Mining and Logging Threat: Logging

MAAP #218: Killing of Environmental Defenders in the Peruvian Amazon

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Peruvian environmental defender Edwin Chota was murdered by illegal loggers in 2014 for attempting to protect his Indigenous community from Exploitation. See Illegal Logging section. Photo: NYT/Tomas Munita.

 

 

 

 

 

 

 

 

 

 

 

 

 

Amazon Conservation’s MAAP program specializes in reporting on the most urgent deforestation threats facing the Amazon and producing big-picture analyses of key Amazon-wide issues.

This report uniquely presents a view into the complicated but critical issue of murders of environmental defenders, examining the relationship between the location of these killings and deforestation in the Peruvian Amazon to provide a better understanding of the context of their deaths.

Between 2010 and 2022, an estimated 29 Peruvian environmentalists and Indigenous leaders were killed while defending various parts of Peru’s Amazon from invaders seeking to exploit its resources (RAISG 2022).

Importantly, the frequency of these murders has increased in recent years, with nearly half (14 out of 29) occurring since 2020.

Our findings indicate that these murders are connected to five major issues in the Peruvian Amazon:
Illegal gold mining, Illegal logging, Illicit crops (coca), Land trafficking, and Protesting.

This report focuses on the first three (Illegal gold mining, Illegal logging, and Illicit crops).

Base Map

Base Map. Location of the 29 environmental defenders murdered in Peru and the suspected causes related to major environmental threats in the region 2010-2022. Sources: IBC, MINJUS, SERNANP, Conservación Amazónica-ACCA.

The Base Map shows the location of the 29 documented environmental defenders killed in Peru between 2010-2022.

It also indicates the environmental threat related to each death as the suspected or confirmed motive for the crime: Illegal Gold Mining, Illegal Logging, Illicit Crops (coca), Land Trafficking, and Protest.

Note that many of the murders occurred in geographic clusters that coincide with the major environmental conflict of that specific area.

For example, gold mining is a major cause of conflict in the southern Peruvian Amazon, while illegal logging and illicit crops are more common threats in the central Peruvian Amazon.

Murders related to Illegal Gold Mining

Illegal gold mining has long been, and continues to be, a major issue in the southern Peruvian Amazon (Madre de Dios region), particularly in Indigenous territories and protected area buffer zones (MAAP#208).

For example, Figure 1 illustrates the extensive gold mining deforestation (indicated in orange) in the Tambopata National Reserve buffer zone and surrounding Indigenous territories.

Figure 1. Three cases of environmental defender deaths related to illegal mining. Sources: IBC, MINJUS, SERNANP, Conservación Amazónica-ACCA.

Since 2015, three environmental defenders have been killed within or near the Tambopata National Reserve buffer zone (see yellow dots in Figure 1). All three cases involved forestry concessionaires trying to defend their concession from illegal mining invasion.

In 2015, Alfredo Vracko Neuenschwander was killed near the critical mining area known as “La Pampa” located in the core of the buffer zone. Note that during the two years prior to his death, more than 1,700 hectares were deforested in La Pampa due to illegal gold mining (MAAP #1). Vracko, who was president of the Madre de Dios Federation of Forestry and Reforestation Concessionaires at the time, is believed to have been killed by illegal miners who were scheduled to be evicted from his forestry concession on the same day. However, his murder remains officially unsolved.

In 2020, Roberto Carlos Pacheco Villanueva was killed just outside the Tambopata buffer zone. Villanueva owned a forestry concession that had been illegally deforested and burned by invaders linked to illegal mining. Having filed legal complaints about the illegal use of his land, Villanueva faced numerous threats against his life in the years leading up to his murder. While still unsolved, it is believed that his murder was committed by the same miners who invaded his concession.

More recently, in 2022, Juan Julio Fernández Hanco was murdered just off the Interoceanic Highway near the edge of the Tambopata buffer zone. During this period (2021-2023), nearly 24,000 hectares were deforested due to gold mining in this area (MAAP #195). The investigation is ongoing, with the suspects being illegal miners who invaded Juan Julio’s reforestation concessions.

Murders related to Illegal Logging

Illegal logging has been a significant problem across the Peruvian Amazon for years. A recent report revealed that over 20% of timber harvested in Peru in 2021 came from illegal origins (OSINFOR, 2024). Loreto, Madre de Dios, Amazonas, and Ucayali were identified as the regions with the highest levels of unauthorized timber extraction.

Figure 2. Four environmental defender deaths related to illegal logging. Sources: IBC, MINJUS, DEVIDA, SERNANP, ACCA.

In 2014, illegal loggers murdered four men from the community of Alto Tamaya-Saweto, in one of the most well-known murder cases of Peruvian environmental defenders. These defenders (Edwin Chota Valera, Francisco Pinedo Ramírez, Jorge Ríos Pérez, and Leoncio Quintisima Meléndez) were killed along the Peru-Brazil border (see orange dots in Figure 2), following a decade of complaints from Chota about the presence of criminal logging groups in their community. Ten years later, in April 2024, a group of loggers were found guilty of the murders and sentenced to nearly 30 years in prison. This case has since been appealed with the expectation of going to Peru’s supreme court.

Murders related to Illicit Crops (Coca)

Official data indicates that the surface area of coca production in Peru continues to increase, particularly in the central Peruvian Amazon along the Andes Mountains (in the regions of Ucayali and Huánuco). Since 2010, ten environmental defenders have been killed in this area related to their fight against coca-related activities (see red dots in Figure 3).

Figure 3. Ten cases of environmental defender deaths related to illegal coca production. Sources: IBC, MINJUS, DEVIDA, SERNANP, Conservación Amazónica-ACCA.

Three environmental defenders (Santiago Vega Chota, Yenes Ríos Bonsano, and Herasmo García Grau) were killed in 2020 and 2021 within or near their communities of Sinchi Roca and Puerto Nuevo in the region of Ucayali, following their attempts to monitor their communities’ territories for coca production. Both communities are located within a coca production zone known as Aguaytía, which experienced a 158% increase in coca cultivation between 2018 and 2022 (DEVIDA 2022).

Between 2010 and 2020, four environmental defenders (Segundo José Reategui, Manuel Tapullima, Justo Gonzales Sangama, and Arbildo Melendez) were murdered in or near the Unipacuyacu Indigenous community. These four deaths have been linked to illegal coca production by outsiders on community lands that have not yet been officially titled by the government, which has facilitated these invasions. Unipacuyacu is located within the Pichis-Palcazu-Pachitea coca production zone spanning the Huánuco and Pasco regions, where coca cultivation increased by more than 450% between 2018 and 2022 (DEVIDA 2022).

Finally, three other environmental defenders (Jesús Berti Antaihua Quispe, Gemerson Pizango Narvaes, and Nusat Parisada Benavides de la Cruz) were killed in 2022 in their communities of Santa Teresa and Cleyton. These two indigenous communities are located within and just outside of the in an area outside of the El Sira Communal Reserve buffer zone. During the four years leading up to their deaths, coca production in El Sira and its buffer zone increased by over 500% (DEVIDA 2022). While unconfirmed, it is believed that these murders were committed by mafias tied to drug trafficking and illegal mining.

Regulatory Basis

Peru ranks among the countries with the highest number of environmental defender deaths worldwide (Global Witness 2023).

Peru’s National Plan for Human Rights 2018-2021, defines an environmental defender as someone who: As an individual or collective, carries out a legitimate activity, paid or not, consisting of demanding and promoting, within the legally permitted framework, in a peaceful and nonviolent manner, the effectiveness of violated rights. Their efforts are usually manifested publicly through demands and raised through regular process channels, conforming with the framework devoted to these fundamental rights.

To address the vulnerability of environmental defenders, the Peruvian government, specifically the Ministry of Justice and Human Rights (MINJUSDH), has developed regulations to ensure their protection. The most important of these are:

Regulation Title Importance
 

Supreme Decree N 002-2018-JUS

  

National Plan for Human Rights 2018-2021

 Establishes that environmental defenders are a group of special protection and requests that the state adopts measures to protect them.
 

Supreme Decree 004-2021-JUS

  

Intersectoral Mechanism for the Protection of Human Rights Defenders

 Establishes the principles, measures, and proceedings to guarantee the prevention, protection, and access to justice for human rights defenders prior to risk situations, being the highest ranking standard in the country.
 

Ministerial Resolution 255-2020-JUS

  

Registry on Risk Situations for Human Rights Defenders

 

 Recognizes, analyzes, and manages information about the risks that human rights defenders face, and adopts actions to prevent threats.

 

Peru has also taken an intersectoral approach by coordinating participation among eight ministries: Ministry of Justice and Human Rights, Ministry of the Interior, Ministry of the Environment, Ministry of Culture, Ministry of Woman and Vulnerable Populations, Ministry of External Relations, Ministry of Energy and Mines, and Ministry of Agriculture and Irrigation Development. A public implementing agency, the National Commission for Development and Life Without Drugs (DEVIDA), also cooperates with this effort.

Despite these efforts, defenders continue to face criminalization, legal harassment, and threats of violence and murder. This shows the urgent need to strengthen their protection and institutional support in Peru.

In response, the Peruvian Congress has recently enacted three new laws to further protect human rights defenders. These include (i) Bill 4686/2022-CR, a law that recognizes and protects defenders of environmental rights, and (ii) Bill 2069/2021-PE, a law for the protection and assistance of communal and/or Indigenous or native leaders at risk. Moving forward, how the ongoing Alto Tamaya-Saweto case proceeds through Peru’s Supreme Court will be crucial to future efforts to protect environmental and human rights defenders.

References

Comisión Nacional Para El Desarrollo y Vida Sin Drogas (DEVIDA). 2023. Perú: Monitoreo de cultivos de coca 2022.

Global Witness 2023. Casi 2.000 personas defensoras de la tierra y el medioambiente asesinadas entre 2012 y 2022 por proteger el planeta.

Organismo de Supervisión de los Recursos Forestales y de Fauna Silvestre (OSINFOR). 2024. Estimación del índice y porcentaje de tala y comercio ilegal de madera en el Perú 2021.

Red Amazónica de Información Socioambiental Georreferenciada (RAISG). 2022. Presiones, amenazas y violencia en la Amazonía peruana.

Acknowledgments

This report was prepared with support from the Instituto de Bien Común (IBC).

Citation

Montoya M, Bonilla A, Novoa S, Tipula P, Salisbury D, Quispe M, Finer M, Folhadella A, Cohen M (2024) Killing of Environmental Defenders in the Peruvian Amazon. MAAP:218.

MAAP #139: Using Satellites to Detect Illegal Logging in Peruvian Amazon

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Image 1. Illegal logging camp. Data: Skysat, MAAP/ACCA.

Illegal logging, in addition to larger-scale deforestation, is a major problem impacting the Peruvian Amazon.

In 2019, a Global Witness report, based on official information from the Peruvian government, estimated that at least 60% of the inspected timber over the past 10 years had an illegal origin. This problem not only directly affects the forest and its biodiversity, but also contributes to carbon loss (Qin et al, 2021) and forest degradation.

Illegal logging often involves the selective cutting of high-value trees in prohibited areas (whereas deforestation clears an entire area).

While numerous satellites can detect deforestation, only specialized satellites that are very high-resolution (less than one meter) can detect illegal logging.

In this report, we present a new emblematic case of illegal logging in the southern Peruvian Amazon.

It is based on a novel technique of tasking and analyzing very high-resolution images (in this case, with the Skysat satellite fleet from Planet) for a specific target area. Thanks to this new technique, we can tackle the problem of illegal logging in real-time, previously one of the biggest obstacles (see “Conclusion” section below).


Emblematic case

We refer to this as an emblematic case given the strong indicators of illegality (see Legal Status section, below) combined with likely significant impacts on an area of Amazon primary forest important for both indigenous peoples and biodiversity.

First, it is often difficult to confirm illegal logging given the frequent lack of updated technical and administrative information. This case study overcomes both obstacles.

Second, the illegal activity would not only be affecting a forestry concession (operated by the company Wood Tropical Forest), but also threatening important surrounding areas. Adjacent to the concession (to the west) is the Madre de Dios Territorial Reserve, a critical area that protects the territory of indigenous peoples in voluntary isolation. And to the south is the renowned Los Amigos Conservation Concession, a key area for the conservation of biodiversity.

Base Map

As part of our core work of continually monitoring the Los Amigos Conservation Concession, we acquired a series of very high-resolution images that also covered the surrounding Wood Tropical Forest forestry concession. These images, taken between February and April 2021, were obtained by the Skysat constellation (with a spatial resolution of 0.5 meters), operated by the satellite company Planet.

Our analysis revealed a serious situation of probable illegal logging: at least 3 active logging camps and 37 recently cut trees within the Wood Tropical Forest concession and close to both the neighboring Territorial Reserve and Conservation Concession (see Base Map).

Base Map. Data: MAAP/ACCA.

Very High-Resolution Skysat Images

The following images show some of the major findings made by our analysis of the Skysat data. Images 1-2 show examples of the logging camps, and Images 3-5 show examples of the likely selective illegal logging of high-value trees.

Image 2. Logging camp. Data: Skysat, MAAP/ACCA.
Image 3. Illegal logging. Data: Skysat, MAAP/ACCA.
Image 4. Illegal logging. Data: Skysat, MAAP/ACCA.
Image 5. Illegal logging. Data: Skysat, MAAP/ACCA.

Conclusion

This report presents a novel technique, based on the strategic capture of very high-resolution images (in this case, Skysat) and rapid analysis to detect selective illegal logging in real-time. Previously, one of the biggest obstacles to effectively addressing illegal logging was the inability of traditional monitoring methods to detect such small-scale, but widespread, illegal activity in the field. In this report, we demonstrate an important new capablility of detecting illegal logging activity in vast and remote areas in unprecedented detail, down to the level of a logging camp or individual cut trees.

Legal Situation (in Spanish)

La concesión forestal con Contrato N.° 17-TAM/C-J-007-02 fue otorgada en el año 2002 a la Empresa Shihuahuaco Timber S.A.C. y cedió su posición contractual a la empresa Wood Tropical Forestal en el año 2010, quien es titular del contrato de concesión hasta la actualidad.

La presunción de ilegalidad de la tala selectiva, evidenciada por nuestras imágenes satelitales, se debe a que la concesión forestal no se encontraría realizando actividades de aprovechamiento forestal enmarcadas en planes de manejo aprobados por el Gobierno Regional de Madre de Dios.

En efecto, tras realizar la consulta al Gobierno Regional de Madre de Dios, en su calidad de Autoridad Regional Forestal y de Fauna Silvestre (ARFFS), se advierte que la concesión se encuentra vigente. No obstante, no ha presentado a la ARFFS planes operativos para el aprovechamiento forestal desde hace más de ocho años. Incluso, desde el 30 de enero de 2020, cuenta con una resolución de la ARFFS que aprueba la suspensión del derecho de obligaciones contractuales (Resolución de Gerencia Regional N.° 065-2020-GOREMAD/GRFFS).

En ese sentido, y en tanto no se han presentado planes operativos en los últimos años, podemos inferir que en el área de la concesión forestal posiblemente no se estén realizando actividades lícitas de tala, por lo menos, desde hace ocho años.

Aunado a ello, en el Informe de Supervisión N.° 007-2019-OSINFOR/08.1.1, de acuerdo a una supervisión a la concesión para verificar obligaciones contractuales, el OSINFOR da cuenta de que la concesionaria presentó diversas denuncias entre los años 2016 al 2018 a la  Fiscalía Especializada en Materia Ambiental (FEMA), al Organismo de Supervisión de los Recursos Forestales y de Fauna Silvestre (OSINFOR) y al Gobierno Regional de Madre de Dios, en las cuales advirtió la presencia de terceras personas al interior de la concesión que estarían realizando tala ilegal, deforestación, instalación de campamentos ilegales, entre otros.

Acknowledgments

We thank E. Ortiz (AAF), Z. Romero (ACCA), G. Palacios (ACA), and A. Felix, J. Carlos Guerra, K. Nielsen, O. Liao, and R. Suarez from USAID’s PREVENT Project, and J. Jara 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 is working with the Government of Peru, civil society, and the private sector to prevent and combat environmental crimes in Loreto, Ucayali and Madre de Dios, in order to conserve the Peruvian Amazon.

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

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

Citation

Finer M, Yupanqui O, Suarez D, Novoa S (2021) Using Satellites to Detect Illegal Logging in Peruvian Amazon. MAAP: 139.

MAAP #136: Amazon Deforestation 2020 (Final)

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

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

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

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

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

We highlight several key findings:

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

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

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

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

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

 

 

 

Brazilian Amazon

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

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

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

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

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

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

Bolivian Amazon

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

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

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

 

 

 

 

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

Peruvian Amazon

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

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

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

 

 

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

Colombian Amazon

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

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

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

 

 

 

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

*Notes and Methodology

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

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

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

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

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

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

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

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

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

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

 

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

Acknowledgements

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

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

Citation

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

MAAP #126: Drones and Legal Action in the Peruvian Amazon

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ACOMAT member flying a drone for monitoring their forestry concession. Source: ACCA.

The southern Peruvian Amazon (Madre de Dios region), is threatened by illegal mining, logging, and illegal deforestation.

In response, an association of forest concessionaires (known as ACOMAT) is implementing a comprehensive monitoring system that links the use of technology (satellites and drones) with legal action.

ACOMAT was formed in 2012 and now comprises 15 forestry concessions, covering 440,000 acres (178,000 hectares) in the southern Peruvian Amazon (see Base Map). Most of the concessions are alternatives to logging, such as Brazil nuts, Conservation, and Ecotourism.

This comprehensive system has three main elements:

1) Real-time, satellite-based forest loss monitoring (such as GLAD alerts) to quickly detect any possible new threats, even across vast and remote areas.

2) Field patrols with drone flights to verify forest alerts (or monitor threatened areas) with very high resolution images.

3) If suspected illegality is documented, initiate a criminal or administrative complaint, utilizing both the satellite and drone-based evidence.

In the case of ACOMAT, during 2019 they conducted 26 drone patrols and filed 15 legal complaints with the regional Environmental Prosecutor’s Office, known as FEMA. Below, we describe several of these cases.

Note that there is high potential to replicate this comprehensive monitoring model at the level of forest custodians (for example, concessionaires and indigenous communities) in the Amazon and other tropical forests.

Key ACOMAT Cases

Next, we describe four cases where comprehensive monitoring was performed (see Insets A-D on the Base Map).

Base Map. ACOMAT concessions. Data: ACCA, MINAM/PNCBMCC, SERNANP.

A. Illegal logging in the Los Amigos Conservation Concession

In October 2019, a patrol was carried out to investigate a threatened area within the Los Amigos Conservation Concession (the world’s first Conservation Consession). During the patrol, which included five drone flights, illegal logging was documented, including stumps with sawn trees , paths for the transfer of wood to a nearby river, and abandoned camps. The drone images were added as evidence in support of the previously filed criminal complaint to the FEMA in Madre de Dios. Below we present two striking images from the drone flights, clearly showing the illegal logging. Status of the Complaint: In Preliminary Investigation.

Case A. Illegal logging in the Los Amigos Conservation Concession, identified with drone overflight. Source: ACCA.
Case A. Illegal logging in the Los Amigos Conservation Concession, identified with drone overflight. Source: ACCA.

B. Ilegal Logging in the MADEFOL Forestry Concession

In May 2019, a field patrol was carried out to investigate a threatened area within the MADEFOL forestry concession. During the patrol, which included two drone flights, illegal logging was documented, including stumps with sawn trees, a recently abandoned camp, and an access road. With the drone images as evidence, a new criminal complaint was filed with the FEMA in Madre de Dios. Below is an image from the drone flights, clearly showing the evidence of illegal logging. Status of the complaint: In qualification.

Case B. Illegal logging in the “MADEFOL” forestry concession identified with drone overflight. Source: ACCA.

C. Illegal Gold Mining in a Conservation Concession

In May 2019, a field patrol was carried out in the “Inversiones Manu SAC” Conservation Concession to investigate an area that had previously been affected by illegal gold miners. During the patrol, which included two drone flights, illegal gold mining was documented in the Malinowski River. With the drone images as evidence, a new criminal complaint was filed with the FEMA in Madre de Dios. Below is a drone image clearly showing the evidence of illegal gold mining. Status of the complaint: Preliminary Investigation.

Caso C. Minería ilegal en la Concesión de Conservación “Inversiones Manu SAC,” identificada con sobrevuelo de dron. Fuente: ACCA.

D. Deforestation in a Brazil Nut Concession

In October 2019, a patrol was carried out to investigate an early warning deforestation alert within the “Sara Hurtado Orozco B” Brazil nut concession.

During the patrol, which included one drone flight, the recent deforestation of five acres (two hectares) was documented. With the drone images, a new criminal complaint was filed with the FEMA of Madre de Dios. It should be noted that this concession was being investigated for a separate illegal deforestation event. Below is one of the images of the drone flight, clearly showing the illegal deforestation. Status of the complaint: In preliminary proceedings.

Caso D. Deforestación en la Concesión Forestal de Castaña “Sara Hurtado Orozco B”. Fuente: ACCA.

Importance of the “ACOMAT Model”

We have started using the term “Acomat model” to refer to the innovative use of the three elements described above (real-time monitoring, drone flights, and criminal complaints) by the ACOMAT concessionaires.

ACOMAT was created in 2012, and since 2017 has received crucial support from the organization Conservation Amazónica-ACCA, supported by funds from Norway’s International Climate and Forest Initiative (NICFI), led by the Norwegian Agency for Development Cooperation (NORAD).

This project has provided training on all three major aspects, satellite-based monitoring alerts, drones, and the legal process. Concessionaires now receive deforestation alerts to their phones, have the ability to organize and conduct field patrols, and some are trained to perform their own drone flights.

Acknowledgments

We thank R. Segura (DAI), M.E. Gutierrez (ACCA), D. Suarez (ACCA), H. Balbuena (ACCA), M. Silman (WFU), and G. Palacios for their helpful comments on this report.

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

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

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

Citation

Finer M, Castañeda C, Novoa S, Paz L (2020) Drones and Legal Action in the Peruvian Amazon. MAAP 126.

 

MAAP #123: Detecting Illegal Logging in the Peruvian Amazon

Posted on by dcadmin
Image 1. Example of a 2019 logging road with signs of illegality. Data: Planet.

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

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

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

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

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

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

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

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

Logging Roads 2019

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

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

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

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

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

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

Cases of Possible Illegality

Logging roads in areas without forestry concessions or permits

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

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

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

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

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

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

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

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

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

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

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

Logging roads in Native Communities

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

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

Methodology

Our analysis included two main steps:

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

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

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

Annex – Logging road data per region

REGION, Logging Roads (Km)

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

TOTAL, 1511.7

References

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

Acknowledgments

We thank R. Valle (OSINFOR), A. Felix (DAI), D. Suarez (ACCA), and G. Palacios for their helpful comments on this report.

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

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

Citation

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

MAAP #105: From satellite to drone to legal action in the Peruvian Amazon

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ACOMAT member flying a drone for monitoring. Source: ACCA.

Amazon Conservation, in collaboration with its Peruvian sister organization, is implementing a project aimed at linking cutting-edge technology (satellites and drones) with legal action, in the southern Peruvian Amazon (Madre de Dios region).

The project is building a comprehensive deforestation monitoring system with a local group of forestry concessionaires, known as ACOMAT,* who manage over 486,000 acres (see Base Map).

The monitoring system has three basic steps:

1) Real-time deforestation monitoring with satellite-based early warning forest loss alerts.*

2) Verify and document the alerts with drone overflights.*

3) Initiate a criminal complaint with the local environmental prosecuter’s office* (or an administrative complaint with the relevant forestry authorities) if suspected illegalities are found.

Below, we describe 6 cases (A-E) that have been generated from this comprehensive monitoring system.

It is important to emphasize that this type of monitoring system, featuring local forest custodians (such as concessionaires and indigenous communities) is possible to replicate in the Amazon and other tropical forests.

This innovative project is largely funded by the Norwegian Agency for Development Cooperation (NORAD) and International Conservation Fund of Canada (ICFC).

Base Map. The 6 Acomat cases (A-F) described in this report. Data: ACCA, MINAM/PNCB, SERNANP.

Case A. Illegal logging in the “Los Amigos” Conservation Concession

This evidence in this case was obtained from a drone overflight of an area that was the subject of an early warning forest loss alert within Los Amigos Conservation Consession (a conservation area where logging is not permitted). The overflight documented the illegal logging of the timber species known locally as tornillo (Cedrelinga cateniformis) within the concession (see image below).  The drone images were presented to the environmental prosecuter’s office in Madre de Dios as part of a criminal complaint.

Case A. Illegal logging in the Conservation Concession “Los Amigos”, identified with a drone flying over. Source: ACCA.

Case B. Illegal mining in the “Sonidos de la Amazonía” Ecotourism Concession      

The owner of the Sonidos de la Amazonía Ecotourism Concession received an early warning forest loss alert on his cellphone. She then organized a drone overflight and documented active illegal gold mining activity, including infrastructure (see image below). The drone images were presented to the environmental prosecuter’s office in Madre de Dios as part of a criminal complaint.

Case B. Illegal mining in the Tourism Concession “Sonidos de la Amazonía,” identified with drone images. Source: ACCA.

Case C. Illegal mining in the “AGROFOCMA” Forestry Concession    

The owner of the AGROFOCMA forestry (logging) concession received an early warning forest loss alert on his cellphone. He then organized a drone overflight and documented active illegal gold mining activity, including infrastructure (see image below). The drone images were presented to the environmental prosecuter’s office in Madre de Dios as part of a criminal complaint.

Case C. Illegal mining in the Forest Concession “AGROFOCMA,” identified with drone images. Source: ACCA.

Case D. Illegal mining in the “Inversiones Manu” Forestry Concession     

The owner of the Inversiones Manu forestry (logging) concession received an early warning forest loss alert on his cellphone. He then organized a drone overflight and documented active illegal gold mining activity, including workers and infrastructure (see image below). The drone images were presented to the environmental prosecuter’s office in Madre de Dios as part of a criminal complaint.

Case D. Illegal mining in the Forest Concession “Inversiones Manu,” identified with drone images. Source: ACCA.

Case E. Illegal logging in the “Sara Hurtado” Brazil Nut Concession 

The owner of the Sara Hurtado Brazil Nut Concession received an early warning forest loss alert on her cellphone. She then organized a drone overflight and documented active illegal logging activity, including cedar wood planks (see image below). The drone images were presented to the environmental prosecuter’s office in Madre de Dios as part of a criminal complaint.

In a related case, drones also captured images of a nearby collection center and transport truck for the recently logged planks. These images were also presented to the environmental prosecuter’s office as part of a sixth case.

Case E. Illegal logging in the Forest Concession “Sara Hurtado” identified with drone images. Source: ACCA.

*Notes

ACOMAT is the “Asociación de Concesionarios Forestales Maderables y no Maderables de las Provincias del Manu, Tambopata y Tahuamanu.”

The early warning alerts are generated by the Peruvian government (Geobosques/MINAM). GLAD alerts can also be used (these are generated by the University of Maryland and presented by Global Forest Watch). In our case, the concessionaires receive Geobosques alerts in their emails.

We used quadricopter drones. Obtained images are very-high resolution (<5 cm).

The local environmental prosecuter’s office is the “Fiscalía Especializada en Materia Ambiental (FEMA) de Madre de Dios.”

Acknowledgements

We thank S. Novoa (ACCA), H. Balbuena (ACCA), E. Ortiz (AAF), T. Souto (ACA), P. Rengifo (ACCA), A. Condor (ACCA), y G. Palacios for helpful comments on earlier drafts of this report.

This work supprted by the following funders:  Norwegian Agency for Development Cooperation (NORAD), International Conservation Fund of Canada (ICFC), MacArthur Foundation, Metabolic Studio.

Citation

Guerra J, Finer M, Novoa S (2019) From satellite to drone to legal action in the Peruvian Amazon. MAAP: 105.

 

MAAP #99: Detecting Illegal Logging in the Peruvian Amazon

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New logging road in the Peruvian Amazon. Data: Planet.

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

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

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

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

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

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

 

 

Base Map. Logging roads. Data: MAAP, SERNANP

Base Map

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

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

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

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

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

 

 

 

 

 

 

Cases of Possible Illegal Logging

A. Logging roads in non-forestry areas

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

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


B. Logging roads in canceled concessions

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

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

C. Logging Roads in Brazil nut concessions

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

Zoom C. Data: Planet, MAAP, OSINFOR


D. Logging roads in protected areas

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

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

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

References

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

Acknowledgments

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

Citation

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

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

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

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

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

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

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

Our principal findings include:

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

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

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

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

I. Deforestation Trends

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

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

Trends by Country

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

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

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

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

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

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

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

Trends by Size

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

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

II. Deforestation Hotspots

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

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

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

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

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

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

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

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

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

 

 

III. Drivers of Deforestation     

MAAP Interactive (screenshot)

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

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

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

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

 

 

 

 

Agriculture – oil palm, cacao, and other crops

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

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

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

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

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

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

 

 

 

 

 

Large-scale Agriculture

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

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

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

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

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

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

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

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

Small and Medium-scale Agriculture

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

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

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

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

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

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

Cattle Ranching

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

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

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

Legend:
Cattle ranching (orange)

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

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

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

 

 

 

Gold Mining

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

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

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

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

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

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

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

Logging

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

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

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

Legend:
Logging Road (purple)

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

 

 

 

 

 

 

Roads

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

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

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

Legend:
Road (gray)

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

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

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

 

 

 

 

Hydroelectric dams

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

Legend:
Hydroelectric Dam (light blue)

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

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

Hydrocarbon (oil and gas)

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

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

Legend:
Hydrocarbon (black)

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

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

 

 

 

 

 

 

 

Climate Change

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

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

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

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

Citation

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

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

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

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

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

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

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

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

 

 

1. Selective logging of high-value trees

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

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

2. Construction of logging roads

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

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

3. Logging camps

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

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

4. Storage and transport

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

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

Annex

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

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

*Notes

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

Citation

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

MAAP #93: Shrinking Primary Forests of the Peruvian Amazon

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

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

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

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

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

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

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

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

 

 

 

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

Southern Peruvian Amazon

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

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

 

 

 

 

 

 

 

 

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

Central Peruvian Amazon

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

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

 

 

 

 

 

 

 

 

 

 

Base Map plus protected areas and indigenous communities.

Northern Peruvian Amazon

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

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

 

 

 

 

 

 

 

 

 

 

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

Annex

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

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Notes

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

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

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

Metodology

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

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

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

Citation:

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