Efforts for Detecting Forest Change in Fiji and Guyana using ALOS PALSAR and Landsat Time-series Data
Reiche, Johannes1; Hoekman, Dirk1; Verbesselt, Jan1; Souza, Carlos2; Herold, Martin1
1Wageningen University, NETHERLANDS; 2IMAZON, BRAZIL
The South Pacific Island state Fiji and the South American Republic of Guyana, both currently undergoing national REDD+ readiness activities, represent two of five EU-funded FP7 ReCover service cases. Such as many tropical countries suffer from persistent cloud cover inhibiting spatially consistent reporting of deforestation and forest degradation for REDD+. Data gaps remain even when compositing Landsat-like optical satellite imagery over one or two years. Instead, medium resolution SAR is capable of providing reliable deforestation information but shows limited capacity to identify forest degradation.
The first part of the paper describes an innovative approach for feature fusion of multi-temporal and medium-resolution SAR and optical sub-pixel fraction information (Normalized Difference Fraction Index (NDFI), Souza et al. 2005). After independently processing SAR and optical input data streams the extracted SAR and optical sub-pixel fraction features are fused using a decision tree classifier. ALOS PALSAR Fine Bean Dual and Landsat imagery of 2007 and 2010 acquired over the main mining district in central Guyana have been used for a proof-of-concept demonstration observing overall accuracies of 88% and 89.3% for mapping forest land cover and detecting deforestation and forest degradation, respectively. Deforestation and degradation rates of 0.1% and 0.08% are reported for the observation period. The paper demonstrates the increase of both spatial completeness and thematic detail when applying the methodology, compared with potential Landsat-only or PALSAR-only approaches for a heavy cloud contaminated tropical environment (Reiche et al. 2013).
The second part of the present paper presents results of ALOS PALSAR (2007-2011) and Landsat (2000-2012) time-series analysis for monitoring different forest dynamics in Fiji, comprising deforestation and regrowth cycles of forest plantations and destructive selective logging in natural tropical forest environment. The generic change detection approach for time series by detecting and characterizing Breaks For Additive Seasonal and Trend (BFAST, Verbesselt et al. 2010) is used for time-series analysis. Special focus is on the remaining effect of strong cloud cover and remaining topography on the Landsat time series analysis.
Reiche, J., Souza, C., Herold, M., Hoekman, D. & J. Verbesselt, 2013, in print, ''Feature level fusion of multi-temporal ALOS PALSAR and Landsat data for mapping and monitoring of tropical deforestation and forest degradation''. DOI 10.1109/JSTARS.2013.2245101.
Souza, C., Roberts, D. & M. Cochrane, 2005, ''Combining spectral and spatial information to map canopy damage from selective logging and forest fires,'' Remote Sensing of Environment, vol. 98, no. 2-3, pp. 329–343.
Verbesselt, J., Hyndman, R., Newnham & G. Culvenor, D., 2010, ''Detecting trend and seasonal changes in satellite image time series'', Remote Sensing of Environment, Vol. 114, pp. 106-115.