Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests
Societal 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 fie...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-05-01
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| Series: | Plants, People, Planet |
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| Online Access: | https://doi.org/10.1002/ppp3.10189 |
| _version_ | 1818384641710620672 |
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| author | Antje Ahrends Mark T. Bulling Philip J. Platts Ruth Swetnam Casey Ryan Nike Doggart Peter M. Hollingsworth Robert Marchant Andrew Balmford David J. Harris Nicole Gross‐Camp Peter Sumbi Pantaleo Munishi Seif Madoffe Boniface Mhoro Charles Leonard Claire Bracebridge Kathryn Doody Victoria Wilkins Nisha Owen Andrew R. Marshall Marije Schaafsma Kerstin Pfliegner Trevor Jones James Robinson Elmer Topp‐Jørgensen Henry Brink Neil D. Burgess |
| author_facet | Antje Ahrends Mark T. Bulling Philip J. Platts Ruth Swetnam Casey Ryan Nike Doggart Peter M. Hollingsworth Robert Marchant Andrew Balmford David J. Harris Nicole Gross‐Camp Peter Sumbi Pantaleo Munishi Seif Madoffe Boniface Mhoro Charles Leonard Claire Bracebridge Kathryn Doody Victoria Wilkins Nisha Owen Andrew R. Marshall Marije Schaafsma Kerstin Pfliegner Trevor Jones James Robinson Elmer Topp‐Jørgensen Henry Brink Neil D. Burgess |
| author_sort | Antje Ahrends |
| collection | DOAJ |
| description | Societal 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. |
| first_indexed | 2024-12-14T03:25:30Z |
| format | Article |
| id | doaj.art-ff4526d845c5432fa555e6f028f20107 |
| institution | Directory Open Access Journal |
| issn | 2572-2611 |
| language | English |
| last_indexed | 2024-12-14T03:25:30Z |
| publishDate | 2021-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plants, People, Planet |
| spelling | doaj.art-ff4526d845c5432fa555e6f028f201072022-12-21T23:18:53ZengWileyPlants, People, Planet2572-26112021-05-013326828110.1002/ppp3.10189Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forestsAntje Ahrends0Mark T. Bulling1Philip J. Platts2Ruth Swetnam3Casey Ryan4Nike Doggart5Peter M. Hollingsworth6Robert Marchant7Andrew Balmford8David J. Harris9Nicole Gross‐Camp10Peter Sumbi11Pantaleo Munishi12Seif Madoffe13Boniface Mhoro14Charles Leonard15Claire Bracebridge16Kathryn Doody17Victoria Wilkins18Nisha Owen19Andrew R. Marshall20Marije Schaafsma21Kerstin Pfliegner22Trevor Jones23James Robinson24Elmer Topp‐Jørgensen25Henry Brink26Neil D. Burgess27Royal Botanic Garden Edinburgh Edinburgh UKEnvironmental Sustainability Research Centre University of Derby Derby UKDepartment of Environment and Geography University of York York UKGeography Staffordshire University Stoke‐on‐Trent UKSchool of GeoSciences University of Edinburgh Edinburgh UKSchool of Earth and Environment University of Leeds Leeds UKRoyal Botanic Garden Edinburgh Edinburgh UKDepartment of Environment and Geography University of York York UKDepartment of Zoology University of Cambridge Cambridge UKRoyal Botanic Garden Edinburgh Edinburgh UKUniversity of East Anglia Norwich UKTanBE Ltd Dar es Salaam TanzaniaSokoine University of Agriculture Morogoro TanzaniaSokoine University of Agriculture Morogoro TanzaniaHerbarium Botany Department University of Dar es Salaam Dar es Salaam TanzaniaTanzania Forest Conservation Group Dar es Salaam TanzaniaNorth Carolina Zoo Asheboro NC USAThe Society for Environmental Exploration (Frontier) London UKThe Society for Environmental Exploration (Frontier) London UKThe Society for Environmental Exploration (Frontier) London UKDepartment of Environment and Geography University of York York UKVrije Universiteit Amsterdam Amsterdam The NetherlandsThe Nature Conservancy Berlin GermanySouthern Tanzania Elephant Program Iringa TanzaniaRoyal Botanic Garden Edinburgh Edinburgh UKThe Society for Environmental Exploration (Frontier) London UKArid Zone Research Institute Alice Spring NT AustraliaUNEP‐WCMC Cambridge UKSocietal 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.https://doi.org/10.1002/ppp3.10189biodiversity conservationcarbon emissionscommunity‐based forest managementEast Africaglobal forest watchhuman disturbance |
| spellingShingle | Antje Ahrends Mark T. Bulling Philip J. Platts Ruth Swetnam Casey Ryan Nike Doggart Peter M. Hollingsworth Robert Marchant Andrew Balmford David J. Harris Nicole Gross‐Camp Peter Sumbi Pantaleo Munishi Seif Madoffe Boniface Mhoro Charles Leonard Claire Bracebridge Kathryn Doody Victoria Wilkins Nisha Owen Andrew R. Marshall Marije Schaafsma Kerstin Pfliegner Trevor Jones James Robinson Elmer Topp‐Jørgensen Henry Brink Neil D. Burgess Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests Plants, People, Planet biodiversity conservation carbon emissions community‐based forest management East Africa global forest watch human disturbance |
| title | Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests |
| title_full | Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests |
| title_fullStr | Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests |
| title_full_unstemmed | Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests |
| title_short | Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests |
| title_sort | detecting and predicting forest degradation a comparison of ground surveys and remote sensing in tanzanian forests |
| topic | biodiversity conservation carbon emissions community‐based forest management East Africa global forest watch human disturbance |
| url | https://doi.org/10.1002/ppp3.10189 |
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