Next Article in Journal
Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China
Previous Article in Journal
Application of GeoSHM System in Monitoring Extreme Wind Events at the Forth Road Bridge
Previous Article in Special Issue
An Integrated GIS and Remote Sensing Approach for Monitoring Harvested Areas from Very High-Resolution, Low-Cost Satellite Images
Open AccessArticle

Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing

1
The Remote Sensing Laboratory, Department of Geography and Human Environment, The Porter School of the Environment and Earth Sciences, Tel-Aviv University, Tel Aviv 699780, Israel
2
The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Faculty of Agriculture, The Hebrew University of Jerusalem, Rehovot 7610001, Israel
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2800; https://doi.org/10.3390/rs11232800
Received: 3 October 2019 / Revised: 21 November 2019 / Accepted: 24 November 2019 / Published: 27 November 2019
(This article belongs to the Special Issue Monitoring Forest Change with Remote Sensing)
In recent years, hyperspectral remote sensing (HRS) has become common practice for remote analyses of the physiognomy and composition of forests. Supervised classification is often used for this purpose, but demands intensive sampling and analyses, whereas unsupervised classification often requires information retrieval out of the large HRS datasets, thereby not realizing the full potential of the technology. An improved principal component analysis-based classification (PCABC) scheme is presented and intended to provide accurate and sequential image-based unsupervised classification of Mediterranean forest species. In this study, unsupervised classification and reduction of data size are performed simultaneously by applying binary sequential thresholding to principal components, each time on a spatially reduced subscene that includes the entire spectral range. The methodology was tested on HRS data acquired by the airborne AisaFENIX HRS sensor over a Mediterranean forest in Mount Horshan, Israel. A comprehensive field-validation survey was performed, sampling 257 randomly selected individual plants. The PCABC provided highly improved results compared to the traditional unsupervised classification methodologies, reaching an overall accuracy of 91%. The presented approach may contribute to improved monitoring, management, and conservation of Mediterranean and similar forests. View Full-Text
Keywords: hyperspectral remote sensing; principal component analysis; plant species; Mediterranean forest; unsupervised classification hyperspectral remote sensing; principal component analysis; plant species; Mediterranean forest; unsupervised classification
Show Figures

Figure 1

MDPI and ACS Style

Dadon, A.; Mandelmilch, M.; Ben-Dor, E.; Sheffer, E. Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing. Remote Sens. 2019, 11, 2800.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop