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

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dc.contributor.authorAhrends, Antje
dc.contributor.authorBulling, Mark T.
dc.contributor.authorPlatts, Philip J.
dc.contributor.authorSwetnam, Ruth
dc.contributor.authorRyan, Casey
dc.contributor.authorDoggart, Nike
dc.contributor.authorHollingsworth, Peter M.
dc.contributor.authorMarchant, Robert
dc.contributor.authorBalmford, Andrew
dc.contributor.authorHarris, David J.
dc.contributor.authorGross-­Camp, Nicole
dc.contributor.authorSumbi, Peter
dc.contributor.authorMunishi, Pantaleo
dc.contributor.authorMadoffe, Seif
dc.contributor.authorMhoro, Boniface
dc.contributor.authorLeonard, Charles
dc.contributor.authorBracebridge, Claire
dc.contributor.authorDoody, Kathryn
dc.contributor.authorWilkins, Victoria
dc.contributor.authorOwen, Nisha
dc.contributor.authorMarshall, Andrew R.
dc.contributor.authorSchaafsma, Marije
dc.contributor.authorPfliegner, Kerstin
dc.contributor.authorJones, Trevor
dc.contributor.authorRobinson, James
dc.contributor.authorTopp-­Jørgensen, Elmer
dc.contributor.authorBrink, Henry
dc.contributor.authorBurgess, Neil D.
dc.date.accessioned2023-06-19T09:23:58Z
dc.date.available2023-06-19T09:23:58Z
dc.date.issued2021-01-08
dc.descriptionResearch Articleen_US
dc.description.abstractTropical 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.en_US
dc.description.sponsorshipDarwin Initiative, Grant/Award Number: 25-­019; Marie Curie Actions, Grant/ Award Number: MEXT-­C T-­2004-­517098; Global Environment Facility; Danish International Development Agency; Scottish Government’s Rural and Environment Science and Analytical Services Division; Finnish International Development Agency; Leverhulme Trusten_US
dc.identifier.urihttp://www.suaire.sua.ac.tz/handle/123456789/5315
dc.language.isoenen_US
dc.publisherPlants, People, Planet (PPP)en_US
dc.subjectBiodiversity conservationen_US
dc.subjectCarbon emissionsen_US
dc.subjectCommunity-­based forest managementen_US
dc.subjectEast Africaen_US
dc.subjectGlobal forest watchen_US
dc.subjectHuman disturbanceen_US
dc.subjectSynthetic aperture radaren_US
dc.subjectVillage land forest reservesen_US
dc.titleDetecting and predicting forest degradation: a comparison of ground surveys and remote sensing in Tanzanian forestsen_US
dc.typeArticleen_US
dc.urlDOI: 10.1002/ppp3.10189en_US

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