Synthetic Aperture Radar data has a unique potential for continuous forest mapping as it is not affected by cloud cover. While longer wavelengths such as L-band are commonly used for forest applications, in this paper we assess the aptitude of C-band Sentinel-1 data for this purpose, for which there is much interest due to its high temporal resolution (5 days) and `free, full and open' data policy. We tested its ability to distinguish forest from non-forest in six study sites, located in Alaska, Colombia, Finland, Florida, Indonesia, and the UK. Using the time series for a full year significantly increases the classification accuracy compared to a single scene (a mean of 84.6% compared to 76.8% across the study sites). Our results show that we can further improve the mean accuracy to 86.4% when only considering the annual mean and standard deviation of VV and VH backscatter. In this case, separation accuracies of up to 93% (in Finland) are possible, though in the worst case (Indonesia) the highest possible accuracy using these variables was 82%. The best overall performance was observed when using a Support Vector Machine classifier, outperforming random forest, k-Nearest-Neighbors and Quadratic Discriminant Analysis. We further show that the small information content we found in the phase data is an artifact of terrain slope orientation and has a negligible impact on classifier performance. We thus conclude that for the purposes of forest mapping the smaller file size and easier to process GRD data is sufficient, with little benefit to downloading the SLC data. Possible uses of the phase data in this context relate to its temporal coherence, which was not tested in this study.
Fires exacerbated during El Niño Southern Oscillation are a serious threat in Indonesia leading to the destruction and degradation of tropical forests and emissions of CO2 in the atmosphere. Forest structural changes which occurred due to the 1997-1998 El Niño Southern Oscillation in the Sungai Wain Protection Forest (East Kalimantan, Indonesia), a previously intact forest reserve have led to the development of a range of landcover from secondary forest to areas dominated by grassland. These structural differences can be appreciated over large areas by remote sensing instruments such as TanDEM-X and LiDAR that provide information that are sensitive to vegetation vertical and horizontal structure. One-point statistics of TanDEM-X coherence (mean and CV) and LiDAR CHM (mean, CV) and derived metrics such as vegetation volume and canopy cover were tested for the discrimination between 4 landcover classes. Jeffries-Matusita (JM) separability was high between forest classes (primary or secondary forest) and non-forest (grassland) while, primary and secondary forest were not separable. The study tests the potential and the importance of potential of TanDEM-X coherence and LiDAR observations to characterize structural heterogeneity based on one-point statistics in tropical forest but requires improved characterization using two-point statistical measures.
Karin Viergever, Pedro Andrade, Manoel Cardoso, Miguel Castillo, Jean-François Exbrayat, Sarah Middlemiss, David Milodowski, Edward T. A. Mitchard, Jean Ometto, Veronique Morel, Richard Tipper, Mathew Williams
Ecometrica, together with partners in the UK, Mexico and Brazil, have collaborated on a UK Space Agency international partnership space programme (IPSP) project to advance EO applications in forests. A key objective was to improve EO derived information management for forest protection. Ecometrica’s cloud-based mapping platform was used to establish regional EO Labs within the partner organizations: ECOSUR (Mexico), INPE and FUNCATE (Brazil) and the University of Edinburgh (UK). The regional networks of EO Labs have provided a unified view of forestry-related data that is easy to access. In Mexico and Brazil the EO Labs enabled collaboration between research organisations and NGOs to develop applications for monitoring forest change in specified study areas and has enabled the compilation of previously unavailable regional EO and other spatial datasets into products that can be used by researchers, NGOs and state governments. Data on forest loss was linked to dynamic earth system models developed by the University of Edinburgh and INPE, utilising the EO Labs to provide an intuitive and powerful environment in which non-expert end- users can investigate the results from the huge datasets produced by multi-run model simulations. This paper demonstrates and discusses examples of mapping applications created on Ecometrica EO Labs by ECOSUR, INPE and the University of Edinburgh as part of this project, illustrating how cloud technology can enhance the field of forest protection.
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