气候变化领域集成服务门户(CCPortal): Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features

CCPortal

DOI	10.3390/rs16101786
	Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features
	Wen, Li; Mason, Tanya; Powell, Megan; Ling, Joanne; Ryan, Shawn; Bernich, Adam; Gufu, Guyo
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	10
卷号	16 期号:10
英文摘要	Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation, and climate change. Classification and mapping of wetlands in agricultural landscapes is crucial for conserving these ecosystems to maintain their ecological integrity amidst ongoing land-use changes and environmental pressures. This study aims to establish a robust framework for wetland classification and mapping in intensive agricultural landscapes using time series of Sentinel-2 imagery, with a focus on the Gwydir Wetland Complex situated in the northern Murray-Darling Basin-Australia's largest river system. Using the Google Earth Engine (GEE) platform, we extracted two groups of predictors based on six vegetation indices time series calculated from multi-temporal Sentinel-2 surface reflectance (SR) imagery: the first is statistical features summarizing the time series and the second is phenological features based on harmonic analysis of time series data (HANTS). We developed and evaluated random forest (RF) models for each level of classification with combination of different groups of predictors. Our results show that RF models involving both HANTS and statistical features perform strongly with significantly high overall accuracy and class-weighted F1 scores (p < 0.05) when comparing with models with either statistical or HANTS variables. While the models have excellent performance (F-score greater than 0.9) in distinguishing wetlands from other landcovers (croplands, terrestrial uplands, and open waters), the inter-class discriminating power among wetlands is class-specific: wetlands that are frequently inundated (including river red gum forests and wetlands dominated by common reed, water couch, and marsh club-rush) are generally better identified than the ones that are flooded less frequently, such as sedgelands and woodlands dominated by black box and coolabah. This study demonstrates that HANTS features extracted from time series Sentinel data can significantly improve the accuracy of wetland mapping in highly fragmentated agricultural landscapes. Thus, this framework enables wetland classification and mapping to be updated on a regular basis to better understand the dynamic nature of these complex ecosystems and improve long-term wetland monitoring.
英文关键词	wetland classification and mapping; Sentinel-2 time series; vegetation indices; HANTS; random forest; benchmark experiment
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001231775600001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/291643
作者单位	University of New South Wales Sydney; Macquarie University
推荐引用方式 GB/T 7714	Wen, Li,Mason, Tanya,Powell, Megan,et al. Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features[J],2024,16(10).
APA	Wen, Li.,Mason, Tanya.,Powell, Megan.,Ling, Joanne.,Ryan, Shawn.,...&Gufu, Guyo.(2024).Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features.REMOTE SENSING,16(10).
MLA	Wen, Li,et al."Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features".REMOTE SENSING 16.10(2024).