Principales autores y elementos de la resiliencia

Forest disturbance information derived from Landsat imagery have proven suitable for detecting forest degradation in tropical environments, but persistent cloud cover can lead to significant data gaps (Souza, Jr et al. 2013). For our study site in Central Guyana data gaps remained even when compositing Landsat imagery over one or two years. We showed that change detection schemes especially were affected. Because data gaps of annual Landsat composites were unevenly distributed, these data gaps accumulated during the multi-temporal analysis. In contrast, L-band SAR data demonstrated its capability to provide reliable deforestation information, but showed limited capacity to identify forest degradation.

To overcome the latter single-sensor limitations, Chapter 2 introduced an innovative approach for feature level fusion of multi-temporal L-band SAR and optical forest disturbance information. Using multi-temporal Landsat and dual- polarised ALOS PALSAR imagery (HH and HV polarisation) acquired for the years 2007 and 2010, we used the approach to map forest land cover and to detect deforestation and forest degradation. Adopting Guyanas’s country specific definitions for forest and degraded forest allowed a comparison of our results with other national products such as Guyana’s REDD+ MRVS reports (Pöyry Management Consulting (NZ) Limited 2011; Indufor Asia Pacific Ltd 2012). Due to the persistent cloud cover in the region and the primary use of optical satellite data only, these reports suffered from spatially incomplete results. By making use of the complementarities of Landsat and ALOS PALSAR, we were able to reduce data gaps in Landsat (cloud cover, SLC-off) and PALSAR (SAR layover and shadow in mountainous area) data sets to a negligible amount. Compared to a potential Landsat- or PALSAR-only approach, thematic detail and spatial consistency improved significantly for the combined Landsat-PALSAR approach. We reported high annual deforestation and degradation rates of 0.1% and 0.08%, respectively for

Chapter 6

the study site in Central Guyana, emphasizing the importance of the region for Guyana’s REDD+ program. Extensive alluvial mining and agricultural expansion were the main drivers of these changes, which clearly exceeded the reported country averages.

Results of Chapter 2 stress the need for implementing SAR-optical approaches to support REDD+ in robustly reporting forest changes in areas with dense cloud cover.

2. How do slope-induced effects on the SAR signal impact forest change

detection in complex terrain?

Driven by agricultural expansion and cost-efficient logging operations, the majority of tropical deforestation occurred in lowland areas (Aide et al. 2013) while large tracts of forest in mountainous terrain remained undisturbed. However, an increasing demand for timber and agricultural areas may shift deforestation operations towards mountainous areas. To manage and conserve these forests, robust satellite-based monitoring is required. Mountainous terrain, however, is notoriously difficult to handle when pre-processing SAR images. For pre-processing ALOS PALSAR data for Central Guyana (Chapter 2), a simple physical single-model (Hoekman 1990) was used that performed well for dense forest areas, but showed remaining slope-induced effects over other land cover when dealing with steep slopes (Hoekman et al. 1994; Hoekman et al. 2010). Since Chapter 2 dealt only with alluvial mining and agricultural expansion occurring in flat and gentle areas directly adjacent to rivers, change detection was not affected by remaining slope effects. Moving from the study site in Central Guyana (Chapter 2) to a managed evergreen forest site in Fiji (Chapter 4 and 5) where deforestation is occurring in complex terrain with steep slopes up to 40 degrees, slope-induced effects over non-forest land cover may negatively affect change detection.

To also account for varying land cover when correcting slope-induced effects on the SAR signal, Chapter 3 presented a two-step approach for multi-model land cover dependent slope correction. The first step uses the simple physical model (Hoekman 1990) used in Chapter 2, while the second step applies a semi-empirical model that accounts for the land cover (terrain type) dependent scatter mechanism. We corrected dual-polarised ALOS PALSAR images (HH and HV polarisation) of the site in Fiji and of an additional site in Brazil. Remaining slope effects were reduced to less than 0.1 dB for steep slopes. We showed that slope-induced effects were mitigated already to a large extent after the first step, but can remain as large as 1.77 dB for low biomass classes (woodland, HH). When estimating biomass this can lead to a systematic under- and/or overestimation purely related to slope effects, confirming the recent findings of Mermoz et al. (2015). When dealing with deforestation activities only, however, remaining slope effects are already sufficiently small after the first step when considering the large change in the L- band backscatter signal caused by the removal of forest. The single-model approach was therefore adequate to pre-process the ALOS PALSAR data of the Fiji site used for

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time series analysis in Chapter 4 and 5. Although the multi-model approach is not required when dealing with deforestation only, for detecting forest degradation and distinguishing smaller biomass changes, implementation will strongly support the analysis.

A further point of concern is the resolution of the DEM used for slope correction. When dealing with long steep slopes, such as for the site in Brazil, the 90 m SRTM resolution is sufficient to retrieve necessary slope angles. However in other areas, such as parts of Guyana (Chapter 3) and Fiji (Chapter 4 and 5) featuring very dissected landscapes with short steep slopes, micro-topography may remain and distort the analysis. Having high resolution 30 m elevation information available for Fiji, enabled us to largely mitigate such effects. To enable remote sensing efforts to provide forest change information consistently at a medium resolution scale and for varying terrain, high resolution DEMs for the tropics like those available from Tandem-X (Zink et al. 2006) should be made available free-of-charge.

3. How can we fuse SAR and optical image time series, and what impact does