Danielsen, F., T. Adrian, S. Brofeldt, M. van Noordwijk, M. K. Poulsen, S. Rahayu, E. Rutishauser, I. Theilade, A. Widayati, N. The An, T. Nguyen Bang, A. Budiman, M. Enghoff, A. E. Jensen, Y. Kurniawan, Q. Li, Z. Mingxu, D. Schmidt-Vogt, S. Prixa, V. Thoumtone, Z. Warta, and N. Burgess. 2013. Community monitoring for REDD+: international promises and field realities. Ecology and Society 18(3): 41.http://dx.doi.org/10.5751/ES-05464-180341

Spectral indices derived from optical remote sensing data have been widely used for fire-severity classification in forests from local to global scales. However, comparative analyses of multiple indices across diverse forest types are few. This represents an information gap for fire management agencies in areas like temperate south-eastern Australia, which is characterised by a diversity of natural forests that vary in structure, and in the fire-regeneration strategies of the dominant trees. We evaluate 10 spectral indices across eight areas burnt by wildfires in 1998, 2006, 2007, and 2009 in south-eastern Australia. These wildfire areas encompass 13 forest types, which represent 86% of the 7.9M ha region’s forest area. Forest types were aggregated into six forest groups based on their fire-regeneration strategies (seeders, resprouters) and structure (tree height and canopy cover). Index performance was evaluated for each forest type and forest group by examining its sensitivity to four fire-severity classes (unburnt, low, moderate, high) using three independent methods (anova, separability, and optimality). For the best-performing indices, we calculated index-specific thresholds (by forest types and groups) to separate between the four severity classes, and evaluated the accuracy of fire-severity classification on independent samples. Our results indicated that the best-performing indices of fire severity varied with forest type and group. Overall accuracy for the best-performing indices ranged from 0.50 to 0.78, and kappa values ranged from 0.33 (fair agreement) to 0.77 (substantial agreement), depending on the forest group and index. Fire severity in resprouter open forests and woodlands was most accurately mapped using the delta Normalised Burnt ratio (dNBR). In contrast, dNDVI (delta Normalised difference vegetation index) performed best for open forests with mixed fire responses (resprouters and seeders), and dNDWI (delta Normalised difference water index) was the most accurate for obligate seeder closed forests. Our analysis highlighted the low sensitivity of all indices to fire impacts in Rainforest. We conclude that the optimal spectral index for quantifying fire severity varies with forest type, but that there is scope to group forests by structure and fire-regeneration strategy to simplify fire-severity classification in heterogeneous forest landscapes.

Reducing emissions from deforestation and forest degradation in developing countries, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks in developing countries (REDD+) is a potentially powerful international policy mechanism that many tropical countries are working towards implementing. Thus far, limited practical consideration has been paid to local rights to forests and forest resources in REDD+ readiness programs, beyond noting the importance of these issues. Previous studies have shown that community members can reliably and cost-effectively monitor forest biomass. At the same time, this can improve local ownership and forge important links between monitoring activities and local decision-making. Existing studies have, however, been static assessments of biomass at one point in time. REDD+ programs will require repeated surveys of biomass over extended time frames. Here, we examine trends in accuracy and costs of local forest monitoring over time. We analyse repeated measurements by community members and professional foresters of 289 plots over two years in four countries in Southeast Asia. This shows, for the first time, that with repeated measurements community members’ biomass measurements become increasingly accurate and costs decline. These findings provide additional support to available evidence that community members can play a strong role in monitoring forest biomass in the local implementation of REDD+.

Wildfires have increased in size and frequency in recent decades in many biomes, but have they also become more severe? This question remains under-examined despite fire severity being a critical aspect of fire regimes that indicates fire impacts on ecosystem attributes and associated post-fire recovery. We conducted a retrospective analysis of wildfires larger than 1000 ha in south-eastern Australia to examine the extent and spatial pattern of high-severity burned areas between 1987 and 2017. High-severity maps were generated from Landsat remote sensing imagery. Total and proportional high-severity burned area increased through time. The number of high-severity patches per year remained unchanged but variability in patch size increased, and patches became more aggregated and more irregular in shape. Our results confirm that wildfires in southern Australia have become more severe. This shift in fire regime may have critical consequences for ecosystem dynamics, as fire-adapted temperate forests are more likely to be burned at high severities relative to historical ranges, a trend that seems set to continue under projections of a hotter, drier climate in south-eastern Australia.

In recent years, the concepts of teleconnections and telecoupling have been introduced into land-use and land-cover change literature as frameworks that seek to explain connections between areas that are not in close physical proximity to each other. The conceptual frameworks of teleconnections and telecoupling seek to explicitly link land changes in one place, or in a number of places, to distant, usually non-physically connected locations. These conceptual frameworks are offered as new ways of understanding land changes; rather than viewing land-use and land-cover change through discrete land classifications that have been based on the idea of land-use as seen through rural–urban dichotomies, path dependencies and sequential land transitions, and place-based relationships. Focusing on the land-use and land-cover changes taking place along the East–West Economic Corridor that runs from Dong Ha City in Quang Tri, Vietnam, through Sepon District, Savannakhet, Lao PDR, into Thailand this paper makes use of data gathered from fieldwork and remote sensing analysis to examine telecouplings between sending, receiving and spill-over systems on both sides of the Vietnam-Lao PDR border. Findings are that the telecouplings are driving changes in rural village and urban systems on both sides of the border, and are enabled by a policy environment that has sought to facilitate the cross-border transportation of goods within the region.

Machine learning and spectral index (SI) thresholding approaches have been tested for fire-severity mapping from local to regional scales in a range of forest types worldwide. While index thresholding can be easily implemented, its operational utility over large areas is limited as the optimum index may vary with forest type and fire regimes. In contrast, machine learning algorithms allow for multivariate fire classifications. This study compared the accuracy of fire-severity classifications from SI thresholding with those from Random Forests (RF). Reference data were from 3730 plots within the boundaries of eight major wildfires across the six temperate forest ‘functional’ groups of Victoria, south-eastern Australia. The reference plots were randomly divided into training and validation datasets (60/40) for each fire-severity class (unburnt, low, moderate, high) and forest functional group. SI fire-severity classifications were conducted using thresholds derived in a previous study based on the same datasets. A RF classification algorithm was trained to derive fire-severity levels based on appropriate spectral indices and their temporal difference. The RF classification outperformed the SI thresholding approach in most cases, increasing overall accuracy by 11% on a forest-group basis, and 16% on an individual wildfire basis. Adding more predictor variables into the RF algorithm did not improve classification accuracy. Greater overall accuracies (by 12% on average) were achieved when in situ data (rather than data from other fires) were used to train the RF algorithm. Our study shows the utility of Random Forest algorithms for streamlining fire-severity mapping across heterogeneous forested landscapes.