Feilhauer H, He KS, Rocchini D (2012)
Publication Status: Published
Publication Type: Journal article, Original article
Publication year: 2012
Publisher: MDPI
Book Volume: 4
Pages Range: 2057-2075
Journal Issue: 7
DOI: 10.3390/rs4072057
Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species. © 2012 by the authors.
APA:
Feilhauer, H., He, K.S., & Rocchini, D. (2012). Modeling species distribution using niche-based proxies derived from composite bioclimatic variables and MODIS NDVI. Remote Sensing, 4(7), 2057-2075. https://dx.doi.org/10.3390/rs4072057
MLA:
Feilhauer, Hannes, Kate S. He, and Duccio Rocchini. "Modeling species distribution using niche-based proxies derived from composite bioclimatic variables and MODIS NDVI." Remote Sensing 4.7 (2012): 2057-2075.
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