DOI: 10.1016/j.jag.2017.06.010
Scopus记录号: 2-s2.0-85032195812
论文题名: Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure
作者: Zhang X ; , Treitz P ; M ; , Chen D ; , Quan C ; , Shi L ; , Li X
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 62 起始页码: 201
结束页码: 214
语种: 英语
英文关键词: Decision trees
; Mangrove forest
; Maximum likelihood
; Normalized Difference Moisture Index (NDMI)
; Normalized Difference Vegetation Index (NDVI)
; Tide level
Scopus关键词: accuracy assessment
; decision support system
; intertidal environment
; Landsat thematic mapper
; mangrove
; maximum likelihood analysis
; NDVI
; remote sensing
; satellite data
; scanning electron microscopy
; spatial distribution
; vegetation mapping
英文摘要: Mangrove forests grow in intertidal zones in tropical and subtropical regions and have suffered a dramatic decline globally over the past few decades. Remote sensing data, collected at various spatial resolutions, provide an effective way to map the spatial distribution of mangrove forests over time. However, the spectral signatures of mangrove forests are significantly affected by tide levels. Therefore, mangrove forests may not be accurately mapped with remote sensing data collected during a single-tidal event, especially if not acquired at low tide. This research reports how a decision-tree −based procedure was developed to map mangrove forests using multi-tidal Landsat 5 Thematic Mapper (TM) data and a Digital Elevation Model (DEM). Three indices, including the Normalized Difference Moisture Index (NDMI), the Normalized Difference Vegetation Index (NDVI) and NDVIL·NDMIH (the multiplication of NDVIL by NDMIH, L: low tide level, H: high tide level) were used in this algorithm to differentiate mangrove forests from other land-cover and land-use types in Fangchenggang City, China. Additionally, the recent Landsat 8 OLI (Operational Land Imager) data were selected to validate the results and compare if the methodology is reliable. The results demonstrate that short-term multi-tidal remotely-sensed data better represent the unique nearshore coastal wetland habitats of mangrove forests than single-tidal data. Furthermore, multi-tidal remotely-sensed data has led to improved accuracies using two classification approaches: i.e. decision trees and the maximum likelihood classification (MLC). Since mangrove forests are typically found at low elevations, the inclusion of elevation data in the two classification procedures was tested. Given the decision-tree method does not assume strict data distribution parameters, it was able to optimize the application of multi-tidal and elevation data, resulting in higher classification accuracies of mangrove forests. When using multi-source data of differing types and distributions to map mangrove forests, a decision-tree method appears to be superior to traditional statistical classifiers. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80002
Appears in Collections: 气候变化事实与影响
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作者单位: School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing, China; Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Meteorological Institute of Hebei Province, Shijiazhuang, China; Department of Geography and Planning, Queen's University, Kingston, ON, Canada
Recommended Citation:
Zhang X,, Treitz P,M,et al. Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,62