MAAP#125: Detecting Illegal Logging With Very High Resolution Satellites

Very high resolution satellite image showing illegal logging in the southern Peruvian Amazon. Data: Maxar. Analysis: MAAP/ACCA.
Very high resolution satellite image showing illegal logging in the southern Peruvian Amazon. Data: Maxar. Analysis: MAAP/ACCA.

Illegal logging in the Peruvian Amazon is mainly selective and, until now, difficult to detect through satellite information.

In this report, we present the enormous potential of very high resolution satellite imagery (<70 cm) to identify illegal logging.

The leading entities that offer this type of data are Planet (Skysat) and Maxar (Worldview).

We emphasize that this technique has the potential to detect the illegal activity in real time, when preventive action is still possible.

This is an important advance because when an intervention normally occurs, such as detaining a boat or truck with illegal timber, the damage is done.

Below, we show a specific case of using very high resolution satellite imagery to detect and confirm probable illegal logging in the southern Peruvian Amazon (Madre de Dios region).

 

Base Map. Illegal logging activities in the Turbina SAC forestry concession. The size of the points is for reference only. Data: MAAP/Amazon Conservation.
Base Map. Illegal logging activities in the Turbina SAC forestry concession. The size of the points is for reference only. Data: MAAP/Amazon Conservation.

Case: Turbina SAC

The Base Map below shows the intensity of probable illegal logging activity* in the Turbina SAC forestry concession, from 2016 to the present. Specifically, it shows the exact points of illegal logging events (felled trees) and logging camps, as identified through our analysis of very high-resolution satellite images. Note that this forestry concession is adjacent to the Los Amigos Conservation Concession, an important long-term (20 years) biodiversity conservation area.

 

Very High Resolution Satellite Imagery

Below, we show a series of very high-resolution satellite images, courtesy of the innovative satellite companies Planet and Maxar.

The first image shows the identification of probable illegal logging between June 2019 (left panel) and August 2020 (right panel). The red circle indicates the exact area (canopy) of the illegally logged tree.

The identification of illegal logging between June 2019 (left panel) and August 2020 (right panel). Click to enlarge. Data: Maxar, Planet, MAAP.
The identification of illegal logging between June 2019 (left panel) and August 2020 (right panel). Click to enlarge. Data: Maxar, Planet, MAAP.

The following image shows the identification of illegal logging in March 2020. The red circle indicates the exact area of the illegally logged trees.

Identification of illegal logging. Data: Maxar, MAAP.
Identification of illegal logging. Data: Maxar, MAAP.

The following image shows the identification of a logging camp in March 2o20. The red circle indicates the area of the camp.

Satellite image of an illegal logging camp. Data: Maxar, MAAP.
Satellite image of an illegal logging camp. Data: Maxar, MAAP.

*Statement on Legality

We determined that this logging activity is illegal from a detailed analysis of official information from the Peruvian Government (specifically, the Peruvian Forestry Service, SERFOR, and forestry oversight agency, OSINFOR). This information indicates that, although the concession is in force (Vigente), its status is classified as Inactive (Inactiva). In addition, 2013 was the last year that this concession had an approved logging plan (Plan Operativo de Aprovechamiento, or POA), and it was for a different sector of the concession from the newly detected logging activity.

To confirm our assumption of illegal activity, we requested the technical opinion from the corresponding regional forestry and wildlife authority, however, as of the date of publication of this report, we have not yet received a response.

Thus, with the information we had at the time of publication, we concluded the logging was illegal as it was not conducted within a current management plan.

Methodology

We carried out the analysis in two main steps:

The first step was the visual interpretation and digitization of new logging events and associated logging camps within the Turbina forestry concession. This analysis was based on the evaluation of submetric images obtained from the satellite companies Planet and Maxar, for the period 2019-20. It is worth noting that for Planet, we had the new ability to “task” new images for a specific area, rather than waiting for an image to appear by other means. Logging in the Peruvian Amazon is usually highly selective for high-value species, thus its detection requires a comparative analysis of images (before and after), in such a way that the trees cut during the study period (2019-20 in this case) can be identified.

The second step focused on an analysis of the legality of the identified logging events. The locations of the logged trees and camps were cross-referenced with spatial information on the state and status of forestry concessions provided by the GeoSERFOR (SERFOR) portal, as well as the areas delimited in the annual operational plans of the concessions, verified by OSINFOR and distributed through the SISFOR portal (WMS). We considered both spatial and temporal aspects to the forestry concession data.

Citation

Novoa S, Villa L, Finer M (2020) Detecting Illegal Logging with Very High Resolution Satellites. MAAP: 125.

 

Acknowledgments

We thank A. Felix (USAID Prevent), M.E. Gutierrez (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.

 

 

Protecting the world’s largest remaining tropical glacier and headwaters essential for climate adaptation

A decade of research, outreach, and negotiations pays off, as Ausangate is officially declared a Regional Conservation Area.

After a decade leading negotiations and building political will in the region, we celebrated a major success: the Peruvian Ministries Council approved the creation of the Ausangate Regional Conservation Area, safeguarding 165,000 acres as an officially designated protected area.

This area’s incredible environmental significance is due in part to the Quelccaya Ice Cap of the Ausangate mountain range. Miguel Ángel Canal, Cusco’s Regional Director of Natural Resources and Environmental Management, stated it best when he said the area, “is considered a global thermometer where the relationship between global warming and glacier melting can be studied.”  

The declaration of Ausangate this year was timely, as there was continued pressure from mining companies petitioning the government to create mining concessions (leasing government land to explore precious minerals) in key parts of  this conservation area. The majority of local communities have strongly opposed mining exploration due to the mercury contamination caused by the extraction process. 

“In the case of Ausangate, I think that the best thing is that the people and the authorities have understood the importance of conserving strategic sites such as this mountain range,” says Marlene Mamani, an Amazon Conservation expert who brings in-depth understanding of the area’s importance from both a climate and cultural perspective. Marlene, a native of the nearby Valle Sagrado community stresses that the city of Cusco gets its water from the mountains and that “people must realize that these snow-capped mountains are vital for our survival.”

With financial support from individuals, foundations, and partners, we were able to help local communities improve their quality of life and incomes by engaging them in sustainable economic activities including managing alpacas and vicuñas for wool production and managing wetlands and pastures that protect their way of life and  natural resources from the impacts from climate change. 

This was a story from our 2019 Impact Report. Click here to read about other conservation successes from 2019.

MAAP #124: Deforestation Hotspots 2020 In The Peruvian Amazon

Base Map. 2020 Forest Loss Hotspots in the Peruvian Amazon. Data: UMD/GLAD, MAAP, SERNANP.

We have entered the peak deforestation season in the Peruvian Amazon, so it is also a critical time for real-time monitoring (MAAP’s specialty).

Here, we highlight the major deforestation events documented so far in 2020 (through August 23).

The Base Map shows the current forest loss hotspots, indicated by the colors yellow, orange and red.

Below, we present the most urgent deforestation cases, caused by gold mining and agriculture (both large and small scale), the current leading deforestation drivers in Peru.

The Letters A-I on the Base Map correspond to the location of the cases described below.

One of the key cases is the new illegal gold mining hotspot along the Pariamanu river (Letter A in the southern Peruvian Amazon).

Another important case is the expanding large-scale agriculture by a Mennonite colony that continues causing an alarming deforestation.

The other cases deal with small-scale agriculture, which cumulatively represent the main deforestation driver in Peru.

Urgent Deforestation Cases 2020

1. Gold Mining

In MAAP #121, we reported that, in general, gold mining deforestation has decreased in the southern Peruvian Amazon following the government’s Operation Mercury, but it does continue in several critical areas. The images below show two of these areas (Pariamanu and Araza) with alarming new deforestation in 2020.

A. Pariamanu

The following image shows the gold mining deforestation of 52 acres (21 hectares) of primary forest along the Pariamanu River in the southern Peruvian Amazon (Madre de Dios region) between January (left panel) and August (right panel) of 2020. We highlight that the Peruvian government has just carried out an operation against the illegal mining activity in this area.

Pariamanu case (illegal gold mining). Data: Planet, MAAP.

B. Araza

The following image shows the gold mining deforestation of 114 acres (46 hectares) along the Chaspa River in the Puno region, between January (left panel) and August (right panel) of 2020.

Araza case. Data: Planet, MAAP.

 

2. Large-scale Agriculture

C. Mennonite Colony (near Tierra Blanca)

We reported last year that a new colony of Mennonites caused the deforestation of 4,200 acres (1,700 hectares) between 2017 and 2019 in the Loreto region (MAAP #112). The following image shows the additional deforestation of 820 acres (332 hectares) in 2020 between January (left panel) and August (right panel).

Mennonite case (near Tierra Blanca). Data: Planet, MAAP.

 

3. Small-scale Agriculture

D. Jeberos

In 2018, we reported on the construction of a new road (65 km) cutting through primary forest in the Loreto region, between the city of Yurimaguas and the town of Jeberos (MAAP #84). The following image shows the deforestation of 40 acres (16 hectares) along the new road in 2020, between January (left panel) and August (right panel).

Jeberos case (near Tierra Blanca). Data: Planet, MAAP.

E. Las Piedras

The following image shows the deforestation of 64 acres (26 hectares) of primary forest in a Brazil-nut concession along the Las Piedras River in the Madre de Dios region, between November 2019 (left panel) and August 2020 (right panel).

Las Piedras case. Data: Planet, MAAP.

F. Bolognesi

The following image shows an example of deforestation (580 acres or 235 hectares) in one of the areas with the highest concentration of forest loss, located in the Ucayali region.

Bolognesi case. Data: Planet, MAAP.

G. Santa Maria de Nieva

The following image shows an example of deforestation(346 acres or 140 hectares) in another one of the areas with the highest concentration of forest loss, located in the Amazonas region

Santa Maria de Nieva case. Data: Planet, MAAP.

H. Mishahua River

The following image shows the recent deforestation of 168 acres (68 hectares) along the Mishahua River, in the Ucayali region. Just to the north, we documented extensive deforestation along the Sepahua River in 2019, where it also appears to be starting up again in 2020.

Mishahua case. Data: Planet, MAAP.

I. South of Sierra del Divisor National Park

The following image shows an example of deforestation (166 acres or 67 hectares) in another one of the areas with the highest concentration of forest loss, located south of the Sierra del Divisor National Park in the Ucayali region.

Mishahua case. Data: Planet, MAAP.

Methodology

The analysis was based on early warning GLAD alerts from the Universidad de Maryland and Global Forest Watch.

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%.

Acknowledgments

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

This work was supported by the following major funders: Erol Foundation, Norwegian Agency for Development Cooperation (NORAD), and International Conservation Fund of Canada (ICFC).

Citation

Finer M, Mamani N (2020) Deforestation Hotspots 2020 in the Peruvian Amazon. MAAP: 124.

MAAP #123: Detecting Illegal Logging In The Peruvian Amazon

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

 

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.

153,000 Acres Of Brazil Nut Forests Protected by Amazon Conservation and Google

 Brazil nut concessionaires walking in forestAmazon Conservation’s sister organization Conservación Amazónica – ACCA, with support from Google.org, just finished up a two-year initiative that trained community members to use cutting-edge satellite and field technologies to combat deforestation in the southern Peruvian Amazon, now protecting over 150,000 acres of lowland forests.

This initiative trained 75 Brazil nut harvesters and their families in forest monitoring technologies, which will help them safeguard forests to be used for sustainable purposes. Preventing deforestation of natural resources is not only environmentally important, but also economically, as the productive forests in and around the Madre de Dios area in Peru provide a sustainable and forest-friendly economic income to around 45,000 people, about 20% of the population.

In Peru, local families or associations can be granted a piece of public forests to be used for specific purposes – called a concession – such as harvesting nuts and berries, or for ecotourism. This system prevents acres of forests from falling victim to destructive activities, such as land squatting, illegal logging, or invasions by gold miners. Additionally, concessionaires are required by law to report on illicit activities in their concessions, which is a way the government gets community support to protect large swaths of forests.

Brazil nut concessionairesBefore this program, concessionaires and their communities lacked capacity to monitor these large, remote areas and a way to rapidly and safely report deforestation in their territories. Our innovative methodology of combining real-time satellite imagery analysis and drone field technology (which includes smartapps and other technologies developed by Google) with legal training, gave concessionaires the ability to detect and report deforestation as it happened in their territory. This is a stark contrast from before, when the only way to monitor thousands of acres of forests was through foot patrols that took days to complete. 

Now 75 Brazil nut harvesters and their families are using satellite imagery, early deforestation alerts, and GPS applications on mobile devices to monitor their forests. Among them, 23 individuals successfully obtained their licenses as drone pilots from the Ministry of Transport and Communications’ General Directorate of Civil Aeronautics. This means they can now their entire territory in minutes, without having to face potential risks of confronting dangerous individuals committing environmental crimes or even running into outsiders who might bring diseases like the novel coronavirus into their communities. 

Brazil nut appetizersThrough this program, over 153,000 acres (62,000 hectares) of forests are now monitored and protected with technology by the local people we empowered. Moreover, technological kits were donated to each individual or local association, each containing a drone, a maintenance kit, a laptop and a printer, giving them the knowledge and tools needed to safeguard forests..

These successes were celebrated with a closing ceremony in the Castaña Amazon Park earlier this year. Local authorities and representatives of local organizations attended, such as the director of the Research Institute of the Peruvian Amazon (IIAP) and members of local harvesting associations. During the ceremony, attendees enjoyed Brazil nut appetizers, while watching presentations about the project, the results achieved, and the collaborators and participants. The event ended with a guided tour of the Brazil Nut Harvesting Center in the Castaña Amazon Park, which is noted as the first living Brazil nut tree park in the world.

Presenter at Brazil Nut Google EventThe project, led by our director of our Southwest Amazon Drone Center, Carlos Castañeda, will continue to provide technical support to maintain the donated drones and training to reinforce what they learned, as well as be available to answer any questions that may arise during monitoring and surveillance of their concessions. Thus, the continuity of the project and its sustainability are ensured.

This Google.org-funded project was the first of its kind nationwide in Peru. After this success, Amazon Conservation continues its mission of conserving the Amazon basin using new technologies. Over the next three years, we hope to strengthen the real-time monitoring of the forests by empowering local people and employing science and technology as a proven way to fight deforestation in the Amazon and create a model for other tropical forests around the world.

Bringing Climate Resilience to Local Communities

For the past year our team in Bolivia and Peru have been working with EUROCLIMA+, an initiative of the European Commission focused on combating climate change in Latin America. We are working with local communities to pilot climate change resilience in their sustainable use of forest resources.

Promoting sustainable forest resources is important for keeping forests standing, and an initiative that we have been expanding for many years now. The Amazon rainforest is full of economically valuable products, such as the Brazil nuts and açaí berries, which are important sources of income for local communities. These fruits can only grow in standing forests, and cannot survive in a monoculture or farmed operation setting. 

 

 

An Economic Essential

Luis Arteaga, our Technical Director in Bolivia, coordinates this project. His team works in the northern part of Bolivia, where local communities heavily depend on these forest goods to make a living. “Almost all the families dedicate themselves to harvesting forest fruits, mainly the Brazil nut, which is their main economic activity.” 

Noting the ecological makeup of the area, one can see why: the northern municipality of the Santa Rosa del Ábuna conservation area has the highest concentration of Brazil nut trees in the department of Pando, Bolivia. These nuts generate up to 90% of local families’ overall income, and although harvesting is a job that requires a lot of dedication and back-breaking work, it generates important opportunities for commercialization and sustainable forest management. Luis puts it simply, “If Brazil nuts didn’t exist or didn’t grow in these forests, they would have already been cut down for another economic activity.” 

bags of brazil nutsTying the importance of conservation of these forests not only to climate change but also to economic sustainability of local and global economies is vital for countries and communities to see the value of forests. In fact, our area of work in Bolivia holds 85% of the Brazil nut production in the world, and keeping those forests standing through sustainable activities will have a significant impact in the fight against deforestation and carbon emissions.

 

 

Confronting Climate Change

A key aspect of our work with EUROCLIMA+ is recognizing how these sustainable forest economies help mitigate the effects of climate change on communities and on the planet, which hadn’t previously been as much on peoples’ minds. This pilot work is also helping local communities become aware of how climate is changing the forests on which they depend, so they can plan for their long-term, sustainable use, without needing to turn to destructive practices like timber extraction and cattle ranching if a harvesting season is affected by global warming. This involves not only making sure we have healthy forests, but also helping communities diversify their source of income sustainably, such as harvesting other complementary forest products like açaí berries and sustainably farming paiche fish. 

“In my opinion,” Luis notes, “one important advancement is that we are learning how climate change has impacted, is impacting, and will impact this vital bi-national region of the Amazon in Peru and Bolivia. Working with EUROCLIMA+ has taught us to use the climate lens to think about all of our future conservation work as well, and this is a good step forward.”

MAAP #122: Amazon Deforestation 2019

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).

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

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.
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

Table 1. Deforestation of primary forest in the Colombian Amazon, 2015-20. Data: Hansen/UMD/Google/USGS/NASA, UMD/GLAD. *Until May 2020
Table 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#121: Reduction Of Illegal Gold Mining In The Peruvian Amazon

Thanks to the support of the USAID, via the Prevent Project, dedicated to the prevention and combat of environmental crimes in the Amazon, we conducted a detailed analysis of recent illegal gold mining deforestation in the southern Peruvian Amazon.

The objective is to understand the trends from early 2017 to June 2020 (which includes the first part of the mandatory quarantine issued by the Peruvian government as of March 16, 2020 due to the coronavirus pandemic).

Base Map. Illegal gold mining deforestacion in the protected area buffer zones of the southern Peruvian Amazon, 2017-2019. Data: MAAP. Click to enlarge image.
Base Map. Illegal gold mining deforestacion in the protected area buffer zones of the southern Peruvian Amazon, 2017-2019. Data: MAAP. Click to enlarge image.

We focus on the buffer zones of two protected areas in the Madre de Dios region: Tambopata National Reserve and Bahuaja Sonene National Park (see Base Map).*

This area includes La Pampa, the current highest intensity illegal mining zone in the country. In February 2019, the Peruvian government launched Operation Mercury  to confront the illegality in La Pampa and surrounding areas.

The Base Map shows that gold mining deforestation in La Pampa decreased over 90% following Operation Mercury.

However, illegal gold mining does continue after Operation Mercury (including during the coronavirus state of emergency), but at lower rates. Thus, current snapshots may be misleading and recent context is important.

On the Base Map, the red arrows indicate the areas with the most recent illegal activity (click the image to enlarge). See below for more details.

 

Main Results

Table 1. Illegal gold mining deforestation before (yellow) and after (red) Operation Mercury in the buffer zones of Madre de Dios. Data: MAAP.
Table 1. Illegal gold mining deforestation before (yellow) and after (red) Operation Mercury in the buffer zones of Madre de Dios. Data: MAAP.

The Base Map and Table 1 illustrate the following key results:

  • In La Pampa, we documented mining deforestation of 173 hectares (428 acres) per month before Operation Mercury (January 2018 – February 2019). After the intervention, deforestation was reduced to 14 hectares (36 acres) per month (March 2019 – May 2020), a decrease of 92%.
    .
  • Upstream, in the Alto Malinowski, we documented the mining deforestation of 61 hectares (150 acres) per month before Operation Mercury. After the intervention, deforestation was reduced to 28 hectares (69 acres) per month, a decrease of 53%.
    .
  • Downstream, in the Apaylon area, we documented the mining deforestation of 2.9 hectares (7 acres) per month, before Operation Mercury. After the intervention, deforestation increased to 4 hectares (10 acres) per month, an increase of 41%. Apaylon is main area in the buffer zone where deforestation has increased.
    .
  • Within Tambopata National Reserve, we documented the mining deforestation of 6.5 hectares (16 acres) per month, before Operation Mercury. After the intervention, deforestation was reduced to 0.5 hectares (1.2 acres) per month, a decrease of 93%.
    .
  • Overall, illegal gold mining does continue in the buffer zones of Madre de Dios, but at lower rates than the previous two years. We documented the gold mining deforestation of 797 hectares (1,670 acres) after Operation Mercury.
    .
  • Regarding the speculation that illegal activity would increase during the coronavirus pandemic, we have not documented any major increase or surge in the buffer zones of Madre de Dios.* Illegal mining does continue, however, we documented the deforestation of 80 hectares (198 acres) during the quarantine.

 

Reduction of 90% in La Pampa

The following images show the major decrease in gold mining deforestation in La Pampa after Operation Mercury. Image 1 shows the rapid deforestation before Operation Mercury, between January 2017 (left panel) and February 2019 (right panel). Image 2 shows how the deforestation decreased after Operation Mercury, between February 2019 (left panel) and May 2020 (right panel). The red dot represents a reference point between the images.

Image 1. Rapid gold mining deforestation in La Pampa before Operation Mercury, between January 2017 (left panel) and February 2019 (right panel). Data: Planet.
Image 1. Rapid gold mining deforestation in La Pampa before Operation Mercury, between January 2017 (left panel) and February 2019 (right panel). Data: Planet.
Image 2. Mining deforestation decreased in La Pampa after Operation Mercury, between February 2019 (left panel) and May 2020 (right panel). Data: Planet.
Image 2. Mining deforestation decreased in La Pampa after Operation Mercury, between February 2019 (left panel) and May 2020 (right panel). Data: Planet.

 

Displaced Miners?

There has also been speculation that the focus of Operation Mercury in La Pampa would lead to illegal miners moving to other areas.* Base Map 2 shows two of the most threatened areas: Camanti and Pariamanu.

Table 2. Deforestation by illegal gold mining before (yellow) and after (red) Operation Mercury in two other threatened areas. Data: MAAP.
Table 2. Deforestation by illegal gold mining before (yellow) and after (red) Operation Mercury in two other threatened areas. Data: MAAP.

These are the main results for these two areas:

  • In Camanti (located in the buffer zone of Amarakaeri Communal Reserve), we documented the gold mining deforestation of 13.3 hectares (33 acres) per month before Operation Mercury. After the intervention, deforestation was reduced to 6.1 hectares (15 acres) per month, a decrease of 54%.
    .
  • In Pariamanu, we documented  the mining deforestation of 2.5 hectares (6 acres) per month before Operation Mercury. After the intervention, it increased to 4.2 hectares (10 acres) per month, an increase of 70%.
  • In summary, illegal gold mining continues in these two areas outside La Pampa. We documented the mining deforestation of 175 hectares (432 acres) after Operation Mercury (including 22 hectares during the pandemic). There is some evidence that miners are being displaced to Pariamanu, but there has not been a surge in Camanti.
Base Map 2. Main mining areas in the south of the Peruvian Amazon. Click to enlarge image.
Base Map 2. Main mining areas in the south of the Peruvian Amazon. Click to enlarge image.

Statement of the Peruvian Protected Area Agency (SERNANP)

El Servicio Nacional de Áreas Naturales Protegidas por el Estado (SERNANP) nos ha comunicado lo siguiente:

  • La actividad de control y vigilancia en la Reserva Nacional Tambopata es permanente y las autoridades (SERNANP, Policía Nacional del Perú, Fiscalías Especializadas en Materia Ambiental, y Marina de Guerra del Perú) continúan interviniendo a todas las actividades de minería ilegal, manteniendo el 100%.
  • Las zonas de amortiguamiento son espacios que están sujetos a la intervención de las autoridades de la Operación Mercurio (no del SERNANP). Se han realizado intervenciones continuas e interdicciones tanto en  las zonas indicadas en el reporte, como en Apaylon y Camanti.
    ,
  • Cabe mencionar que la Operación Mercurio, durante el 2019 y sobre todo en el 2020 (Incluyendo el período de cuarentena) ha ampliado sus operativos mas allá de la Pampa, lo cual explica porque en Camanti las cifras también se ha reducido.  En el segundo semestre de 2020 y en el 2021, se espera que los operativos es amplíen a otras zonas de Madre de Dios.

 

*Notes

 

Acknowledgments

We thank R. Segura, M. Castro, E. Ortiz, M. Silman, M. E. Gutierrez, S. Novoa, H. Balbuena, M. Allemant, 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.

USAID logo

Citation

Finer M, Mamani N (2020) Reduction of Illegal Gold Mining in the Peruvian Amazon. MAAP:

MAAP Synthesis: 2019 Amazon Deforestation Trends and Hotspots

MAAP, an initiative of Amazon Conservation, specializes in satellite-based, real-time deforestation monitoring of the Amazon. Our geographic focus covers five countries: Bolivia, Brazil, Colombia, Ecuador, and Peru (see Base Map).

We found that, since 2001, this vast area lost 65.8 million acres (26.6 million hectares) of primary forest, an area equivalent to the size of the United Kingdom (or the U.S. state of Colorado).

Base Map. Amazon Deforestation, 2001-2019. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, MAAP
Base Map. Amazon Deforestation, 2001-2019. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, MAAP. Click to see image in high resolution.

In 2019, we published 18 high-impact reports on the most urgent cases of deforestation. 2019 highlights include:

  • Fires in the Brazilian Amazon actually burned freshly deforested areas (MAAP #113);
  • Effective illegal gold mining crackdown in the Peruvian Amazon as a result of the government’s Operation Mercury (MAAP #104);
  • Illegal invasion of protected areas in the Colombian Amazon (MAAP #106);
  • Construction of oil-drilling platforms in the mega-diverse Yasuni National Park of the Ecuadorian Amazon (MAAP #114).

Here, in our annual Synthesis Report, we go beyond these emblematic cases and look at the bigger picture for 2019, describing the most important deforestation trends and hotspots across the Amazon.

*Note: to download a PDF, click the “Print” button below the title.


Synthesis Key Findings

Trends: We present a GIF comparing deforestation trends for each country since 2001. The preliminary 2019 estimates have several important headlines:
  • Possible major deforestation decrease in the Colombian Amazon following a dramatic increase over the previous three years;
  • Likely major deforestation increase in the Bolivian Amazon due to forest fires;
  • Downward deforestation trend continues in the Peruvian Amazon, but still historically high;
  • Deforestation of 2.4 million acres in the Brazilian Amazon, but the trend depends on the data source.
Hotspots: We present a Base Map highlighting the major deforestation hotspots in 2019. Results emphasize the deforestation and fires in the Brazilian Amazon, along with several key areas in Colombia, Peru, and Bolivia.

Deforestation Trends 2001-2019

The following GIF shows deforestation trends for each country between 2001 and 2019 (see descriptive notes below). Click here for static versions of each graph.

Three important points about the data: First, as a baseline, we use annual forest loss from the University of Maryland to have a consistent source across all five countries (thus it may differ from official national data). Second, we applied a filter to only include loss of primary forest (see Methodology). Third, the 2019 data represents a preliminary estimate based on early warning alerts.

maaproject.org-maap-synthesis-2019-amazon-deforestation-trends-and-hotspots

  1. Deforestation in the Ecuadorian Amazon is relatively low, reaching a maximum of 18,800 hectares (46,500 acres) in 2017. The estimate for 2019 is 11,400 hectares (28,000 acres).
    .
  2. In the Bolivian Amazon, deforestation decreased in 2018 to 58,000 hectares (143,000 acres) after a peak in 2016 of 122,000 hectares (302,000 acres). However, with the recent widespread forest fires, deforestation increased again in 2019, to 135,400 hectares (334,465 acres).
    .
  3. The Colombian Amazon experienced a deforestation boom starting in 2016 (coinciding with the FARC peace accords), reaching an historical high of 153,800 hectares (380,000 acres) in 2018. However, the deforestation estimate for 2019 is back to pre-boom levels at 53,800 hectares (133,000 acres).
    .
  4. Deforestation in the Peruvian Amazon declined in 2018 (compared to 2017) to 140,000 hectares (346,325 acres), but remained relatively high compared to historical data. The official deforestation data from the Peruvian government for 2018 is slightly higher at 154,700 hectares (382,272 acres), but also represents an important reduction compared to 2017. The deforestation estimate for 2019 indicates the continued downward trend to 134,600 hectares (332,670 acres).
    .
  5. Deforestation in the Brazilian Amazon is on another level compared to the other four countries. The 2019 deforestation estimate of 985,000 hectares (2.4 million acres) is consistent with the official data of the Brazilian government. The trend, however, is quite different; we show a decrease in deforestation compared to the previous three years, but the official data indicates an increase. To better understand the differences between data sources (including spatial resolution, inclusion of burned areas, and timeframe), consult this blog by Global Forest Watch.

Deforestation Hotspots 2019

Base Map shows the most intense deforestation hotspots during 2019.

maap-synthesis-2019-amazon-deforestation-trends-and-hotspots-BaseMap-Letters
Base Map. Amazon Deforestation, 2001-2019. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, MAAP. Click to see image in high resolution.

Many of the major deforestation hotspots were in Brazil. The letters A indicate areas deforested between March and July, and then burned starting in August, covering over 735,000 acres in the states of Rondônia, Amazonas, Mato Grosso, Acre, and Pará (MAAP #113). They also indicate areas where fire escaped into the surrounding primary forest, impacting an additional 395,000 acres. There is a concentration of these hotspots along the Trans-Amazonian Highway. The letter B indicates uncontrolled forest fires earlier in the year (March) in the state of Roraima (MAAP #109).

Bolivia also had an intense 2019 fire season. Letter C indicates the area where fires in Amazonian savanna ecosystems escaped to the surrounding forests.

In Colombia, the letter D indicates an area of high deforestation surrounding and within four protected areas: Tinigua, Chiribiquete, and Macarena National Parks, and the Nukak National Reserve (MAAP #106).

In Peru, there are several key areas to highlight. Letter E indicates a new Mennonite colony that has caused the deforestation of 2,500 acres in 2019, near the town of Tierra Blanca in the Loreto region (MAAP #112). Letter F indicates an area of high concentration of small-scale deforestation in the central Amazon (Ucayali and Huánuco regions), with cattle ranching as one of the main causes (MAAP #37). Letter G indicates an area of high concentration of deforestation along the Ene River (Junín and Ayacucho regions). In the south (Madre de Dios region), letter H indicates expanding agricultural activity around the town of Iberia (MAAP #98) and letter I indicates deforestation caused by a combination of gold mining and agricultural activity.


 

Methodology

As noted above, there are three important considerations about the data in our analysis: First, as a baseline, we use annual forest loss from the University of Maryland to have a consistent source across all five countries. Thus, the values may differ from official national data. Second, we applied a filter to only include loss of primary forest in order to better approximate the official methodology and data. Third, the 2019 data represents a preliminary estimate based on early warning alerts.

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.

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: Peru and Ecuador 18 South, Colombia 18 North, Western Brazil 19 South and Bolivia 20 South.

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, 9.88 acres)
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%.


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.

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

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

Agradecemos a S. Novoa (ACCA), R. Botero (FCDS), A. Condor (ACCA) y G. Palacios por sus útiles comentarios a este reporte.

Acknowledgements

We thank S. Novoa (ACCA), R. Botero (FCDS), A. Condor (ACCA), A. Folhadella (Amazon Conservation), M. Cohen, 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) MAAP Synthesis: 2019 Amazon Deforestation Trends and Hotspots. MAAP Synthesis #4.

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

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.