MAAP #214: Agriculture in the Amazon: New data reveals key patterns of crops & cattle pasture

Figure 1. Agricultural and pasture data in a section of the Brazilian Amazon.

A burst of new data and online visualization tools are revealing key land use patterns across the Amazon, particularly regarding the critical topic of agriculture. This type of data is particularly important because agriculture is the leading cause of overall Amazonian deforestation.

These new datasets include:

  • Crops. The International Food Policy Research Institute (IFPRI), a leading agriculture and food systems research authority, recently launched the latest version of their innovative crop monitoring product, the Spatial Production Allocation Model (SPAM).1 This latest version, developed with support from WRI’s Land & Carbon Lab, features spatial data for 46 crops, including soybean, oil palm, coffee, and cocoa. This data is mapped at 10-kilometer resolution across the Amazon and updated through 2020.2
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  • Cattle pasture. The Atlas of Pastures,3 developed by the Federal University of Goiás, facilitates access to data regarding Brazilian cattle pastures generated by MapBiomas. This data is mapped at 30-kilometer resolution and updated through 2022. We use Collection 5 from Mapbiomas for the rest of the Amazonian countries.4
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  • Gold mining. New mining data is included for additional context. Amazon Mining Watch uses machine learning to map open-pit gold mining.5 This data is mapped at 10-kilometer resolution across the Amazon and updated through 2023.

We merged and analyzed these new datasets to provide our first overall estimate of Amazonian land use, the most detailed effort to date across all nine countries of the biome. Figure 1 shows an example of this merged data in a section of the Brazilian Amazon.

Below, we present and illustrate the following major findings across the Amazon, and then zoom in on several regions across the Amazon to show the data in greater detail.

Major Findings

The Base Map illustrates several major findings detailed below.

Base Map. Overview of the merged datasets noted above for crops, pasture, and gold mining. Double-click to enlarge. Data: IFRI/SPAM, Lapig/UFG, Mapbiomas, AMW, ACA/MAAP.

1) Crops
We found that 40 crops in the SPAM dataset overlap with the Amazon, covering over 106 million hectares (13% of the Amazon biome).

Soybean covers over 67.5 million hectares, mostly in southern Brazil and Bolivia. Maize covers slightly more area (70 million hectares) but we consider this a secondary rotational crop with soy (thus, there is considerable overlap between these two crops).

Oil palm covers nearly 8 million hectares, concentrated in eastern Brazil, central Peru, northern Ecuador, and northern Colombia.

In the Andean Amazon zones of Peru, Ecuador, and Colombia, cocoa covers over 8 million hectares and the two types of coffee (Arabica and Robusta) cover 6.7 million hectares.

Other major crops across the Amazon include rice (13.8 million hectares), sorghum (10.9 million hectares), cassava (9.8 million hectares), sugarcane (9.6 million hectares), and wheat (5.8 million hectares).

2) Cattle Pasture
Cattle Pasture covers 76.3 million hectares (9% of the Amazon biome). The vast majority (92%) of the pasture is in Brazil, followed by Colombia and Bolivia.

3) Crops & Cattle Pasture
Overall, accounting for overlaps between the data, we estimate that crops and pasture combined cover 115.8 million hectares. This total is the equivalent of 19% of the Amazon biome.

In comparison, open-pit gold mining covered 1.9 million hectares (0.23% of the Amazon biome).

Zooms across the Amazon

Eastern Brazilian Amazon

Figure 2 shows the transition from the soy frontier to the cattle pasture frontier in the eastern Brazilian Amazon. Also note a mix of other crops, such as oil palm, sugarcane, and cassava, and some gold mining.

Figure 2. Eastern Brazilian Amazon. Data: IFRI/SPAM, Lapig/UFG, Mapbiomas, AMW, ACA/MAAP.

Andean Amazon (Peru and Ecuador)

Figure 3. Andean Amazon. Data: IFRI/SPAM, Lapig/UFG, Mapbiomas, AMW, ACA/MAAP.

The land use patterns are quite different in the Andean Amazon regions of Peru and Ecuador.

Figure 3 shows, that instead of soy and cattle pasture, there is instead oil palm, rice, coffee, and cocoa.

Also note the extension of the cattle pasture frontier in the western Brazilian Amazon, towards Peru and Bolivia.

 

 

 

 

 

 

 

 

 

 

 

 

Northeast Amazon (Venezuela, Guyana, Suriname, French Guiana)

Figure 4 shows the general lack of crops in the core Amazon regions Guyana, Suriname, and French Guiana, which is surely a major factor they are all considered High Forest cover, Low Deforestation countries (HFLD). In contrast, note there is abundant gold mining activity throughout this region.

Figure 4. Northeastern Amazon. Data: IFRI/SPAM, Lapig/UFG, Mapbiomas, AMW, ACA/MAAP.

Methods

For the SPAM data, we used the physical area, which is measured in a hectare and represents the actual area where a crop is grown (not counting how often production was harvested from it). We only considered values ​​greater than or equal to 100 ha per pixel.

For the Base Map, due to their importance as primary economic crops, we layered soybean and oil palm as the top two layers, respectively. From there, crops were layered in order of their total physical area across the Amazon. Thus, the full extensions of some crops are not shown if they overlap pixels with other crops that have greater physical area. For overlaps with crops and pasture, we favored the crops.

Notes & Data Sources

1 International Food Policy Research Institute (IFPRI), 2024, “Global Spatially-Disaggregated Crop Production Statistics Data for 2020 Version 1.0” https://doi.org/10.7910/DVN/SWPENT, Harvard Dataverse, V1

Spatial Production Allocation Model (SPAM)
SPAM 2020 v1.0 Global data (Updated 2024-04-16)

2 Note that the spatial resolution is rather low (10-kilometers) so all crop coverage data above should be interpreted as referential only.

3 The Atlas of Pastures (Atlas das Pastagens), open to the public, was developed by the Image Processing and Geoprocessing Laboratory of the Federal University of Goiás (Lapig/UFG), to facilitate access to results and products generated within the MapBiomas initiative, regarding Brazilian pastures.

https://atlasdaspastagens.ufg.br/

4 MapBiomas Collection 5;  https://amazonia.mapbiomas.org/en/

5 See MAAP #212 for more information on Amazon Mining Watch.

Citation

Finer M, Ariñez A (2024) Agriculture in the Amazon: New data reveals key patterns of crops & cattle pasture. MAAP: 214.

MAAP #134: Agriculture and Deforestation in the Peruvian Amazon

Peru’s first National Agricultural Area Map. Source: MIDAGRI.

For the first time, Peru has a detailed National Agricultural Area Map.

This unique map, produced with high-resolution satellite imagery, was published by the Peruvian Ministry of Agrarian Development (MIDAGRI) in January.*

This map reveals that the agricultural area at the national level is 11.6 million hectares, as of 2018.

Here, we analyze this new information in relation to annual forest loss data, generated by the Peruvian Environment Ministry (Geobosques).

The goal is to better understand the critical link between agriculture and deforestation in the Peruvian Amazon.

Specifically, we analyze the agricultural area of 2018 in relation to the preceding forest loss between 2001 and 2017.

Below are two main sections:

First, we present our Base Map that illustrates the major results.

Second, we show a series of zoomed images of select areas to illustrate key results in detail. These areas include major deforestation events related to oil palm, cacao, and other crops.

 

 

 

 

 

Base Map showing our major results. Data: MAAP, MIDAGRI, MINAM/Geobosques. Double click to enlarge.

Major Results

  • We found that 43% (4.9 million hectares) of Peru’s total agricultural area in 2018 was located in the Amazon basin.
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  • Of these Amazonian agricultural areas, more than 1.1 million hectares (24%) came from forest lost between 2001 and 2017 (indicated in red on the Base Map).
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  • Expressed another way, over half (56%) of the forest loss in the Peruvian Amazon between 2001 and 2017 corresponds to an agricultural area in 2018.
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  • The Base Map also shows, in brown, the agricultural area that is not linked to recent forest loss. The vast majority is located outside the Amazon basin (western Peru).
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  • Finally, the Base Map shows, in black, the recent forest loss not linked to agriculture. Much of this loss corresponds to gold mining (southeastern Peru), logging roads, and natural loss such as landslides.

 

 

 

 

 

 

Zooms of Key Areas

A. United Cacao (Loreto)

Image A shows the large-scale deforestation associated with the company United Cacao between 2013 and 2016, in the Loreto region  (MAAP # 128). The clearing, as the name indicates, was for the installation of Peru’s first and only industrial-style cacao plantation. In total, the deforestation for the plantation reached 2,380 hectares.

Zoom A. United Cacao (Loreto region). Data: MAAP, MIDAGRI, MINAM/Geobosques.

B. Oil Palm (Shanusi, Loreto)

Image B shows the large-scale deforestation of more than 16,800 hectares associated with oil palm plantations between 2006 and 2015, along the border of the Loreto and San Martin regions (MAAP #116). Of this total, the deforestation of 6,975 hectares was linked to two plantations managed by the company Grupo Palmas company. The remainder occurred in the private areas surrounding the company’s plantations.

Zoom B. Oil palm deforestation around Shanusi (Loreto region). Data: MAAP, MIDAGRI, MINAM/Geobosques.

C. Oil Palm (Ucayali)

Image C shows the large-scale deforestation of more than 12,000 hectares for two oil palm plantations between 2011 and 2015, in the Ucayali region (MAAP #41).

Zoom C. Oil palm deforestation (Ucayali region). Data: MAAP, MIDAGRI, MINAM/Geobosques.

D. Iberia (Madre de Dios)

Image D shows the expanding agriculture-related deforestation around the town of Iberia, near the border with Brazil and Bolivia (MAAP #75). The major cause, according to local sources, is the increase in corn, papaya, and cacao plantations. We have documented the deforestation of more than 3,000 hectares in this area since 2014.

Zoom D. Agriculture related deforestation around Iberia (Madre de Dios region). Data: MAAP, MIDAGRI, MINAM/Geobosques.

E. Zona Minera (Madre de Dios)

Finally, Image E shows deforestation in the gold mining hotspot known as La Pampa, in the Madre de Dios region. The non-agricultural deforestation in the center is the major illegal gold mining front. Around that area, and along the Interoceanic Highway, there is extensive agriculture-related deforestation.

Zoom E. Mining and agriculture deforestation in southern Peru (Madre de Dios region). Data: MAAP, MIDAGRI, MINAM/Geobosques.

*Notes and Methodology

According to MIDAGRI, the National Agricultural Area Map was “generated based on satellite images from RapidEye and later updated with satellite images from Sentinel-2 and the Google Earth platform, which allowed the mapping and precise measurement of the agricultural surface throughout the national territory.”

The data include “agricultural land with cultivation and without cultivation.” We assume that these data include cattle pasture.

The identification and quantification of deforested areas (2001-2017) that correspond to agricultural area in 2018 results from the analysis carried out in GIS by the superposition of both geospatial layers (MINAM and MIDAGRI).

Amazonian agricultural areas that came from forest lost between 2001 and 2017 = 1,185,722 hectares (indicated in red on the Base Map).

Acknowledgments

We thank E. Ortiz (AAF), S. Novoa (ACCA) and G. Palacios for their helpful comments on this report.

This work was supported by NORAD (Norwegian Agency for Development Cooperation), ICFC (International Conservation Fund of Canada), and EROL Foundation.

Citation

Vale Costa H, Finer M (2021) Agriculture and Deforestation in the Peruvian Amazon. MAAP: 134.

MAAP #128: United Cacao Case – 7 Years After Massive Deforestation in the Peruvian Amazon

Image 1. The first sign of large-scale deforestation, near the town of Tamshiyacu, in June 2013. Data: Planet (RapidEye). First time published. Click to enlarge.

Here, we confirm the massive deforestation of primary forest (more than 2,000 hectares) in the Peruvian Amazon by the company United Cacao between 2013 and 2016.

We present a series of recently obtained (and never before published) satellite images to emphasize the reality and importance of a deforestation case that is still being debated at the highest levels in the Peruvian government, seven years later.

In June 2013, a high-resolution satellite, through scattered clouds, revealed the start of massive deforestation of primary forest near the town of Tamshiyacu, in the Loreto region (Image 1).

In August of that same year, the clouds cleared a bit more, giving a better view. Image 2 (see below) shows the rapid deforestation of primary forest in 2013.

By September 2015, the deforestation had reached 2,380 hectares, or 5,880 acres (Image 3).

Most recently, in October 2020, a new very high-resolution image shows cacao crops in areas that, seven years ago, were primary forest (Image 4).

Below, we show, for the first time, these high and very-high resolution images (5 and 0.5 meters, respectively) recently obtained from the satellite company Planet, clearly showing the situation from 2012 -2020. Additionally, we show the historical context with Landsat images dating back to 1985, and briefly describe the current political situation regarding the case.

Deforestation 2013

Image 2 shows the first stage of large-scale deforestation (1,100 hectares) by the company United Cacao, between August 2012 (left panel) and August 2013 (right panel).

Image 2. Large-scale deforestation by United Cacao between August 2012 (left panel) and August 2013 (right panel). Data: Planet (RapidEye). First time published. Click to enlarge.

Deforestation 2015

Image 3 shows the total large-scale deforestation (2,380 hectares, or 5,880 acres) by United Cacao between August 2012 (left panel) and September 2015 (right panel). Insets A-C indicate the locations of the zooms below.

Image 3. Large-scale deforestation by United Cacao between August 2012 (left panel) and September 2015 (right panel). Data: Planet (RapidEye). First time published. Click to enlarge.

2020 – Cacao Crops in Deforested Areas

Images 4-6 show current cacao crops (right panel) in areas that, seven years ago, were primary forest (left panel).

Image 4. Current cacao crops (right panel) in areas that, seven years ago, were primary forest (left panel). Data: Planet, Airbus. First time published. Click to enlarge.
Image 5. Current cacao crops (right panel) in areas that, seven years ago, were primary forest (left panel). Data: Planet, Airbus. First time published. Click to enlarge.
Image 6. Current cacao crops (right panel) in areas that, seven years ago, were primary forest (left panel). Data: Planet, Airbus. First time published. Click to enlarge.

Current Policy Situation

In 2014, the Ministry of Agriculture (MINAGRI) ordered United Cacao (Cacao del Perú Norte SAC) to halt its development and production activities, but the company did not comply.

In 2019, MINAGRI rejection the Program for  Adequacy and Environmental Management (PAMA) presented by the company that took over the operation, Tamshi S.A.C. The PAMA is a type of environmental impact evaluation for projects already in operation.

Also in 2019, environmental enforcement of the case was transferred from MINAGRI to the Agency for Environmental Assessment and Enforcement (OEFA).

Most recently, OEFA issued a major fine, equivalent to around $35 million, to the company Tamshi S.A.C. for carrying out activities without having an approved environmental management plan. OEFA also issued 17 corrective measures, one of which was the immediate stoppage of activities.

Acknowledgments

We thank C. Ipenza, A. Felix, C. Noriega, S. Novoa, and G. Palacios for their helpful comments on this report.

This report was conducted with technical assistance from USAID, via the Prevent project. Prevent is an initiative that, over the next 5 years, will work with the Government of Peru, civil society, and the private sector to prevent and combat environmental crimes in Loreto, Ucayali and Madre de Dios, in order to conserve the Peruvian Amazon.

This publication is made possible with the support of the American people through USAID. Its content is the sole responsibility of the authors and does not necessarily reflect the views of USAID or the US government.

This work was also supported by NORAD (Norwegian Agency for Development Cooperation), and ICFC (International Conservation Fund of Canada), and EROL Foundation.

Citation

Finer M, Mamani N (2020) United Cacao Case – 7 Years After Massive Deforestation in the Peruvian Amazon. MAAP: 128.

MAAP #81: Carbon loss from deforestation in the Peruvian Amazon

Base Map. Data: MINAM/PNCB, Asner et al 2014

When tropical forests are cleared, the enormous amount of carbon stored in the trees is released to the atmosphere, making it a major source of global greenhouse gas emissions (CO2) that drive climate change.

In fact, a recent study revealed that deforestation and degradation are turning tropical forests into a new net carbon source for the atmosphere, exacerbating climate change.1

The Amazon is the world’s largest tropical forest, and Peru is a key piece of that. Researchers (led by Greg Asner at the Carnegie Institution for Science) recently published the first high-resolution estimate of aboveground carbon in the Peruvian Amazon, documenting 6.83 billion metric tons.2

Here, we analyze this same dataset to estimate the total carbon emissions from deforestation in the Peruvian Amazon between 2013 and 2017. We estimate the loss of 59 million metric tons of carbon during these last five years, the equivalent of around 4% of annual United States fossil fuel emissions.3

We present a series of zoom images to show how carbon loss happened in several key areas impacted by the major deforestation drivers: gold mining, large-scale oil palm and cacao plantations, and smaller-scale agriculture. The labels A-G correspond to the zooms below.

We also show how protected areas are protecting hundreds of millions of metric tons of carbon in some of the most important areas in the country.

On the positive side, having this detailed information may provide added incentives to slow deforestation and degradation as part of critical climate change strategies.

 

 

Major Findings

Data: Asner et al 2014

The base map (see above) shows, in shades of green, carbon densities across Peru. It also shows, in red, the forest loss layer from 2013 to 2017.

We calculated the estimated amount of carbon emissions from forest loss during these five years: 59.029 teragrams, or 59 million metric tons.

The regions with the most carbon loss are 1) Loreto (13.4 million metric tons), 2) Ucayali (13.2 million), 3) Huánuco (7.3 million), 4) Madre de Dios (7 million), and 5) San Martin (6.9 million).

These values include some natural forest loss. Overall, however, they should be considered underestimates because they do not include forest degradation (for example, selective logging).

A recent study revealed that degradation may account for 70% of emissions, thus total carbon emissions from forests in the Peruvian Amazon may be closer to 200 million metric tons.

Next, we show a series of zoom images to show how carbon loss happened in several key areas. We also show how protected areas and conservation concessions are protecting the most important carbon reserves.

 

 

 

 

Zoom A: Central Peruvian Amazon

Image A shows the loss of 2.8 million metric tons of carbon in a section of the central Peruvian Amazon (Ucayali region). On the east side of image, note the loss due to two large-scale oil palm plantations (649,000 metric tons); on the west side, note small-scale agriculture penetrating deeper into high carbon density forest.

Image A. Central Peruvian Amazon. Data: Asner et al 2014, MINAM/PNCB

Zoom B: Southern Peruvian Amazon (gold mining) 

Image B shows the loss of 756 thousand metric tons of carbon due to gold mining in the southern Peruvian Amazon (Madre de Dios region). On the east side of image is the sector known as La Pampa; west side is Upper Malinowski.

Image B. Gold mining. Data: Asner et al 2014, MINAM/PNCB

Zoom C: Southern Peruvian Amazon (agriculture)

Image C shows the loss of 876 thousand metric tons of carbon in the southern Peruvian Amazon around the town of Iberia (Madre de Dios region). Note the expanding carbon loss along both sides of the Interoceanic Highway that crosses the image.

Image C. Iberia. Data: Asner et al 2014, MINAM/PNCB

Zoom D: United Cacao

Image D shows the loss of 291 thousand metric tons of carbon for a large-scale cacao project (United Cacao) in the northern Peruvian Amazon (Loreto region). Note that nearly all the forest clearing occurred in high carbon density forest. This is another line of evidence that the company cleared primary forest, contrary to their claims that the area was already degraded.

Image D. United Cacao. Data: Asner et al 2014, MINAM/PNCB

Zoom E: Yaguas National Park

Image E shows how three protected areas, including the new Yaguas National Park, are effectively safeguarding 202 million metric tons of carbon in the northeastern Peruvian Amazon. This area is home to some of the highest carbon densities in the country.

Image E. Yaguas. Data: Asner et al 2014, MINAM/PNCB

Zoom F: Los Amigos Conservation Concession

Image F shows how Los Amigos, the world’s first conservation concession, is effectively safeguarding 15 million metric tons of carbon in the southern Peruvian Amazon. Two surrounding protected areas, Manu National Park and Amarakaeri Communal Reserve, safeguard an additional 194 million metric tons. This area is home to some of the highest carbon densities in the country.

Image F. Los Amigos. Data: Asner et al 2014, MINAM/PNCB

Zoom G: Sierra del Divisor National Park

Image G. Data: Asner et al 2014, MINAM/PNCB

Image G shows how three protected areas, including the new Sierra del Divisor National Park, are effectively safeguarding 270 million metric tons of carbon in the eastern Peruvian Amazon.

This area is home to some of the highest carbon densities in the country.

 

 

 

 

 

 

 

 

 

 

 

 

Methodology

Para el análisis se utilizó los datos de carbono sobre el suelo  generados por Asner et al 2014, y los datos de pérdida de bosques identificados por el Programa Nacional de Conservación de Bosques (PNBC-MINAM) de los años 2013 al 2016 así como las alertas tempranas del año 2017. Primero uniformizamos los datos de pérdida de bosque 2013-2016 con las alertas tempranas del año 2017 para evitar superposición y tener un solo dato 2013-2017. Posteriormente, extraemos los datos de carbono de las áreas de pérdida de bosque del 2013-2017, este proceso permitió obtener la densidad de carbono (por hectárea) en relación al área de la pérdida de bosque para finalmente estimar el total de stocks de carbono perdido entre el año 2013 al 2017.

References

Baccini A, Walker W, Carvalho L, Farina M, Sulla-Menashe D, Houghton RA (2017) Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science. 13;358(6360):230-4.

Asner GP et al (2014). The High-Resolution Carbon Geography of Perú. Carnegie Institution for Science.

Boden TA, Andres RJ, Marland G (2017) National CO2 Emissions from Fossil-Fuel Burning, Cement Manufacture, and Gas Flaring: 1751-2014. DOI 10.3334/CDIAC/00001_V2017

Citation

Finer M, Mamani N (2017). Carbon loss from deforestation in the Peruvian Amazon. MAAP: 81.

 

 

MAAP #80: Amazon Beauty, in High-Resolution

Image 80. Base Map. Data: SERNANP, MAAP

MAAP tracks the most urgent deforestation cases in the Andean Amazon, thus it can be a bit depressing. However, it is important to remember why we do it: the Amazon is spectacular.

Here, we present a series of high-resolution satellite images to show the incredible beauty of the Peruvian Amazon, and help remind us all why it is so important to protect.

All the images, obtained from DigitalGlobe, are both recent and very high resolution (less than 0.5 meters). Together, they form an art exhibition, starring the forests, rivers, and mountains of the Peruvian Amazon.

The categories of the images are: “Protected Areas” and “Threatened Areas.”

The Protected Areas include National Parks (Yaguas, Sierra del Divisor, and Manu); National Reserve (Tambopata); Communal Reserve (Amarakaeri); and Regional Conservation Area (Choquequirao).

The Threatened Areas include areas at risk due to gold mining, road construction, hydroelectric dams, and new oil palm and cacao plantations.

Click on each image to enlarge. See the base map for the location of each image (A-M).

 

 

 

 

Protected Areas

A. Yaguas National Park (Loreto)

As Peru’s newest national park, created in January 2018, Yaguas National Park now protects a large (2,147,345 acres) and nearly intact stretch of the northern Peruvian Amazon. In featured Image A, we show the Yaguas River meandering through the primary forest of the eastern section of the new park.

Image 80_A. Parque Nacional Yaguas. Data: DigitalGlobe (Nextview)

B. Sierra del Divisor National Park (Ucayali)

The second newest national park in Peru is Sierra del Divisor, created in 2015. Sierra del Divisor National Park protects more than three million acres in the remote central Peruvian Amazon, along the border with Brazil. Featured Image B shows an aerial view of the famous cone mountain in the southern part of the park.

Image B. Parque Nacional Sierra del Divisor. Data: DigitalGlobe (Nextview)

C. Tambopata National Reserve (Madre de Dios)

Tambopata National Reserve made headlines in 2015 due to an illegal gold mining invasion that has since been contained (MAAP #61). Fortunately, Tambopata, located in the southern Peruvian Amazon, is best known for its world-renowned biodiversity. Featured Image C shows a meandering tributary of the Tambopata River and the subsequent formation of oxbow lakes.

Image C. Reserva Nacional Tambopata. Data: DigitalGlobe (Nextview)

D. Amarakaeri Communal Reserve (Madre de Dios)

Amarakaeri Communal Reserve is an important protected area in the southern Peruvian Amazon that is jointly managed by indigenous communities (ECA Amarakaeri) and the national protected areas agency (SERNANP). Featured Image D shows a wild river winding through the rugged foothills of the southern portion of the reserve.

Image D. Reserva Comunal Amarakaeri. Data: DigitalGlobe (Nextview), SERNANP

E. Manu National Park (Cusco sector)

Manu is one of the most famous national parks in the world, known for its diversity of habitats in the southern Peruvian Amazon, including lowland rainforest. Featured Image E shows the other extreme, the highlands and the transition beyond treeline to an ecosystem known as puna. Interestingly, this image shown an example of the upper-most headwaters where Amazonian rivers are born.

Image E. Parque Nacional Manu. Data: DigitalGlobe (Nextview), SERNANP

F. Regional Conservation Area Choquequirao (Cusco)

Choquequirao, one of the first examples of a regional conservation area in southern Peru, is located next to Machu Picchu. Featured Image F shows a high-elevation scene in the heart of the reserve, near the mountain peak known as Nevado Sacsarayoc.

Image F. Choquequirao. Data: DigitalGlobe (Nextview)

G. Los Amigos Conservation Concession (Madre de Dios)

It’s not technically a protected area, but a forestry concession in the southern Peruvian Amazon. In fact, Los Amigos is the first private conservation concession in the world. Featured Image G shows the meandering course of a tributary of the Los Amigos river, and the surrounding primary forest, deep in the concession.

Image G. Los Amigos. Data: DigitalGlobe (Nextview)

Threatened Areas

H. Tamshiyacu (Loreto)

The company United Cacao clearcut 5,880 acres of primary forest near the town of Tamshiycacu in the northern Peruvian Amazon between 2013 and 2015 (MAAP #35). Featured Image H shows the abrupt transition between plantation and primary forest at the eastern end of the project area, where plans exist to expand for more large-scale cacao operations.

Image H. United Cacao. Data: DigitalGlobe (Nextview)

I. Manu-Amarakaeri Highway (Madre de Dios)

A controversial road construction project would cross the buffer zones of two important protected areas in the southern Peruvian Amazon, Amarakaeri Communal Reserve and Manu National Park. Initial construction began in 2015 before being halted by the courts, but the project continues to be a long-term threat to the area. Featured Image I shows the leading edge of the road construction, surrounded by primary forest.

Image I. Amarakaeri/Manu road. Data: DigitalGlobe (Nextview)

J. La Pampa (Madre de Dios)

MAAP has documented the rapid expansion of gold mining deforestation in an area known as La Pampa, in the southern Peruvian Amazon (MAAP #75). Alarmingly, over 11,250 acres has been cleared since 2013. Featured Image J shows the most active mining deforestation front penetrating the primary forests to the east. Note the temporary and mobile mining camp city that has been formed near the leading edge of mining deforestation.

Image J. La Pampa. Data: DigitalGlobe (Nextview)

K. Tierra Blanca (Loreto)

The Peruvian company Grupo Romero had plans to clearcut thousands of hectares of primary forest for four large-scale oil palm plantations. There are reports that the company has abandoned the projects, in part due to pressure from civil society. Featured Image K shows the spared primary forest in one of the proposed plantations, Tierra Blanca. Note the recent construction (2014) of a logging road that still endangers the area.

Image K. Tierra Blanca. Data: DigitalGlobe (Nextview)

L. Amarakaeri Communal Reserve (Madre de Dios)

Immediately following a gold mining invasion in 2015, the co-administrators of Amarakaeri Communal Reserve (SERNANP and ECA Amarakaeri) took action against the illegal activities (see MAAP #44). Featured Image L shows the spared primary forest surrounding the abandoned invasion front at the border of the reserve.

Image L. Reserva Comunal Amarakaeri. Data: DigitalGlobe (Nextview), SERNANP

Marañon River (sector Amazonas/Cajamarca)

Featured Image M shows the exact location of a proposed hydroelctric dam, Chadín 2. It is one of the most advanced of the controversial 20 proposed dams along the Marañón River in the western Peruvian Amazon. It would be a large dam with the capacity to produce 600 MW of energy, and will create a flooding reservoir of over 8,000 acres. The project’s environmental impact study was approved in 2014, but construction has not yet started.

Image M. Rio Maranon. Data: DigitalGlobe (Nextview)

Coordinates

A. Yaguas: -2.72314, -70.746635
B. Sierra del Divisor: -7.962626, -73.781751
C. Tambopata: -12.93985, -69.233005
D. Amarakaeri: -13.073707, -70.966423
E. Manu: -12.816693, -71.886345
F. Choquequirao: -13.30926, -72.808164
G. Los Amigos: -12.377288, -70.380948
H. Tamshiyacu: -3.983962, -73.013498
I. Carretera Manu/Amarakaeri: -12.473042, -71.114976
J. La Pampa: -12.997284, -69.94845
K. Tierra Blanca: -6.517934, -75.366485
L. Amarakaeri: -12.88521, -70.626946
M. Chadin 2: -6.423889, -78.223333

Citation

Finer M, Mamani N (2018) Amazon Beauty, in High-Resolution. MAAP: 80.

MAAP Interactive: Deforestation Drivers in the Andean Amazon

Since its launch in April 2015, MAAP has published over 70 reports related to deforestation (and natural forest loss) in the Andean Amazon. We have thus far focused on Peru, with several reports in Colombia and Brazil as well.

These reports are meant to be case studies of the most important and urgent deforestation events. We often use forest loss alerts (known as GLAD) to guide us, and satellite imagery (from Planet and DigitalGlobe) to identify the deforestation driver.

Here we present an interactive map highlighting the drivers identified in all published MAAP reports. These drivers include gold mining, agriculture (e.g. oil palm and cacao), cattle pasture, roads, and dams (see icon legend below map). We also include natural causes such as floods and blowdowns (fire included under agriculture since most human caused). Furthermore, we highlight deforestation events within protected areas. Note that you can filter by driver by checking boxes of interest.

We hope the result is one of the most detailed and up-todate resources on patterns and drivers of deforestation in the Andean Amazon. Over the coming year we will continue to focus on Peru and Colombia, and begin to include Ecuador and Bolivia as well.

To view the interactive map, please visit:

MAAP Interactive: Deforestation Drivers in the Andean Amazon
https://www.maapprogram.org/interactive/

For more information on patterns and drivers of deforestation in the Peruvian Amazon, see our latest Synthesis report 

MAAP #64: Good News Deforestation Stories (Peruvian Amazon)

We admit that most MAAP stories are about the bad news of Amazon deforestation. But fortunately there is good news as well.

Here we highlight 5 good news stories from the Peruvian Amazon that show how near real-time monitoring may lead to halting deforestation from emerging threats, such as gold mining and large-scale agriculture (oil palm and cacao plantations).

The detailed cases are:
A) United Cacao (cacao),
B) Plantations of Pucallpa (oil palm),
C) Grupo Romero (oil palm),
D) Amarakaeri Comunal Reserve (gold mining), and
E) Tambopata National Reserve (gold mining).

 

 

 

United Cacao

Image 64a. Data: NASA/USGS

The rapid deforestation of primary forest for a large-scale cacao plantation in the northern Peruvian Amazon took everyone by surprise in 2013. Civil society led the way in exposing and tracking the deforestation with satellite imagery and the government eventually confirmed the forest loss data. For its part, MAAP published 6 articles (for example MAAP #35 and MAAP #2).

Although total deforestation eventually reached 5,880 acres (2,380 hectares), the company, due to a complicated combination of factors, was suspended from the London Stock Exchange and no new deforestation has been detected in over a year.

Image 64a shows that the cacao project area was covered by intact forest in late 2012, followed by large-scale deforestation of primary forest in 2013. The deforestation slowed, and then stopped, between 2014 and 2017. The yellow circle indicates the cacao plantation area over time.

Plantations of Pucallpa (oil palm)

In a remarkable case, satellite imagery was used to demonstrate that an oil palm company (Plantations of Pucallpa) had breached the Code and Conduct of the RSPO (Roundtable on Sustainable Palm Oil), a non-profit entity founded to develop and implement global standards for sustainable palm oil.

In 2015, the Native Community of Santa Clara de Uchunya (with the support of the NGO Forest Peoples Programme) presented an official complaint to the RSPO against Plantations of Pucallpa, a member of the roundtable. An important component of the complaint alleged massive deforestation, but the company adamantly denied it. MAAP articles showing the deforestation of 15,970 acres (6,460 hectares) were used as evidence (MAAP #4, MAAP #41), as was independent government analysis.

In April 2017, the RSPO concluded that Plantations of Pucallpa cleared 14,145 acres (5,725 hectares) despite declaring no land-clearing, thus breaching the Code and Conduct. Several months prior this decision, the company divested its oil palm estates and withdrew from the RSPO. We have not detected any new deforestation in the project area in over a year.

Image 64b shows the massive deforestation for two large-scale oil palm plantations in the central Peruvian Amazon (Plantations of Pucallpa is the plantation to the north). The yellow circles indicate the oil palm plantation project areas over time. Note that the project area was a mix of primary and secondary forest in 2011, immediately prior to the deforestation, which began in 2012. The deforestation intensified in 2013 before nearly reaching its maximum extent in 2015. We have not detected any new deforestation since 2016.

Image 64b. Data: NASA/USGS, MAAP

Grupo Romero (oil palm)

Perhaps the best news of the bunch is about four large-scale oil palm plantations that were stopped before any deforestation occurred. As detailed in a recent report by Environmental Investigation Agency, the Peruvian business conglomerate Grupo Romero conducted environmental impact studies for four new oil palm plantations in the northern Peruvian Amazon. Analysis of these studies revealed that these plantations would cause the massive deforestation of 56,830 acres (23,000 hectares) of primary forest. After strong pushback from civil society, including legal action, a recent report from Chain Reaction Research revealed that Grupo Romero is now working towards a zero-deforestation supply chain and thus found that the four planned plantations are no longer feasible and abandoned the projects.

Image 64c shows how the project area for two of the proposed oil palm plantations (in yellow), Santa Catalina and Tierra Blanca, is largely covered by intact, primary forest.

Image 64c. Data: NASA/USGS, Grupo Palmas (Grupo Romero)

Amarakaeri Communal Reserve (gold mining)

In June 2015, we revealed the deforestation of 11 hectares in Amarakaeri Communal Reserve due to an illegal gold mining invasion. The Reserve, located in the southern Peruvian Amazon, is an important protected area that is co-managed by indigenous communities (ECA Amarakaeri) and SERNANP, Peru’s protected areas agency (see MAAP #6). In the following weeks, the Peruvian government, led by SERNANP, cracked down on the illegal mining activities. A year later, we showed that the deforestation had been stopped, with no further expansion into the Reserve (MAAP #44). In fact, we showed that there were signs of recovering vegetation on the recently mined areas.

Image 64d shows the gold mining deforestation approaching (2011-12) and entering (2013-15) Amarakaeri Communal Reserve (yellow circles indicate areas of invasion). However, it also shows how, following action by the government and ECA Amarakaeri, the deforestation was halted and did not expand in 2016-17.

Image 64d. Data: NASA/USGS, Sentinel/ESA, RapidEye/Planet

Tambopata National Reserve (gold mining)

In September 2015, illegal gold miners started to invade Tambopata National Reserve, an important protected area in the southern Peruvian Amazon with world renowned biodiversity. In a series of MAAP articles, we tracked the invasion as it intensified in 2016, and eventually reached 1,360 acres (550 hectares) by early 2017. However, by late 2016, the Peruvian Government intensified its interventions against the illegal mining activity, and the rate of deforestation quickly and sharply decreased. In the most recent satellite imagery, we have not detected any major new expansion of illegal gold mining within the Reserve.

Image 64e shows the initial invasion of Tambopata National Reserve between September 2015 and January 2016. The deforestation within the Reserve intensifies during 2016, but slows significantly in 2017. The yellow circles indicate areas of invasion.

Image 64e. Data: Planet, SERNANP

References

Planet Team (2017). Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. https://api.planet.com.

Citation

Finer M, Novoa S, Olexy T, Scott A (2017) Good News Deforestation Stories (Peruvian Amazon). MAAP: 64.

MAAP SYNTHESIS #2: PATTERNS AND DRIVERS OF DEFORESTATION IN THE PERUVIAN AMAZON

We present our second synthesis report, building off our first report published in September 2015. This synthesis is largely based on the 50 MAAP reports published between April 2015 and November 2016. The objective is to synthesize all the information to date regarding deforestation trends, patterns and drivers in the Peruvian Amazon.

MAAP methodology includes 4 major components: Forest loss detection, Prioritize big data, Identify deforestation drivers, and Publish user-friendly reports. See Methodology section below for more details.

Our major findings include:

  • Trends. During the 15 years between 2001 and 2015, around 4,448,000 acres (1,800,000 hectares) of Peruvian Amazon forest has been cleared, with a steadily increasing trend. 2014 had the highest annual forest loss on record (438,775 acres), followed by a slight decrease  in 2015. The preliminary estimate for 2016 indicates that forest loss remains relatively high. The vast majority (80%) of forest loss events in the Peruvian Amazon are small-scale (<5 hectares), while large-scale events (> 50 hectares) pose a latent threat due to new agro-industrial projects.
  • Hotspots. We have identified at least 8 major deforestation hotspots. The most intense hotspots are located in the central Amazon (Huánuco and Ucayali). Other important hotspots are located in Madre de Dios and San Martin. Two protected areas (Tambopata National Reserve and El Sira Communal Reserve) are threatened by these hotspots.
  • Drivers. We present an initial deforestation drivers map for the Peruvian Amazon. Analyzing high-resolution satellite imagery, we have documented six major drivers of deforestation and degradation: small/medium-scale agriculture, large-scale agriculture, cattle pasture, gold mining, illegal coca cultivation, and roads. Small-scale agriculture and cattle pasture are likely the most dominant drivers overall. Gold mining is a major driver in southern Peru. Large-scale agriculture and major new roads are latent threats. Logging roads are likely a major source of forest degradation in central Peru.

Deforestation Trends

Image 1 shows forest loss trends in the Peruvian Amazon from 2001 to 2015, including a breakdown of the size of the forest loss events. This includes the official data from the Peruvian Environment Ministry, except for 2016, which is a preliminary estimate based on GLAD forest loss alerts.

Image 1. Data: PNCB/MINAM, UMD/GLAD. *Estimate based on GLAD alerts.

During the 15 years between 2001 and 2015, around 4,448,000 acres (1,800,000 hectares) of Peruvian Amazon forest has been cleared (see green line). This represents a loss of approximately 2.5% of the existing forest as of 2001.There have been peaks in 2005, 2009, and 2014, with an overall increasing trend. In fact, 2014 had the highest annual forest loss on record (386,626 acres). Forest loss decreased in 2015 (386,732 acres), but is still the second highest recorded. The preliminary estimate for 2016 indicates that forest loss continues to be relatively high.

It is important to note that the data include natural forest loss events (such as storms, landslides, and river meanders), but overall serves as our best proxy for anthropogenic deforestation. The non-anthropogenic forest loss is estimated to be approximately 3.5% of the total.1

The vast majority (81%) of forest loss events in the Peruvian Amazon are small-scale (<5 hectares, equivalent of 12 acres), see the yellow line. Around 16% of the forest loss events are medium-scale (5-50 hectares, equivalent of 12-124 acres), see the orange line. Large-scale (>50 hectares, equivalent of 124 acres) forest loss events, often associated with industrial agriculture, pose a latent threat. Although the average is only 2%, large-scale forest loss rapidly spiked to 8% in 2013 due to activities linked with a pair of new oil palm and cacao plantations. See MAAP #32 for more details on the patterns of sizes of deforestation events.

Deforestation Patterns

Image 2 shows the major deforestation hotspots in 2012-14 (left panel) relative to 2015-16 (right panel), based on a kernel density analysis.We have identified at least 8 major deforestation hotspots, labeled as Hotspots A-H.

Image 2. Data: PNCB/MINAM, GLAD/UMD. Click to enlarge.

The most intense hotspots, A and B, are located in the central Amazon. Hotspot A, in northwest Ucayali, was dominated by two large-scale oil palm projects in 2012-14, but then shifted a bit to the west in 2015-16, where it was dominated by cattle pasture and small-scale oil palm. Hotspot B, in eastern Huánuco, is dominated by cattle pasture (MAAP #26).

Hotspots C and D are in the Madre de Dios region in the southern Amazon. Hotspot C indicates the primary illegal gold mining front in recent years (MAAP #50). Hotspot D highlights the emerging deforestation zone along the Interoceanic Highway, particularly around the town of Iberia (MAAP #28).

Hotspots E-H are agriculture related. Hotspot E indicates the rapid deforestation for a large-scale cacao plantation in 2013-14, with a sharp decrease in forest loss 2015-16 (MAAP #35). Hotspot F indicates the expanding deforestation around two large-scale oil palm plantation (MAAP #41). Hotspot G indicates the intensifying deforestation for small-scale oil palm plantations (MAAP #48).

Hotspot H indicates an area impacted by intense wildfires in 2016.

Protected Areas, in general, are effective barriers against deforestation (MAAP #11). However, several protected areas are currently threatened, most notably Tambopata National Reserve (Hotspot C; MAAP #46). and El Sira Communal Reserve (Hotspot B; MAAP #45).

Deforestation Drivers

Image 3. Data: MAAP, SERNANP. Click to enlarge.

Surprisingly, there is a striking lack of precise information about the actual drivers of deforestation in the Peruvian Amazon. According to an important paper published in 2016, much of the existing information is vague and outdated, and is based solely on a general analysis of the size of deforestation events.3  

As noted above, one of the major advances of MAAP has been using high-resolution imagery to better identify deforestation drivers.

Image 3 shows the major deforestation drivers identified thus far by our analysis. As far as we know, it represents the first spatially explicit deforestation drivers map for the Peruvian Amazon.

To date, we have documented six major direct drivers of deforestation and degradation in the Peruvian Amazon: small/medium-scale agriculture, large-scale agriculture, cattle pasture, gold mining, illegal coca cultivation, and roads.

At the moment, we do not consider the hydrocarbon (oil and gas) and hydroelectric dam sectors as major drivers in Peru, but this could change in the future if proposed projects move forward.

We describe these major drivers of deforestation and degradation in greater detail below.

Small/Medium-scale Agriculture

The literature emphasizes that small-scale agriculture is the leading cause of deforestation in the Peruvian Amazon.However, there is little actual empirical evidence demonstrating that this is true.3 The raw deforestation data is dominated by small-scale clearings that are most likely for agriculture or cattle pasture. Thus, it is likely that small-scale agriculture is a major driver, but a definitive study utilizing high-resolution imagery and/or extensive field work is still needed to verify the assumption.

In several key case studies, we have shown specific examples of small-scale agriculture being a deforestation driver. For example, using a combination of high-resolution imagery, photos from the field, and local sources, we have determined that:

  • Oil Palm, in the form of small and medium-scale plantations, is one of the main drivers within deforestation Hotspot B (Ucayali; MAAP #26), Hotspot G (northern Huánuco; MAAP #48), and Hotspot F (Loreto-San Martin;MAAP #16). This was also shown for Ucayali in a recent peer-reviewed study.4 See below for information about large-scale oil palm.
  • Cacao is causing rapid deforestation along the Las Piedras River in eastern Madre de Dios (MAAP #23, MAAP #40). See below for information about large-scale cacao.
  • Papaya is an important new driver in Hotspot D, along the Interoceanic Higway in eastern Madre de Dios (MAAP #42).
  • Corn and rice plantations may also be an important driver in Hotspot D in eastern Madre de Dios (MAAP #28).

Large-scale Agriculture

Large-scale, agro-industrial deforestation remains a latent threat in Peru, particularly in the central and northern Amazon regions. This issue was put on high alert in 2013, with two cases of large-scale deforestation for oil palm and cacao plantations, respectively.

In the oil palm case, two companies that are part of the Melka group,5 cleared nearly 29,650 acres in Hotspot A in Ucayali between 2012 and 2015 (MAAP #4, MAAP #41). In the cacao case, another company in the Melka group (United Cacao) cleared 5,880 acres in Hotspot E in Loreto between 2013 and 2015 (MAAP #9, MAAP #13, MAAP #27, MAAP #35). Dennis Melka has explicitly stated that his goal is to bring the agro-industrial production model common in Southeast Asia to the Peruvian Amazon.6

Prior to these cases, large-scale agricultural deforestation occurred between 2007 and 2011, when oil palm companies owned by Grupo Palmas7 cleared nearly 17,300 acres for plantations in Hotspot H along the Loreto-San Martin border (MAAP #16). Importantly, we documented the additional deforestation of 24,215 acres for oil palm plantations surrounding the Grupo Palmas projects (MAAP #16).

In contrast, large-scale agricultural deforestation was minimal in 2015 and 2016. However, as noted above, it remains a latent threat. Both United Cacao and Grupo Palmas have expansion plans that would clear over 49,420 acres of primary forest in Loreto.8

Cattle Pasture

Using an archive of satellite imagery, we documented that deforestation for cattle pasture is a major issue in the central Peruvian Amazon. Immediately following a deforestation event, the scene of hundreds or thousands of recently cut trees often looks the same whether the cause is agriculture or cattle pasture. However, by using an archive of imagery and studying deforestation events from previous years, one can more easily determine the drivers of the forest loss. For example, after a year or two, agriculture and cattle pasture appear very differently in the imagery and thus it is possible to distinguish these two drivers.

Using this technique, we determined that cattle pasture is a major driver in Hotspots A and B, in the central Peruvian Amazon (MAAP #26, MAAP #37).

We also used this technique to determine that much of the deforestation in the northern section of El Sira Communal Reserve is due to cattle pasture (MAAP #45).

Maintenance of cattle pasture, and small-scale agriculture, are likely important factors behind the escaped fires that degrade the Amazon during intense dry seasons (MAAP #45, MAAP #47).

Gold Mining

Gold mining is one of the major drivers of deforestation in the southern Peruvian Amazon (Hotspot C). An important study found that gold mining cleared around 123,550 acres up through 2012.9 We built off this work, and by analyzing hundreds of high resolution imageres, found that gold mining caused the loss of an additional 30,890 acres between 2013 and 2016 (MAAP #50). Thus, gold mining is thus far responsible for the total loss of around 154,440 acres in southern Peru. Much of the most recent deforestation is illegal due to its occurrence in protected areas and buffer zones strictly off-limits to mining activities.

Most notably, we have closely tracked the illegal gold mining invasion of Tambopata National Reserve, an important protected area in the Madre de Dios region with renowned biodiversity and ecotourism. The initial invasion occurred in November 2015 (MAAP #21), and has steadily expanded to over 1,110 acres (MAAP #24, MAAP #30, MAAP #46). As part of this invasion, miners have modified the natural course of the Malinowski River, which forms the natural northern border of the reserve (MAAP #33). In addition, illegal gold mining deforestation continues to expand within the reserve’s buffer zone, particularly in an area known as La Pampa (MAAP #12, MAAP #31).

Further upstream, illegal gold mining is also expanding on the upper Malinowski River, within the buffer zone of Bahuaja Sonene National Park (MAAP #19, MAAP #43).

In contrast to the escalating situation in Tambopata, we also documented that gold mining deforestation has been contained in the nearby Amarakaeri Communal Reserve, an important protected area that is co-managed by indigenous communities and Peru’s national protected areas agency. Following an initial invasion of 27 acres in 2014 and early 2015, satellite imagery shows that management efforts have prevented any subsequent expansion within the protected area (MAAP #6, MAAP #44).

In addition to the above cases in Madre de Dios, gold mining deforestation is also increasingly an issue in the adjacent regions of Cusco and Puno (MAAP #14).

There are several small, but potentially emerging, gold mining frontiers in the central and northern Peruvian Amazon (MAAP #49). The Peruvian government has been working to contain the illegal gold mining in the El Sira Communal Reserve (MAAP #45). Further north in Amazonas region, there is gold mining deforestation along the Rio Santiago (MAAP #36, MAAP #49), and in the remote Condor mountain range along the border with Ecuador (MAAP #49).

Roads

Roads are a well-documented driver of deforestation in the Amazon, particularly due to their ability to facilitate human access to previously remote areas.10 Roads often serve as an indirect driver, as most of the deforestation directly associated with agriculture, cattle pasture, and gold mining is likely greatly facilitated by proximity to roads. We documented the start of a controversial road construction project that would cut through the buffer zones of two important protected areas, Amarakaeri Communal Reserve and Manu National Park (MAAP #29).

Logging Roads

In relation to general roads described above, we distinguish access roads that are constructed to gain entry to a particular project. The most notable type of access roads in Peru are logging roads, which are likely a leading cause of forest degradation as they facilitate selective logging of valuable timber species in remote areas.

One of the major recent advances in forest monitoring is the ability to quickly identify the construction of new logging roads. The unique linear pattern of these roads appears quite clearly in Landsat-based tree cover loss alerts such as GLAD and CLASlite. This advance is important because it is difficult to detect illegal logging in satellite imagery because loggers in the Amazon often selectively cut high value species and do not produce large clearings. But now, although it remains difficult to detect the actual selective logging, we can detect the roads that indicate that selective logging is taking place in that area.

In a series of articles, we highlighted the recent expansion of logging roads, including the construction of 1,134 km between 2013 and 2015 in the central Peruvian Amazon (MAAP #3, MAAP #18). Approximately one-third of these roads were within the buffer zones of Cordillera Azul and Sierra del Divisor National Parks (MAAP #15).

We documented the construction of an additional 83 km of logging roads during 2016,  (MAAP #40, MAAP #43) including deeper into the buffer zone of Cordillera Azul National Park.

Another major finding is the rapid construction of the logging roads. In several cases, we documented the construction rate of nearly five kilometers per week (MAAP #18, MAAP #40, MAAP #43).

Determining the legality of these logging roads is complex, partly because of the numerous national and local government agencies involved in the authorization process. Many of these roads are near logging concessions and native communities, whom may have obtained the rights for logging from the relevant forestry authority (in many cases, the regional government).

Coca

According to a recent United Nations report, the Peruvian land area under coca cultivation in 2015 (99,580 acres) was the lowest on record (since 2001) and part of a declining trend since 2011 (154,440 acres).11 There are 13 major coca growing zones in Peru, but it appears that only a few of them are actively causing new deforestation. Most important are two coca zonas in the region of Puno that are causing deforestation within and around Bahuaja Sonene National Park (MAAP #10, MAAP #14). Several coca zones in the regions of Cusco and Loreto may also be causing some new deforestation.

Hydroelectric Dams

Although there is a large portfolio of potential new hydroelectric dam projects in the Peruvian Amazon,12 many of not advanced to implementation phase. Thus, forest loss due to hydroelectric dams is not currently a major issue, but this could quickly change in the future if these projects are revived. For example, in adjacent western Brazil, we documented the forest loss of 89,205 acres associated with the flooding caused by two dams on the upper Madeira River (MAAP #34).

Hydrocarbon (Oil & Gas)

During the course of our monitoring, we have not yet detected major deforestation events linked to hydrocarbon-related activities. As with dams, this could change in the future if oil and gas prices rise and numerous projects in remote corners of the Amazon move forward.

Methodology

MAAP methodology has 4 major components:

  1. Forest Loss Detection. MAAP reports rely heavily on early-warning tree cover loss alerts to help us identify where new deforestation is happening. Currently, our primary tool is GLAD alerts, which are developed by the University of Maryland and Google,13 and presented by WRI’s Global Forest Watch and Peru’s GeoBosques. These alerts, launched in Peru in early 2016, are based on 30-meter resolution Landsat satellite images and updated weekly. We also occasionally incorporate CLASlite, forest loss detection software based on Landsat (and now Sentinel-2) developed by the Carnegie Institution for Science, and the moderate resolution (250 meters) Terra-i alerts. We are also experimenting with Sentinel-1 radar data (freely available from the European Space Agency), which has the advantage of piercing through cloud cover in order to continue monitoring despite persistent cloudy conditions
  2. Prioritize Big Data. The early warning systems noted above yield thousands of alerts, thus a procedure to prioritize the raw data is needed. We employ numerous prioritization methods, such as creation of hotspot maps (see below), focus on key areas (such as protected areas, indigenous territories, and forestry concessions), and identification of striking patterns (such as linear features or large-scale clearings).
  1. Identify Deforestation Drivers. Once priority areas are identified, the next challenge is to understand the cause of the forest loss. Indeed, one of the major advances of MAAP over the past year has been using high-resolution satellite imagery to identify key deforestation drivers. Our ability to identify these deforestation drivers has been greatly enhanced thanks to access to high-resolution satellite imagery provided by Planet 14
    (via their Ambassador Program) and Digital Globe (via the NextView Program, courtesy of an agreement with USAID). We also occasionally purchase imagery from Airbus(viaApollo Mapping).
  2. Publish User-Friendly Reports. The final step is to publish technical, but accessible, articles highlighting novel and important findings on the MAAP web portal. These articles feature concise text and easy-to-understand graphics aimed at a wide audience, including policy makers, civil society, researchers, students, journalists, and the public at large. During preparation of these articles, we consult with Peruvian civil society and relevant government agencies in order to improve the quality of the information.

Endnotes

MINAM-Peru (2016) Estrategia Nacional sobre Bosques y Cambio Climático.

Methodology: Kernel Density tool from Spatial Analyst Tool Box of ArcGis. The 2016 data is based on GLAD alerts, while the 2012-15 data is based on official annual forest loss data

Ravikumar et al (2016) Is small-scale agriculture really the main driver of deforestation in the Peruvian Amazon? Moving beyond the prevailing narrative. Conserv. Lett. doi:10.1111/conl.12264

4 Gutiérrez-Vélez VH et al (2011). High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon. Environ. Res. Lett., 6, 044029.

Environmental Investigation Agency EIA (2015) Deforestation by Definition.

NG J (2015) United Cacao replicates Southeast Asia’splantation model in Peru, says CEO Melka. The Edge Singapore, July 13, 2015.

Palmas del Shanusi & Palmas del Oriente; http://www.palmas.com.pe/palmas/el-grupo/empresas

Hill D (2015) Palm oil firms in Peru plan to clear 23,000 hectares of primary forest. The Guardian, March 7, 2015.

Asner GP, Llactayo W, Tupayachi R,  Ráez Luna E (2013) Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring. PNAS 46: 18454. They reported 46,417 hectares confirmed and 3,268 hectares suspected (49,865 ha total).

10 Laurance et al (2014) A global strategy for road building. Nature 513:229; Barber et al (2014) Roads, deforestation, and the mitigating effect of protected areas in the Amazon.  Biol Cons 177:203.

11 UNODC/DEVIDA (2016) Perú – Monitoreo de Cultivos de Coca 2015.

12 Finer M, Jenkins CN (2012) Proliferation of Hydroelectric Dams in the Andean Amazon and Implications for Andes-Amazon Connectivity. PLoS ONE 7(4): e35126.

13 Hansen MC et al (2016) Humid tropical forest disturbance alerts using Landsat data. Environ Res Lett 11: 034008.

14 Planet Team (2017). Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. https://api.planet.com

Citation

Finer M, Novoa S (2017) Patterns and Drivers of Deforestation in the Peruvian Amazon. MAAP: Synthesis #2.

MAAP #40: Early Warning Deforestation Alerts in the Peruvian Amazon

GLAD alerts are a powerful new tool to monitor forest loss in the Peruvian Amazon in near real-time. This early warning system, created by the GLAD (Global Land Analysis and Discovery) laboratory at the University of Maryland and supported by Global Forest Watch, was launched in March 2016 as the first Landsat-based (30-meter resolution) forest loss alert system (previous systems were based on lower-resolution imagery). The alerts are updated weekly and can be accessed through Global Forest Watch (Image 40a, left panel) or GeoBosques (Image 40a, right panel), a web portal operated by the Peruvian Ministry of Environment.

Imagen 41a. Datos: UMD/GLAD, WRI/GFW, PNCB/MINAM
Image 40a. Data: UMD/GLAD, WRI/GFW, PNCB/MINAM

In MAAP, we often combine these alerts with analysis of high-resolution satellite imagery (courtesy of the Planet Ambassador Program and Digital Globe NextView service) to better understand patterns and drivers of deforestation in near real-time. In this article, we highlight 3 examples of this type of innovative analysis from across the Peruvian Amazon:

Example 1: Logging Roads in central Peru (Ucayali)
Example 2: Invasion of Ecotourism Concessions in southern Peru (Madre de Dios)
Example 3: Buffer Zone of Cordillera Azul National Park (Loreto)

Example 1: Logging Roads in central Peru (Ucayali)

In the previous MAAP #18, we documented the proliferation of logging roads in the central Peruvian Amazon during 2015. In recent weeks, we have seen the start of rapid new logging road construction for 2016. Image 40b shows the linear forest loss associated with two new logging roads along the Tamaya river in the remote central Peruvian Amazon (Ucayali region). Red indicates the 2016 road construction (35.8 km). Insets A and B indicate the areas shown in the high-resolution zooms below.

Image 40b. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, MINAGRI
Image 40b. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, MINAGRI

The following images show, in high-resolution, the rapid construction of logging roads in 2016. Image 40c shows the construction of 16.1 km between March (left panel) and July (right panel) 2016 in the area indicated by Inset A. Image 40d shows the construction of 19.7 km between June (left panel) and July (right panel) 2016 in the area indicated by Inset B.

Image 40c. Data: Planet
Image 40c. Data: Planet
Image 40d. Data: Planet
Image 40d. Data: Planet

Example 2: Invasion of Ecotourism Concessions in southern Peru (Madre de Dios)

Image 40e shows the recent deforestation within two ecotourism concessions along the Las Piedras River in the Madre de Dios region. Red indicates the 2016 GLAD alerts (67.3 hectares). Note that the Las Piedras Amazon Center (LPAC) Ecotourism Concession represents an effective barrier against deforestation occurring in the surrounding concessions. According to local sources, the main drivers of deforestation in the area are related to the establishment of cacao plantations and cattle pasture (see s MAAP #23). Inset A indicates the areas shown in the high-resolution zoom below.

Image 40e. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, MINAGRI
Image 40e. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, MINAGRI

Image 40f shows high-resolution images of the area indicated by Inset A between April (left panel) and July (right panel) 2016. The yellow circles indicate areas of deforestation between these dates.

Image 40f. Data: Planet, DigitalGlobe (Nextview)
Image 40f. Data: Planet, DigitalGlobe (Nextview)

Example 3: Buffer Zone of Cordillera Azul National Park (Loreto)

Image 40g shows the recent deforestation within the western buffer zone of the Cordillera Azul National Park in the Loreto region. Red indicates the 2016 GLAD alerts (87.3 hectares). It is worth noting that this area is classified as Permanent Production Forest, not as an agricultural area.

Image 40g. Data: SERNANP, Landsat, UMD/GLAD, Hansen/UMD/Google/USGS/NASA
Image 40g. Data: SERNANP, Landsat, UMD/GLAD, Hansen/UMD/Google/USGS/NASA

Image 40h shows high-resolution images of the area indicated by Inset A between December 2015 (left panel), January 2016 (central panel), and July 2016 (right panel). The yellow circles indicate areas that were deforested between these dates. The driver of the deforestation appears to be the establishment of small-scale agricultural plantations.

Image 40h. Data: RapidEye/Planet, Digital Globe (Nextview)
Image 40h. Data: RapidEye/Planet, Digital Globe (Nextview)

Citation

Finer M, Novoa S, Goldthwait E (2016) Early Alerts of Deforestation in the Peruvian Amazon. MAAP: 40.

MAAP #38: United Cacao deforestation in area classified as “Forest Production”

The Peruvian Ministry of Agriculture and Irrigation (MINAGRI) recently issued a resolution approving the Update of the Soil and Optimum Land Use Suitability Studies for Areas in the Loreto Region. It is important to emphasize that “Optimum Land Use” (Capacidad de Uso Mayor in Spanish)  is not determined by forest cover, but the quantitative interpretation of the soil, climate, and topography.

This new resolution represents an important advance in forest management in Peru because, according to both the previous1 and current2 Forestry Law, if the Optimum Land Use of a particular area is classified as Forest Production or Protection, it is illegal to change the land use to agriculture and cause deforestation. Thus, it is only possible to request land use change if the area has been classified as “Agriculture” (Optimum Land Use Annual Crop, Permanent Crop, or Pasture).3

Here, we analyze the spatial data corresponding to the new resolution. In Image 38a, we show that 92.6% (2,200 hectares) of the deforestation4 associated with the United Cacao project occurred on areas with an Optimum Land Use classification of Forest Production5. This classification “groups the lands in which climatic, terrain and soil conditions are not favorable for intensive cultivation, permanent crops, nor pastures, but for the production of timber species.”

Imagen 38a. Datos: MINAGRI
Image 38a. Data: MINAGRI 2016. Red lines indicate areas deforested by United Cacao between 2013 and 2016. Green indicates areas with Optimum Land Use classification of Forest Production, while the yellows indicate areas with Optimum Land Use classification of Agriculture.

In addition, 3.8% of the deforestation occurred in areas with an Optimum Land Use classification of Pasture/Forestry, while the remaining 3.6% occurred in areas with classification of Pasture. However, it is important to emphasize that even in these areas with an agricultural classification, our analysis of satellite imagery found that they were actually covered with primary forest (see Image 38b).

In conclusion, the vast majority of deforestation caused by United Cacao occurred in areas classified as optimally suited for forest production, where changes in land-use and associated deforestation are not permitted.

Imagen 38b. Datos: Landsat/NASA/USGS
Imagen 38b. Data: Landsat/NASA/USGS

Notes

1Ley 27308 Articulo 7. Decreto Supremo 014-2001-AG, Reglamento de la Ley Forestal y de Fauna Silvestre, Art. 36.

2 LEY FORESTAL Y DE FAUNA SILVESTRE (LEY Nº 29763), Artículo 37

3 Decreto Legislativo No. 653, Ley de Promocion de las Inversiones en el Sector Agrario (1991)

4 See MAAP #35 for more information regarding this deforestation.

Specifically, this area is classified as F2s: Tierras Aptas para Producción Forestal (Símbolo F), Clase – Calidad Agrológica Media (Símbolo F2),  Subclase – Limitación por Suelo (Símbolo “s”)

Citation

Finer M, Novoa S, Cruz C (2016) United Cacao deforestation in area classified as “Forest Production.” MAAP: 38.