Journal
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
Volume 21, Issue -, Pages 218-229Publisher
ELSEVIER
DOI: 10.1016/j.jag.2012.09.002
Keywords
Conservation; Isometric Feature Mapping; Optical remote sensing; Partial Least Squares regression; Subclass information; Vegetation
Categories
Funding
- German Research Foundation [SCHM 2153/2-1, SCUM 2153/2-2]
- German Ministry for Economics through the German Aerospace Center [50EE1033]
- Division Of Undergraduate Education
- Direct For Education and Human Resources [1028125] Funding Source: National Science Foundation
Ask authors/readers for more resources
Assessing and mapping patterns of (semi-)natural vegetation types at a large spatial scale is a difficult task. The challenge increases if the floristic variation within vegetation types (i.e., subtype variation of species composition) is the target. A desirable way to deal with this task may be to address such vegetation patterns with remote-sensing approaches. In particular data from multispectral sensors are easy to obtain, globally accessible, and often provide a high temporal resolution. They hence offer a comprehensive basis for vegetation mapping. The potential of such sensors for vegetation mapping has, however, never been thoroughly investigated. In particular, a systematic test regarding the spectral capabilities of these data for an assessment of detailed floristic variation has not been implemented to date. We thus addressed in this study the question how the ability of optical sensors to map floristic variation is affected by their respective spectral coverage and number of bands. To answer this question, we simulated monotemporal and multiseasonal data of eleven multispectral sensors. These data were used to model gradual transitions in species composition (i.e., floristic gradients) within three types of spontaneous vegetation typical for Central Europe using Partial Least Squares regression. Comparison of the model fits (ranging up to R-2 = 0.76 in cross-validation) illustrated the potential of multispectral data for detailed vegetation mapping. The results show that spectral coverage of the entire solar-reflective domain is the most important sensor characteristic for a successful assessment of floristic variation. Model and sensor performances as well as limitations are thoroughly discussed, and recommendations for sensor development are made based on the final conclusions of this study. (C) 2012 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available