Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests

Funder: Critical Ecosystem Partnership Fund; Id: http://dx.doi.org/10.13039/100013724Funder: Global Environment Facility; Id: http://dx.doi.org/10.13039/100011150Funder: Danish International Development Agency; Id: http://dx.doi.org/10.13039/501100011054Funder: Scottish Government’s Rural and Environment Science and Analytical Services DivisionFunder: Finnish International Development AgencyFunder: Leverhulme Trust; Id: http://dx.doi.org/10.13039/501100000275Societal Impact Statement: Large 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

Conservation and the botanist effect

Over the last few decades, resources for descriptive taxonomy and biodiversity inventories have substantially declined, and they are also globally unequally distributed. This could result in an overall decline in the quality of biodiversity data as well as geographic biases, reducing the utility and reliability of inventories. We tested this hypothesis with tropical tree records (n = 24,024) collected from the Eastern Arc Mountains, Tanzania, between 1980 and 2007 by 13 botanists, whose collections represent 80% of the total plant records for this region. Our results show that botanists with practical training in tropical plant identification record both more species and more species of conservation concern (20 more species, two more endemic and one more threatened species per 250 specimens) than untrained botanists. Training and the number of person-days in the field explained 96% of the variation in the numbers of species found, and training was the most important predictor for explaining recorded numbers of threatened and endemic species. Data quality was related to available facilities, with good herbarium access significantly reducing the proportions of misidentifications and misspellings. Our analysis suggests that it may be necessary to account for recorder training when comparing diversity across sites, particularly when assessing numbers of rare and endemic species, and for global data portals to provide such information. We also suggest that greater investment in the training of botanists and in the provisioning of good facilities would substantially increase recording efficiency and data reliability, thereby improving conservation planning and implementation on the ground