NDLI: The impact of inter-annual variability in remote sensing time series on modeling tree species distributions

Content Provider	IEEE Xplore Digital Library
Author	Cord, A. Klein, D. Dech, S.
Copyright Year	2011
Description	Author affiliation: University of Würzburg, Department of Geography and Geology, Am Hubland, 97074 Würzburg, Germany (Klein, D.) \|\| German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, 82234, Wessling, Germany (Cord, A.; Dech, S.)
Abstract	Predictions of species occurrence as indicators of ecosystem integrity are of high relevance for decision-makers in conservation biology, invasive species' management, and climate change research. Remote sensing data can serve as valuable input for Species Distribution Models (SDMs) since they provide information on current habitat conditions and disturbance factors besides bioclimatic suitability which is commonly derived from climatic data. However, little is known about the usefulness of multi-temporal remote sensing data in general for modeling species distributions and the related effects of inter-annual variability on the extent and accuracy of modeled distribution ranges. This study investigates the above-mentioned questions for two tropical tree species, Brosimum alicastrum and Liquidambar macrophylla, in Mexico. From the MODIS 16-day vegetation index product (MOD13A2), 18 annual phenological metrics (time-related, NPP-related and seasonality-related) were computed for the period from 2001 to 2009 and combined to a set of multi-year average values (covering 3, 5, 7, and 9 years). The results show that inter-annual variability has a significant impact on model predictions and that models based on longer composite periods show less deviance from observed species presence-absence field data.
Starting Page	181
Ending Page	184
File Size	818453
Page Count	4
File Format	PDF
ISBN	9781457712029
e-ISBN	9781457712036
DOI	10.1109/Multi-Temp.2011.6005078
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-12
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Measurement Data models Remote sensing Vegetation Predictive models Time series analysis Mexico Maximum Entropy MODIS Species Distribution Model Phenology Biodiversity
Content Type	Text
Resource Type	Article

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