ON-LINE CHANGE MONITORING WITH TRANSFORMED MULTI-SPECTRAL TIME SERIES, A STUDY CASE IN TROPICAL FOREST
In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances from satellite image time series based...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-10-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/987/2016/isprs-archives-XLI-B7-987-2016.pdf |
| Summary: | In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using
satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances
from satellite image time series based on empirical fluctuation tests, is particularly used for near real-time deforestation monitoring,
and it has been shown to be robust in detecting forest disturbances. Typically, a vegetation index that is transformed from spectral
bands into feature space (e.g. normalised difference vegetation index (NDVI)) is used as input for BFAST Monitor. However, using
a vegetation index for deforestation monitoring is a major limitation because it is difficult to separate deforestation from multiple
seasonality effects, noise, and other forest disturbance. In this study, we address such limitation by exploiting the multi-spectral band
of satellite data. To demonstrate our approach, we carried out a case study in a deciduous tropical forest in Bolivia, South America. We
reduce the dimensionality from spectral bands, space and time with projective methods particularly the Principal Component Analysis
(PCA), resulting in a new index that is more suitable for change monitoring. Our results show significantly improved temporal delay in
deforestation detection. With our approach, we achieved a median temporal lag of 6 observations, which was significantly shorter than
the temporal lags from conventional approaches (14 to 21 observations). |
|---|---|
| ISSN: | 1682-1750 2194-9034 |