Browsing by Author "Ahrends, Antje"

Now showing 1 - 2 of 2

Detecting and predicting forest degradation: a comparison of ground surveys and remote sensing in Tanzanian forests
(Plants, People, Planet (PPP), 2021-01-08) Ahrends, Antje; Bulling, Mark T.; Platts, Philip J.; Swetnam, Ruth; Ryan, Casey; Doggart, Nike; Hollingsworth, Peter M.; Marchant, Robert; Balmford, Andrew; Harris, David J.; Gross-Camp, Nicole; Sumbi, Peter; Munishi, Pantaleo; Madoffe, Seif; Mhoro, Boniface; Leonard, Charles; Bracebridge, Claire; Doody, Kathryn; Wilkins, Victoria; Owen, Nisha; Marshall, Andrew R.; Schaafsma, Marije; Pfliegner, Kerstin; Jones, Trevor; Robinson, James; Topp-Jørgensen, Elmer; Brink, Henry; Burgess, Neil D.
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 ex- ample 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 opti- cal remote-sensing maps of complete tree cover loss only detected severe im- pacts (≥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 spa- tial 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 im- portant in areas where forest degradation is more widespread than deforestation, such as in eastern and southern Africa.
Towards regional, error-bounded landscape carbon storage estimates for data-deficient areas of the world
(PLOS ONE, 2012-09-14) Willcock, Simon; Phillips, Oliver L.; Platts, Philip J; Balmford, Andrew; Burgess, Neil D.; Lovett, Jon C.; Ahrends, Antje; Mbilinyi, Boniface; Lewis, Simon L.
Monitoring landscape carbon storage is critical for supporting and validating climate change mitigation policies. These may be aimed at reducing deforestation and degradation, or increasing terrestrial carbon storage at local, regional and global levels. However, due to data-deficiencies, default global carbon storage values for given land cover types such as ‘lowland tropical forest’ are often used, termed ‘Tier 1 type’ analyses by the Intergovernmental Panel on Climate Change (IPCC). Such estimates may be erroneous when used at regional scales. Furthermore uncertainty assessments are rarely provided leading to estimates of land cover change carbon fluxes of unknown precision which may undermine efforts to properly evaluate land cover policies aimed at altering land cover dynamics. Here, we present a repeatable method to estimate carbon storage values and associated 95% confidence intervals (CI) for all five IPCC carbon pools (aboveground live carbon, litter, coarse woody debris, belowground live carbon and soil carbon) for data-deficient regions, using a combination of existing inventory data and systematic literature searches, weighted to ensure the final values are regionally specific. The method meets the IPCC ‘Tier 2’ reporting standard. We use this method to estimate carbon storage over an area of33.9 million hectares of eastern Tanzania, reporting values for 30 land cover types. We estimate that this area stored 6.33 (5.92–6.74) Pg C in the year 2000. Carbon storage estimates for the same study area extracted from five published Africa-wide or global studies show a mean carbon storage value of ,50% of that reported using our regional values, with four of the five studies reporting lower carbon storage values. This suggests that carbon storage may have been underestimated for this region of Africa. Our study demonstrates the importance of obtaining regionally appropriate carbon storage estimates, and shows how such values can be produced for a relatively low investment.