Analyzing fine-scale wetland composition using high resolution imagery and texture features

Szantoi, Zoltan; Escobedo, Francisco; Abd-Elrahman, Amr; Smith, Scot; Pearlstine, Leonard

doi:https://doi.org/10.1016/j.jag.2013.01.003

Ítem

Solo Metadatos

Analyzing fine-scale wetland composition using high resolution imagery and texture features

https://repository.urosario.edu.co/handle/10336/26962
https://doi.org/10.1016/j.jag.2013.01.003

Autores

Szantoi, Zoltan

Escobedo, Francisco

Abd-Elrahman, Amr

Smith, Scot

Pearlstine, Leonard

Fecha

2013-01-01

Editor

Elsevier

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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).