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Texture Analysis for Mapping Tamarix parviflora Using Aerial Photographs along the Cache Creek, California.

Authors :
Shaokui Ge
Carruthers, Raymond
Peng Gong
Herrera, Angelica
Source :
Environmental Monitoring & Assessment; Mar2006, Vol. 114 Issue 1-3, p65-83, 19p, 1 Color Photograph, 1 Diagram, 8 Charts, 2 Graphs, 1 Map
Publication Year :
2006

Abstract

Natural color photographs were used to detect the coverage of saltcedar, Tamarix parviflora, along a 40 km portion of Cache Creek near Woodland, California. Historical aerial photographs from 2001 were retrospectively evaluated and compared with actual ground-based information to assess accuracy of the assessment process. The color aerial photos were sequentially digitized, georeferenced, classified using color and texture methods, and mosaiced into maps for field use. Eight types of ground cover ( Tamarix, agricultural crops, roads, rocks, water bodies, evergreen trees, non-evergreen trees and shrubs (excluding Tamarix)) were selected from the digitized photos for separability analysis and supervised classification. Due to color similarities among the eight cover types, the average separability, based originally only on color, was very low. The separability was improved significantly through the inclusion of texture analysis. Six types of texture measures with various window sizes were evaluated. The best texture was used as an additional feature along with the color, for identifying Tamarix. A total of 29 color photographs were processed to detect Tamarix infestations using a combination of the original digital images and optimal texture features. It was found that the saltcedar covered a total of 3.96 km<superscript>2</superscript> (396 hectares) within the study area. For the accuracy assessment, 95 classified samples from the resulting map were checked in the field with a global position system (GPS) unit to verify Tamarix presence. The producer's accuracy was 77.89%. In addition, 157 independently located ground sites containing saltcedar were compared with the classified maps, producing a user's accuracy of 71.33%. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01676369
Volume :
114
Issue :
1-3
Database :
Complementary Index
Journal :
Environmental Monitoring & Assessment
Publication Type :
Academic Journal
Accession number :
20467659
Full Text :
https://doi.org/10.1007/s10661-006-1071-z