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Remote Sens. 2012, 4(7), 2057-2075; doi:10.3390/rs4072057
Article

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

1
, 2,*  and 3
Received: 20 May 2012; in revised form: 30 June 2012 / Accepted: 2 July 2012 / Published: 9 July 2012
(This article belongs to the Special Issue Remote Sensing of Biological Diversity)
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Abstract: 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.
Keywords: floristic gradient; climatic variables; MODIS NDVI; Partial Least Squares regression; plant species composition; remote sensing; vegetation mapping and modeling; North Carolina; South Carolina floristic gradient; climatic variables; MODIS NDVI; Partial Least Squares regression; plant species composition; remote sensing; vegetation mapping and modeling; North Carolina; South Carolina
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Feilhauer, H.; He, K.S.; Rocchini, D. Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI. Remote Sens. 2012, 4, 2057-2075.

AMA Style

Feilhauer H, He KS, Rocchini D. Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI. Remote Sensing. 2012; 4(7):2057-2075.

Chicago/Turabian Style

Feilhauer, Hannes; He, Kate S.; Rocchini, Duccio. 2012. "Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI." Remote Sens. 4, no. 7: 2057-2075.


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