Abstract
Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.
Highlights
Approaches to monitoring forest degradation Long-term and consistent monitoring is key to discriminating degraded and intact forest, and separating change due to anthropogenic impacts and seasonal/cyclic change [64]
The purpose of this paper is to review current remote sensing approaches to forest degradation monitoring in the context of MRV and REDD+
Concluding summary not exhaustive, this review has captured a range of practical approaches, and identified some of the limitations associated with the remote sensing detection and monitoring of forest degradation
Summary
Together with deforestation, are placed second to burning of fossil fuels in terms of contributing to greenhouse gas (GHG) emissions [81]; a key driver of global climate change [44]. Satellite observations can be used to estimate the area of forest classes (including degraded and intact forest states), for which volume and biomass densities can be extrapolated using field-based measurements [17] Repeat observations of both EO and field data allow for ongoing assessments of changes in forest carbon stocks. An integrated approach that combines multi-sensor EO and in situ data could form part of a systematic framework for monitoring changes in forest cover and carbon stocks This would allow the implementation of a more complete MRV system, whereby the disturbance history, i.e., degradation type and long-term loss of carbon stocks in forest land, is needed to account for emissions arising from forest degradation. The datasets provide a long-term consistent record of change, from which degradation, drivers, albeit indirectly, and policy actions can be determined
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