Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests : A Test Case in Continental Southeast Asia

Show full item record



Permalink

http://hdl.handle.net/10138/298955

Citation

Langner , A , Miettinen , J , Kukkonen , M , Vancutsem , C , Simonetti , D , Vieilledent , G , Verhegghen , A , Gallego , J & Stibig , H-J 2018 , ' Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests : A Test Case in Continental Southeast Asia ' , Remote Sensing , vol. 10 , no. 4 , 544 . https://doi.org/10.3390/rs10040544

Title: Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests : A Test Case in Continental Southeast Asia
Author: Langner, Andreas; Miettinen, Jukka; Kukkonen, Markus; Vancutsem, Christelle; Simonetti, Dario; Vieilledent, Ghislain; Verhegghen, Astrid; Gallego, Javier; Stibig, Hans-Juergen
Contributor: University of Helsinki, Department of Geosciences and Geography
Date: 2018-04
Language: eng
Number of pages: 21
Belongs to series: Remote Sensing
ISSN: 2072-4292
URI: http://hdl.handle.net/10138/298955
Abstract: This study presents an approach to forest canopy disturbance monitoring in evergreen forests in continental Southeast Asia, based on temporal differences of a modified normalized burn ratio (NBR) vegetation index. We generate NBR values from each available Landsat 8 scene of a given period. A step of ' self-referencing' normalizes the NBR values, largely eliminating illumination/topography effects, thus maximizing inter-comparability. We then create yearly composites of these self-referenced NBR (rNBR) values, selecting per pixel the maximum rNBR value over each observation period, which reflects the most open canopy cover condition of that pixel. The ArNBR is generated as the difference between the composites of two reference periods. The methodology produces seamless and consistent maps, highlighting patterns of canopy disturbances (e. g., encroachment, selective logging), and keeping artifacts at minimum level. The monitoring approach was validated within four test sites with an overall accuracy of almost 78% using very high resolution satellite reference imagery. The methodology was implemented in a Google Earth Engine (GEE) script requiring no user interaction. A threshold is applied to the final output dataset in order to separate signal from noise. The approach, capable of detecting sub-pixel disturbance events as small as 0.005 ha, is transparent and reproducible, and can help to increase the credibility of monitoring, reporting and verification (MRV), as required in the context of reducing emissions from deforestation and forest degradation (REDD+).
Subject: evergreen forest
continental Southeast Asia
canopy disturbance
forest degradation
selective logging
change detection
NBR
self-referencing
LANDSAT TIME-SERIES
BRAZILIAN AMAZON
COVER CHANGE
TROPICAL FORESTS
CARBON EMISSIONS
FRACTION IMAGES
CLOUD SHADOW
DEFORESTATION
DEGRADATION
MAP
4112 Forestry
1172 Environmental sciences
Rights:


Files in this item

Total number of downloads: Loading...

Files Size Format View
remotesensing_10_00544_v2.pdf 30.96Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record