Deforestation Journal Articles

[Rosalie Checca]

Figure1. Latitude of net zero biophysical effect of forests on local temperature varies from 30 to 56N. Above the line, forest cover causes local warming; below the line, forest cover causes local cooling. The thickness of the line indicates the number of studies that show forest cooling up to that threshold. Data sources as indicated.

Table 1. Forest effects on global temperature in modeling experiments from biogeochemical (CO2) versus biophysical impacts (albedo, evapotranspiration and roughness as well as changes in atmospheric and ocean circulation, snow and ice, and clouds).

Figure 3. Local temperature change in response to deforestation by season and time of day in the various climate zones as determined by comparing neighboring forested and open land (space for time approach) or measuring forest change over time. Warm/dry season response, averaged over the entire diurnal cycle, in red shading and cold/wet season response in blue shading. Daytime response, averaged over the entire annual cycle, in yellow shading and nighttime response in gray shading. See Supplementary Information 3 for data sources.

Figure 4. Effect of complete deforestation on local annual temperature by climate factor, averaged across the land surface within a 10 latitudinal band. Complete deforestation was implemented globally and analyzed by 10 latitudinal bands (Davin and de Noblet-Ducoudr, 2010). The CO2 effect was determined from total aboveground biomass in each 10 band after Walker et al. (2020) and scaled by CERA-derived sensitivity by latitude. Inset distinguishes the sum of all local biophysical effects from local CO2 effects.

Copyright 2022 Lawrence, Coe, Walker, Verchot and Vandecar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Palm oil is the most widely traded vegetable oil globally, with demand projected to increase substantially in the future. Almost all oil palm grows in areas that were once tropical moist forests, some of them quite recently. The conversion to date, and future expansion, threatens biodiversity and increases greenhouse gas emissions. Today, consumer pressure is pushing companies toward deforestation-free sources of palm oil. To guide interventions aimed at reducing tropical deforestation due to oil palm, we analysed recent expansions and modelled likely future ones. We assessed sample areas to find where oil palm plantations have recently replaced forests in 20 countries, using a combination of high-resolution imagery from Google Earth and Landsat. We then compared these trends to countrywide trends in FAO data for oil palm planted area. Finally, we assessed which forests have high agricultural suitability for future oil palm development, which we refer to as vulnerable forests, and identified critical areas for biodiversity that oil palm expansion threatens. Our analysis reveals regional trends in deforestation associated with oil palm agriculture. In Southeast Asia, 45% of sampled oil palm plantations came from areas that were forests in 1989. For South America, the percentage was 31%. By contrast, in Mesoamerica and Africa, we observed only 2% and 7% of oil palm plantations coming from areas that were forest in 1989. The largest areas of vulnerable forest are in Africa and South America. Vulnerable forests in all four regions of production contain globally high concentrations of mammal and bird species at risk of extinction. However, priority areas for biodiversity conservation differ based on taxa and criteria used. Government regulation and voluntary market interventions can help incentivize the expansion of oil palm plantations in ways that protect biodiversity-rich ecosystems.

Copyright: 2016 Vijay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data associated with each of the analyses performed in this paper: site analysis, vulnerable forest analysis and biodiversity prioritization are available through the Dryad data repository (doi:10.5061/dryad.2v77j) and Supporting Information.

Funding: This material is based upon work supported by the National Science Foundation (www.nsf.gov) under Grant No.1106401. CNJ received support from Cincia Sem Fronteiras (A025_2013). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

In this study, we seek to identify where oil palm has recently replaced tropical forests because this may best anticipate where future deforestation may occur. Furthermore, we wish to understand where future deforestation may cause the most harm to biodiversity.

Cutting carbon emissions from tropical deforestation could play a critical role in limiting the impacts of climate change and contribute toward global mitigation efforts aimed at reaching the agreed goal of

Different scenarios of oil palm development will lead to very different outcomes in terms of deforestation and carbon emissions, such as the development of degraded land versus peatlands in Indonesia [11]. In recent years, consumers and non-governmental organizations (NGOs) have increasingly called on consumer goods companies to buy responsibly produced palm oil and companies have begun to adopt voluntary measures [12]. The main organization responsible for the certification of sustainable palm oil is the Roundtable on Sustainable Palm Oil (RSPO), a group composed of oil palm producers, palm oil processors and traders, manufacturers, retailers, investors and NGOs. This certification system requires the producers to follow several criteria including transparency of management, conservation of natural resources and the execution of social and environmental impact assessments [13].

Currently, there are 3.51 million hectares of RSPO certified oil palm plantations producing 13.18 million tonnes of palm oil, making up 21% of global palm oil production [14]. NGOs have raised concerns about the monitoring and enforcement of standards for certification [15, 16, 17]. Furthermore, while primary forests and High Conservation Value forests (those deemed to have significant biodiversity or cultural value, or that provide ecosystem services) are protected under RSPO regulations, secondary, disturbed or regenerating forests are unprotected. RSPO certification has been criticized as insufficient from an environmental perspective [18]. Finally, there are concerns about the sources of palm oil that lacks certification, much of which is processed or traded by RSPO member companies and sold in the global marketplace [19].

Other reasons past assessments may have focused on only one or two countries are the many obstacles that face regional and global assessments of land cover changes and land use history. Assembling imagery across many countries using local resources is prohibitively labour intensive. While global satellite datasets are available, such as Landsat Thematic Mapper (TM) imagery from 1984 to the present, identifying land cover transitions from these images can be difficult, especially in humid tropical areas with frequent cloud cover. This means that transitions between distinct cover types (e.g. forest and row crops) are more reliably identified than those between similar cover types (e.g. fragmented forests and shifting cultivation). Thus, while availability of high-resolution imagery over much of the globe makes it possible to identify current land cover with great accuracy, sometimes even specific crops such as oil palm, the assessment of historical land cover is limited to broad categories in global assessments. For example, when Gibbs et al. [21] made a global assessment of land cover changes for the expansion of agriculture in the tropics, they decided to classify using only five land cover types to reduce these types of errors.

We studied oil palm plantations in 20 countries in four regions of interest: 1.) South America; 2.) Central America, Mexico and Caribbean (which we will refer to as Mesoamerica); 3.) Africa; and 4.) Southeast Asia. In each region, we selected the five countries with the largest values of FAO 2013 palm oil production.

We selected individual sample sites with oil palm monoculture using high-resolution imagery available from Google Earth of sufficient resolution to identify visually the pattern of individual oil palm trees. Whenever possible, we verified sample sites using corroborating news articles, geotagged photos, government and company records, or scholarly articles. We also used these sources to identify regions within each country (e.g. states and provinces) where oil palm is produced and examined each for oil palm to improve the spatial distribution of such sites within each country. A fully random selection of sites based on age would have been prohibitively time consuming, if even possible with available satellite imagery and mapping algorithms. The sampled oil palm areas covered at least 3% of the FAO 2013 total oil palm harvested area for each sample country. The percentage of sampled area was much higher for many lower production countries (Table 1).

We did not evaluate regrowth for this study because we were interested in the earliest identifiable deforestation events in areas currently occupied by oil palm. Finally, to facilitate analyses at larger spatial scales, we linearly interpolated annual deforested area between image dates to produce an annual time series of deforested area in each sample. We used 1989 as a start date for analysis since satellite imagery for the first sample point of most sites was available by that date (85%). The latest starting sample was 1991.

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