Ítem
Solo Metadatos
Analyzing fine-scale wetland composition using high resolution imagery and texture features
Título de la revista
Autores
Szantoi, Zoltan
Escobedo, Francisco
Abd-Elrahman, Amr
Smith, Scot
Pearlstine, Leonard
Fecha
2013-01-01
Directores
ISSN de la revista
Título del volumen
Editor
Elsevier
Citations
Métricas alternativas
Resumen
Abstract
In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features).
Palabras clave
Keywords
Wetland mapping , High resolution imagery , Image texture , Support Vector Machine
