International Journal of Remote Sensing Applications Volume 4 Issue 2, June 2014 doi: 10.14355/ijrsa.2014.0402.01
http://www.ijrsa.org
High Spatial-Resolution Land Cover Classification and Wetland Mapping over Large Areas Using Integrated Geospatial Technologies Philipp Nagel1, Bradley J. Cook2, Fei Yuan*3 1,3
Department of Geography, Minnesota State University, Mankato, MN 56001, USA
2
Department of Biological Sciences, Minnesota State University, Mankato, MN 56001, USA
1
philipp.nagel@mnsu.edu; 2bradley.cook@mnsu.edu; *3fei.yuan@mnsu.edu
Received 21th November 2013; Accepted 09th February 2014; Published 03rd June 2014 Š 2014 Science and Engineering Publishing Company
Abstract
Keywords
Land Use and Land Cover (LULC) and wetland classification maps are an important prerequisite for many environmental studies. In order to produce accurate LULC and wetland maps at high spatialresolution, a new approach was developed to integrate image classifications, spatial data layers, and analysis methods using Python scripting. Both Maximum Likelihood and Object-based Feature Extraction were adopted into the LULC classification. A spatial analysis approach was applied to wetland mapping based on available wetland inventories and soil data. Python scripts were created and used to automate these processes for each of the 30 reference sites across Minnesota and Wisconsin of the United States, which encompassed the entire study site. Results demonstrated that the proposed method allowed for the integration of geospatial data of varying sources and qualities to produce accurate LULC and wetland maps effectively. The results of accuracy assessment indicated that the classification maps for Minnesota and Wisconsin were of comparable quality. The objectbased classifier extracted LULC effectively from the Wisconsin imagery with acceptable accuracy despite lacking of the NIR spectral band. These maps were used as inputs to create a hydro geomorphicap-proach (HGM) guidebook (Hauer and Smith 1998) for both states (Cook et al. unpublished). The Python-based technique was found to be especially beneficial when dealing with big datasets over large study areas, as it allowed batch processing.
High Spatial-resolution; Land Cover Classification; Wetland Mapping; Python-based Technique; Big Datasets
Introduction Monitoring of Land Use and Land Cover (LULC) and wetlands is fundamental to environmental studies and conservation efforts. Past land use practices and current development pressures threaten the ecological integrity of many streams and their associated riparian wetlands (Mensing et al. 1998). Due to expanding human settlement and intensifying development, more than 80% of the riparian corridor areas of North America and Europe have disappeared in the last 200 years (Naiman et al. 1993). Many earlier studies on LULC and wetland mapping have been conducted mainly with 30 m Land sat or coarser spatial resolution imagery (Ozesmi and Bauer 2002; Yuan et al. 2005a; Yuan et al. 2005b; Wright and Gallant 2007). The United States Geological Survey (USGS) has a history of providing national and global land cover products at 30 m ground sample distance, but larger scale classification maps are required for many environmental studies in order to assess the influences of environmental changes on our ecosystem more accurately. Although high spatial-resolution satellite remote sensing images have become increasingly available in the last decade, their use is limited by cost and the larger resources required for processing, which is particularly true for studies at regional or national scales.
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