Selena Chavez , Shimon Wdowinski , David Lagomasino , Edward Castañeda-Moya
{"title":"监测南佛罗里达大沼泽地红树林对季节变化、飓风扰动和恢复的响应:2013-2023年Landsat-8十年观测的时空分析","authors":"Selena Chavez , Shimon Wdowinski , David Lagomasino , Edward Castañeda-Moya","doi":"10.1016/j.rsase.2025.101688","DOIUrl":null,"url":null,"abstract":"<div><div>Mangrove forests play a critical role in coastal ecosystems by buffering shorelines against the destructive forces of storms and storm surges, but in doing so, they often endure significant structural damage, including defoliation, tree snapping, and branch loss. Using decade-long remote sensing Landsat 8 data, we calculated the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to assess patterns and trends within the decade-long time series for each index in mangrove forests of southwestern Florida Everglades. Before calculating NDVI and NDMI, we cloud-filtered and calculated the monthly spectral means of the study region from March 2013 to March 2023. Using both NDVI and NDMI, we found seasonal variations in the value of both indices, in which the value increased during the wet season and decreased during the dry season of the Everglades. We also detected the impact of Hurricane Irma on mangroves in 2017 due to a sudden drop in the indices’ values right after the storm. The time series showed a slow recovery of indices values compared to pre-storm values. Using an exponential recovery model, we calculated that most mangrove areas recovered within two to four years. However, some small mangrove areas show no recovery, which we attribute to saltwater ponding and areas without freshwater flow and hydrological connectivity.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101688"},"PeriodicalIF":4.5000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Monitoring mangrove responses to seasonal changes, hurricane-induced disturbance, and recovery in the South Florida Everglades: A spatio-temporal analysis of decade-long (2013–2023) Landsat-8 observations\",\"authors\":\"Selena Chavez , Shimon Wdowinski , David Lagomasino , Edward Castañeda-Moya\",\"doi\":\"10.1016/j.rsase.2025.101688\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Mangrove forests play a critical role in coastal ecosystems by buffering shorelines against the destructive forces of storms and storm surges, but in doing so, they often endure significant structural damage, including defoliation, tree snapping, and branch loss. Using decade-long remote sensing Landsat 8 data, we calculated the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to assess patterns and trends within the decade-long time series for each index in mangrove forests of southwestern Florida Everglades. Before calculating NDVI and NDMI, we cloud-filtered and calculated the monthly spectral means of the study region from March 2013 to March 2023. Using both NDVI and NDMI, we found seasonal variations in the value of both indices, in which the value increased during the wet season and decreased during the dry season of the Everglades. We also detected the impact of Hurricane Irma on mangroves in 2017 due to a sudden drop in the indices’ values right after the storm. The time series showed a slow recovery of indices values compared to pre-storm values. Using an exponential recovery model, we calculated that most mangrove areas recovered within two to four years. However, some small mangrove areas show no recovery, which we attribute to saltwater ponding and areas without freshwater flow and hydrological connectivity.</div></div>\",\"PeriodicalId\":53227,\"journal\":{\"name\":\"Remote Sensing Applications-Society and Environment\",\"volume\":\"39 \",\"pages\":\"Article 101688\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing Applications-Society and Environment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352938525002411\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Applications-Society and Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352938525002411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Monitoring mangrove responses to seasonal changes, hurricane-induced disturbance, and recovery in the South Florida Everglades: A spatio-temporal analysis of decade-long (2013–2023) Landsat-8 observations
Mangrove forests play a critical role in coastal ecosystems by buffering shorelines against the destructive forces of storms and storm surges, but in doing so, they often endure significant structural damage, including defoliation, tree snapping, and branch loss. Using decade-long remote sensing Landsat 8 data, we calculated the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to assess patterns and trends within the decade-long time series for each index in mangrove forests of southwestern Florida Everglades. Before calculating NDVI and NDMI, we cloud-filtered and calculated the monthly spectral means of the study region from March 2013 to March 2023. Using both NDVI and NDMI, we found seasonal variations in the value of both indices, in which the value increased during the wet season and decreased during the dry season of the Everglades. We also detected the impact of Hurricane Irma on mangroves in 2017 due to a sudden drop in the indices’ values right after the storm. The time series showed a slow recovery of indices values compared to pre-storm values. Using an exponential recovery model, we calculated that most mangrove areas recovered within two to four years. However, some small mangrove areas show no recovery, which we attribute to saltwater ponding and areas without freshwater flow and hydrological connectivity.
期刊介绍:
The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems