Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods
文献类型： 外文期刊
作者： Dronova, Iryna 1 ; Gong, Peng 1 ; Clinton, Nicholas E. 4 ; Wang, Lin 2 ; Fu, Wei 2 ; Qi, Shuhua 6 ; Liu, Ying 7 ;
作者机构： 1.Univ Calif Berkeley, Dept Environm Sci Policy & Management, Div Ecosyst Sci, Berkeley, CA 94720 USA
2.Chinese Acad Sci, Inst Remote Sensing Applicat, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
3.Beijing Normal Univ, Beijing 100101, Peoples R China
4.Tsinghua Univ, Ctr Earth Syst Sci, Beijing 100084, Peoples R China
5.Chinese Acad Fishery Sci, Beijing 100141, Peoples R China
6.Jiangxi Normal Univ, Sch Geog & Environm, Nanchang 330027, Peoples R China
7.Jiangxi Normal Univ, Sch Geog & Environm, Na
关键词： Accuracy;Classification;Heterogeneity;Landsat;Landscape;Machine-learning;OBIA;Object-based;Plant functional types;Poyang Lake;China;Remote sensing;Texture;Uncertainty;Vegetation;Wetland
期刊名称：REMOTE SENSING OF ENVIRONMENT （ 影响因子：10.164； 五年影响因子：11.057 ）
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收录情况： SCI
摘要： Remote sensing-based analyses of vegetation function such as photosynthesis and productivity are challenging in wetlands with complex cover and difficult field access. Recent advances in object-based image analysis (OBIA) and machine-learning algorithms offer new image classification tools; however, few comparisons of different approaches have been discussed to date. We applied OBIA to delineate wetland plant functional types (PFTs) for Poyang Lake, the largest freshwater lake in China and Ramsar wetland conservation site, from a spring 2008 Landsat TM image. We targeted major PFTs that represent dominant vegetation groups along wetland inundation gradients and affect ecosystem biogeochemical cycles and ecological habitats. Classification results were compared among: a) several "small" object segmentation scales (with average object sizes 1350-9000m 2); b) algorithms from six families of statistical machine-learning classifiers (Bayesian, Logistic, Neural Network, Decision Trees, K-Nearest Neighbors and Support Vector Machines) and c) two hierarchical levels of vegetation classification, a generalized 3-class set and a more specific 6-class set. We also examined the response of classification accuracy to four basic object-level texture metrics. The highest accuracies (>85-90%) and best agreement among algorithms occurred at coarser object scales rather than close-to-pixel scales. No single machine-learning algorithm was consistently superior at all scales, although support vector machine, k-nearest neighbor and artificial neural network most frequently provided the highest overall and PFT-specific accuracies. Including texture metrics had both positive and negative low-magnitude effects on classification accuracy that were not consistent among scale values, algorithms or PFT classes. Individual PFTs differed in scales at which they were best discriminated from others, reflecting their unique landscape positions, ecology of dominant species and disturbance agents. There was a 29-35% disagreement between mapped areas of generalized PFTs and their respective subclasses, suggesting potential mismatches between the ecological classification scheme and PFT landscape patch structure, and raising concern on error propagation in multi-scale classifications. We conclude that OBIA with machine-learning classifiers is useful for landscape vegetation analyses, however, considerations of spatial scale and image segmentation outcomes are critical in mapping PFTs and should be more thoroughly investigated in future work.
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