Remote sensing assessment of ecological quality of Baiyangdian wetland in response to extreme rainfall

IF 3.8 Q2 ENVIRONMENTAL SCIENCES Remote Sensing Applications-Society and Environment Pub Date : 2024-06-26 DOI:10.1016/j.rsase.2024.101284
Hongxing Luo , Yanmei Xu , Qi Han , Liqiu Zhang , Li Feng
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Abstract

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.

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白洋淀湿地生态质量对极端降雨的遥感评估
随着全球变暖的加剧,极端天气已成为威胁生态环境的主要挑战之一。如何快速、准确地检测和评估这些极端天气事件对生态系统的影响仍然是一项挑战。2023 年 7 月,超强台风 "杜苏芮 "造成的极端降雨袭击了华北地区,导致白洋淀湿地植被大量死亡。为快速评估白洋淀湿地的生态损失,本研究获取了降雨前后的无云遥感影像,通过基于遥感的生态指数(RSEI)量化生态环境质量,并与基于水效益的生态指数(WBEI)进行对比,然后进行空间自相关分析,揭示研究区域生态环境质量的空间异质性。结果表明,WBEI 从 0.50 降至 0.44,RSEI 从 0.68 降至 0.64。全球莫兰指数从 0.681 变为 0.801,表明生态环境质量的空间分布特征呈正相关。利用两种遥感指数准确捕捉并量化了极端降雨导致的生态环境质量恶化。此外,空间关联聚类图显示,极端降雨后,枣家店子区的高-高聚类消失,表明退耕还林湿地的生态恢复能力低于白洋淀天然湿地。这项研究为评估极端降水的影响提供了一个新的视角。通过量化生态环境质量的响应,为湿地生态保护工作提供了科学指导。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
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
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