Towards operational monitoring of forest canopy disturbance in evergreen rain forests: A test case in continental Southeast Asia

Langner Andreas, Miettinen Jukka, Kukkonen Markus, Vancutsem Christelle, Simonetti Dario, Vieilledent Ghislain, Verhegghen Astrid, Gallego Javier, Stibig Hans-Jürgen. 2018. Towards operational monitoring of forest canopy disturbance in evergreen rain forests: A test case in continental Southeast Asia. Remote Sensing, 10 (4):544, 21 p.

https://doi.org/10.3390/rs10040544

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Url - jeu de données - Entrepôt autre : https://doi.org/10.5281/zenodo.1014728

Quartile : Q1, Sujet : REMOTE SENSING

Résumé : 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 ΔrNBR 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+).

Mots-clés géographiques Agrovoc : Cambodge, République démocratique populaire lao

Mots-clés libres : Evergreen forest, Continental Southeast Asia, Canopy disturbance, Forest degradation, Selective logging, Change detection, NBR, Self-referencing

Classification Agris : K01 - Foresterie - Considérations générales
K70 - Dégâts causés aux forêts et leur protection
P01 - Conservation de la nature et ressources foncières

Champ stratégique Cirad : Axe 6 (2014-2018) - Sociétés, natures et territoires

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