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Pendrill F, Gardner TA, Meyfroidt P, Persson UM, Adams J, Azevedo T, Bastos Lima MG, Baumann M, Curtis PG, De Sy V, Garrett R, Godar J, Goldman ED, Hansen MC, Heilmayr R, Herold M, Kuemmerle T, Lathuillière MJ, Ribeiro V, Tyukavina A, Weisse MJ, West C. Disentangling the numbers behind agriculture-driven tropical deforestation. Science 2022; 377:eabm9267. [PMID: 36074840 DOI: 10.1126/science.abm9267] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Tropical deforestation continues at alarming rates with profound impacts on ecosystems, climate, and livelihoods, prompting renewed commitments to halt its continuation. Although it is well established that agriculture is a dominant driver of deforestation, rates and mechanisms remain disputed and often lack a clear evidence base. We synthesize the best available pantropical evidence to provide clarity on how agriculture drives deforestation. Although most (90 to 99%) deforestation across the tropics 2011 to 2015 was driven by agriculture, only 45 to 65% of deforested land became productive agriculture within a few years. Therefore, ending deforestation likely requires combining measures to create deforestation-free supply chains with landscape governance interventions. We highlight key remaining evidence gaps including deforestation trends, commodity-specific land-use dynamics, and data from tropical dry forests and forests across Africa.
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Affiliation(s)
- Florence Pendrill
- Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
| | - Toby A Gardner
- Stockholm Environment Institute (SEI), Stockholm, Sweden
| | - Patrick Meyfroidt
- Georges Lemaître Earth and Climate Research Centre, Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium.,Fonds de la Recherche Scientifique F.R.S.-FNRS, Brussels, Belgium
| | - U Martin Persson
- Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
| | - Justin Adams
- Tropical Forest Alliance, World Economic Forum, Geneva, Switzerland
| | | | | | - Matthias Baumann
- Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Veronique De Sy
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, Netherlands
| | - Rachael Garrett
- Environmental PolicyLab, Department of Humanities, Social, and Political Sciences, ETH Zurich, Zürich, Switzerland.,Department of Geography and Cambridge Conservation Initiative, Cambridge University, Cambridge, UK
| | - Javier Godar
- Stockholm Environment Institute (SEI), Stockholm, Sweden
| | | | - Matthew C Hansen
- Department of Geographical Sciences, University of Maryland, College Park, Maryland, USA
| | - Robert Heilmayr
- Environmental Studies Program, University of California, Santa Barbara, Santa Barbara, California, USA.,Bren School of Environmental Science and Management, University of California, Santa Barbara, Santa Barbara, California, USA
| | - Martin Herold
- Helmholz GFZ Research Centre for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg, Potsdam, Germany
| | - Tobias Kuemmerle
- Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany.,Integrated Research Institute for Transformations in Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Vivian Ribeiro
- Stockholm Environment Institute (SEI), Stockholm, Sweden
| | - Alexandra Tyukavina
- Department of Geographical Sciences, University of Maryland, College Park, Maryland, USA
| | - Mikaela J Weisse
- Global Forest Watch, World Resources Institute, Washington, DC, USA
| | - Chris West
- Stockholm Environment Institute York, Department of Environment and Geography, University of York, York, UK
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Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method. REMOTE SENSING 2022. [DOI: 10.3390/rs14133238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s. However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. The lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment. Here we improved the forest loss and gain detections by integrating the LandTrendr change detection algorithm with the Random Forest (RF) machine-learning method and applied it to assess forest loss and gain patterns in the Zhejiang, Jiangxi, and Guangxi Provinces of the subtropical vegetation in China. The accuracy evaluation indicated that our approach can adequately detect the spatial and temporal distribution patterns in forest gain and loss, with an overall accuracy of 93% and the Kappa coefficient of 0.89. The forest loss area was 8.30 × 104 km2 in the Zhejiang, Jiangxi, and Guangxi Provinces during 1986–2019, accounting for 43.52% of total forest area in 1986, while the forest gain area was 20.25 × 104 km2, accounting for 106.19% of total forest area in 1986. Although the interannual variation patterns were similar among three provinces, the forest loss and gain area and the magnitude of change trends were significantly different. Guangxi has the largest forest loss and gain area and increasing trends, followed by Jiangxi, and the least in Zhejiang. The variations in annual forest loss and gain area can be mostly explained by the timelines of major forestry policies and regulations. Our study would provide an applicable method and data for assessing the impacts of forest disturbance events and forestry policies and regulations on the spatial and temporal patterns of forest loss and gain in China, and further contributing to regional and national forest carbon and greenhouse gases budget estimations.
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