Hongxing Luo , Yanmei Xu , Qi Han , Liqiu Zhang , Li Feng
{"title":"白洋淀湿地生态质量对极端降雨的遥感评估","authors":"Hongxing Luo , Yanmei Xu , Qi Han , Liqiu Zhang , Li Feng","doi":"10.1016/j.rsase.2024.101284","DOIUrl":null,"url":null,"abstract":"<div><p>As global warming intensifies, extreme weather has become one of the major challenges threatening the ecological environment. It remains challenging to detect and evaluate the effects of these extreme weather events on ecosystems quickly and accurately. In July 2023, extreme rainfall caused by Super Typhoon Doksuri hit North China, resulting in massive vegetation mortality in the Baiyangdian Wetland. To quickly assess the ecological loss of Baiyangdian wetland, this study obtained cloud-free remote sensing images before and after the rainfall, quantified the eco-environmental quality by RSEI (Remote Sensing-based Ecological Index) with the comparison of WBEI (Water Benefit-based Ecological Index); and then conducted spatial autocorrelation analysis to reveal the spatial heterogeneity of eco-environmental quality in the study area. The results showed that the WBEI decreased from 0.50 to 0.44 and the RSEI decreased from 0.68 to 0.64. The global Moran's Index varies from 0.681 to 0.801, demonstrating a positive correlation in the spatial distribution characteristics of eco-environmental quality. The deterioration of eco-environmental quality due to extreme rainfall was accurately captured and quantified using two remote sensing indices. Additionally, the cluster map of spatial association indicates that the High-High cluster in the sub-area Zaozhadian disappeared after the extreme rainfall, suggesting that the ecological resilience of the wetland returned from farmland was lower than that of the natural wetland in Baiyangdian. This study offers a new perspective on evaluating the impacts of extreme precipitation. By quantifying the response of eco-environmental quality, it provides scientific guidance for wetland ecological conservation efforts.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101284"},"PeriodicalIF":3.8000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Remote sensing assessment of ecological quality of Baiyangdian wetland in response to extreme rainfall\",\"authors\":\"Hongxing Luo , Yanmei Xu , Qi Han , Liqiu Zhang , Li Feng\",\"doi\":\"10.1016/j.rsase.2024.101284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>As global warming intensifies, extreme weather has become one of the major challenges threatening the ecological environment. It remains challenging to detect and evaluate the effects of these extreme weather events on ecosystems quickly and accurately. In July 2023, extreme rainfall caused by Super Typhoon Doksuri hit North China, resulting in massive vegetation mortality in the Baiyangdian Wetland. To quickly assess the ecological loss of Baiyangdian wetland, this study obtained cloud-free remote sensing images before and after the rainfall, quantified the eco-environmental quality by RSEI (Remote Sensing-based Ecological Index) with the comparison of WBEI (Water Benefit-based Ecological Index); and then conducted spatial autocorrelation analysis to reveal the spatial heterogeneity of eco-environmental quality in the study area. The results showed that the WBEI decreased from 0.50 to 0.44 and the RSEI decreased from 0.68 to 0.64. The global Moran's Index varies from 0.681 to 0.801, demonstrating a positive correlation in the spatial distribution characteristics of eco-environmental quality. The deterioration of eco-environmental quality due to extreme rainfall was accurately captured and quantified using two remote sensing indices. Additionally, the cluster map of spatial association indicates that the High-High cluster in the sub-area Zaozhadian disappeared after the extreme rainfall, suggesting that the ecological resilience of the wetland returned from farmland was lower than that of the natural wetland in Baiyangdian. This study offers a new perspective on evaluating the impacts of extreme precipitation. By quantifying the response of eco-environmental quality, it provides scientific guidance for wetland ecological conservation efforts.</p></div>\",\"PeriodicalId\":53227,\"journal\":{\"name\":\"Remote Sensing Applications-Society and Environment\",\"volume\":\"36 \",\"pages\":\"Article 101284\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-06-26\",\"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/S2352938524001484\",\"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/S2352938524001484","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Remote sensing assessment of ecological quality of Baiyangdian wetland in response to extreme rainfall
As global warming intensifies, extreme weather has become one of the major challenges threatening the ecological environment. It remains challenging to detect and evaluate the effects of these extreme weather events on ecosystems quickly and accurately. In July 2023, extreme rainfall caused by Super Typhoon Doksuri hit North China, resulting in massive vegetation mortality in the Baiyangdian Wetland. To quickly assess the ecological loss of Baiyangdian wetland, this study obtained cloud-free remote sensing images before and after the rainfall, quantified the eco-environmental quality by RSEI (Remote Sensing-based Ecological Index) with the comparison of WBEI (Water Benefit-based Ecological Index); and then conducted spatial autocorrelation analysis to reveal the spatial heterogeneity of eco-environmental quality in the study area. The results showed that the WBEI decreased from 0.50 to 0.44 and the RSEI decreased from 0.68 to 0.64. The global Moran's Index varies from 0.681 to 0.801, demonstrating a positive correlation in the spatial distribution characteristics of eco-environmental quality. The deterioration of eco-environmental quality due to extreme rainfall was accurately captured and quantified using two remote sensing indices. Additionally, the cluster map of spatial association indicates that the High-High cluster in the sub-area Zaozhadian disappeared after the extreme rainfall, suggesting that the ecological resilience of the wetland returned from farmland was lower than that of the natural wetland in Baiyangdian. This study offers a new perspective on evaluating the impacts of extreme precipitation. By quantifying the response of eco-environmental quality, it provides scientific guidance for wetland ecological conservation efforts.
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