Abstract
Societal Impact StatementLarge areas of tropical forest are degraded. While global tree cover is being mapped with increasing accuracy from space, much less is known about the quality of that tree cover. Here we present a field protocol for rapid assessments of forest condition. Using extensive field data from Tanzania, we show that a focus on remotely‐sensed deforestation would not detect significant reductions in forest quality. Radar‐based remote sensing of degradation had good agreement with the ground data, but the ground surveys provided more insights into the nature and drivers of degradation. We recommend the combined use of rapid field assessments and remote sensing to provide an early warning, and to allow timely and appropriately targeted conservation and policy responses.Summary Tropical forest degradation is widely recognised as a driver of biodiversity loss and a major source of carbon emissions. However, in contrast to deforestation, more gradual changes from degradation are challenging to detect, quantify and monitor. Here, we present a field protocol for rapid, area‐standardised quantifications of forest condition, which can also be implemented by non‐specialists. Using the example of threatened high‐biodiversity forests in Tanzania, we analyse and predict degradation based on this method. We also compare the field data to optical and radar remote‐sensing datasets, thereby conducting a large‐scale, independent test of the ability of these products to map degradation in East Africa from space. Our field data consist of 551 ‘degradation’ transects collected between 1996 and 2010, covering >600 ha across 86 forests in the Eastern Arc Mountains and coastal forests. Degradation was widespread, with over one‐third of the study forests—mostly protected areas—having more than 10% of their trees cut. Commonly used optical remote‐sensing maps of complete tree cover loss only detected severe impacts (≥25% of trees cut), that is, a focus on remotely‐sensed deforestation would have significantly underestimated carbon emissions and declines in forest quality. Radar‐based maps detected even low impacts (<5% of trees cut) in ~90% of cases. The field data additionally differentiated types and drivers of harvesting, with spatial patterns suggesting that logging and charcoal production were mainly driven by demand from major cities. Rapid degradation surveys and radar remote sensing can provide an early warning and guide appropriate conservation and policy responses. This is particularly important in areas where forest degradation is more widespread than deforestation, such as in eastern and southern Africa.
Highlights
Large areas of tropical forest are degraded through human impacts such as overexploitation, fragmentation, pollution, exotic species invasion and fire (Sloan & Sayer, 2015)
Recent studies have shown that carbon emissions from forest degradation may have been underestimated and could account for as much as 25%–69% of the combined gross carbon losses due to deforestation and degradation in the tropics (Baccini et al, 2017; Berenguer et al, 2014; Pearson et al, 2017)
In order to broadly investigate whether the model for tree harvesting (>15 cm dbh) was able to indicate areas under future threat, we extrapolated the model to ~2020 and compared the predictions to tree cover losses recorded by GFW between 2000 and 2018 (Figure 3) and local reports
Summary
Large areas of tropical forest are degraded through human impacts such as overexploitation, fragmentation, pollution, exotic species invasion and fire (Sloan & Sayer, 2015). Radar data hold particular promise as they overcome the challenges presented by cloud cover and variable phenology, and they correlate with changes in biomass (McNicol et al, 2018; Mitchell et al, 2017; Ryan et al, 2012) Using such data sources for detecting and quantifying degradation from space remains limited by the extent to which degradation is associated with a reduction in canopy cover and/or biomass (Ryan et al, 2012). While countries increasingly monitor wall-to-wall forest cover change using remote sensing, and they have some inventory data, they still lack representative quantitative data on forest degradation (Romijn et al, 2015). How ground data collected using this protocol compare to remotely-sensed datasets; radar-based maps of biomass change (McNicol et al, 2018) and commonly used maps of complete tree cover loss (which underpin ‘Global Forest Watch’; Hansen et al, 2013); 2. The overall aim is to assess whether these rapid assessments are a useful addition to remote sensing and detailed vegetation assessments in (permanent) plots in informing conservation policy and practice
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