Paper Info
Title
Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor.
Abstract
Forest cover and vegetation degradation was monitored across the Kavango-Zambezi Transfrontier Conservation Area (KAZA) in southern Africa and the performance of three different methods in detecting degradation was assessed using reference data. Breaks for Additive Season and Trend (BFAST) Monitor was used to identify potential forest cover and vegetation degradation using Landsat Normalized Difference Moisture Index (NDMI) time series data. Parametric probability-based magnitude thresholds, non-parametric random forest in conjunction with Soil-Adjusted Vegetation Index (SAVI) time series, and the combination of both methods were evaluated for their suitability to detect degradation for six land cover classes ranging from closed canopy forest to open grassland. The performance of degradation detection was largely dependent on tree cover and vegetation density. Satisfactory accuracies were obtained for closed woodland (user's accuracy 87%, producer's accuracy 71%) and closed forest (user's accuracy 92%, producer's accuracy 90%), with lower accuracies for open canopies. The performance of the three methods was more similar for closed canopies and differed for land cover classes with open canopies. Highest user's accuracy was achieved when methods were combined, and the best performance for producer's accuracy was obtained when random forest was used.
Year
DOI
Venue
2018
10.3390/rs10111850
REMOTE SENSING
Keywords
Field
DocType
southern Africa,degradation detection,remote sensing,BFAST Monitor,time series
Reference data (financial markets),Woodland,Time series,Vegetation,Hydrology,Remote sensing,Grassland,Geology,Random forest,Land cover,Canopy
Journal
Volume
Issue
Citations 
10
11
0
PageRank 
References 
Authors
0.34
4
5
Name
Order
Citations
PageRank
Michael Schultz100.34
Aurélie Shapiro200.34
Jan G. P. W. Clevers315319.42
Craig Beech400.34
herold martin510126.05