Combating deforestation: From satellite to intervention

Finer, Matt; Novoa, Sidney; Weisse, Mikaela; Petersen, Rachael; Mascaro, Joseph; Souto, Tamia; Stearns, Forest; Martinez, Raúl García

doi:10.1126/science.aat1203

Cited by 129 publications

(95 citation statements)

References 12 publications

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“…Deforestation monitoring programs, like the newly established National System of Monitoring and Control in Peru, can target the unique spatial patterning associated with oil palm development within areas shown to be at risk. The implementation of real-time monitoring using satellite imagery enhances this approach [68]. In habitats disproportionately at risk of oil palm deforestation and with little protected area coverage, it may be advisable to establish new protected areas, increase protection levels in existing ones, and effectively implement consultation with indigenous communities.…”

Section: Discussionmentioning

confidence: 99%

Deforestation risks posed by oil palm expansion in the Peruvian Amazon

Vijay

Reid

Finer

et al. 2018

Environ. Res. Lett.

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Further expansion of agriculture in the tropics is likely to accelerate the loss of biodiversity. One crop of concern to conservation is African oil palm (Elaeis guineensis). We examined recent deforestation associated with oil palm in the Peruvian Amazon within the context of the region's other crops. We found more area under oil palm cultivation (845 km 2 ) than did previous studies. While this comprises less than 4% of the cropland in the region, it accounted for 11% of the deforestation from agricultural expansion from 2007-2013. Patches of oil palm agriculture were larger and more spatially clustered than for other crops, potentially increasing their impact on local habitat fragmentation. Modeling deforestation risk for oil palm expansion using climatic and edaphic factors showed that sites at lower elevations, with higher precipitation, and lower slopes than those typically used for intensive agriculture are at long-term risk of deforestation from oil palm agriculture. Within areas at long-term risks, based on CART models, areas near urban centers, roads, and previously deforested areas are at greatest short-term risk of deforestation. Existing protected areas and officially recognized indigenous territories cover large areas at long-term risk of deforestation for oil palm (>40%). Less than 7% of these areas are under strict (IUCN I-IV) protection. Based on these findings, we suggest targeted monitoring for oil palm deforestation as well as strengthening and expanding protected areas to conserve specific habitats.

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Section: Discussionmentioning

confidence: 99%

Deforestation risks posed by oil palm expansion in the Peruvian Amazon

Vijay

Reid

Finer

et al. 2018

Environ. Res. Lett.

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“…size, shape, location relative to infrastructure). Such models could facilitate more rapid and systematic assessment of deforestation drivers over time (Finer et al 2018). For example, Richards and Friess trained a model to predict drivers of mangrove forest loss in Southeast Asia (Richards and Friess 2016), and recently Curtis et al trained a model to predict drivers of deforestation globally (Curtis et al 2018).…”

Section: New Methods For Systematic Monitoring Of Deforestation Driversmentioning

confidence: 99%

What causes deforestation in Indonesia?

Austin

Schwantes

et al. 2019

Environ. Res. Lett.

321

227

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We investigate the causes of deforestation in Indonesia, a country with one of the highest rates of primary natural forest loss in the tropics, annually between 2001 and 2016. We use high spatial resolution imagery made available on Google Earth to characterize the land cover types following a random selection of deforestation events, drawn from the Global Forest Change dataset. Notorious in the region, large-scale oil palm and timber plantations together contributed more than two-fifths of nationwide deforestation over our study period, with a peak in late aughts followed by a notable decline up to 2016. Conversion of forests to grasslands, which comprised an average of one-fifth of national deforestation, rose sharply in dominance in years following periods of considerable fire activity, particularly in 2016. Small-scale agriculture and small-scale plantations also contributed onefifth of nationwide forest loss and were the dominant drivers of loss outside the major islands of Indonesia. Although relatively small contributors to total deforestation, logging roads were responsible for a declining share of deforestation, and mining activities were responsible for an increasing share, over the study period. Direct drivers of deforestation in Indonesia are thus spatially and temporally dynamic, suggesting the need for forest conservation policy responses tailored at the subnational level, and new methods for monitoring the causes of deforestation over time.

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“…Conversely, nascent initiatives such as Radiant High resolution satellite imagery acquisitions are needed to accurately interpret land cover. Planet labs' aspirations to acquire and provide such imagery [50,51], as well as datasets from DigitalGlobe [45] could prove to be useful for the photo-interpretation of land cover over large areas [52]. However, the benefit of acquiring and processing commercial high-resolution imagery must be substantial to overcome the limitations of coarser resolution free imagery such as Landsat.…”

Section: Data Issuesmentioning

confidence: 99%

Contextualizing Landscape-Scale Forest Cover Loss in the Democratic Republic of Congo (DRC) between 2000 and 2015

Molinario

Hansen

Potapov

et al. 2020

Land

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Shifting cultivation has been shown to be the primary cause of land use change in the Democratic Republic of Congo (DRC). Traditionally, forested and fallow land are rotated in a slash and burn cycle that has created an agricultural mosaic, including secondary forest, known as the rural complex. This study investigates the land use context of new forest clearing (during 2000–2015) in primary forest areas outside of the established rural complex. These new forest clearings occur as either rural complex expansion (RCE) or isolated forest perforations (IFP), with consequent implications on the forest ecosystem and biodiversity habitat. During 2000–2015, subsistence agriculture was the dominant driver of forest clearing for both extension of settled areas and pioneer clearings removed from settled areas. Less than 1% of clearing was directly attributable to land uses such as mining, plantations, and logging, showing that the impact of commercial operations in the DRC is currently dwarfed by a reliance on small-holder shifting cultivation. However, analyzing the landscape context showed that large-scale agroindustry and resource extraction activities lead to increased forest loss and degradation beyond their previously-understood footprints. The worker populations drawn to these areas create communities that rely on shifting cultivation and non-timber forest products (NTFP) for food, energy, and building materials. An estimated 12% of forest loss within the RCE and 9% of the area of IFP was found to be within 5 km of mines, logging, or plantations. Given increasing demographic and commercial pressures on DRC’s forests, it will be crucial to factor in this landscape-level land use change dynamic in land use planning and sustainability-focused governance.

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Combating deforestation: From satellite to intervention

Abstract: Near–real-time monitoring and response are possible

Cited by 129 publications

References 12 publications

Deforestation risks posed by oil palm expansion in the Peruvian Amazon

Deforestation risks posed by oil palm expansion in the Peruvian Amazon

What causes deforestation in Indonesia?

Contextualizing Landscape-Scale Forest Cover Loss in the Democratic Republic of Congo (DRC) between 2000 and 2015

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