Performance of vegetation indices from Landsat time series in deforestation monitoring

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

Research output: Contribution to journalArticleAcademicpeer-review

45 Citations (Scopus)

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
Original languageEnglish
Pages (from-to)318-327
JournalInternational Journal of applied Earth Observation and Geoinformation
Volume52
DOIs
Publication statusPublished - 2016

Fingerprint Dive into the research topics of 'Performance of vegetation indices from Landsat time series in deforestation monitoring'. Together they form a unique fingerprint.

  • Cite this