Combating deforestation: From satellite to intervention
Abstract
Near–real-time monitoring and response are possible
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ML can be used with remote sensing imagery to pinpoint changes in forest cover, or proxies for deforestation such as smoke from fires set to clear vegetation, as well as to differentiate selective cutting from clearcutting.
“ML can be used with remote sensing imagery to pinpoint changes in forest cover [241,329,369], or proxies for deforestation such as smoke from fires set to clear vegetation [505], as well as to differentiate selective cutting from clearcutting [342,487].”
From Tackling climate change with machine learning (2023) · cites this paper as [241]
Cites
High-Resolution Global Maps of 21st-Century Forest Cover Change
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., et al. (2013) · Science · doi:10.1126/science.1244693 · Cited by 11,704 · see citing works
Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains
Nepstad, D., McGrath, D., Stickler, C., Alencar, A., Azevedo, A., Swette, B., Bezerra, T., DiGiano, M., Shimada, J., Seroa da Motta, R., et al. (2014) · Science · doi:10.1126/science.1248525 · Cited by 1,202 · see citing works
Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation
Barlow, J., Lennox, G. D., Ferreira, J., Berenguer, É., Lees, A. C., Nally, R. M., Thomson, J. R., Ferraz, S. F. D. B., Louzada, J., Oliveira, V. H. F., et al. (2016) · Nature · doi:10.1038/nature18326 · Cited by 1,151 · see citing works
Li, J., & Roy, D. P. (2017) · Remote Sensing · doi:10.3390/rs9090902 · Cited by 662 · see citing works
Belward, A. S., & Skøien, J. O. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.03.009 · Cited by 440 · see citing works
Deforestation slowdown in the Brazilian Amazon: prices or policies?
Assunção, J., Gandour, C., & Rocha, R. (2015) · Environment and Development Economics · doi:10.1017/s1355770x15000078 · Cited by 305 · see citing works
Humid tropical forest disturbance alerts using Landsat data
Hansen, M. C., Krylov, A., Tyukavina, A., Potapov, P. V., Turubanova, S., Zutta, B., Ifo, S., Margono, B., Stolle, F., & Moore, R. (2016) · Environmental Research Letters · doi:10.1088/1748-9326/11/3/034008 · Cited by 303 · see citing works
Using spatial statistics to identify emerging hot spots of forest loss
Harris, N. L., Goldman, E. D., Gabris, C., Nordling, J., Minnemeyer, S., Ansari, S., Lippmann, M., Bennett, L., Raad, M., Hansen, M. C., et al. (2017) · Environmental Research Letters · doi:10.1088/1748-9326/aa5a2f · Cited by 252 · see citing works
Pervasive Rise of Small-scale Deforestation in Amazonia
Kalamandeen, M., Gloor, E. U., Mitchard, E. T. A., Quincey, D. J., Ziv, G., Spracklen, D. V., Spracklen, B. D., Adami, M., Aragão, L. E. O. C., & Galbraith, D. R. (2018) · Scientific Reports · doi:10.1038/s41598-018-19358-2 · Cited by 195 · see citing works
Choose satellites to monitor deforestation
Lynch, J. M., Maslin, M. A., Balzter, H., & Sweeting, M. N. (2013) · Nature · doi:10.1038/496293a · Cited by 84 · see citing works
Butler, D. (2014) · Nature · doi:10.1038/505143a · Cited by 51 · see citing works
Design and Implementation of an Interactive Web-Based Near Real-Time Forest Monitoring System
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Places to Watch: Identifying High-Priority Forest Disturbance from Near–Real Time Satellite Data
Weisse, M., Petersen, R., Sargent, S., et al. (2017) · Cited by 5 · see citing works
Self-citation
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Finer, M., Novoa, S., Weisse, M. J., Petersen, R., Mascaro, J., Souto, T., Stearns, F., & Martinez, R. G.
- Affiliations
- Amazon Conservation Association (US)
- Asociación para la Conservación de la Cuenca Amazónica (PE)
- World Resources Institute (US)
- Planet Labs PBC (United States) (US)
- Venue
- Science
- Published
- 2018-06-22
- Volume
- 360
- Issue
- 6395
- Pages
- 1303-1305
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2809079570
- Cited by
- 177
- References
- 13
- Citations by year
- 2026: 13 · 2025: 19 · 2024: 22 · 2023: 22 · 2022: 28 · 2021: 23 · 2020: 29 · 2019: 17 · 2018: 4
- FWCI
- 10.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Conservation, Biodiversity, and Resource Management
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Engineering › Ocean Engineering › Wetland Management and Conservation
- Keywords
- deforestation, forest monitoring, satellite remote sensing, near real-time monitoring, forest conservation
- MeSH terms
- Conservation of Natural Resources, Environmental Monitoring, Spacecraft, Satellite Imagery
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