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 507004
Title Performance of vegetation indices from Landsat time series in deforestation monitoring
Author(s) Schultz, Michael; Clevers, Jan G.P.W.; Carter, Sarah; Verbesselt, Jan; Avitabile, Valerio; Quang, Hien Vu; Herold, Martin
Source International Journal of applied Earth Observation and Geoinformation 52 (2016). - ISSN 0303-2434 - p. 318 - 327.
Department(s) Laboratory of Geo-information Science and Remote Sensing
Publication type Refereed Article in a scientific journal
Publication year 2016
Abstract The performance of Landsat time series (LTS) of eight vegetation indices (VIs) was assessed for monitoring deforestation across the tropics. Three sites were selected based on differing remote sensing observation frequencies, deforestation drivers and environmental factors. The LTS of each VI was analysed using the Breaks For Additive Season and Trend (BFAST) Monitor method to identify deforestation. A robust reference database was used to evaluate the performance regarding spatial accuracy, sensitivity to observation frequency and combined use of multiple VIs. The canopy cover sensitive Normalized Difference Fraction Index (NDFI) was the most accurate. Among those tested, wetness related VIs (Normalized Difference Moisture Index (NDMI) and the Tasselled Cap wetness (TCw)) were spatially more accurate than greenness related VIs (Normalized Difference Vegetation Index (NDVI) and Tasselled Cap greenness (TCg)). When VIs were fused on feature level, spatial accuracy was improved and overestimation of change reduced. NDVI and NDFI produced the most robust results when observation frequency varies
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