干旱区生态环境知识资源中心(Arid): Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space-Time Remote Sensing

Arid

DOI	10.3390/rs14143365
	Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space-Time Remote Sensing
	Hickey, Sharyn M.; Radford, Ben
通讯作者	Hickey, SM
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:14
英文摘要	Mangroves are a globally important ecosystem experiencing significant anthropogenic and climate impacts. Two subtypes of mangrove are particularly vulnerable to climate-induced impacts (1): tidally submerged forests and (2) those that occur in arid and semi-arid regions. These mangroves are either susceptible to sea level rise or occur in conditions close to their physiological limits of temperature and freshwater availability. The spatial extent and impacts on these mangroves are poorly documented, because they have structural and environmental characteristics that affect their ability to be detected with remote sensing models. For example, tidally submerged mangroves occur in areas with large tidal ranges, which limits their visibility at high tide, and arid mangroves have sparse canopy cover and a shorter stature that occur in fringing and narrow stands parallel to the coastline. This study introduced the multi-dimensional space-time randomForest method (MSTRF) that increases the detectability of these mangroves and applies this on the North-west Australian coastline where both mangrove types are prevalent. MSTRF identified an optimal four-year period that produced the most accurate model (Accuracy of 80%, Kappa value 0.61). This model was able to detect an additional 32% (76,048 hectares) of mangroves that were previously undocumented in other datasets. We detected more mangrove cover using this timeseries combination of annual median composite Landsat images derived from scenes across the whole tidal cycle but also over climatic cycles such as ENSO. The median composite images displayed less spectral differences in mangroves in the intertidal and arid zones compared to individual scenes where water was present during the tidal cycle or where the chlorophyll reflectance was low during hot and dry periods. We found that the MNDWI (Modified Normalised Water Index) and GCVI (Green Chlorophyll Vegetation Index) were the best predictors for deriving the mangrove layer using randomForest.
英文关键词	mangroves Landsat Google Earth Engine intertidal temporal
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000833201500001
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394167
推荐引用方式 GB/T 7714	Hickey, Sharyn M.,Radford, Ben. Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space-Time Remote Sensing[J],2022,14(14).
APA	Hickey, Sharyn M.,&Radford, Ben.(2022).Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space-Time Remote Sensing.REMOTE SENSING,14(14).
MLA	Hickey, Sharyn M.,et al."Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space-Time Remote Sensing".REMOTE SENSING 14.14(2022).