Infrastructure Threat: Roads

MAAP #157: New and Proposed Roads Across the Western Amazon

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Amazon Roads Base Map 1. Data: ACA/MAAP, MTC, MINAM, MI, ABT, GAD Napo, FCDS, EcoCiencia, Diálogo Chino, CSF, RAISG, ACCA, ACEAA.

Extensive deforestation, especially along the major road networks, has shockingly turned the eastern Brazilian Amazon into a net carbon source (see MAAP #144).

Fortunately, the greater Amazon across all nine countries is still a net carbon sink, largely thanks to the still intact core of the western Amazon.

The biggest long-term threat to this core Amazon is likely new roads, as they are a leading cause of opening up vast and previously remote areas to deforestation and degradation (Vilela et al 2020).

Here, we present an initial analysis of new and proposed roads across the western Amazon.

Although it’s difficult to predict what proposed projects are actually likely to eventually move forward, we do find the potential of a major road expansion across the core western Amazon (see Base Map 1).

Moreover, even by just focusing on the most advanced or actively discussed projects, we find the risk of major negative impact.

Below, we discuss our initial Amazon Roads Base Map and present a series of zooms showing the primary forest at risk if select road projects move forward.

Amazon Roads Base Map

Base Map 2 highlights new, proposed, and existing roads (red, yellow, and black lines, respectively), in relation to protected areas and indigenous territories for context. We focus on the still largely intact core of the western Amazon (Bolivia, Colombia, Ecuador, Peru, and western Brazil).

Most of the new roads were constructed in the past five years and were digitized from satellite imagery. Note that for some of these new roads, just initial construction of a rough road started and there is still potential for future impacts from road improvement and paving.

Most of the proposed roads were obtained from official government data sets. As noted above, it’s difficult to predict what proposed road projects are actually likely to eventually move forward. Nonetheless, it is clear to see there is the potential to greatly divide the remaining core western Amazon with the portfolio of proposed roads.

Amazon Roads Base Map 2. Data: ACA/MAAP, MTC, MINAM, MI, ABT, GAD Napo, FCDS, EcoCiencia, Diálogo Chino, CSF, RAISG, ACCA, ACEAA.

Zooms of High-Impact New & Proposed Roads

In this section, we focus on the currently most advanced or actively discussed projects (see Letters A-F on Amazon Roads Base Map). We highlight their potential impacts to vast sections of the core western Amazon protected areas and indigenous terrritories.

A. Boca Manu Road (Peru)

The new/proposed road that we refer to here as the Boca Manu road would serve as a new connection between Cusco and Madre de Dios regions. It is notable due its sensitive route between Manu National Park and Amarakaeri Communal Reserve to Boca Manu, and from there between Los Amigos Conservation Concession and Amarakaeri Communal Reserve to Boca Colorado. In addition to likely impacting these protected areas and the concession, the road also has the potential to impact the nearby territory of  indigenous groups in voluntary isolation. See this recent report from Diálogo Chino for more information about this road and its status and impacts.

Zoom A. Boca Manu Road. Data: MTC, MINAM, ACA, ACCA, RAISG.

B. Pucallpa – Cruzeiro do Sul Road (Peru – Brazil)

This proposed road would connect the Peruvian city of Pucallpa with the edge of the existing road network in western Brazil, near the town of Cruzeiro do Sul. Although the potential route has several options, it would sure cut through or near Sierra del Divisor National Park in Peru and the adjacent Serra do Divisor National Park in Brazil. This area is characterized by vast primary forests, thus creating a new binational route connecting the deforestation fronts in each country could obviously trigger significant impacts. See this recent report from Diálogo Chino for more information about this road and its status and impacts.

Zoom B. Pucallpa – Cruzeiro do Sul Road. Data: MTC, MINAM, ACA, CSF, Diálogo Chino, RAISG.

C. Yurua Road (Peru)

The new/proposed road that we refer to here as the Yurua road would connect the Peruvian towns of Nueva Italia on the Ucayali River and Breu on the Yurua River. This 200 km route was originally built as a logging road in the late 1980s to access remote timber areas in the central Peruvian Amazon, but had fallen into disrepair by the early 2000s. A recent MAAP analysis (see MAAP #146) found that between 2010 and 2021 much of the route had been rehabilitated, triggering elevated deforestation along the way. If this road were ever to be paved then impacts would likely continue to rise, including with native communities along the route. See MAAP #146 for more information about this road and its status and impacts.

Zoom C. Yurua Road. Data: MTC, MINAM, ACA, ACCA, RAISG.

D. Genaro Herrera – Angamos Road (Peru)

This new/proposed road would build off an old track through the vast forests connecting the northern Peruvian towns of Genaro Herrera and Angamos, in the region of Loreto. In 2021, clearing began along this route, advancing over 100 kilometers from both ends. If completed and paved, the final road project would impact protected areas on both sides (including the Matsés National Reserve to the south) and pose a major threat to indigenous people in voluntary isolation reportedly living to the north. See this recent report for more information about this road and its status and impacts.

Zoom D. Genaro Herrera – Angamos Road. Data: MTC, ACA, RAISG.

E. Cachicamo – Tunia Road (Chiribiquete National Park, Colombia)

Chiribiquete National Park, located in the heart of the Colombian Amazon, has been experiencing increasing deforestation pressures, partly due to expanding road networks around and even within the park. For example, the Cachicamo-Tunia Road, constructed in 2020, has triggered a new deforestation front in the northwest section of the park. Note this road is also impacting an adjacent Indigenous Reserve.

Zoom E. Cachicamo – Tunia Road. Data: FCDS, RAISG, ACA.

F.  Manaus – Porto Velho Road (BR-319, Brazil)

Arguably the most controversial project on the list: paving the middle section of BR-319 in the heart of the Brazilian Amazon. This nearly 900 km road connects the remote city of Manaus (otherwise only reachable by air or water) with the rest of Brazilian road network in Humaitá and Porto Velho to the south. It was built in the early 1970s but abandoned and impassable by the late 1980s, isolating Manaus once again. Since 2015, a basic maintenance program has made the road generally passable, but the main project remains: paving the 400 km middle section that passes through the core western Amazon. This paving would effectively connect Manaus with the existing highways in the south, and most likely trigger massive forest loss by extending the arc of deforestation northwards, including within and around the protected areas that surround the road. This road project has been the subject of numerous recent press reports, including investigative pieces by the Washington Post and El Pais.

Zoom F. Manaus – Porto Velho Road. Data: Ministério da Infraestrutura, ACA, RAISG.

G. Ixiamas – Chivé Road (Bolivia)

In recent years, Bolivia has been seeking financing for a 250 km road linking the current frontier town Ixiamas with the isolated town Chivé, located near the Peruvian border on the Madre de Dios river. This road would cross extensive tracts of primary Amazon forest and savannah in the north of the La Paz department, including the newly created Bajo Madidi Municipal Conservation Area and the Tacana II indigenous territory.

Zoom G. Ixiamas – Chivé Road. Data: ABT, ACEAA, ACA, RAISG.

Methodology

Our analysis and maps focus on the western Amazon (Bolivia, Colombia, Ecuador, Peru, and western Brazil).

Most of the new roads were constructed in the past five years and were digitized from satellite imagery. Note that for some of these new roads, just initial rehabilitation/improvement of a rough road started and there is still potential for future impacts from paving.

Most of the proposed roads were obtained from official government data sets (and complemented by civil society reports).

We credit the following data sources: Ministerio de Transportes y Comunicaciones (Peru), Geobosques/MINAM (Peru), Ministério da Infraestrutura (Brazil),  Autoridad de Fiscalización y Control Social de Bosques y Tierra – ABT (Bolivia), Gobierno Autonomo Descentralizado Provincial de Napo (Ecuador), Fundación para la Conservación y el Desarrollo Sostenible – FCDS (Colombia), Fundación EcoCiencia (Ecuador), Diálogo Chino, Conservation Strategy Fund, RAISG, Conservación Amazónica – ACCA (Peru), Conservación Amazónica – ACEAA (Bolivia), and Amazon Conservation (digitalization of some new and proposed roads).

Reference:
Vilela et al (2020) A better Amazon road network for people and the environment. PNAS 17 (13) 7095-7102.

Acknowledgments

We especially thank Diálogo Chino for their support of this report. We also thank E. Ortiz, S. Novoa, S. Villacis, D. Larrea, M. Terán, and D. Larrea for helpful comments on earlier drafts of the text and images.

Citation

Finer M, Mamani N (2022) New and Proposed Roads Across the Western Amazon. MAAP: 157.

MAAP #153: Amazon Deforestation Hotspots 2021

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Amazon Base Map. Deforestation hotspots across the Amazon in 2021 (as of September 18). Data: UMD/GLAD, ACA/MAAP.

We present a first look at the major 2021 Amazon deforestation hotspots.*

The Amazon Base Map illustrates several key findings:p

  • We estimate the loss of over 1.9 million hectares (4.8 million acres) of primary forest loss across the nine countries of the Amazon biome in 2021.
    k
  • This matches the previous two years, bringing the total deforestation to 6 million hectares (15 million acres) since 2019, roughly the size of the state of West Virginia.
    p
  • In 2021, most of the deforestation occurred in Brazil (70%), followed by Bolivia (14%), Peru (7%), and Colombia (6%).
    p
  • In Brazil, hotspots are concentrated along the major road networks. Many of these areas were also burned following the deforestation.
    j
  • In Bolivia, fires once again impacted several important ecosystems, including the Chiquitano dry forests.
    p
  • In Peru, deforestation continues to impact the central region, most notably from large-scale clearing for a new Mennonite colony.
    p
  • In Colombia, there continues to be an arc of deforestation impacting numerous protected areas and indigenous territories.

Below, we zoom in on the four countries with the highest deforestation (Brazil, Bolivia, Peru, and Colombia), with additional maps and analysis.

Brazil Base Map. Deforestation hotspots in Brazilian Amazon. Data: UMD/GLAD, ACA/MAAP.

Brazilian Amazon

The Brazil Base Map shows the notable concentration of deforestation hotspots along the major roads (especially roads 163, 230, 319, and 364) in the states of Acre, Amazonas, Pará, and Rondônia.

 

 

 

 

 

 

 

 

 

 

 

Bolivia Base Map. Deforestation hotspots in Bolivian Amazon. Data: UMD/GLAD, ACA/MAAP.

Bolivian Amazon

The Bolivia Base Map shows the concentration of hotspots due to major fires in the Chiquitano dry forest biome, largely located in the department of Santa Cruz in the southeast section of the Amazon.

 

 

 

 

 

 

 

 

 

 

 

Peru Base Map. Deforestation hotspots in the Peruvian Amazon. Data: UMD/GLAD, ACA/MAAP.

Peruvian Amazon

The Peru Base Map shows the concentration of deforestation in the central Amazon (Ucayali region).

We highlight the rapid deforestation (365 hectares) for a new Mennonite colony in 2021, near the town of Padre Marquez (see MAAP #149).

Also, note some additional hotspots in the south (Madre de Dios region), but these are largely from expanding agriculture instead of the historical driver of gold mining.

Indeed, gold mining deforestation has been greatly reduced due to government actions, but this illegal activity still threatens several key areas and indigenous territories (MAAP #130).

 

 

 

 

 

 

 

Colombia Base Map. Deforestation hotspots in northwest Colombian Amazon. Data: UMD/GLAD, ACA/MAAP.

Colombian Amazon

As described in previous reports (see MAAP #120), the Colombia Base Map shows there continues to be an “arc of deforestation” in the northwest Colombian Amazon (Caqueta, Meta, and Guaviare departments).

This arc impacts numerous Protected Areas (particularly Tinigua and Chiribiquete National Parks) and Indigenous Reserves (particularly Yari-Yaguara II and Nukak Maku).

 

 

 

 

 

 

 

 

 

*Notes and Methodology

The analysis was based on 10-meter resolution primary forest loss alerts (GLAD+) produced by the University of Maryland and also presented by Global Forest Watch. These alerts are derived from the Sentinel-2 satellite operated by the European Space Agency.

We emphasize that this data represents a preliminary estimate and more definitive annual data will come later in the year.

We also note that this data does include forest loss caused by natural forces and burned areas.

Our geographic range for the Amazon is a hybrid between both the biogeographic boundary (as defined by RAISG) and watershed boundary, designed for maximum inclusion.

To identify the deforestation hotspots, we conducted a kernel density estimate. This type of analysis calculates the magnitude per unit area of a particular phenomenon, in this case, forest cover loss. We conducted this analysis using the Kernel Density tool from the Spatial Analyst Tool Box of ArcGIS. We used the following parameters:

Search Radius: 15000 layer units (meters)
Kernel Density Function: Quartic kernel function
Cell Size in the map: 200 x 200 meters (4 hectares)
Everything else was left to the default setting.

For the Base Map, we used the following concentration percentages: Medium: 5-7%; High: 7-14%; Very High: >14%.

Acknowledgements

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

Citation

Finer M, Mamani N, Spore J (2022) Amazon Deforestation Hotspots 2021. MAAP: 153.

MAAP #147: Amazon Deforestation Hotspots 2021 (1st Look)

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Base Map. Deforestation hotspots across the Amazon in 2021 (as of September 18). Data: UMD/GLAD, ACA/MAAP.

We present a first look at the major deforestation hotspots across all nine countries of the Amazon in 2021 (as of September 18).*

The Base Map illustrates several key findings thus far in 2021:p

  • We estimate the loss of over 860,000 hectares (2.1 million acres) of primary forest loss across the nine countries of the Amazon.
    p
  • Amazon deforestation has been concentrated in three countries: Brazil (79%), Peru (7%), Colombia (6%).
    p
  • The vast majority of deforestation (79%) occurred in the Brazilian Amazon, where massive hotspots stretched across the major road networks. Many of these areas were also burned following the deforestation.
    p
  • There continues to be an arc of deforestation in the northwestern Colombian Amazon, impacting numerous protected areas and indigenous territories.
    p
  • In the Peruvian Amazon, deforestation continues to impact the central region, most notably from a new Mennonite colony and large-scale rice plantation.
    p
  • In Bolivia, fires are once again impacting several important ecosystems, including the Beni grasslands and Chiquitano dry forests of the Amazon, and Chaco scrub forest outside the Amazon.

Below, we zoom in on the three countries with the highest deforestation (Brazil, Colombia, and Peru) and show a series of high-resolution satellite images that illustrate some of the major 2021 deforestation events.

Widespread Deforestation in the Brazilian Amazon

The Brazil Base Map shows the notable concentration of deforestation hotspots along the major roads (especially roads 163, 230, 319, and 364). Zooms A-C show high-resolution examples of this deforestation, which largely appears to be associated with clearing rainforests for pasture.

Brazil Base Map. Deforestation hotspots in Brazilian Amazon (as of September 18). Data: UMD/GLAD, ACA/MAAP.
Zoom A. Deforestation in the Brazilian Amazon near road 230 (TransAmazian Highway) between February (left panel) and September (right panel) of 2021. Data: Planet.
Zoom B. Deforestation in the Brazilian Amazon along road 319 in Amazonas state between May (left panel) and September (right panel) of 2021. Data: Planet, ESA.
Zoom C. Deforestation in the Brazilian Amazon along road 163 between November 2020 (left panel) and September 2021 (right panel). Data: Planet, ESA.
Colombia Base Map. Deforestation hotspots in northwest Colombian Amazon (as of September 18). Data: UMD/GLAD, ACA/MAAP.

Arc of Deforestation in the Colombian Amazon

As described in previous reports (see MAAP #120), the Colombia Base Map shows there continues to be an “arc of deforestation” in the northwest Colombian Amazon (Caqueta, Meta, and Guaviare departments).

This arc impacts numerous protected areas (particularly Tinigua and Chiribiquete National Parks) and Indigenous Reserves (particularly Yari-Yaguara II and Nukak Maku).

Zooms D & E show high-resolution examples of this deforestation, which largely appears to be associated with clearing rainforests for pasture.

Zoom D. Deforestation in the Colombian Amazon (Caqueta) between December 2020 (left panel) and September 2021 (right panel). Data: Planet.
Zoom E. Deforestation in the Colombian Amazon between January (left panel) and September (right panel) of 2021. Data: Planet, ESA.
Peru Base Map. Deforestation hotspots in the Peruvian Amazon (as of September 18). Data: UMD/GLAD, ACA/MAAP.

Deforestation in the central Peruvian Amazon

The Peru Base Map shows the concentration of deforestation in the central Peruvian Amazon (Ucayali, Huanuco, and southern Loreto regions).

Zooms F & G show two notable examples of this deforestation: the rapid deforestation in 2021 for a new Mennonite colony (299 hectares) and large-scale rice plantation (382 hectares), respectively.

Also note some additional hotspots in the south (Madre de Dios region) from gold mining and medium-scale agriculture.

The hotspot in the north (Loreto region) is natural forest loss from a windstorm.

Zoom F. Deforestation (299 hectares) in the Peruvian Amazon for a new Mennonite colony between January (left panel) and September (right panel) of 2021 in southern Loreto region. Data: Planet.
Zoom G. Deforestation (382 ha) in the Peruvian Amazon for a new large-scale rice plantation between January (left panel) and September (right panel) of 2021 in Ucayali region. Data: Planet.

*Notes and Methodology

The analysis was based on 10-meter resolution primary forest loss alerts (GLAD+) produced by the University of Maryland and also presented by Global Forest Watch. These alerts are derived from the Sentinel-2 satellite operated by the European Space Agency.

We emphasize that this data represents a preliminary estimate and more definitive annual data will come later next year.

We also note that this data does include forest loss caused by natural forces and burned areas.

Our geographic range for the Amazon is a hybrid between both the biogeographic boundary (as defined by RAISG) and watershed  boundary, designed for maximum inclusion.

To identify the deforestation hotspots, we conducted a kernel density estimate. This type of analysis calculates the magnitude per unit area of a particular phenomenon, in this case forest cover loss. We conducted this analysis using the Kernel Density tool from Spatial Analyst Tool Box of ArcGIS. We used the following parameters:

Search Radius: 15000 layer units (meters)
Kernel Density Function: Quartic kernel function
Cell Size in the map: 200 x 200 meters (4 hectares)
Everything else was left to the default setting.

For the Base Map, we used the following concentration percentages: Medium: 7-10%; High: 11-20%; Very High: >20%.

Acknowledgements

We thank E. Ortiz and A. Ariñez for their helpful comments on this report.

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

Citation

Finer M, Mamani N, Spore J (2020) Amazon Deforestation Hotspots 2021. MAAP: 147.

MAAP #136: Amazon Deforestation 2020 (Final)

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Base Map. Forest loss hotspots across the Amazon in 2020. Data: Hansen/UMD/Google/USGS/NASA, RAISG, MAAP. The letters A-E correspond to the zoom examples below.

*To download the report, click “Print” instead of “Download PDF” at the top of the page.

In January, we presented the first look at 2020 Amazon deforestation based on early warning alert data (MAAP #132).

Here, we update this analysis based on the newly released, and more definitive, annual data.*

The Base Map illustrates the final results and indicates the major hotspots of primary forest loss across the Amazon in 2020.

We highlight several key findings:

  • The Amazon lost nearly 2.3 million hectares (5.6 million acres) of primary forest loss in 2020 across the nine countries it spans.
    g
  • This represents a 17% increase in Amazon primary forest loss from the previous year (2019), and the third-highest annual total on record since 2000 (see graph below).
    j
  • The countries with the highest 2020 Amazon primary forest loss are 1) Brazil, 2) Bolivia, 3) Peru, 4) Colombia, 5) Venezuela, and 6) Ecuador.
    h
  • 65% occurred in Brazil (which surpassed 1.5 million hectares lost), followed by 10% in Bolivia, 8% in Peru, and 6% in Colombia (remaining countries all under 2%).
    k
  • For Bolivia, Ecuador, and Peru, 2020 recorded historical high Amazon primary forest loss. For Colombia, it was the second highest on record.

In all of the data graphs, orange indicates the 2020 primary forest loss and red indicates all years with higher totals than 2020.

For example, the Amazon lost nearly 2.3 million hectares in 2020 (orange), the third highest on record behind only 2016 and 2017 (red).

Note that the three highest years (2016, 2017, and 2020) had one major thing in common: uncontrolled forest fires in the Brazilian Amazon.

See below for country-specific graphs, key findings, and satellite images for the top four 2020 Amazon deforestation countries (Brazil, Bolivia, Peru, and Colombia).

 

 

 

Brazilian Amazon

2020 had the sixth-highest primary forest loss on record (1.5 million hectares) and a 13% increase from 2019.

Many of the 2020 hotspots occurred in the Brazilian Amazon, where massive deforestation stretched across nearly the entire southern region.

A common phenomenon observed in the satellite imagery through August was that rainforest areas were first deforested and then later burned, causing major fires due to the abundant recently-cut biomass (Image A). This was also the pattern observed in the high-profile 2019 Amazon fire season. Much of the deforestation in these areas appears to associated with expanding cattle pasture areas.

In September 2020 (and unlike 2019), there was a shift to actual Amazon forest fires (Image B). See MAAP #129 for more information on the link between deforestation and fire in 2020.

Note that the three highest years (2016, 2017, and 2020) had one major thing in common: uncontrolled forest fires in the Brazilian Amazon.

Image A. Deforestation in Brazilian Amazon (Amazonas state) of 2,540 hectares between January (left panel) and November (right panel) 2020. Data: Planet.
Image B. Forest fire in Brazilian Amazon (Para state) that burned 9,000 hectares between March (left panel) and October (right panel) 2020. Data: Planet.

Bolivian Amazon

2020 had the highest primary forest loss on record in the Bolivian Amazon, surpassing 240,000 hectares.

Indeed, the most intense hotspots across the entire Amazon ocurred in southeast Bolivia, where fires raged through the drier Amazon forests (known as the Chiquitano and Chaco ecosystems).

Image C shows the burning of a massive area (over 260,000 hectares) in the Chiquitano dry forests (Santa Cruz department).

 

 

 

 

Image C. Forest fire in Bolivian Amazon (Santa Cruz) that burned over 260,000 hectares between April (left panel) and November (right panel) 2020. Data: ESA.

Peruvian Amazon

2020 also had the highest primary forest loss on record in the Peruvian Amazon, surpassing 190,000 hectares.

This deforestation is concentrated in the central region. On the positive, the illegal gold mining that plagued the southern region has decreased thanks to effective government action (see MAAP #130).

Image D shows expanding deforestation (over 110 hectares), and logging road construction (3.6 km), in an indigenous territory south of Sierra del Divisor National Park in the central Peruvian Amazon (Ucayali region). The deforestation appears to be associated with an expanding small-scale agriculture or cattle pasture frontier.

 

 

Image D. Deforestation and logging road construction in Peruvian Amazon (Ucayali region) between March (left panel) and November (right panel) 2020. Data: Planet.

Colombian Amazon

2020 had the second-highest primary forest loss on record in the Colombian Amazon, nearly 140,000 hectares.

As described in previous reports (see MAAP #120), there is an “arc of deforestation” concentrated in the northwest Colombian Amazon. This arc impacts numerous protected areas (including national parks) and Indigenous Reserves.

For example, Image E shows the recent deforestation of over 500 hectares in Chiribiquete National Park. Similar deforestation in that sector of the park appears to be conversion to cattle pasture.

 

 

 

Image E. Deforestation in Colombian Amazon of over 500 hectares in Chiribiqete National Park between January (left panel) and December (right panel) 2020. Data: ESA, Planet.

*Notes and Methodology

To download the report, click “Print” instead of “Download PDF” at the top of the page.

The analysis was based on 30-meter resolution annual data produced by the University of Maryland (Hansen et al 2013), obtained from the “Global Forest Change 2000–2020” data download page. It is also possible to visualize and interact with the data on the main Global Forest Change portal.

Importantly, this data detects and classifies burned areas as forest loss. Nearly all Amazon fires are human-caused. Also, this data does include some forest loss caused by natural forces (landslides, wind storms, etc…).

Note that when comparing 2020 to early years, there are several methodological differences from the University of Maryland introduced to data after 2011. For more details, see “User Notes for Version 1.8 Update.”

It is worth noting that we found the early warning (GLAD) alerts to be a good (and often conservative) indicator of the final annual data.

Our geographic range includes nine countries and consists of a combintion of the Amazon watershed limit (most notably in Bolivia) and Amazon biogeographic limit (most notably in Colombia) as defined by RAISG. See Base Map above for delineation of this hybrid Amazon limit, designed for maximum inclusion. Inclusion of the watershed limit in Bolivia is a recent change incorporated to better include impact to the Amazon dry forests of the Chaco.

We applied a filter to calculate only primary forest loss. For our estimate of primary forest loss, we intersected the forest cover loss data with the additional dataset “primary humid tropical forests” as of 2001 (Turubanova et al 2018). For more details on this part of the methodology, see the Technical Blog from Global Forest Watch (Goldman and Weisse 2019).

To identify the deforestation hotspots, we conducted a kernel density estimate. This type of analysis calculates the magnitude per unit area of a particular phenomenon, in this case forest cover loss. We conducted this analysis using the Kernel Density tool from Spatial Analyst Tool Box of ArcGIS. We used the following parameters:

Search Radius: 15000 layer units (meters)
Kernel Density Function: Quartic kernel function
Cell Size in the map: 200 x 200 meters (4 hectares)
Everything else was left to the default setting.

For the Base Map, we used the following concentration percentages: Medium: 7-10%; High: 11-20%; Very High: >20%.

 

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53.

Acknowledgements

We thank E. Ortiz (AAF), M. Silman (WFU), M. Weisse (WRI/GFW) for their helpful comments on this report.

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

Citation

Finer M, Mamani N (2020) Amazon Deforestation Hotspots 2020 (Final). MAAP: 136.

MAAP #123: Detecting Illegal Logging in the Peruvian Amazon

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Image 1. Example of a 2019 logging road with signs of illegality. Data: Planet.

In the Peruvian Amazon, the widespread illegal logging is difficult to detect with satellites because it is selective for high-value species (not clearcutting).

It is possible, however, to detect the associated logging roads.

In this report, we present a novel technique to identify illegal logging: analyze new logging roads in relation to detailed land use data available from government agencies.

Thus, our new method detects the crime in real-time and preventive action is still possible. This is important because when an intervention against illegal logging normally occurs, stopping a boat or truck with illegal timber, the damage is done.

This analysis has two parts. First, we identified the new logging roads built in the Peruvian Amazon during 2019, updating our previous work for 2015-18 (see Base Map).

Second, we analyzed the new logging road data in relation to governmental land use information in order to identify possible illegality.

This data is from 2019, but we are now applying this technique in real time during 2020.

Base Map. 2019 Logging roads, in relation to 2015-18 logging roads. Data: MAAP.

Logging Roads 2019

The Base Map illustrates the location of logging roads built in the Peruvian Amazon during the last 5 years.

Previously (MAAP #99), we documented the construction of 3,300 kilometers of logging roads between 2015 and 2018.

Here, we estimate the construction of an additional 1,500 kilometers in 2019 (see red).

Note that forest roads are mainly concentrated in the Ucayali, Madre de Dios and Loreto regions.

Below, we show three types of possible illegality that detected in 2019:

  • Logging roads in areas without forestry concessions or permits (Cases 1-2).
    .
  • Logging roads in existing forest concessions, but whose current status is defined as “Non-Active or Undefined” (Cases 3-5).
    .
  • Logging roads in Native Communities (Case 6).

Cases of Possible Illegality

Logging roads in areas without forestry concessions or permits

Case 1. We detected the opening of a new logging road network (55 km) in an area without forestry concessions or permits, between the limits of the Loreto and San Martín regions. The image shows the digitized logging roads (red, left panel), and non-digitized satellite image (right panel). The arrows provide reference points between panels.

Case 1. Data: MAAP, Planet. Click to enlarge.

Case 2. We detected the construction of a new logging road network (5.8 km) in the buffer zone of Asháninka Communal Reserve, reaching only 300 meters from the protected area. The image shows the digitized logging roads (red, left panel), and non-digitized satellite image (right panel). The arrows provide reference points between panels.

Case 2. Data: MAAP, Planet, IBC, SERNANP. Click to enlarge.

Logging roads in existing forest concessions, but whose current state is labelled as “Non-Active or Undefined” 

Case 3. We detected the construction of a new logging road (45.3 km) that crosses a native community and reaches a forest concession whose status is defined as “Undefined,” in the Loreto region just north of Pacaya Samiria National Reserve. The image shows the digitized logging roads (red, left panel), and non-digitized satellite image (right panel). The arrows provide reference points between panels.

Case 3. Data: MAAP, ESA, IBC, SERFOR. Click to enlarge.

Case 4. We detected the construction of a new logging road network (53.2 km), of which nearly half (21.4 km) crosses a forest concession whose status is defined is “Non Active”, near the town of Sepahua in the Ucayali region. The image shows the digitized logging roads (red, left panel), and non-digitized satellite image (right panel). The arrows provide reference points between panels.

Case 4. Data: MAAP, Planet, IBC, SERFOR. Click to enlarge.

Case 5. We detected the construction of a new logging road (17.7 km) in a forestry concession whose current status is defined as “Non Active,” in the Madre de Dios region. The image shows the digitized logging roads (red, left panel), and non-digitized satellite image (right panel). The arrows provide reference points between panels.

Case 5. Data: MAAP, ESA, IBC, SERFOR. Click to enlarge.

Logging roads in Native Communities

Case 6. We detected the construction of a logging road (23.4 km) within an indigenous community in the Ucayali region. We did not find evidence of a permit for this activity. The image shows the digitized logging roads (red, left panel), and non-digitized satellite image (right panel). The arrows provide reference points between panels.

Case 6. Data: MAAP, Planet, SERNANP, IBC, SERFOR. Click to enlarge.

Methodology

Our analysis included two main steps:

The first step consisted of evaluating linear patterns in the 2019 early warning and final forest loss data, available from Global Forest Watch (data from the University of Maryland) and Geobosques (data from the Peruvian Ministry of the Environment). From the linear patterns, we distinguished between logging access roads and other types of roads and highways. Logging roads tend to have linear patterns that branch into the interior of the forest where the commercial timber is found. Other types of roads have a more defined destination, such as towns or farms. Once logging roads were identified, we downloaded the associated high-resolution imagery (3 meters) from Planet Explorer and digitized the roads in ArcGIS. During this process, additional logging roads detected in the high resolution images were also digitized.

The second step focused on the legality analysis. The new logging road data were overlaid with other types of land use information, such as forestry concessions on the GeoSERFOR portal (SERFOR), permits and concessions on the SISFOR portal (OSINFOR), indigenous communities (IBC 2019), protected areas (SERNANP), population centers (INEI 2019), and official road networks (MTC 2018). For example, as shown above, this process identified logging roads near protected areas, within indigenous communities, and within non-active forest concessions.

We analyzed information on several websites now available from national and regional authorities, such as SISFOR (OSINFOR), GEOSERFOR (SERFOR), and IDERs (Spatial Data Infrastructure of Regional governments). These new resources provide valuable information, however may have limitations in ability to constantly update information on the status of concessions and forest permits, especially from regional governments.

Annex – Logging road data per region

REGION, Logging Roads (Km)

LORETO, 231.2
MADRE DE DIOS, 477.8
UCAYALI, 720.0
HUANUCO, 45.5
JUNÍN, 19.8
PASCO, 15.1
SAN MARTIN, 2.4

TOTAL, 1511.7

References

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

Acknowledgments

We thank R. Valle (OSINFOR), A. Felix (DAI), D. Suarez (ACCA), 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.

Citation

Finer M, Paz L, Novoa S, Villa L (2020) Detecting Illegal Logging in the Peruvian Amazon. MAAP: 123.

MAAP #117: New Oil Road Deeper into Yasuni National Park (Ecuador), Towards Uncontacted Indigenous Reserve

Posted on by dcadmin

Yasuní National Park, located in the heart of the Ecuadorian Amazon, is one of the most biodiverse spots in the world and overlaps ancestral Waorani territory. In the recent MAAP #114, we showed the construction of four new oil drilling platforms (and access road) in the controversial ITT oil block, located in the heart of Yasuní.

Here, we show that, beginning in mid-March 2020, we detected the construction of a new access road heading further south from the last platform (Image 1). As of early May, this road construction was 4.7 km through primary forest.

Updated: June 30 (4.7 km); June 14 (3.7 km); May 17 (2.2 km).

Image 1. Construction of a new 4.7 km oil access road deeper into Yasuni National Park between March (left panel) and June (right panel) 2020. Click to enlarge.

Implications of the New Oil Road

This finding is concerning because it brings oil development closer to the “Zona Intangible,” a reserve created to protect the territory of indigenous people in voluntary isolation (Tagaeri, Taromenane), isolated relatives of the Waorani.

In Image 2, the location of the new road (indicated in red) is shown approaching several planned oil drilling platforms just outside the buffer zone of the Zona Intangible. Image 3 shows a zoom of this area.

It is also concerning because construction is occurring during the coronavirus pandemic.

Image 2. The new oil acces road (in red) approaching the Zona Intangible.

Very High Resolution Image

We have also obtained a very high resolution satellite image (Skysat, 0.8 meters) of the new oil access road. Below are two examples of this image; the first shows the complete route of the new highway and the second is a zoom of the most recent expansion to the south. Click to enlarge.

Finer M, Mamani N (2020) New Oil Road Deeper into Yasuni National Park. MAAP: 116.

MAAP #99: Detecting Illegal Logging in the Peruvian Amazon

Posted on by dcadmin
New logging road in the Peruvian Amazon. Data: Planet.

In the Peruvian Amazon, most of the logging is selective (not clearcutting), with the targets being higher-value species. Thus, illegal logging is difficult to detect with satellite imagery.

In MAAP #85, however, we presented the potential of satellite imagery in identifying logging roads, which are one of the main indicators of logging activity in the remote Amazon.

Here, we go a step further and show how to combine logging road data with additional land use data, such as forestry licenses and concessions, to identify possible illegal logging.

This analysis, based in the Peruvian Amazon, has two parts. First, we identify the construction of new logging roads in 2018, updating our previous dataset from 2015-17 (see Base Map).

Second, we analyze these new logging roads in relation to addition spatial information now available on government web portals,* in order to identify possible illegality.

*We analyzed information on several websites now available from national and regional authorities, such as SISFOR (OSINFOR), GEOSERFOR (SERFOR), and IDERs (Spatial Data Infrastructure of Regional governments). These new resources provide valuable information, however may have limitations in ability to constantly update information on the status of concessions and forest permits.

 

 

Base Map. Logging roads. Data: MAAP, SERNANP

Base Map

The Base Map illustrates the precise location of logging roads built in the Peruvian Amazon over the last four years.

Previously (MAAP #85), we estimated the construction of 2,200 kilometers of logging roads during 2015-17 (yellow).

Here, we estimate the construction of an additional 1,100 km in 2018 (pink).

Thus, in total, we have documented the construction of 3,300 km of logging roads over the last four years (2015-18).

Note that these logging roads are concentrated mainly in the regions of Ucayali, Madre de Dios (northeast), and Loreto (south).

 

 

 

 

 

 

Cases of Possible Illegal Logging

A. Logging roads in non-forestry areas

Zoom A shows the construction of a logging road past the border of a forestry permit, into a non-forestry area. In this case, the road extends close (200 meters) to the border of a protected area (Ashaninka Communal Reserve). It is important to point out that this type of analysis requires frequently updated information from the entities that grant forest permits, such as regional governments.  

Zoom A. Data: Planet, MAAP, SERNANP, OSINFOR, IBC


B. Logging roads in canceled concessions

Zoom B shows the construction of logging roads within logging concessions classified as “Caducado,” or cancelled (no longer active). This type of analysis also requires frequently updated information on the status of forestry concessionaries.

Zoom B. Data: Planet, MAAP, OSINFOR, GOREU

C. Logging Roads in Brazil nut concessions

Zoom C shows the construction of logging roads within a Brazil nut forestry concession. While some managed timber extraction is allowed in Brazil nut concessions, the extensive construction of two logging roads, along with the irregular logging area boundaries, drew attention. A detailed investigation by the Peruvian Forestry Service (SERFOR) and environmental prosecutor (FEMA) revealed the illegality of this logging activity (see this article from Mongabay for more information).

Zoom C. Data: Planet, MAAP, OSINFOR


D. Logging roads in protected areas

Zoom D shows part of a logging road entering a protected area (El Sira Communal Reserve). It appears that this section of the reserve overlaps with a forestry permit obtained after the creation of the protected area. It is worth emphasizing that according to Peruvian law, timber extraction is not permitted within protected areas such as El Sira.

Zoom D. Data: Planet, MAAP, SERNANP, OSINFOR, GOREU, IBC

SERNANP (the Peruvian National Service of Natural Protected Areas) has communicated these facts to the region of Ucayali’s Provincial Prosecutor’s Office Specialized in Environment (Atalaya headquarters). Also, SERNANP is managing the process of nullifying the permit, given that it doesn’t have the technical opinion of SERNANP, a requirement as stated by the current regulation.

References

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

Acknowledgments

We thank OSINFOR, SERNANP Alfredo Cóndor (ACCA) and Lorena Durand (ACCA) for helpful comments to this report.

Citation

Villa L, Finer M (2019) Detecting Illegal Logging in the Peruvian Amazon. MAAP: 99.

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

Posted on by dcadmin
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 #94: Detecting Logging in the Peruvian Amazon with High Resolution Imagery

Posted on by dcadmin
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 #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.