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

Antje Ahrends; Mark Bulling; Philip John Platts; Ruth D. Swetnam; Casey Ryan; Nike Doggart; Peter M Hollingsworth; Robert Marchant; Andrew Balmford; David J. Harris; Nicole Gross-Camp; Peter Sumbi; Pantaleon K T Munishi; Seif Madoffe; Boniface E. Mhoro; Charles Leonard; Claire Bracebridge; Kathryn Doody; Victoria Wilkins; Nisha Owen; Andy Marshall; Marije Schaafsma; Kerstin Pfliegner; Trevor Jones; James Robinson; Elmer J Topp-Jorgensen; Henry Brink; Neil D. Burgess

doi:10.1002/ppp3.10189

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

Antje Ahrends, Mark Bulling, Philip John Platts, Ruth D. Swetnam, Casey Ryan, Nike Doggart, Peter M Hollingsworth, Robert Marchant, Andrew Balmford, David J. Harris, Nicole Gross-Camp, Peter Sumbi, Pantaleon K T Munishi, Seif Madoffe, Boniface E. Mhoro, Charles Leonard, Claire Bracebridge, Kathryn Doody, Victoria Wilkins, Nisha OwenAndy Marshall, Marije Schaafsma, Kerstin Pfliegner, Trevor Jones, James Robinson, Elmer J Topp-Jorgensen, Henry Brink, Neil D. Burgess

Environment and Geography

Research output: Contribution to journal › Article › peer-review

Abstract

• 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.

Original language	English
Pages (from-to)	268-281
Number of pages	14
Journal	Plants, People, Planet
Volume	3
DOIs	https://doi.org/10.1002/ppp3.10189
Publication status	Published - 27 Apr 2021

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Cite this

Ahrends, A., Bulling, M., Platts, P. J., Swetnam, R. D., Ryan, C., Doggart, N., Hollingsworth, P. M., Marchant, R., Balmford, A., Harris, D. J., Gross-Camp, N., Sumbi, P., Munishi, P. K. T., Madoffe, S., Mhoro, B. E., Leonard, C., Bracebridge, C., Doody, K., Wilkins, V., ... Burgess, N. D. (2021). Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests. Plants, People, Planet, 3, 268-281. https://doi.org/10.1002/ppp3.10189

@article{356ec13afe324b99872f1d721760aa8d,

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

abstract = "• 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 {\textquoteleft}degradation{\textquoteright} 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.",

author = "Antje Ahrends and Mark Bulling and Platts, {Philip John} and Swetnam, {Ruth D.} and Casey Ryan and Nike Doggart and Hollingsworth, {Peter M} and Robert Marchant and Andrew Balmford and Harris, {David J.} and Nicole Gross-Camp and Peter Sumbi and Munishi, {Pantaleon K T} and Seif Madoffe and Mhoro, {Boniface E.} and Charles Leonard and Claire Bracebridge and Kathryn Doody and Victoria Wilkins and Nisha Owen and Andy Marshall and Marije Schaafsma and Kerstin Pfliegner and Trevor Jones and James Robinson and Topp-Jorgensen, {Elmer J} and Henry Brink and Burgess, {Neil D.}",

year = "2021",

month = apr,

day = "27",

doi = "10.1002/ppp3.10189",

language = "English",

volume = "3",

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journal = "Plants, People, Planet",

issn = "2572-2611",

publisher = "Wiley-Blackwell",

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Ahrends, A, Bulling, M, Platts, PJ, Swetnam, RD, Ryan, C, Doggart, N, Hollingsworth, PM, Marchant, R, Balmford, A, Harris, DJ, Gross-Camp, N, Sumbi, P, Munishi, PKT, Madoffe, S, Mhoro, BE, Leonard, C, Bracebridge, C, Doody, K, Wilkins, V, Owen, N, Marshall, A, Schaafsma, M, Pfliegner, K, Jones, T, Robinson, J, Topp-Jorgensen, EJ, Brink, H & Burgess, ND 2021, 'Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests', Plants, People, Planet, vol. 3, pp. 268-281. https://doi.org/10.1002/ppp3.10189

TY - JOUR

T1 - Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests

AU - Ahrends, Antje

AU - Bulling, Mark

AU - Platts, Philip John

AU - Swetnam, Ruth D.

AU - Ryan, Casey

AU - Doggart, Nike

AU - Hollingsworth, Peter M

AU - Marchant, Robert

AU - Balmford, Andrew

AU - Harris, David J.

AU - Gross-Camp, Nicole

AU - Sumbi, Peter

AU - Munishi, Pantaleon K T

AU - Madoffe, Seif

AU - Mhoro, Boniface E.

AU - Leonard, Charles

AU - Bracebridge, Claire

AU - Doody, Kathryn

AU - Wilkins, Victoria

AU - Owen, Nisha

AU - Marshall, Andy

AU - Schaafsma, Marije

AU - Pfliegner, Kerstin

AU - Jones, Trevor

AU - Robinson, James

AU - Topp-Jorgensen, Elmer J

AU - Brink, Henry

AU - Burgess, Neil D.

PY - 2021/4/27

Y1 - 2021/4/27

N2 - • 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.

AB - • 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.

U2 - 10.1002/ppp3.10189

DO - 10.1002/ppp3.10189

M3 - Article

SN - 2572-2611

VL - 3

SP - 268

EP - 281

JO - Plants, People, Planet

JF - Plants, People, Planet

ER -