Abstract
Satellite data have been widely used in the detection of vegetation area changes, however, the lack of historical training samples seriously limits detection accuracy. In this research, an iterative intersection analysis algorithm (IIAA) is proposed to solve this problem, and employed to improve the change detection accuracy of Phragmites area in the Detroit River International Wildlife Refuge between 2001 and 2010. Training samples for 2001, 2005, and 2010 were constructed based on NAIP, DOQQ high-resolution imagery and ground-truth data; for 2002–2004 and 2006–2009, because of the shortage of training samples, the IIAA was employed to supply additional training samples. This method included three steps: first, the NDVI image for each year (2002–2004, 2006–2009) was calculated with Landsat TM images; secondly, rough patches of the land-cover were acquired by density slicing using suitable thresholds; thirdly, a GIS overlay analysis method was used to acquire the Phragmites information in common throughout the ten years and to obtain training patches. In the combination with training samples of other land cover types, supervised classifications were employed to detect the changes of Phragmites area. In the experiment, we analyzed the variation of Phragmites area from 2001 to 2010, and the result showed that its distribution areas increased from 5156 acres to 6817 acres during this period, which illustrated that the invasion of Phragmites remains a serious problem for the protection of biodiversity.
Highlights
Phragmites is an invasive species of particular concern in the wetlands of the North American Great Lakes [1,2]
The Great Lakes region has a long history of biological invasions with over 40% of established exotic species being wetland plants, it is not known exactly when the initial invasion started [4]
Combined with Landsat TM images, DOQQ (Digital Orthophoto Quarter Quads), images of the USGS (United States Geological Survey), and NAIP (National Agriculture Imagery Program) images of the USDA (United States Department of Agriculture), this paper presents an automatic sample recognition algorithm, to study the invasion and changing dynamics of Phragmites in the Detroit River International Wildlife Refuge (DRIWR)
Summary
Phragmites is an invasive species of particular concern in the wetlands of the North American Great Lakes [1,2]. At least 10% of invasive species in this region have caused well-documented environmental problems and substantial economic losses [4] One such problematic invasive species is Phragmites, an aggressive non-native genotype [2] that has expanded throughout the Great Lakes [5] and other regions. This rapid expansion of a monotypic plant community has resulted in adverse ecological, economic, and social impacts on the natural resources and people of the Great Lakes.
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