub Date : 2026-01-01DOI: 10.1016/j.rsase.2026.101875
Abhista Fawwaz Sahitya , Muhammad Kamal , Nur Mohammad Farda
Mangroves are vital ecosystems that require careful monitoring to prevent degradation, particularly in terms of canopy cover. Deep learning techniques are favored for developing intricate models from remote sensing data. One promising approach is deep forest, which employs tree-based learning for mapping. However, it has yet to be fully utilized in mangrove research, especially for assessing canopy cover. This study aims to examine the hyperparameter tuning of the random forest and deep forest algorithms, test the influence of input variables on canopy cover mapping, and apply and evaluate the performance of both algorithms. The random forest (RF) and deep forest (DF) algorithms were applied to PlanetScope SuperDove imagery. Several simulations were conducted to identify the optimal model, employing hyperparameter tuning through grid search optimization and a thorough analysis of input variables. The DF algorithm achieved the highest accuracy at 93.23 %, while the RF algorithm attained 88.05 %, with maximum depth being a key parameter for both. However, under different input scenarios, the RF model outperformed DF, reaching an accuracy of 69.79 % compared to DF's 68.17 %. The texture variable and the transformation index proved essential for classifying mangrove canopy cover. Overall, both algorithms effectively map mangrove canopy cover, although further research is necessary to evaluate performance across various class numbers and geographic areas.
{"title":"Assessing the performance of random forest and deep forest algorithms for Mangrove canopy cover mapping using PlanetScope SuperDove imagery","authors":"Abhista Fawwaz Sahitya , Muhammad Kamal , Nur Mohammad Farda","doi":"10.1016/j.rsase.2026.101875","DOIUrl":"10.1016/j.rsase.2026.101875","url":null,"abstract":"<div><div>Mangroves are vital ecosystems that require careful monitoring to prevent degradation, particularly in terms of canopy cover. Deep learning techniques are favored for developing intricate models from remote sensing data. One promising approach is deep forest, which employs tree-based learning for mapping. However, it has yet to be fully utilized in mangrove research, especially for assessing canopy cover. This study aims to examine the hyperparameter tuning of the random forest and deep forest algorithms, test the influence of input variables on canopy cover mapping, and apply and evaluate the performance of both algorithms. The random forest (RF) and deep forest (DF) algorithms were applied to PlanetScope SuperDove imagery. Several simulations were conducted to identify the optimal model, employing hyperparameter tuning through grid search optimization and a thorough analysis of input variables. The DF algorithm achieved the highest accuracy at 93.23 %, while the RF algorithm attained 88.05 %, with maximum depth being a key parameter for both. However, under different input scenarios, the RF model outperformed DF, reaching an accuracy of 69.79 % compared to DF's 68.17 %. The texture variable and the transformation index proved essential for classifying mangrove canopy cover. Overall, both algorithms effectively map mangrove canopy cover, although further research is necessary to evaluate performance across various class numbers and geographic areas.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101875"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977178","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}
Pub Date : 2026-01-01DOI: 10.1016/j.rsase.2026.101874
Luis M. Hernández Beleño , Gregori de Arruda Moreira , Eliana Vergara-Vásquez , Yiniva Camargo Caicedo , David J. O'Connor , Andrés M. Vélez-Pereira
The interplay between emissions and atmospheric boundary-layer dynamics shapes urban air quality (AQ) in Colombia's complex topography. This study assesses the influence of the atmospheric boundary layer on AQ across contrasting physiographic regions. The ERA5 reanalysis dataset was used to obtain hourly ABLH and VC estimates for the period 2020–2024, while COSMIC-2 profiles were used to derive Temperature Elevation Profile (TEP) variables, including inversion-base height and thermal gradients. Urban AQ data from 78 monitoring stations were obtained from SISAIRE, focusing on PM10, PM2.5, and O3. The analysis combines exceedance rates (98th-percentile thresholds), diurnal and seasonal cycles, nonparametric correlations, and Gaussian linear models stratified by stable/unstable ABL conditions and dry/wet seasons. Our results show frequent exceedances in Antioquia and Bogotá, where PM2.5 daily exceedance medians reach 1.11 % and 0.87 %, respectively. Norte de Santander exhibits the highest PM2.5 median exceedance rate (7.18 %), while departments such as Cesar and Magdalena show low-to-moderate levels. O3 responses are strongly modulated by thermal structure, with direct associations between ABLH, inversion strength, and O3 peaks, particularly in high-elevation terrains. Physiography and circulation patterns explain regional contrasts, with stagnation-prone basins showing stronger pollution accumulation. We conclude that ventilation conditions strongly influence particulate pollution, whereas peak O3 is governed primarily by precursor emissions and temperature-driven photochemistry. These findings highlight the need for meteorology-aware AQ management strategies, especially in densely populated Andean basins.
{"title":"Influence of atmospheric boundary-layer dynamics on air quality of the middle- and high-density urban areas of Colombia","authors":"Luis M. Hernández Beleño , Gregori de Arruda Moreira , Eliana Vergara-Vásquez , Yiniva Camargo Caicedo , David J. O'Connor , Andrés M. Vélez-Pereira","doi":"10.1016/j.rsase.2026.101874","DOIUrl":"10.1016/j.rsase.2026.101874","url":null,"abstract":"<div><div>The interplay between emissions and atmospheric boundary-layer dynamics shapes urban air quality (AQ) in Colombia's complex topography. This study assesses the influence of the atmospheric boundary layer on AQ across contrasting physiographic regions. The ERA5 reanalysis dataset was used to obtain hourly ABLH and VC estimates for the period 2020–2024, while COSMIC-2 profiles were used to derive Temperature Elevation Profile (TEP) variables, including inversion-base height and thermal gradients. Urban AQ data from 78 monitoring stations were obtained from SISAIRE, focusing on PM<sub>10</sub>, PM<sub>2.5</sub>, and O<sub>3</sub>. The analysis combines exceedance rates (98th-percentile thresholds), diurnal and seasonal cycles, nonparametric correlations, and Gaussian linear models stratified by stable/unstable ABL conditions and dry/wet seasons. Our results show frequent exceedances in Antioquia and Bogotá, where PM<sub>2.5</sub> daily exceedance medians reach 1.11 % and 0.87 %, respectively. Norte de Santander exhibits the highest PM<sub>2.5</sub> median exceedance rate (7.18 %), while departments such as Cesar and Magdalena show low-to-moderate levels. O<sub>3</sub> responses are strongly modulated by thermal structure, with direct associations between ABLH, inversion strength, and O<sub>3</sub> peaks, particularly in high-elevation terrains. Physiography and circulation patterns explain regional contrasts, with stagnation-prone basins showing stronger pollution accumulation. We conclude that ventilation conditions strongly influence particulate pollution, whereas peak O<sub>3</sub> is governed primarily by precursor emissions and temperature-driven photochemistry. These findings highlight the need for meteorology-aware AQ management strategies, especially in densely populated Andean basins.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101874"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926096","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}
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