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 437789
Title Mapping land cover gradients through analysis of hyper-temporal NDVI imagery
Author(s) Ali, A.; Bie, C.A.J.M. de; Skidmore, A.K.; Scarrott, R.G.; Hamad, A.; Venus, V.; Lymberakis, P.
Source International Journal of applied Earth Observation and Geoinformation 23 (2013). - ISSN 0303-2434 - p. 301 - 312.
DOI http://dx.doi.org/10.1016/j.jag.2012.10.001
Department(s) Resource Ecology
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2013
Keyword(s) multitemporal modis images - remotely-sensed imagery - time-series - fuzzy-sets - species composition - accuracy assessment - neural-networks - classification - vegetation - boundaries
Abstract The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but typically fail to express such differences as gradients. Present interpretation techniques still make insufficient use of freely available spatial-temporal Earth Observation (EO) data that allow detection of existing land cover gradients. This study explores the use of hyper-temporal NDVI imagery to detect and delineate land cover gradients analyzing the temporal behavior of NDVI values. MODIS-Terra MVC-images (250 m, 16-day) of Crete, Greece, from February 2000 to July 2009 are used. The analysis approach uses an ISODATA unsupervised classification in combination with a Hierarchical Clustering Analysis (HCA). Clustering of class-specific temporal NDVI profiles through HCA resulted in the identification of gradients in landcover vegetation growth patterns. The detected gradients were arranged in a relational diagram, and mapped. Three groups of NDVI-classes were evaluated by correlating their class-specific annual average NDVI values with the field data (tree, shrub, grass, bare soil, stone, litter fraction covers). Multiple regression analysis showed that within each NDVI group, the fraction cover data were linearly related with the NDVI data, while NDVI groups were significantly different with respect to tree cover (adj. R 2 = 0.96), shrub cover (adj. R 2 = 0.83), grass cover (adj. R 2 = 0.71), bare soil (adj. R 2 = 0.88), stone cover (adj. R 2 = 0.83) and litter cover (adj. R 2 = 0.69) fractions. Similarly, the mean Sorenson dissimilarity values were found high and significant at confidence interval of 95% in all pairs of three NDVI groups. The study demonstrates that hyper-temporal NDVI imagery can successfully detect and map land cover gradients. The results may improve land cover assessment and aid in agricultural and ecological studies.
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