Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 408737
Title Comparing direct image and wavelet transform-based approaches to analysing remote sensing imagery for predicting wildlife distribution
Author(s) Murwira, A.; Skidmore, A.K.
Source International Journal of Remote Sensing 31 (2010)24. - ISSN 0143-1161 - p. 6425 - 6440.
Department(s) Resource Ecology
Publication type Refereed Article in a scientific journal
Publication year 2010
Keyword(s) spatial heterogeneity - species richness - patterns - vegetation - elephants - landscape - movements - ndvi
Abstract In this study we tested the ability to predict the probability of elephant (Loxodonta africana) presence in an agricultural landscape of Zimbabwe based on three methods of measuring the spatial heterogeneity in vegetation cover, where vegetation cover was measured using the Landsat Thematic Mapper (TM)-derived normalized difference vegetation index (NDVI). The three methods of measuring spatial heterogeneity were: one wavelet-derived spatial heterogeneity measure; and two direct image measures. The wavelet-derived spatial heterogeneity measure consists of the intensity, which measures the maximum contrast in the vegetation cover, and the dominant scale, which determines the scale at which this intensity occurs. The two direct image measures use the NDVI average and the NDVI coefficient of variation (NDVIcv). The results show that the wavelet-derived spatial heterogeneity significantly explains 80% of the variance in elephant presence compared with 60% and 48% variance explained by the NDVI average and NDVIcv, respectively. We conclude that the wavelet transform-based approach predicts elephant distribution better than the direct image measures of spatial heterogeneity
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