Annual Maps of Forests in Australia from Analyses of Microwave and Optical Images with FAO Forest Definition

The Australian governmental agencies reported a total of 149 million ha forest in the Food and Agriculture Organization of the United Nations (FAO) in 2010, ranking sixth in the world, which is based on a forest definition with tree height>2 meters. Here, we report a new forest cover data product that used the FAO forest definition (tree cover>10% and tree height>5 meters at observation time or mature) and was derived from microwave (Phased Array type L-band Synthetic Aperture Radar, PALSAR) and optical (Moderate Resolution Imaging Spectroradiometer, MODIS) images and validated with very high spatial resolution images, Light Detection and Ranging (LiDAR) data from the Ice, Cloud, and land Elevation Satellite (ICESat), and in situ field survey sites. The new PALSAR/MODIS forest map estimates 32 million ha of forest in 2010 over Australia. PALSAR/MODIS forest map has an overall accuracy of ~95% based on the reference data derived from visual interpretation of very high spatial resolution images for forest and nonforest cover types. Compared with the canopy height and canopy coverage data derived from ICESat LiDAR strips, PALSAR/MODIS forest map has 73% of forest pixels meeting the FAO forest definition, much higher than the other four widely used forest maps (ranging from 36% to 52%). PALSAR/MODIS forest map also has a reasonable spatial consistency with the forest map from the National Vegetation Information System. This new annual map of forests in Australia could support cross-country comparison when using data from the FAO Forest Resource Assessment Reports.

Forest Cover Mapping Based on a Combination of Aerial Images and Sentinel-2 Satellite Data Compared to National Forest Inventory Data

Research Highlights: This study developed the first remote sensing-based forest cover map of Baden-Württemberg, Germany, in a very high level of detail. Background and Objectives: As available global or pan-European forest maps have a low level of detail and the forest definition is not considered, administrative data are often oversimplified or out of date. Consequently, there is an important need for spatio-temporally explicit forest maps. The main objective of the present study was to generate a forest cover map of Baden-Württemberg, taking the German forest definition into account. Furthermore, we compared the results to NFI data; incongruences were categorized and quantified. Materials and Methods: We used a multisensory approach involving both aerial images and Sentinel-2 data. The applied methods are almost completely automated and therefore suitable for area-wide forest mapping. Results: According to our results, approximately 37.12% of the state is covered by forest, which agrees very well with the results of the NFI report (37.26% ± 0.44%). We showed that the forest cover map could be derived by aerial images and Sentinel-2 data including various data acquisition conditions and settings. Comparisons between the forest cover map and 34,429 NFI plots resulted in a spatial agreement of 95.21% overall. We identified four reasons for incongruences: (a) edge effects at forest borders (2.08%), (b) different forest definitions since NFI does not specify minimum tree height (2.04%), (c) land cover does not match land use (0.66%) and (d) errors in the forest cover layer (0.01%). Conclusions: The introduced approach is a valuable technique for mapping forest cover in a high level of detail. The developed forest cover map is frequently updated and thus can be used for monitoring purposes and for assisting a wide range of forest science, biodiversity or climate change-related studies.

Response to Comment on “The extent of forest in dryland biomes”

Griffith et al. do not question the quality of our analysis, but they question our results with respect to the definition of forest we employed. In our response, we explain why the differences we report result from a difference of technique and not of definition, and how anyone can adapt—as we did—our data set to any forest definition and tree cover threshold of interest.

Comment on “The extent of forest in dryland biomes”

Bastin et al. (Reports, 12 May 2017, p. 635) infer forest as more globally extensive than previously estimated using tree cover data. However, their forest definition does not reflect ecosystem function or biotic composition. These structural and climatic definitions inflate forest estimates across the tropics and undermine conservation goals, leading to inappropriate management policies and practices in tropical grassy ecosystems.

Definition of Forest for REDD+: an Unresolved Issue

Debate over arrangements for REDD+ in a post-Kyoto climate policy framework has been continuing in United Nations Framework Convention on Climate Change (UNFCCC) negotiations; however, no review of the applicability of the existing forest definition to future REDD+ implementation has been undertaken. This paper highlights the need to review the definition of forest, and examines proposals to improve existing definitions. The impacts of the current forestry definition are discussed with reference to Indonesia as a case study. It is concluded that careless definition of selected forest-related terms has had a negative impact on Indonesia’s involvement in the implementation of Clean Development Mechanism (CDM) afforestation and reforestation (A/R) projects. In the global context, this paper concludes that there should be clear definitions for definition of forest that are flexible enough to accommodate the various interests of UNFCCC parties. At the national level, each country should specify their forest definition carefully, taking into consideration the guidance provided at the global level and its own forest characteristics and management. Failing to do so can limit the involvement of these nations in future REDD+ schemes and reduce their capacity to mitigate climate change impacts.