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MAAP Synthesis #3: Deforestation in the Andean Amazon (Trends, Hotspots, Drivers)

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Satellite image of the deforestation produced by United Cacao. Source: DigitalGlobe (Nextview)

MAAP, an initiative of the organization Amazon Conservation, uses cutting-edge satellite technology to monitor deforestation in near real-time in the megadiverse Andean Amazon (Peru, Colombia, Ecuador, and Bolivia).

The monitoring is based on 5 satellite systems: Landsat (NASA/USGS), Sentinel (European Space Agency), PeruSAT-1, and the companies Planet and DigitalGlobe. For more information about our innovative methodology, see this recent paper in Science Magazine.

Launched in 2015, MAAP has published nearly 100 high-impact reports on the major Amazonian deforestation issues of the day.

Here, we present our third annual synthesis report with the objective to concisely describe the bigger picture: Deforestation trends, patterns, hotspots and drivers across the Andean Amazon.

Our principal findings include:

Trends: Deforestation across the Andean Amazon has reached 4.2 million hectares (10.4 million acres) since 2001. Annual deforestation has been increasing in recent years, with a peak in 2017 (426,000 hectares). Peru has had the highest annual deforestation, followed by surging Colombia (in fact, Colombia surpassed Peru in 2017). The vast majority of the deforestation events are small-scale (‹5 hectares).

Hotspots: We present the first regional-scale deforestation hotspots map for the Andean Amazon, allowing for spatial comparisons between Peru, Colombia, and Ecuador.  We discuss six of the most important hotspots.

Drivers: We present MAAP Interactive, a dynamic map with detailed information on the major deforestation drivers: gold mining, agriculture (oil palm and cacao), cattle ranching, logging, and dams. Agriculture and ranching cause the most widespread impact across the region, while gold mining is most intense southern Peru.

Climate Change. We estimated the loss of 59 million metric tons of carbon in the Peruvian Amazon during the last five years (2013-17) due to forest loss. In contrast, we also show that protected areas and indigenous lands have safeguarded 3.17 billion metric tons of carbon.

I. Deforestation Trends

Image 1 shows forest loss trends in the Andean Amazon between 2001 and 2017.*  The left graph shows data by country, while the right graph shows data by forest loss event size.

Image 1. Annual forest loss by country and size. Data: Hansen/UMD/Google/USGS/NASA, UMD/GLAD, Global Forest Watch, MINAM/PNCB, RAISG.

Trends by Country

Over the past 17 years (2001-2017), deforestation has surpassed 4.2 million hectares (10.4 million acres) in the Andean Amazon (see green line). Of this total, 50% is Peru (2.1 million hectares/5.2 million acres), 41% Colombia (1.7 million hectares/4.27 million acres), and 9% Ecuador (887,000 acres/359,000 hectares). This analysis did not include Bolivia.

Since 2007, there has been an increasing deforestation trend, peaking during the past two years (2016-17). In fact, 2017 has the highest annual forest loss on record with 426,000 hectares (over one million acres), more than double the total forest loss in 2006.

Peru had the highest average annual Amazonian deforestation between 2009 and 2016. The past four years have the highest annual deforestation totals on record in the country, with peaks in 2014 (177,566 hectares/439,000 acres) and 2016 (164,662 hectares/406,888 acres). According to new data from the Peruvian Environment Ministry, there was an important decline in 2017 (155,914 hectares/385,272 acres), but it is still the fourth highest annual total on record.

There has been a surge of deforestation in Colombia during the past two years. Note that in 2017, Colombia surpassed Peru with a record high of 214,700 hectares (530,400 acres) deforested.

Deforestation is also increasing in Ecuador, with highs of 32,000 hectares (79,000 acres) in 2016 and 55,500 hectares (137,000) acres in 2017.

For context, Brazil has had an average deforestation loss rate of 639,403 hectares (1.58 million acres) over the past several years.

* Data: Colombia & Ecuador: Hansen/UMD/Google/USGS/NASA; Peru: MINAM/PNCB, UMD/GLAD. While this information includes natural forest loss events, it serves as our best estimate of deforestation resulting from anthropogenic causes.  It is estimated that the non-anthropic loss comprises approximately 3.5% of the total loss. Note that the analysis does not include Bolivia.

Trends by Size

The pattern related to the size of deforestation events in the Andean Amazon remained relatively consistent over the last 17 years. Most noteworthy: the vast majority (74%) of the deforestation events are small-scale (‹5 hectares). Only 2% of deforestation events are large-scale (>100 hectares). The remaining 24% are medium-scale (5-100 hectares).

These results are important for conservation efforts.  Addressing this complex situation – in which most of the deforestation events are small-scale – requires significantly more attention and resources.  In addition, while large-scale deforestation (usually associated with agro-industrial practices) is not that common, it nonetheless represents a serious latent threat, due to the fact that only a small number of agro-industrial projects (for example, oil palm) are able to rapidly destroy thousands of acres of primary forest.

II. Deforestation Hotspots

Image 2: Deforestation hotspots 2015-2017. Data: Hansen/UMD/Google/USGS/NASA.

We present the first regional-scale deforestation hotspots map across the Andean Amazon (Colombia, Ecuador, Peru).  Image 2 shows the results for the past three, 2015 – 2017.

The most critical zones (“high” deforestation density) are indicated in red. They include:

A. Central Peruvian Amazon: Over the last 10 years, this zone, located in the Ucayali and Huánuco regions, has consistently had one of the largest concentrations of deforestation in Peru (Inset A).  Its principal drivers include oil palm and cattle grazing.

B. Southern Peruvian Amazon: This zone, located in the Madre de Dios region, is impacted by gold mining (Inset B1), and increasingly by small- and medium-scale agriculture along the Interoceanic Highway (Inset B2).

C. Central Peruvian Amazon: A new oil palm plantation located in the San Martín region has been identified as a recent large-scale deforestation event in this zone (Inset C).

D. Southwestern Colombian Amazon: Cattle grazing is the principal deforestation driver documented in this zone, located in the departments of Caquetá and Putumayo (Inset D).

E. Northern Colombian Amazon: There is expanding deforestation along a new road in this zone, located in the department of Guaviare (Inset E).

F. Northern Ecuadoran Amazon: This zone is located in the Orellana province, where small- and medium-scale agriculture, including oil palm, is the principal driver of deforestation (Inset F).

 

 

III. Drivers of Deforestation     

MAAP Interactive (screenshot)

One of the main objectives of MAAP is to improve the availability of precise and up-to-date information regarding the current drivers (causes) of deforestation in the Andean Amazon.  Indeed, one of our most important advances has been the use of high-resolution imagery to identify current deforestation drivers.

In order to improve the analysis and understanding of the identified drivers, we have created an Interactive Map that displays the spatial location of each driver associated with every MAAP report.  An important characteristic of this map is the ability to filter the data by driver, by selecting the boxes of interest.

Image 3 shows a screenshot of the Interactive Map.  Note that it contains detailed information on these principal drivers: gold mining, oil palm, cacao, small-scale agriculture, cattle pasture, logging roads, and dams.  It also includes natural causes such as floods, forest fires, and blowdowns.  In addition, it highlights deforestation events in protected areas.

Below, we discuss the principal drivers of deforestation and degradation in greater detail.

 

 

 

 

Agriculture – oil palm, cacao, and other crops

Image 4: Interactive Map, agriculture. Data: MAAP.

Image 4 shows the results of the interactive map when applying the agriculture-related filters.

Legend:
Oil palm (bright green)
Cacao (brown)
Other crops (dark green)

Agricultural activity is one of the principal causes of deforestation in the Andean Amazon.

The majority of agriculture-related deforestation is caused by small- and medium-scale plantations (‹50 hectares).

Deforestation for large-scale, agro-industrial plantations is much less common, but represents a critical latent threat.

 

 

 

 

 

Large-scale Agriculture

We have documented five major deforestation events produced by large-scale plantations since 2007:  four of these occurred in Peru (three of which are related to oil palm and one to cacao) and one in Bolivia (resulting from sugar cane plantations).

First, between 2007 and 2011, two large-scale oil palm plantations caused the deforestation of 7,000 hectares on the border between Loreto and San Martín (MAAP #16).  Subsequent plantations in the surrounding area caused the additional deforestation of 9,800 hectares.

It is importnat to note that the Peruvian company Grupo Palmas is now working towards a zero deforestation value chain and has a new sustainability policy (see Case C of MAAP #64).

Next, between 2012 and 2015, two other large-scale oil palm plantations deforested 12,000 hectares in Ucayali  (MAAP #4, MAAP #41).

Between 2013 and 2015, the company United Cacao deforested 2,380 hectares for cacao plantations in Loreto (MAAP #9, MAAP #13, MAAP #27, MAAP #35).

Deforestation from large-scale agriculture decreased in Peru between 2016 and 2017, but there was one notable event: an oil palm plantation of 740 hectares in San Martín (MAAP #78).

Another notable case of deforestation related to large-scale agriculture has been occurring in Bolivia, where a new sugarcane plantation has caused the deforestation of more than 2,500 hectares in the department of La Paz.

Additionally, we found three new zones in Peru characterized by the deforestation pattern produced by the construction of organized access roads which have the potential of becoming large-scale agriculture areas (MAAP #69).

Small and Medium-scale Agriculture

Deforestation caused by small- and medium-scale agriculture is much more widespread, but it is often difficult to identify the driver from satellite imagery.

We have identified some specific cases of oil palm in Huánuco, Ucayali, Loreto, and San Martín (MAAP #48, MAAP #26, MAAP #16).

Cacao and papaya are emerging drivers in Madre de Dios.  We have documented cacao deforestation along the Las Piedras River (MAAP #23, MAAP #40) and papaya along the Interoceanic Highway (MAAP #42).

Corn and rice cultivation appear to be turning the area around the town of Iberia into a deforestation hotspot (MAAP #28).  In other cases, we have documented deforestation resulting from small- and medium-scale agriculture, though it has not been possible to identify the type of crop (MAAP #75, MAAP #78).

Additionally, small-scale agriculture is possibly a determining factor in the forest fires that degrade the Amazon during the dry season (MAAP #45, MAAP #47).

The cultivation of illicit coca is a cause of deforestation in some areas of Peru and Colombia.  For example, in southern Peru, the cultivation of coca is generating deforestation within the Bahuaja Sonene National Park and its surrounding areas.

Cattle Ranching

Image 5: Interactive Map, cattle ranching. Data: MAAP.

By analyzing high-resolution satellite imagery, we have developed a methodology for identifying areas deforestated by cattle ranching.*

Image 5 shows the results of the Interactive Map when applying the “Cattle pasture” filter, indicating the documented examples in Peru and Colombia.

Legend:
Cattle ranching (orange)

Cattle ranching is the principal driver of deforestation in the central Peruvian Amazon (MAAP #26, MAAP #37, MAAP #45, MAAP #78). We also identified recent deforestation from cattle ranching in northeastern Peru (MAAP #78).

In the Colombian Amazon, cattle ranching is one the primary direct drivers in the country’s most intense deforestation hotspots (MAAP #63, MAAP #77).

* Immediately following a major deforestation event, the landscape of felled trees is similar for both agriculture and cattle pasture.  However, by studying an archive of images and going back in time to analyze older deforestation cases, it is possible to distinguish between the drivers.  For example, after one or two years, agriculture and cattle pasture appear very different in the images. Ther former tends to have organized rows of new plantings, while the latter is mostly grassland.

 

 

 

Gold Mining

Image 6: Interactive Map, gold mining. Data: MAAP.

Image 6 shows the results of the Interactive Map when applying the “Gold mining” filter.

Legend:
Gold Mining (yellow)
*With dot indicates within protected area

The area that has been most impacted by gold mining is clearly the southern Peruvian Amazon, where we estimate the total deforestation of more than 63,800 hectares. Of this, at least 7,000 hectares have been lost since 2013.  The two most critical zones are La Pampa and Alto Malinowski in Madre de Dios (MAAP #87, MAAP #75, MAAP #79).  Another critical area exists in Cusco in the buffer zone of the Amarakaeri Communal Reserve, where mining deforestation is now less than one kilometer from the boundary of the protected area (MAAP #71).

It is important to highlight two important cases in which the Peruvian government has taken effective actions to halt illegal mining within protected areas (MAAP #64).  In September 2015, illegal miners invaded Tambopata National Reserve and deforested 550 hectares over the course of a two-year period.  At the end of 2016, the government intensified its interventions and the invasion was halted in 2017. In regards to Amarakaeri Communal Reserve, in June 2015 we revealed the mining invasion deforestation of 11 hectares.  Over the course of the following weeks, SERNANP and ECA Amarakaeri implemented measures and rapidly halted the illegal activity.

Other small gold-mining fronts are emerging in the northern and central Peruvian Amazon (MAAP #45, MAAP #49).

In addition, we have also documented deforestation linked to illegal gold-mining activities in the Puinawai National Park in the Colombian Amazon.

Logging

Image 7: Interactive Map, logging roads. Data: MAAP.

In MAAP #85 we proposed a new tool to address illegal logging in the Peruvian Amazon: utilize satellite imagery to monitor construction of logging roads in near real-time.

Image 7 shows the results of the Interactive Map when applying the “Logging roads” filter.

Legend:
Logging Road (purple)

We estimate that 2,200 kilometers of forest roads have been constructed in the Peruvian Amazon during the last three years (2015-2017).  The roads are concentrated in southern Loreto, Ucayali, and northwestern Madre de Dios.

 

 

 

 

 

 

Roads

Image 8: Interactive map, roads. Data: MAAP.

It has been well-documented that roads are one of the most important drivers of deforestation in the Amazon, particularly due to the fact that they facilitate human access and activities related to agriculture, cattle ranching, mining, and logging.

Image 8 shows the results of the Interactive Map when applying the “Roads” filter.

Legend:
Road (gray)

We have analyzed two controversial proposed roads in Madre de Dios, Peru.

The Nuevo Edén – Boca Manu – Boca Colorado road would traverse the buffer zone of two protected areas: Amarakaeri Communal Reserve and Manu National Park (MAAP #29).

The other, the Puerto Esperanza-Iñapari road, would traverse the Purús National Park and threaten the territory of the indigenous peoples in voluntary isolation who live in this remote area (MAAP #76).

 

 

 

 

Hydroelectric dams

Image 9 shows the results of the Interactive Map when applying the “Dams” filter.

Legend:
Hydroelectric Dam (light blue)

To date, we have analyzed three hydroelectric dams located in Brazil.  We have documented the loss of 36,100 hectares of forest associated with flooding produced by two dams (San Antonio and Jirau) on the Madeira River near the border with Bolivia (MAAP #34).  We also analyzed the controversial Belo Monte hydroelectrical complex located on the Xingú River, adn estimate that 19,880 hectares of land have been flooded. According to the imagery, this land is a combination of forested areas and agricultural areas (MAAP #66).

Additionally, we show a very high-resolution image of the exact location of the proposed Chadín-2 hydroelectric dam on the Marañón River in Peru (MAAP #80).

Hydrocarbon (oil and gas)

Image 10: Interactive map, hidrocarbon. Data: MAAP.

Image 10 shows the results of the Interactive Map when applying the “Hydrocarbon“ filter.

Legend:
Hydrocarbon (black)

Our first report on this sector focused on Yasuní National Park in the Ecuadorian Amazon.  We documented the direct and indirect deforestation amounts of 417 hectares (MAAP #82).

We also show the location of recent deforestation in two hydrocarbon block in Peru: Block 67 in the north and Blocks 57 in the south.

 

 

 

 

 

 

 

Climate Change

Tropical forests, especially the Amazon, sequester huge amounts of carbon, one of the main greenhouse gases driving climate change.

In MAAP #81, we estimated the loss of 59 million metric tons of carbon in the Peruian Amazon during the last five years (2013-17) due to forest loss, especially deforestation from mining and agricultural activities. This finding reveals that forest loss represents nearly half (47%) of Peru’s annual carbon emissions, including from burning fossil fuels.

In contrast, in MAAP #83 we show that protected areas and indigenous lands have safeguarded 3.17 billion metric tons of carbon, as of 2017. That is the equivalent to 2.5 years of carbon emissions from the United States.

The breakdown of results are:
1.85 billion tons safeguarded in the Peruvian national protected areas system;
1.15 billion tons safeguarded in titled native community lands; and
309.7 million tons safeguarded in Territorial Reserves for indigenous peoples in voluntary isolation.

Citation

Finer M, Mamani N (2018) Deforestation in the Andean Amazon (Trends, Hotspots, Drivers). MAAP Synthesis #3.

MAAP #95: Oil Palm Baseline for the Peruvian Amazon

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High-resolution satellite image of oil palm plantation in Peruvian Amazon. Imagery: DigitalGlobe. Click to enlarge.

In previous reports, we have documented that oil palm is one of the deforestation drivers in the Peruvian Amazon (MAAP #41, #48). However, the full extent of this sector’s deforestation impact is not well known.

A newly published study assessed the deforestation impacts and risks posed by oil palm expansion in the Peruvian Amazon. Here, we review some of the key findings.

We first present a Base Map of oil palm in the Peruvian Amazon, highlighting the plantations that have caused recent deforestation. We then show two zooms of the most important oil palm areas, located in the central and northern Peruvian Amazon, respectively.

In summary, we document over 86,600 hectares (214,000 acres) of oil palm, of which we have confirmed the deforestation of at least  31,500 hectares for new plantations (equivalent to nearly 59,000 American football fields).

In other words, yes oil palm does cause Amazon deforestation, but not nearly as much as Asia.

Baseline Map. Oil palm in Peruvian Amazon. Data: MAAP, Vijay et al 2018, Planet

Base Map

A detailed analysis of high-resolution satellite imagery (DigitalGlobe and Planet) revealed that oil palm plantations now cover 86,623 hectares (214,050 acres) in the Peruvian Amazon (see Base Map).

In the Base Map, both yellow and red indicates the documented oil palm plantations, with red corresponding to those that caused deforestation.

The plantations are concentrated in the central and northern Peruvian Amazon (Ucayali, San Martin, Huánuco, and Loreto regions).

Deforestation

In the Base Map, as noted above, red indicates oil palm plantations that caused deforestation since 2000.

A satellite imagery time series analysis revealed that oil palm has directly led to the deforestation of at least 31,500 hectares (77,838 acres) since 2000.

This analysis is timely because the National Palm Oil Board of Peru (Junpalma) recently announced that “the producers have set their goal to reach 250,000 hectares of oil palm plantations by 2019, in order to cover the entire national market “(Source: Gestion).

For example, it is important to note that the Peruvian company Grupo Palmas several years ago proposed four new plantations that would cause the deforestation of 23,000 hectares of primary forest (see MAAP #64).

Clarification: It is important to note that, as indicated in MAAP #64 (case C), one of the most positive news stories in 2017 was that these 4 large-scale oil palm plantations were stopped before any deforestation event occurred. Grupo Palmas is now working towards a zero deforestation value chain and has a new sustainability policy, as indicated in that analysis.

Zoom Central Peruvian Amazon

Image 1 shows a zoom of the oil palm plantations in the central Peruvian Amazon. Most notable is the deforestation for two large-scale oil palm plantations near Pucallpa (MAAP #41). We have also described the growing oil palm deforestation in northern Huanuco (MAAP #48).

Image 1. Oil palm in central Peruvian Amazon. Data: MAAP, Vijay et al 2018, Planet

Zoom Northern Peruvian Amazon

Image 2 shows a zoom of the oil palm plantations in the northern Peruvian Amazon. Most notable is the deforestation for large-scale oil palm plantations along the Loreto-San Martin border (MAAP #16). More recently, we also described new large-scale oil palm deforestation in San Martin (MAAP #92).

Image 2. Oil palm in northern Peruvian Amazon. Data: MAAP, Vijay et al 2018, Planet

Methodology

Vijay et al (2018) identified oil palm plantations within areas deforested between 2000 and 2015 based on visual analysis of very high-resolution (≤ 0.5 m) Worldview-2 and Worldview-3 satellite imagery (from 2014-2016) obtained from DigitalGlobe (NextView). The total oil palm identified from this source is 84,500 hectares.

We also included data for 2016-18 (as of September 2018) based on analysis of high (Planet) and very high-resolution (DigitalGlobe) satellite imagery by the MAAP team. The total oil palm identified from this source is an additional 2,123 hectares.

For areas of interest (Shanusi, Tocache, North Ucayali, San Martin East, Plantations of Pucallpa), we developed a “time series” analysis of satellite images to determine if oil palm has directly caused the observed deforestation.

References

Vijay V et al (2018) Deforestation risks posed by oil palm expansion in the Peruvian Amazon. Environ. Res. Lett. 13 114010. Link: Link: http://iopscience.iop.org/article/10.1088/1748-9326/aae540/meta

Interactive map: https://sites.google.com/view/oilpalmperu

Citation

Finer M, Vijay V, Mamani N (2018) Oil Palm Baseline for the Peruvian Amazon. MAAP: 95.

MAAP #94: Detecting Logging in the Peruvian Amazon with High Resolution Imagery

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Base Map. Logging Activities. Source: ACCA/ACA.

In MAAP # 85, we showed how medium and high-resolution satellites (such as Landsat, Planet and Sentinel-1) could be used to monitor the construction of logging roads in near-real time.

Here, we show the potential of very high-resolution satellites (such as DigitalGlobe and Planet’s Skysat), to identify the activities associated with logging, including illegal logging.

These activities include (see Base Map):
1. Selective logging of high-value trees,
2. Construction of logging roads (access roads),
3. Logging camps
4. Storage and transport

Next, we show a series of very high-resolution images (>50 centimeters), which allow clear identification of these activities.

Note that we show images of both possible legal logging in authorized areas (Images 1,2,5,6,7,9,10) and confirmed illegal logging in unauthorized areas (Images 3,4,8,11,12).*

 

 

1. Selective logging of high-value trees

The following images (1-4) show examples of selective logging. Importantly, note that Images 3 and 4 show examples of confirmed illegal logging.

Image 1: Selective logging in a forestry area (Ucayali). Data: DigitalGlobe
Image 2: Selective logging in a forestry area (Ucayali). Data: DigitalGlobe
Image 3: Confirmed illegal logging in unauthorized area. Data: DigitalGlobe
Image 4: Confirmed illegal logging in unauthorized area. Data: DigitalGlobe

2. Construction of logging roads

The following images (5-8) show examples of the construction of logging roads for access to logging areas and subsequent transport of the wood to collection areas. In Image 7, note that it is possible to identify down to the level of logging trucks. Image 8 shows an example of an illegal logging path in an unauthorized area.

Image 5. Logging road (Loreto). Data: DigitalGlobe
Image 6. Logging road (Ucayali). Data: DigitalGlobe
Image 7. Logging road and logging trucks. Data: Skysat (Planet)
Image 8. Illegal logging path. Data: DigitalGlobe

3. Logging camps

The following images (9-12) show examples of logging camps. Note that Images 11 and 12 show illegal camps in unauthorized areas.

Image 9. Logging camp in forestry area (Loreto). Data: DigitalGlobe.
Image 10. Logging camp in forestry area (Ucayali). Data: DigitalGlobe.
Image 11. Illegal logging camp in unauthorized area. Data: DigitalGlobe
Image 12. Illegal logging camp in unauthorized area. Data: DigitalGlobe

4. Storage and transport

The following images (13-15) show examples of large timber storage areas along major rivers, and the subsequent river transport by boat to the sawmills. In Figure 15, note that radar satellites (such as Sentinel-1) can relatively clearly identify timber transport ships.

Image 13. Timber storage area. Data: DigitalGlobe.
Image 14. Timber storage area. Data: DigitalGlobe.
Image 15. Detecting timber transport boats. Data: ESA (Sentinel-1B)

Annex

Before and after images. Here we show some of the images as above, but with an additional panel showing what the area looked like before the logging activity.

Image 1: Selective logging in a forestry area (Ucayali). Data: DigitalGlobe
Image 8. Illegal logging path. Data: DigitalGlobe
Image 10. Logging camp in forestry area (Ucayali). Data: DigitalGlobe.
Image 11. Illegal logging camp in unauthorized area. Data: DigitalGlobe

*Notes

We determined illegal logging by incorporating additional spatial information regarding forestry and conservation areas. Although very high resolution images allow the detection of activities related to selective logging, the determination of the legality of these activities often requires complementary and detailed information from the corresponding government entities.

Citation

Villa L, Finer M (2018) Detecting Logging in the Peruvian Amazon with High Resolution Imagery. MAAP: 94.

MAAP #93: Shrinking Primary Forests of the Peruvian Amazon

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Base Map. Data: SERNANP, IBC, Hansen/UMD/Google/USGS/NASA, PNCB/MINAM, GLCF/UMD, ANA.

The primary forests of the Peruvian Amazon, the second largest stretch of the Amazon after Brazil, are steadily shrinking due to deforestation.

Here, we analyze both historic and current data to identify the patterns.

The good news: As the Base Map shows, the Peruvian Amazon is still home to extensive primary forest.* We estimate the current extent of Peruvian Amazon primary forest to be 67 million hectares (165 million acres), greater than the total area of France.

Importantly, we found that 48% of the current primary forests (32.2 million hectares) are located in officially recognized protected areas and indigenous territories (see Annex).**

The bad news: The Peruvian Amazon primary forests are steadily shrinking.

We estimate the original extent of primary forests to be 73.1 million hectares (180.6 million acres). Thus, there has been a historic loss of 6.1 million hectares (15 million acres), or 8% of the original. A third of the historic loss (2 million hectares) has occurred since 2001.

Below, we show three zooms (in GIF format) of the expanding deforestation, and shrinking primary forests, in the southern, central, and northern Peruvian Amazon.

 

 

 

GIF of deforestation in the southern Peruvian Amazon. Data: see Base Map

Southern Peruvian Amazon

Note these three important trends in the GIF (click to enlarge):

  • Increasing deforestation all along the route of the Interoceanic Highway;
  • Increasing gold mining deforestation across several different fronts near the southwestern section of the highway;
  • Increasing agricultural deforestation around Iberia, along the northern section of the highway near the border with Brazil.

 

 

 

 

 

 

 

 

GIF of deforestation in the central Peruvian Amazon. Data: see Base Map

Central Peruvian Amazon

Note these three important trends in the GIF (click to enlarge):

  • The substantial historic (pre 1990) deforestation around the cities Pucallpa and Tarapoto;
  • Increasing deforestation along the road leading west from Pucallpa;
  • Large-scale deforestation for oil palm plantations outside of Pucallpa and Yurimaguas.

 

 

 

 

 

 

 

 

 

 

Base Map plus protected areas and indigenous communities.

Northern Peruvian Amazon

Note these three important trends in the GIF (click to enlarge):

  • The historic (pre 1990) deforestation around Iquitos;
  • Increasing deforestation along the Iquitos-Nauta road;
  • Large-scale deforestation for United Cacao plantation near the town of Tamshiyacu.

 

 

 

 

 

 

 

 

 

 

Base Map plus protected areas and indigenous communities. Data: SERNANP, IBC, Hansen/UMD/Google/USGS/NASA, PNCB/MINAM, GLCF/UMD, RAISG, Ministerio de Cultura.

Annex

The Base Map with three additional categories: Protected Areas, titled Native Communities, and Indigenous Reserves.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Notes

*Defining primary forest: According to the Supreme Decree (No. 018-2015-MINAGRI) approving the Regulations for Forest Management under the framework of the new 2011 Forestry Act (No. 29763), the official definition of primary forest in Peru is: “Forest with original vegetation characterized by an abundance of mature trees with species of superior or dominant canopy, which has evolved naturally.” Using methods of remote sensing, our interpretation of that definition are areas that from the earliest available image are characterized by dense closed-canopy coverage and experienced no major clearing events.

It should be emphasized that our definition of primary forest does not mean that the area is pristine. These primary forests may have been degraded by selective logging and hunting.

**Historical Peruvian Amazon primary forests: 73,188,344 hectares. Current Peruvian Amazon primary forests: 67,043,378 hectares. Of this total, 27.6% are located in designated protected areas (18.5 million hectares), 18% in titled Native Communities (12 million hectares), and 4% in Indigenous Reserves/ Territories designated for indigenous peoples in voluntary isolation (2.9 million hectares). There is some overlap between these three categories, and the final combined percentage (48%) takes this into account.

Metodology

To generate the estimate of original (historical) expanse of primary forests in the Peruvian Amazon, we combined two satellite-based data sources. First, we used data from the Global Land Cover Facility (GLCF 2014), which established a forest cover baseline as of 1990 (The GLCF products are based on the Landsat Global Land Survey collection, which were compiled for years circa 1975, 1990, 2000 and 2005). Areas with no data due to shadows and clouds were filled in with GLCF data covering 2000-2005 time frame. The historical primary forest layer was created by combining the following three GLCF data layers: “Persistent Forest,” “Forest Gain,” and “Forest Loss.” Next, we incorporated the “Hydrography” data layer generated by the Peruvian Environment Ministry (Programa Nacional de Conservación de Bosques) to avoid including water bodies. We defined the limit of the analysis as the hydrographical basin of the Amazon. We generally define “historical Peruvian Amazon primary forest” as the expanse of primary forests before the European colonization of Peru (around 1750).

To generate the estimate of current primary forests, we subtracted areas determined to experience deforestation or forest loss from 1990 to 2017. For data covering 1990-2000, we incorporated two datasets: GLCF forest loss 1990-2000 and “No Forest as of 2000” (“No Bosque al 2000”) generated by the Peruvian Environment Ministry. For data covering 2001-2016, we used annual data generated by the Peruvian Environment Ministry. Finally, for 2017, we used early warning alert data generated by the Peruvian Environment Ministry. As a result, we define current primary forests as an area of historical forest with no observable (30 meter resolution) forest loss from 1990 to 2017.

Global Land Cover Facility (GLCF) and Goddard Space Flight Center (GSFC). 2014. GLCF Forest Cover Change 2000 2005, Global Land Cover Facility,University of Maryland, College Park.

Citation:

Finer M, Mamani N (2018) Shrinking Primary Forests of the Peruvian Amazon. MAAP: 93.

MAAP #92: New Deforestation Threats in the Peruvian Amazon (Part 2: Agriculture Expansion)

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Base Map. Data: SERNANP, MAAP

In this ongoing series, we describe major new projects that may lead to the rapid deforestation of large areas of primary Amazon forest.

The first report (MAAP #84) described the deforestation associated with the construction of the Yurimaguas – Jeberos road (see Base Map), which crosses extensive primary forest and a priority site for conservation in the Loreto region.

The current report describes the deforestation associated with major agricultural expansion in three areas in the northern Peruvian Amazon, referred to here as the “Imiria,” “Orellana“, and “San Martin” cases.

These three cases are important because they present characteristics of large-scale, agro-industrial activities (linear plots organized around an extensive new access road network).

In all three cases, early warning alerts (GLAD/Global Forest Watch) initially detected the deforestation in 2017 (see MAAP #69) and their subsequent expansion in 2018. The total deforestation documented to date in these three cases is 3,600 acres.

Below, we show satellite images of the most recent deforestation due to agricultural expansion in these three areas. In these images, yellow circles indicate 2016-17 deforestation and red circles/arrows indicate the most recent 2018 deforestation.

 

 

 

 

 

 

Imiría case (Ucayali)

Just to the north of the Imiría Regional Conservation Area, we documented the deforestation of 872 acres between June 2017 (left panel) and July 2018 (right panel). In the following image, note the organized deforestation around a new access road network. The red circles indicate the most recent 2018 deforestation. Also, note that the access road just reached the boundary of the Imiría Regional Conservation Area. Regarding the cause of deforestation, a recent news article indicates that a nearby indigenous community (Ceylan en Masisea) has reported the expansion of industrial-scale rice plantations.

Imiría case. (ACR = Regional Conservation Area) Data: Planet, SERNANP, MAAP

Orellana case (Loreto)

In the Loreto region, near the town of Orellana, we documented the deforestation of 902 acres between December 2016 (left panel) and July 2018 (right panel). In the following image, again note the organized deforestation around a new access road network. The red arrows indicate the new access roads built in 2018.

Orellana case. Data: Planet, MAAP

San Martin Case

In northeastern San Martín region, we documented the recent deforestation of 1,828 acres between December 2016 (left panel) and August 2018 (right panel) related to a new oil palm plantation. The red circle highlights the most recent 2018 deforestation, which indicates a major expansion of the plantation.

San Martin case. Data: Planet, MAAP

Coordinates

Imiria case: -8.733077,-74.369202
Orellana case: -6.569118,-75.357971
San Martín case: -6.26539,-75.800171

Citation

Finer M, Villa L (2018) New Deforestation Threats in the Peruvian Amazon (Part 2: Agriculture Expansion). MAAP: 92.

MAAP #91: Introducing PeruSAT-1, Peru’s new High-resolution Satellite

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PeruSat-1. Credit: Airbus DS

In September 2016, Peru’s first satellite, PeruSAT-1, launched. It is Latin America’s most powerful Earth observation satellite, capturing images at a resolution of 0.70 meters.

The cutting-edge satellite was constructed by Airbus (France) and is now operated by the Peruvian Space Agency, CONIDA.

The organization Amazon Conservation was granted early access to the imagery to boost efforts related to near real-time deforestation monitoring.

Below, we present a series of PeruSAT images that demonstrate their powerful utility in terms of detecting and understanding deforestation in the Peruvian Amazon.

 

 

 

 

Gold Mining

We have reported extensively on the continuing gold mining deforestation in the southern Peruvian Amazon (see MAAP #87). We are now using PeruSAT to identify active and emerging mining deforestation fronts. For example, in the following images of an active mining zone, it is possible to clearly observe the environmental impact, and identify mining camps and wastewater pools.

PeruSAT-1 image of active gold mining. Data: ®CONIDA (2018), Distribution CONIDA, Peru; All rights reserved.
PeruSAT-1 image (zoom) of active gold mining. Data: ®CONIDA (2018), Distribution CONIDA, Peru; All rights reserved.

Agricultural Expansion

The following image shows a papaya plantation that appeared after a recent deforestation event near the Interoceanic highway in the southern Peruvian Amazon (Mavila, Madre de Dios). See MAAP #42 for more details on papaya emerging as new deforestation driver in this area.

PeruSAT-1 image of papaya plantation. Data: ®CONIDA (2018), Distribution CONIDA, Peru; All rights reserved.

Logging Roads

The following image shows, in high-resolution, a new logging road crossing primary forest in the southern Peruvian Amazon (district of Iñapari, Madre de Dios).

PeruSAT-1 image of logging road. Data: ®CONIDA (2018), Distribution CONIDA, Peru; All rights reserved.

Citation

Villa L, Finer M (2018) Introducing PeruSAT-1, Peru’s new High-resolution Satellite. MAAP: 91.

MAAP #90: Using Drones to monitor Deforestation and Illegal Logging

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Drone types: helipcopter and fixed-wing (plane)

For the past three years, the organization Amazon Conservation has been working to establish a sustainable, local-based drones program for environmental monitoring in the southern Peruvian Amazon (Madre de Dios region).

This program is based on two types of drones, multi-rotor (helicopter style) and fixed-wing (airplane style).

One of the main objectives is to improve the near real-time monitoring of deforestation and illegal logging.

The monitoring is currently focused on three priority areas: 1) Brazil nut concessions, 2) forestry concessions of the local association ACOMAT, and 3) along the Interoceanic Highway (see Base Map).

Below, we show a series of drone images that we have used to identify the drivers of recent deforestation events. These drivers include gold mining, agriculture, illegal logging, cattle pasture, and natural forest loss.

Base Map. Priority areas of the Amazon Conservation drones initiative.

Interoceanic Highway

In March 2018, in collaboration with the organization ProPurús, we realized drone flights along the Interoceanic Highway in an effort to demonstrate the possible threats of building a new road along the border with Brazil (see MAAP #76). The following images show the two main threats to the area: gold mining and small/medium-scale agriculture (<50 hectares).

A. Drone image: gold mining.
B. Drone image: Deforestation from agriculture (corn)

Brazil Nut Concessions

In 2018, Amazon Conservation launched a new project, funded by Google Challenge, to develop a monitoring program for Brazil nut concessions covering a million hectares (2.47 million acres) in southern Peru. For example, the following image shows the invasion of a papaya plantation that caused the recent deforestation of five acres inside a concession.

C. Drone image: Invasión of papaya in Brazil nut concession.

ACOMAT Forestry Concessions

Since 2017, Amazon Conservation has been working on a project, financed by the Norwegian Agency for Development Cooperation (NORAD), to improve the monitoring of forest concessions of the local association ACOMAT (Association of Timber and Non-Timber Forest Concessionaires of the Provinces from Manu and Tambopata). The following images show examples of forest loss and degradation due to illegal logging, cattle grazing, natural loss (windstorm), and gold mining.

D. Drone image: illegal logging.
E. Drone image: cattle pasture.
F. Drone image: natural forest loss from windstorm.
G. Drone image: gold mining.

Citation

Garcia R, Novoa S, Castañeda C, Rengifo P, Jimenez M, Finer M (2018) Using Drones to monitor Deforestation and Illegal Logging. MAAP: 90.

MAAP #87: Gold Mining deforestation continues in the Peruvian Amazon

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Expansión hacia el este de mineria aurífera en La Pampa. Fuente: Planet.

We have reported extensively on the ongoing gold mining deforestation crisis in the southern Peruvian Amazon (see Archive), estimating the loss of over 17,500 acres in the five years between 2013 and 2017.

Here, we present new analysis showing that the destruction continues in 2018: we estimate an additional 4,265 acres during the first six months (January – June). This most recent deforestation is concentrated in two critical areas: La Pampa and Alto Malinowski. Most, if not all, of the mining appears to be illegal (see Annex).

This brings the total gold mining deforestation since 2013 to over 21,750 acres.

Next, we show a series of satellite images of the recent deforestation in La Pampa and Alto Malinowski.

 

 

Base Map

The Base Map highlights the most recent (2018) gold mining deforestation in red. We estimate this deforestation to be around 4,265 acres in the two most critical zones: La Pampa and Alto Malinowski. The yellow boxes indicate the location of the zooms described below. At the end of the article, in the Annex, we present the same base map but with all the overlapping land designations as well to illustrate the complexity of the situation.

Base Map. 2018 gold mining deforestation in southern Peruvian Amazon. Data: Planet, UMD/GLAD, MINAM/PNCB

La Pampa

The following images show the gold mining deforestation in the area known as “La Pampa” between January (left panel) and May (right panel) 2018. Note that the second image is in slider format.

Zoom de La Pampa. Datos: Planet, MAAP

[twenty20 img1=”7415″ img2=”7416″ width=”80%” offset=”0.5″]

Alto Malinowski

The following images show the gold mining deforestation in the area known as “Alto Malinowski” between January (left panel) and May (right panel) 2018. Note that the second image is in slider format.

[twenty20 img1=”7417″ img2=”7418″ width=”80%” offset=”0.5″]

Annex

We present the same base map as above, but also with relevant land designations.  Note that much of the deforestation is concentrated in forestry concessions (ironically, in “reforestation” concessions) and in the Kotsimba Native Community, both of which are outside the legal mining corridor and within the buffer zones of Tambopata National Reserve and Bahuaja Sonene National Park. Thus, most, if not all, of the mining activity appears to be illegal.

Citation

Finer M, Villa L, Mamani N (2018) Gold Mining continues to ravage the Peruvian Amazon. MAAP: 87.

MAAP #85: Illegal Logging in the Peruvian Amazon, and how Satellites can help address it

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Example of new logging road in the Peruvian Amazon. Data: Planet

We propose a new tool to address illegal logging in the Peruvian Amazon: using cutting-edge satellites to monitor logging road construction in near real-time.

Illegal logging in the Amazon is difficult to detect because it is selective logging of individual valuable trees, not large clear-cuts.

However, a new generation of satellites can quickly detect new logging roads, which in turn may indicate the leading edge of illegal logging.

Here, we analyzed satellite imagery to identify all new logging roads built in the Peruvian Amazon over the past three years (2015-17).

We then show how it is possible to track logging road construction in near-real time, using three satellite-based systems: GLAD alerts, Sentinel-1 (radar satellites), and Planet (optical satellites).

 

 

 

 

 


The Technology

GLAD alerts. Source: GFW

GLAD alerts quickly detect areas of recent forest loss (based on 30 meter resolution Landsat imagery) and highlight those pixels. For example, the image on the right shows GLAD alerts for a recent logging road. The satellites described below can then zoom in on these highlighted areas and continue the monitoring in near real-time.

The European Space Agency’s Sentinel-1 satellites freely offer a new image every 12 days no matter the weather conditions, as radar technology allows it to penetrate clouds (see MAAP #79).

The company Planet has a fleet of 175+ mini satellites, lined up like pearls in a necklace, that are able to capture a high-resolution optical image almost daily, though clouds remain an issue (see MAAP #59).

 

 

 

 

Key Findings

Base Map. Logging roads in the Peruvian Amazon. Data: MAAP, SERNANP, IBC. Click to enlarge.

The Base Map illustrates the location of all logging roads built in the Peruvian Amazon since 2001.

We estimate the construction of 1,365 miles (2,200 km) of logging roads during the last three years (2015-17). We indicate these roads in red.

Note that the roads are concentrated in three zones:

  • Southern Loreto, between Cordillera Azul and Sierra del Divisor National Parks;
  • Southern Ucayali; and
  • Northeast Madre de Dios.

Another important finding is the potential rapid speed of logging road construction: up to 1.5 miles (2.5 km) per week.

Next, we focus on two emblematic logging roads (near Sierra del Divisor and Cordillera Azul National Parks, respectively) to demonstrate the feasibility of near real-time monitoring based on Sentinel-1 and Planet satellites.

 

 

 

 

 

A. Logging Road near Sierra del Divisor

Image A1 is a GIF that shows a series of radar images (Sentinel-1) of the construction of a logging road between 2015 and 2017 just north of Sierra del Divisor National Park. The length of the road is 43 miles (69 km). Image A2 is a Planet image showing the status of the road as of the end of 2017.

Image A1. Construction of logging road near Sierra del Divisor. Data: ESA
Image A2. Logging road near Sierra del Divisor. Data: Planet

B. Logging Road near Cordillera Azul

Image B1 is a GIF that shows a series of radar images (Sentinel-1) of the construction of a logging road between 2015 and 2017 east of Cordillera Azu National Park. The length of the road is 33 miles (53 km). Image B2 is a Planet image showing the status of the road as of the end of 2017.

Image B1. Construction of logging road near Cordillera Azul. Data: ESA
Image B2. Logging road near Cordillera Azul. Data: Planet

Notes

1 Not all illegal logging requires roads, but logging roads may indicate some of the most organized, financed, and large-scale operations.

Coordinates

Zona A: -6.966982,-74.6521
Zona B: -7.650428,-75.552979

Citation

Villa L, Finer M (2018) Illegal Logging in the Peruvian Amazon, and how Satellites can help. MAAP: 85.

MAAP #84: New Threats to the Peruvian Amazon (Part 1: Yurimaguas-Jeberos Road)

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Image A: New Yurimaguas-Jeberos road crossing primary forest. Data: Planet

The efforts and international commitments of the Peruvian Government to reduce deforestation may be compromised by new projects do not have adequate environmental assessment.

In this series, we address the most urgent of these projects, those that threaten large areas of primary Amazonian forest.

We believe that these projects require urgent attention from both government and civil society to ensure an adequate response and avoid irreversible damage. For example, in the case below, it is not known whether there is an environmental impact study.

The first report of this series focuses on a new road (Jeberos – Yurimaguas) that threatens a large expanse of primary forest in the northern Peruvian Amazon (see Image A).

 

 

Yurimaguas-Jeberos Road

Image B. Data: GLAD/UMD, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA

Early warning forest loss alerts (GLAD alerts from the University of Maryland and Global Forest Watch) have detected the construction of a new road between the city of Yurimaguas and the town of Jeberos, in southern Loreto region (see Image B).

We estimate that the new road is 65 km (40 miles). In the image, the arrows indicate part of the route crossing primary forest (indicated in dark green).

Although the road improves the connectivity of an isolated town, the problem is that much of it crosses primary Amazon forest and may trigger massive deforestation. It is well documented that roads are one of the main drivers of deforestation in the Amazon (see MAAP #76).

In addition, most of the route crosses “Permanent Production Forest“, a legal land classification restricted to forestry activities, not agriculture or infrastructure (Image D). The route also crosses a regional conservation priority site (Image D).

It is important to note that the Regional Government of Loreto, which is promoting and financing the project, specifically said in a press statement that the road will “encourage the expansion of the agricultural and livestock frontier in this part of the region.” That phrase can be interpreted as frankly stating that the road will cause extensive deforestation. It is a particularly troubling scenario given that Yurimaguas is already a deforestation hotspot.

 

 

 

 

Image C shows the beginning of road construction between August 2017 (left panel) and April 2018 (right panel).

Image C. Road construction. Data: Planet.

Image D shows how the road crosses Permanent Production Forest and a regional conservation priority site.

Image D. Data: GOREL, MINAGRI, MAAP

Citation

Finer M, Mamani N (2018) New Threats to the Peruvian Amazon (Part 1: Yurimaguas-Jeberos Road). MAAP: 84.