Land Use: Protected Areas

Amazon Fire Tracker 2020: Over 200 Major Fires as of Aug 10

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Brazilian Amazon Fire #76, July 2020. Imagery: Planet. Click to Enlarge.

Our innovative new app for Real-time Amazon Fire Monitoring has detected over 200 major fires in 2020.

The app specializes in filtering out thousands of the traditional heat-based fire alerts to prioritize only those burning large amounts of biomass (defined here as a major fire).*

Our key findings include:

  • We have detected 227 major Amazon fires (Brazil 220, Bolivia 6; Peru 1), as of August 10.
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  • The vast majority of major fires have been in the Brazilian Amazon, where a strikingly high number (85%) have burned recently deforested areas. Thus, the fires are actually a smoking indicator of the rampant deforestation now in Brazil.
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  • In Brazil, we have detected two forest fires, but this risk increases as we get deeper into the dry season. The rest of the fires have been on older fields.
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  • In Brazil, the vast majority (94%) of the major fires have been illegal, in violation of the state and national fire moratoriums established in July. In fact, despite the moratoriums, the number of major fires is accelerating: 143 so far in August following 77 in May through July.
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  • In Brazil, 14 of the fires have been in Protected Areas.
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  • In the Bolivian and Peruvian Amazon, we have recently started detecting fires in the drier ecosystems (savannahs and grasslands).

See below for a more detailed breakdown of the results.

Additional Results

The Base Map is a screen shot of the app’s “Major Amazon Fires 2020” layer.

Base Map. Major Fires 2020. Data: MAAP.

 

The vast majority of the fires have been in the Brazilian Amazon: Pará (37%) and Amazonas (39%), followed by Mato Grosso (17%) and Rondônia (8%).

Importantly, the vast majority of the major fires in the Brazilian Amazon (85%) have burned recently deforested areas (cleared between 2018 and 2020) covering 280,000 acres (113,000 hectares). Thus, we argue that the central issue is actually deforestation and the fires are actually a smoking indicator of this forest loss.

We have detected the first two forest fires, burning 388 acres (1,447 hectares) in Mato Grosso and Para.

The rest of the major fires have been on older cattle or agricultural lands (deforested prior to 2018).

The most impacted protected areas are Jamanxim and Altamira National Forests in Pará. We emphasize, however, that these fires were burning recently deforested areas (not forest fires) and so, again, the primary issue is deforestation.

In Brazil, the vast majority of the major fires (94%) appear to be illegal as they violate the state and national government mandated fire moratoriums established in July. In fact, despite the moratoriums, the number of major fires is accelerating: 143 so far in August, following 64 in July, 12 in June, and the first one in May.

In the Bolivian Amazon, we have recently started detecting fires in the savannahs in the department of Beni. We also detected one fire in a recently deforested area in the Santa Cruz department.

In the Peruvian Amazon, we have recently started detecting fires in the upper elevation grasslands. The biggest one was actually within a protected area (Otishi National Park). There have also been smaller grassland fires near the buffer zone of upper Manu National Park.

Key Examples of 2020 Fires

Overall our key finding is that most major Brazilian Amazon fires are burning recently deforested areas, and not raging forest fires. Below is a series of satellite image time-lapse videos showing examples of recent deforestation followed by a major 2020 fire.

Brazilian Amazon Fire #54, July 2020

 

Brazilian Amazon Fire #59, July 2020

 

Brazilian Amazon Fire #76, July 2020

 

Brazilian Amazon Fire #110, August 2020

*Notes and Methodology

In a novel approach, the app combines data from the atmosphere (aerosol emissions in smoke) and the ground (heat anomaly alerts) to effectively detect and visualize major Amazon fires.

When fires burn, they emit gases and aerosols. A new satellite (Sentinel-5P from the European Space Agency) detects these aerosol emissions. Thus, the major feature of the app is detecting elevated aerosol emissions which in turn indicate the burning of large amounts of biomass. For example, the app distinguishes small fires clearing old fields (and burning little biomass) from larger fires burning recently deforested areas or standing forest (and burning lots of biomass).

We define “major fire” as one showing elevated aerosol emission levels on the app, thus indicating the burning of elevated levels of biomass. This typically translates to an aerosol index of >1 (or cyan-green to red on the app). To identify the exact source of the elevated emissions, we reduce the intensity of aerosol data in order to see the underlying terrestrial heat-based fire alerts. Typically for major fires, there is a large cluster of alerts. The major fires are then confirmed, and burn areas estimated, using high-resolution satellite imagery from Planet Explorer.

See MAAP #118 for additional details.

No fires permitted in the Brazilian state of Mato Grosso after July 1, 2020. No fires permitted in all of Brazilian Amazon after July 15, 2020. Thus, we defined “illegal” as any major fires detected after these respective dates.

There was no available Sentinel-5 aerosol data on July 4, 15, and 26.

Acknowledgements

This analysis was done by Amazon Conservation in collaboration with SERVIR Amazonia.

Citation

Finer M, Nicolau A, Villa L (2020) 200 Major Amazon Fires in 2020: Tracker Analysis. MAAP.

MAAP #122: Amazon Deforestation 2019

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Table 1. Amazon 2019 primary forest loss for 2019 (red) compared to 2018 (orange). Data: Hansen/UMD/Google/USGS/NASA, MAAP.

Newly released data for 2019 reveals the loss of over 1.7 million hectares (4.3 million acres) of primary Amazon forest in our 5 country study area (Bolivia, Brazil, Colombia, Ecuador, and Peru).* That is twice the size of Yellowstone National Park.

Table 1 shows 2019 deforestation (red) in relation to 2018 (orange).

Primary forest loss in the Brazilian Amazon (1.29 million hectares) was over 3.5 times higher than the other four countries combined, with a slight increase in 2019 relative to 2018. Many of these areas were cleared in the first half of the year and then burned in August, generating international attention.

Primary forest loss rose sharply in the Bolivian Amazon (222,834 hectares), largely due to uncontrolled fires escaping into the dry forests of the southern Amazon.

Primary forest loss rose slightly in the Peruvian Amazon (161,625 hectares) despite a relatively successful crackdown on illegal gold mining, pointing to small-scale agriculture (and cattle) as the main driver.

On the positive side, primary forest loss decreased in the Colombian Amazon (91,400 hectares) following a major spike following the 2016 peace accords (between the government and FARC). It is worth noting, however, that we have now documented the loss of 444,000 hectares (over a million acres) of primary forest in the Colombian Amazon in the past four years since the peace agreement (see Annex).

*Two important points about the data. First, we use annual forest loss from the University of Maryland to have a consistent source across all five countries. Second, we applied a filter to only include loss of primary forest (see Methodology).

2019 Deforestation Hotspots Map

The Base Map below shows the major 2019 deforestation hotspots across the Amazon.

2019 deforestation hotspots across the Amazon. Data: Hansen/UMD/Google/USGS/NASA, MAAP.

Many of the major deforestation hotspots were in Brazil. Early in the year, in March, there were uncontrolled fires up north in the state of Roraima. Further south, along the Trans-Amazonian Highway, much of the deforestation occurred in the first half of the year, followed by the high profile fires starting in late July. Note that many of these fires were burning recently deforested areas, and were not uncontrolled forest fires (MAAP #113).

The Brazilian Amazon also experienced escalating gold mining deforestation in indigenous territories (MAAP #116).

Bolivia also had an intense 2019 fire season. Unlike Brazil, many were uncontrolled fires, particularly in the Beni grasslands and Chiquitano dry forests of the southern Bolivian Amazon (MAAP #108).

In Peru, although illegal gold mining deforestation decreased (MAAP #121), small-scale agriculture (including cattle) continues to be a major driver in the central Amazon (MAAP #112) and an emerging driver in the south.

In Colombia, there is an “arc of deforestation” in the northwestern Amazon. This arc includes four protected areas (Tinigua, Chiribiquete and Macarena National Parks, and Nukak National Reserve) and two Indigenous Reserves (Resguardos Indígenas Nukak-Maku and Llanos del Yari-Yaguara II) experiencing substantial deforestation (MAAP #120). One of the main deforestation drivers in the region is conversion to pasture for land grabbing or cattle ranching.

Annex – Colombia peace accord trend

Annex 1. Deforestation of primary forest in the Colombian Amazon, 2015-20. Data: Hansen/UMD/Google/USGS/NASA, UMD/GLAD. *Until May 2020

Methodology

The baseline forest loss data presented in this report were generated by the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland (Hansen et al 2013) and presented by Global Forest Watch. Our study area is strictly what is highlighted in the Base Map.

For our estimate of primary forest loss, we used the annual “forest cover loss” data with density >30% of the “tree cover” from the year 2001. Then 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).

For boundaries, we used the biogeographical limit (as defined by RAISG) for all countries except Bolivia, where we used the Amazon watershed limit (see Base Map).

All data were processed under the geographical coordinate system WGS 1984. To calculate the areas in metric units, the projection was: Peru and Ecuador UTM 18 South, Bolivia UTM 20 South, Colombia MAGNA-Bogotá, and Brazil Eckert IV.

Lastly, 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%.

References

Goldman L, Weisse M (2019) Explicación de la Actualización de Datos de 2018 de Global Forest Watch. https://blog.globalforestwatch.org/data-and-research/blog-tecnico-explicacion-de-la-actualizacion-de-datos-de-2018-de-global-forest-watch

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. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest.

Turubanova S., Potapov P., Tyukavina, A., and Hansen M. (2018) Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environmental Research Letters  https://doi.org/10.1088/1748-9326/aacd1c 

Acknowledgements

We thank G. Palacios for helpful comments to earlier versions of this report.

This work was supported by the following major funders: Norwegian Agency for Development Cooperation (NORAD), Gordon and Betty Moore Foundation, International Conservation Fund of Canada (ICFC), Metabolic Studio, Erol Foundation, MacArthur Foundation, and Global Forest Watch Small Grants Fund (WRI).

Citation

Finer M, Mamani N (2020) 2019 Amazon Deforestation. MAAP: 122.

MAAP #120: Deforestation in the Colombian Amazon – 2020

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Table 1. Deforestation of primary forest in the Colombian Amazon, 2015-20. Data: Hansen/UMD/Google/USGS/NASA, UMD/GLAD. *Until May 2020

Here we present a first look at 2020 deforestation of primary forest in the Colombian Amazon, in relation to the new published annual data for 2019.*

This new data confirms that deforestation decreased in 2019 (91,400 hectares) after a peak in 2018 (153,900 hectares).

Table 1 shows the recent trend: a major deforestation spike following the 2016 peace agreement (between the Colombian government and the FARC) with a peak in 2018, followed by a major decrease in 2019.

In our first look at 2020, we estimate the deforestation of 76,200 hectares (188,295 acres) of primary forest through June.

Note that we have documented the deforestation of 444,000 hectares (over a million acres) of primary forest in the Colombian Amazon in the past four years since the peace agreement.

*Global Forest Watch recently released the annual forest loss data for 2019.

Deforestation Hotspots – 2020

Base Map. 2020 Deforestation hotspots in the Colombian Amazon. Data: UMD/GLAD.

The Base Map shows the 2020 deforestation hotspots.*

As in previous years, they are concentrated in an “arc of deforestation” in the northwest Colombian Amazon.

This arc includes four protected areas (Tinigua, Chiribiquete and Macarena National Parks, and Nukak National Reserve) that lost 0ver 7,700 hectares (19,000 acres) of primary forest in 2020 (see Table 2).

Tinigua National Park is the most impacted protected area with the deforestation of 5,100 hectares (12,600 acres). Note the rare occurrence of a major deforestation hotspot in the middle of a national park.

Chiribiquete National Park lost 510 hectares (1,260 acres) in the recently expanded sections of the park.

The arc of deforestation also includes two Indigenous Reserves (Resguardos Indígenas Nukak-Maku and Llanos del Yari-Yaguara II) that lost 4,000 hectares (9,885 acres) so far in 2020.

*To see detailed map of the 2019-20 primary forest deforestation in the Colombian Amazon, click here.

Deforestation in Protected Areas and Indigenous Lands – 2020

Below, we show 2020 examples within the arc of deforestation in the northwest Colombian Amazon.

Image 1 illustrates the extensive deforestation within Tinigua National Park over the last five years continuing in 2020.

Image 2 shows an example of deforestation within Chiribiquete National Park (western sector) between January (left panel) and April (right panel) of 2020.

Image 3 shows an example of deforestation within the Llanos del Yari-Yaguara II Indigenous Reserve between January (left panel) and April (right panel) of 2020.

Image 1. Extensive deforestation within Tinigua National Park over the last five years, continuing in 2020. Data: Hansen/UMD/Google/USGS/NASA, UMD/GLAD.
Image 2. Deforestation in Chirbiquete National Park (western sector) between January (left panel) and April (right panel) of 2020. Data: ESA, Planet, MAAP.
Image 3. Deforestation in Llanos del Yari-Yaguara II Indigenous Reserve. Data: ESA, Planet, MAAP.

Deforestation in Protected Areas, 2015-20

Table 2 shows the loss of primary forest in four protected areas located in the arc of deforestation arc in the northwestern Colombian Amazon, between 2015 and 2020.

Table 2. Primary forest loss in four protected areas in the northwestern Colombian Amazon, between 2015 and 2020. Data: Hansen/UMD/Google/USGS/NASA, UMD/GLAD.

Methodology

The data presented in this report were generated by the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland (Hansen et al 2013) and presented by Global Forest Watch. For the years 2015-18, we used annual forest loss data. For the years 2019-20, we used early warning alerts (GLAD alerts), and thus represent an estimate. Note that some forest loss detected early in the year may include events from late the preceding year.

Our study area is the Amazon biogeographical limit (not strict Amazon watershed) as highlighted in the Base Map.

Specifically, for our estimate of forest cover loss, we multiplied the annual “forest cover loss” data by the density percentage of the “tree cover” from the year 2001 (values >30%).

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).

All data were processed under the geographical coordinate system WGS 1984. To calculate the areas in metric units the UTM (Universal Transversal Mercator) projection was used: Colombia 18 North.

Lastly, 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: 10%-20%; High: 21%-35%; Very High: >35%.

Acknowledgements

We thank R. Botero (FCDS), E. Ortiz (AAF), and G. Palacios for helpful comments to earlier versions of this report.

This work was supported by the following major funders: Norwegian Agency for Development Cooperation (NORAD), Gordon and Betty Moore Foundation, International Conservation Fund of Canada (ICFC), Metabolic Studio, Erol Foundation, MacArthur Foundation, and Global Forest Watch Small Grants Fund (WRI).

Citation

Finer M, Mamani N (2020) Deforestation in the Colombian Amazon – 2020. MAAP #120.

MAAP #115: Illegal Gold Mining in the Amazon, part 1: Peru

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Base Map. The main illegal gold mining areas in the Peruvian Amazon. Data: MAAP.

In a new series, we highlight the main illegal gold mining frontiers in the Amazon.

Here, in part 1, we focus on Peru. In the upcoming part 2, we will look at Brazil.

The Base Map indicates our focus areas in Peru*:

  • Southern Peru (A. La Pampa, B. Alto Malinowski, C. Camanti, D. Pariamanu);
  • Central Peru (E. El Sira).

Notably, we found an important reduction in gold mining deforestation in La Pampa (Peru’s worst gold mining area) following the government’s launch of Operation Mercury in February 2019.

Illegal gold mining continues, however, in three other major areas of the southern Peruvian Amazon (Alto Malinowski, Camanti, and Pariamanu), where we estimate the mining deforestation of 5,300 acres (2,150 hectares) since 2017.

Of that total, 22% (1,162 acres) occurred in 2019, indicating that displaced miners from Operation Mercury have NOT caused a surge in these three areas.

Below, we show a series of satellite videos of the recent gold mining deforestation (2017-19) in each area.

*Recent press reports indicate the increase in illegal gold mining activity in northern Peru (Loreto region), along the Nanay and Napo Rivers, but we have not yet detected associated deforestation.

A. La Pampa (Southern Peru)

In MAAP #104, we reported a major reduction (92%) of gold mining deforestation in La Pampa during the first four months of Operation Mercury, a governmental mega-operation to confront the illegal mining crisis in this area.

The following video shows how gold mining deforestation has declined considerably since February 2019, the beginning of the operation. Note the rapid deforestation during the years 2016-18, followed by a sudden stop in 2019.

B. Alto Malinowski (Southern Peru)

The following video shows gold mining deforestation in a section of the upper Malinowski River (Madre de Dios region). We estimate the mining deforestation of 4,120 acres (1,668 hectares) throughout the Alto Malinowski area during the 2017 – 2019 period.

Of that total, 20% (865 acres) occurred in 2019, indicating that displaced miners from Operation Mercury have not caused a surge in this area adjacent to La Pampa.

According to our analysis of governmental information (see Annex 2), the recent mining activity is likely illegal because: a) much of it occurs outside of titled mining concessions, b) and all of it occurs outside of the mining corridor established for legal mining activity (see Annex 1).

Note that the mining deforestation is within the Kotsimba Indigenous Community territory. However, it has not penetrated Bahuaja Sonene National Park, in part due to the actions of the Peruvian Protected Areas Service (SERNANP).

C. Camanti (Southern Peru)

The following video shows the gold mining deforestation of 944 acres (382 hectares) in the Camanti district (Cusco region), during the 2017 – 2019 period.

Of that total, 21% (198 acres) occurred in 2019, indicating that there has been no increase in mining activity in this area since the beginning of Operation Mercury in February (in contrast to press reports that have suggested that many displaced miners have moved to this area).

According to governmental information (see Annex 2), this mining activity is likely illegal because: a) much of it occurs outside of titled mining concessions, b) all occurs outside of the mining corridor, and c) all occurs inside both a protected forest (Bosque Protector) and buffer zone of the Amarakaeri Communal Reserve.

SERNANP (Peruvian Protected Areas Service) informed us that in December 2019, as part of Operation Mercury, the Public Ministry (Ministerio Público) led an interdiction with the support of law enforcement. Machinery, mining camps, and mercury were destroyed or removed during the raid. In 2020, as part of an extension of Operation Mercury, the Environmental Prosecutor’s Office (FEMA) of the Public Ministry announced that the buffer zone of the Amarakaeri Communal Reserve will be constantly monitored.

D. Pariamanu (Southern Peru)

The following video shows gold mining activity along a section of the Pariamanu River (Madre de Dios region). We estimate the gold mining deforestation of 245 acres (99 hectares) in the Pariamanu area, during the 2017 – 2019 period.

Of that total, 40% (99 acres) occurred in 2019, indicating that there has been a slight increase in mining activity since the beginning of Operation Mercury in February. This finding suggests that displaced miners may be moving to this area.

According to governmental information (see Annex 2), this mining activity is likely illegal because it is not within active mining concessions and outside the mining corridor. Morevoer,  the mining deforestation is within Brazil nut forestry concessions.

E. El Sira (Central Peru)

The following video shows the gold mining deforestation of 52 acres (21 hectares) in the buffer zone of El Sira Communal Reserve (Huánuco region), during the 2017 – 2019 period.

 

Although the mining activity occurs in an active mining concession, a recent report indicates that it is illegal because it does not have the deforestation authorization.

Annex 1: Mining Corridor

The mining corridor is the area that the Peruvian Government has defined as potentially legal for mining activity in the Madre de Dios region via a formalization process. As of 2019, over 100 miners have been formalized in Madre de Dios.

In general, mining activity in the corridor is considered legal, either formaly (the formalization process is completed with environmental and operational permits approved) or informaly (in the process of formalization). Thus, mining activity within the corridor is not considered illegal since it is not a prohibited area.

The following two videos show examples of gold mining deforestation in the mining corridor during 2019.

Annex 2: Land Use Map

For greater context, we present a map of qualifying titles directly related to the mining sector, in southern Peru. Layers include the mining corridor (see above), mining concession status (titled, pending, revoked), indigenous territories, and protected areas.

Land use map for southern Peruvian Amazon mining areas. Data: GEOCATMIN/INGEMMET. Click to enlarge.

Acknowledgements

We thank E. Ortiz (AAF), A. Flórez (SERNANP), P. Rengifo (ACCA), A. Condor (ACCA), A. Folhadella (Amazon Conservation), and G. Palacios for helpful comments to earlier versions of this report.

This work was supported by the following major funders: NASA/USAID (SERVIR), Norwegian Agency for Development Cooperation (NORAD), Gordon and Betty Moore Foundation, International Conservation Fund of Canada (ICFC), Metabolic Studio, Erol Foundation, MacArthur Foundation, and Global Forest Watch Small Grants Fund (WRI).

Citation

Finer M, Mamani N (2020) Illegal Gold Mining Frontiers, part 1: Peru. MAAP: 115.

MAAP #106: Deforestation impacts 4 protected areas in the Colombian Amazon (2019)

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Table 1. Deforestation in the Colombian Amazon. Data: Hansen/UMD/Google/USGS/NASA

We continue our focus on the northwest Colombian Amazon,* one of the most intense deforestation hotspots in the western Amazon (see MAAP# 100).

Here, we analyze deforestation data over the past five years (2015-19) to better understand current trends and patterns.

We found a major increase in deforestation as of 2016. The Colombian Amazon lost nearly 1.2 million acres (478,000 hectares) of forest between 2016 and 2018. Of this, 73% (860,000 acres) was primary forest (see Table 1).

One of the main deforestation drivers in the region is conversion to pasture for land grabbing or cattle ranching.

Next, we provide a real-time update of 2019, based on early warning forest alerts (GLAD alerts) from the University of Maryland/Global Forest Watch), updated through July 25, 2019.

*MAAP in Colombia represents a collaboration between Amazon Conservation and its Colombian partner, the Foundation for Conservation and Sustainable Development (FCDS).”

Base Map. Deforestation hotspots in Colombian Amazon. Data: UMD/GLAD, RUNAP, RAISG

Deforestation 2019

The GLAD alerts estimate the additional loss of 150,000 acres (60,654 hectares) in the first 7 months of 2019 (through end of July). Of  this, 75% (113,000 acres) was primary forest.

The Base Map shows that 2019 deforestation primarily impacts 4 protected areas* in the northwest Colombian Amazon: Tinigua, Serranía de Chiribiquete, and Sierra de la Macarena National Parks, and Nukak National Reserve.

Next, we detail the recent deforestation in these four protected areas of the Colombian Amazon, including the presentation of a series of satellite-based images.

*There are other protected areas in the Colombian Amazon with recent deforestation (such as Picachos and La Paya National Parks), but here we focus on the four with the highest deforestation thus far during 2019.

 

 

 

Protected Areas Zoom Map. Deforestation in four protected areas of the Colobian Amazon. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, RUNAP, RAISG

Deforestation in Protected Areas

We conducted a deforestation analys within the 4 protected areas noted above (Chiribiquete, Tinigua, Macarena, and Nukak), generating the following key results:

  • From 2016-18, deforestation claimed over  70,000 acres (29,000 ha) in the four protected areas, 86% of which were primary forests (62,000 acres).
    .
  • Thus far in 2019 (through July 25), deforestation claimed an additional 10,600 acres (4,300 ha), 87% of which were primary forests (9,200 acres).
    .
  • Tinigua National Park has been the most impacted protected area, as deforestation claimed 39,500 acres (16,000 ha) from 2017-19 (96% of which were primary forests). Note the major deforestation spike in 2018.
    .
  • Deforestation has claimed 6,400 acres (2,600 ha) in Chiribiquete National Park since its expansion in July 2018 (96% of which were primary forests).

Zoom A: Deforestation in Tinigua, Chiribiquete, and Macarena National Parks

See location of Zooms A-C in Protected Areas Zoom Map above. Data updated through July 25, 2019.

Zoom A. Deforestation in Tinigua, Serranía de Chiribiquete, and Sierra de la Macarena National Parks, *through July 25, 2019. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, RUNAP, RAISG

Zoom B. Deforestation in Chiribiquete National Park (western sector)

Zoom B. Deforestation Serranía de Chiribiquete National Park (western sector), *through July 25, 2019. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, RUNAP, RAISG

Zoom C. Deforestation in Nukak National Reserve

Zoom C. Deforestation in Nukak National Reserve *through July 25, 2019. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, RUNAP, RAISG

Annex 1: Table
Deforestation of Primary Forest in four protected areas (2015-18)

Annex 2: Map
Deforestation of Primary Forest in four protected areas (2016-19)

Annex 2. Data: Turubanova 2018, UMD/GLAD, Hansen/UMD/Google/USGS/NASA, RUNAP, RAISG

Methodology

We primarily used data generated by the GLAD laboratory of the University of Maryland, available on Global Forest Watch. This data is based on moderate resolution Landsat imagery (30 m). For 2017-18, we analyzed annual data (Hansen et al 2013), and for 2019 we analyzed GLAD alerts (Hansen et al 2016).

For our deforestation estimates, we multiplied the annual “forest cover loss” data by the density percentage of the “tree cover” from the year 2000 (values >30%). Including this percentage allows us to look at the precise area of each pixel, thus improving the preciseness of the results.

We define primary forest as “mature natural humid tropical forest cover that has not been completely cleared and regrown in recent history,” following the definition from Turubanova et al 2018. For our primary forest deforestation estimates, 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).

All data were processed under the geographical coordinate system WGS 1984. To calculate the areas in metric units the UTM (Universal Transversal Mercator) projection was used: Colombia 18 North.

To identify the deforestation hotspots in the Base Map, 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: 10%-25%; High: 26%-50%; Very High: >50%.

References

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.

Hansen, M.C., A. Krylov, A. Tyukavina, P.V. Potapov, S. Turubanova, B. Zutta, S. Ifo, B. Margono, F. Stolle, and R. Moore. 2016. Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11 (3).

Hansen, M.C., A. Krylov, A. Tyukavina, P.V. Potapov, S. Turubanova, B. Zutta, S. Ifo, B. Margono, F. Stolle, and R. Moore. 2016. Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11 (3).

Turubanova S., Potapov P., Tyukavina, A., and Hansen M. (2018) Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environmental Research Letters.

Acknowledgements

We thank R. Botero (FCDS), A. Rojas (FCDS) y G. Palacios for helpful comments to earlier versions of this report.

This work was supported by the following major funders: MacArthur Foundation, International Conservation Fund of Canada (ICFC), Metabolic Studio, and Global Forest Watch Small Grants Fund (WRI).

Citation

Finer M, Mamani N (2019) Deforestation impacts 4 protected areas in the Colombian Amazon (2019). MAAP: 106.

MAAP #93: Shrinking Primary Forests of the Peruvian Amazon

Posted on by dcadmin
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 #83: Climate Change Defense: Amazon Protected Areas and Indigenous Lands

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

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

Here, we show the importance of protected areas and indigenous lands to safeguard these carbon stocks.

In MAAP #81, 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, 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.1,2

In contrast, here we show that protected areas and indigenous lands have safeguarded 3.17 billion metric tons of carbon, as of 2017.3,4

The Base Map (on the right) shows, in shades of green, the current carbon densities in relation to these areas.

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.

The total safeguarded carbon (3.17 billion metric tons) is the equivalent to 2.5 years of carbon emissions from the United States.5

Below, we show several examples of how protected areas and indigenous lands are safeguarding carbon reservoirs in important areas, indicated by insets A-E.

A. Yaguas National Park

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

Image 83a. Yaguas. Data: Asner et al 2014, MINAM/PNCB, SERNANP

B. Manu National Park, Amarakaeri Communal Reserve, CC Los Amigos

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

Image 83b. Los Amigos-Manu-Amarakaeri. Data: Asner et al 2014, MINAM/PNCB, SERNANP, ACCA

C. Tambopata National Reserve, Bahuaja Sonene National Park

The following Image C shows how two important natural protected areas, Tambopata National Reserve and Bahuaja Sonene National Park, are helping conserve carbon stocks in an area with intense illegal gold mining activity.

D. Sierra del Divisor National Park, National Reserve Matsés

Image 83d. Data: Asner et al 2014, MINAM/PNCB, SERNANP

The following Image D shows how four protected areas, including the new Sierra del Divisor National Park, and adjacent National Reserve Matsés are effectively safeguarding 270 million metric tons of carbon in the eastern Peruvian Amazon.

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

E. Murunahua Indigenous Reserve

The following Image E shows the carbon protected in the Murunahua Indigenous Reserve (for indigenous peoples in voluntary isolation) and the surrounding titled native communities.

Imagen 83e. Datos: Asner et al 2014, MINAM/PNCB, SERNANP

References

1  UNFCCC. Emissions Summary for Peru. http://di.unfccc.int/ghg_profile_non_annex1

2  No incluye las emisiones por la degradación de bosques

3 Asner GP et al (2014). The High-Resolution Carbon Geography of Perú. Carnegie Institution for Science. ftp://dge.stanford.edu/pub/asner/carbonreport/CarnegiePeruCarbonReport-English.pdf

4 Sistema de Áreas Naturales Protegidas del Perú, que incluye áreas de administración nacional, regional, y privado. Datos de las tierras indígenas son de Instituto de Bien Común. Datos de pérdida forestal son de la Programa Nacional de Conservación de Bosques para la Mitigación del Cambio Climático (MINAM/PNCB).

5 UNFCCC. Emissions Summary for United States. http://di.unfccc.int/ghg_profile_annex1

Citation

Finer M, Mamani N (2017). Climate Change Defense: Amazon Protected Areas and Indigenous Lands. MAAP: 83.

Acknowledgments

This report was made possible by the generous support of the Norwegian Agency for Development Cooperation (NORAD).

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

Posted on by dcadmin
Base Map. Data: MINAM/PNCB, Asner et al 2014

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

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

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

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

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

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

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

 

 

Major Findings

Data: Asner et al 2014

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

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

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

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

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

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

 

 

 

 

Zoom A: Central Peruvian Amazon

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

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

Zoom B: Southern Peruvian Amazon (gold mining) 

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

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

Zoom C: Southern Peruvian Amazon (agriculture)

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

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

Zoom D: United Cacao

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

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

Zoom E: Yaguas National Park

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

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

Zoom F: Los Amigos Conservation Concession

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

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

Zoom G: Sierra del Divisor National Park

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

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

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

 

 

 

 

 

 

 

 

 

 

 

 

Methodology

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

References

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

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

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

Citation

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

Acknowledgments

This report was made possible by the generous support of the Norwegian Agency for Development Cooperation (NORAD).

 

 

MAAP #80: Amazon Beauty, in High-Resolution

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Image 80. Base Map. Data: SERNANP, MAAP

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

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

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

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

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

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

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

 

 

 

 

Protected Areas

A. Yaguas National Park (Loreto)

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

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

B. Sierra del Divisor National Park (Ucayali)

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

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

C. Tambopata National Reserve (Madre de Dios)

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

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

D. Amarakaeri Communal Reserve (Madre de Dios)

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

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

E. Manu National Park (Cusco sector)

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

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

F. Regional Conservation Area Choquequirao (Cusco)

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

Image F. Choquequirao. Data: DigitalGlobe (Nextview)

G. Los Amigos Conservation Concession (Madre de Dios)

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

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

Threatened Areas

H. Tamshiyacu (Loreto)

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

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

I. Manu-Amarakaeri Highway (Madre de Dios)

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

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

J. La Pampa (Madre de Dios)

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

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

K. Tierra Blanca (Loreto)

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

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

L. Amarakaeri Communal Reserve (Madre de Dios)

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

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

Marañon River (sector Amazonas/Cajamarca)

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

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

Coordinates

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

Citation

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

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

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

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

Our major findings include:

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

Deforestation Trends

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

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

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

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

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

Deforestation Patterns

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

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

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

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

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

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

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

Deforestation Drivers

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

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

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

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

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

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

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

Small/Medium-scale Agriculture

The literature emphasizes that small-scale agriculture is the leading cause of deforestation in the Peruvian Amazon.1 However, there is little actual empirical evidence demonstrating that this is true.3 The raw deforestation data is dominated by small-scale clearings that are most likely for agriculture or cattle pasture. Thus, it is likely that small-scale agriculture is a major driver, but a definitive study utilizing high-resolution imagery and/or extensive field work is still needed to verify the assumption.

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

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

Large-scale Agriculture

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

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

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

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

Cattle Pasture

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

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

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

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

Gold Mining

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

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

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

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

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

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

Roads

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

Logging Roads

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

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

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

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

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

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

Coca

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

Hydroelectric Dams

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

Hydrocarbon (Oil & Gas)

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

Methodology

MAAP methodology has 4 major components:

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

Endnotes

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

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

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

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

5 Environmental Investigation Agency EIA (2015) Deforestation by Definition.

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

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

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

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

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

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

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

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

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

Citation

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