Details

Autor(en) / Beteiligte

• Schultz, Michael
• Clevers, Jan G.P.W.
• Carter, Sarah
• Verbesselt, Jan
• Avitabile, Valerio
• Quang, Hien Vu
• Herold, Martin

Titel

Performance of vegetation indices from Landsat time series in deforestation monitoring

Ist Teil von

• ITC journal, 2016-10, Vol.52, p.318-327

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2016

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Data fusion of vegetation index can improve mapping performance, particularly in complex ecosystems.•The Normalized Difference Fraction Index is most suitable for mapping deforestation across ecosystems while the Normalized Difference Vegetation Index performs comparatively low.•For Landsat time series based deforestation mapping wetness related vegetation indices outperform greenness related indices.•Mapping performance sensitivity to observation frequency varies by used vegetation index. 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.