MAAP #112: Mennonite Colonies – New Deforestation Driver in the Amazon

Time-lapse deforestation in the “Tierra Blanca” Mennonite colony in Loreto, Peru. Data: Planet.

The Mennonites, a religious (Christian) group often dedicated to organized agriculture, are increasingly inhabiting the western Amazon (Peru and Bolivia).

Here, we reveal the recent deforestation of 18,500 acres (7,500 hectares) in three Mennonite colonies (see the Base Map below).

The two colonies in Peru (Tierra Blanca and Masisea) are new, causing the deforestation of 6,200 acres since 2017 (including 3,500 acres in 2019) in the Loreto and Ucayali regions.

The colony in Bolivia (Río Negro) is older, but deforestation recently began to increase again, causing the deforestation of 12,350 acres since 2017 in the department of Beni.

Next, we present a series of satellite image videos showing the deforestation in the three Mennonite colonies.

MAAP #107: Seeing The Amazon Fires With Satellites

Recent fire (late July 2019) in the Brazilian Amazon. Data: Maxar.
Recent fire (late July 2019) in the Brazilian Amazon. Data: Maxar.

Fires now burning in the Amazon, particularly Brazil and Bolivia, have become headline news and a viral topic on social media.

Yet little information exists on the impact on the Amazon rainforest itself, as many of the detected fires originate in or near agricultural lands.

Here, we advance the discussion on the impact of the fires by presenting the first Base Map of current “fire hotspots” across three countries (Bolivia, Brazil, and Peru). We also present a striking series of satellite images that show what the fires look like in each hotspot and how they are impacting Amazonian forests. Our focus is on the most recent fires in August 2019.

Our key findings include:

  • Fires are burning Amazonian forest in BoliviaBrazil, and Peru.
    .
  • The fires in Bolivia are concentrated in the dry Chiquitano forests in the southern Amazon.
    .
  • The fires in Brazil are much more scattered and widespread, often associated with agricultural lands. Thus, we warn against using fire detection data alone as a measure of impact as many are clearing fields. However, many of the fires are at the agriculture-forest boundary and maybe expanding plantations or escaping into forest.
    .
  • Although not as severe, we also detected fires burning forest in southern Peru, in an area that has become a deforestation hotspot along the Interoceanic Highway.

Given the nature of the fires in Bolivia and Brazil, estimates of total burned forest area are still difficult to determine. We will continue monitoring and reporting on the situation over the coming days.

Base Map

The Base Map shows “fire hotspots” for the Amazonian regions of Bolivia, Brazil, and Peru in August 2019. The data comes from a NASA satellite that detects fires at 375 meter resolution. The letters (A-G) correlate to the satellite image zooms below.

Base Map. Fire Hotspots in the Amazon during August 2019. Data- VIIRS:NASA.
Base Map. Fire Hotspots in the Amazon during August 2019. Data- VIIRS:NASA.

Zoom A: Southern Bolivian Amazon

Fires are concentrated in the dry Chiquitano of southern Bolivia. It is part of the largest tropical dry forest in the world. The fires coincide with areas that have been part of cattle ranching expansion in recent decades (References 1 and 2), suggesting that poor burning practices could be the cause of the fires. Ranching using sown pastures has previously been referred to as a direct cause of forest loss in Bolivia (References 2 and 3). The Bolivian National Service of Meteorology and Hydrology (SENAMHI) issued high wind alerts in July and August for southern Bolivia, which could have led to the expansion of poorly managed fires. Also, August is usually the driest month of the year in this region. These conditions could explain the origin (poor fire practice) and expansion (little rain and strong winds) of the current fires.

Zoom A1. Fire in southern Bolivian Amazon. Data- ESA
Zoom A1. Fire in southern Bolivian Amazon. Data- ESA
Zoom A2. Fire in southern Bolivian Amazon. Data- ESA
Zoom A2. Fire in southern Bolivian Amazon. Data- ESA
Zoom A3. Fire in southern Bolivian Amazon. Data- Planet
Zoom A3. Fire in southern Bolivian Amazon. Data- Planet

Zooms B, C, E, F, G: Western Brazilian Amazon

The major fires in western Brazil seem to be at the agriculture-forest boundary. Note that Zoom B shows fire in a protected area in Amazonas state; Zoom C seems to show fire escaping (or deliberately set) in the primary forests in Rondonia state; and Zooms F and G seems to show fire expanding plantation into forest in Amazonas and Mato Grosso states, respectively.

Zoom B. Fire in a protected area in Amazonas state. Data- ESA
Zoom B. Fire in a protected area in Amazonas state. Data- ESA
Zoom C. Fires at agriculture-forest boundary in Rondonia state. Data- Sentinel
Zoom C. Fires at agriculture-forest boundary in Rondonia state. Data- Sentinel
Zoom E. Fire escaping (or deliberately set) in the primary forests in Rondonia state. Data- Planet
Zoom E. Fire escaping (or deliberately set) in the primary forests in Rondonia state. Data- Planet
Zoom F. Fire that seems to be expanding plantation into forest in Amazonas state. Data- Planet.
Zoom F. Fire that seems to be expanding plantation into forest in Amazonas state. Data- Planet.
Zoom G. Fire that seems to be expanding plantation into forest in Mato Grosso state. Data- Planet
Zoom G. Fire that seems to be expanding plantation into forest in Mato Grosso state. Data- Planet
Bonus Zoom. Recent fire in Brazilan Amazon. Data- Planet
Bonus Zoom. Recent fire in Brazilan Amazon. Data- Planet

 

Zoom D: Southern Peruvian Amazon

Fires burning forest near the town of Iberia, an area along the Interoceanic Highway that has become a deforestation hotspot in the region of Madre de Dios (see MAAP #28 and MAAP #47).

Zoom D. Fire in southern Peruvian Amazon (near Iberia, Madre de Dios). Data- ESA
Zoom D. Fire in southern Peruvian Amazon (near Iberia, Madre de Dios). Data- ESA

Additional References

We have these to be some of the most informative additional references:

New York Times, Aug 24

Global Forest Watch, Aug 23

Technical References

1 Müller, R., T. Pistorius, S. Rohde, G. Gerold & P. Pacheco. 2013. Policy options to reduce deforestation based on a systematic analysis of drivers and agents in lowland Bolivia. Land Use Policy 30(1): 895-907. http://dx.doi.org/10.1016/j. landusepol.2012.06.019

Muller, R., Larrea-Alcázar, D.M., Cuéllar, S., Espinoza, S. 2014.  Causas directas de la deforestación reciente (2000-2010) y modelado de dos escenarios futuros  en las tierras bajas de Bolivia. Ecología en Bolivia 49: 20-34.

Müller, R., P. Pacheco & J. C. Montero. 2014. El contexto de la deforestación y degradación de los bosques en Bolivia: Causas, actores e instituciones. Documentos Ocasionales CIFOR 100, Bogor. 89 p.

Acknowledgements

We thank  J. Beavers, D. Larrea, T. Souto, M. Silman, A. Condor, and G. Palacios for helpful comments to earlier versions of this report.

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

Citation

Novoa S, Finer M (2019) Seeing the Amazon Fires with Satellites. MAAP: 107.

MAAP #105: From Satellite To Drone To Legal Action In The Peruvian Amazon

ACOMAT member flying a drone for monitoring. Source- ACCA
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
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 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 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 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 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
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 #104: Major Reduction In Illegal Gold Mining From Peru’s Operation Mercury

Graph 1. Illegal gold mining deforestation in La Pampa, 2017-19. Data- ACA, MAAP.
Graph 1. Illegal gold mining deforestation in La Pampa, 2017-19. Data- ACA, MAAP.

In February 2019, the Peruvian government launched Operation Mercury (Operación Mercurio), a major multi-sectoral crackdown on the illegal gold mining crisis in the area known as La Pampa,* located  in the southern Peruvian Amazon (Madre de Dios region). Note that this area is not within Tambopata National Reserve, but in its buffer zone.

In this report, we present the results of our analysis on the initial impacts of this Operation.

We found a major reduction in gold mining deforestation in La Pampa in 2019, compared to the same time period (February – June) of the previous two years (see Graph 1).

In fact, the gold mining deforestation decreased 92% between 2018 (900 hectares) and 2019 (67 hectares), representing the situation before and after the start of Operation Mercury.

The Base Map illustrates how the expansion of gold mining deforestation greatly dropped in 2019 compared to the two previous years, especially in the eastern front. The letters (A-C) correspond to the location of the Zooms, below.

The analysis also reveals, however, that the gold mining deforestation in La Pampa has not yet been completely eradicated and continues in numerous remote and isolated areas.

 

Base Map. Illegal gold mining deforestation in La Pampa. Data- ACCA, MAAP, SERNANP
Base Map. Illegal gold mining deforestation in La Pampa. Data- ACCA, MAAP, SERNANP

Zoom A1 shows the critical eastern front of the gold mining deforestion between February (left panel) and June (right panel) 2019, the first five months of Operation Mercury. While the rapid eastward expansion of the front has greatly decreased, the red circles indicate areas where we have detected isolated mining activity.

Zoom A1. Eastern front of the gold mining deforestation in La Pampa. Data- ESA, MAAP
Zoom A1. Eastern front of the gold mining deforestation in La Pampa. Data- ESA, MAAP

High Resolution Zooms

 

Zoom B shows the eradication of one of the biggest mining camps in La Pampa between 2018 (left panel) and 2019 (right panel).

Zoom B. Eradication of major gold mining camp. Data- Maxar
Zoom B. Eradication of major gold mining camp. Data- Maxar

The following Zooms show examples of the persistence of isolated illegal gold mining activity and infrastructure in La Pampa, with recent (June 2019) high resolution satellite and drone images. The letters (A2, C1, C2) correspoind to the Base Map, above.

Zoom A2. Data- Maxar, MAAP
Zoom A2. Data- Maxar, MAAP

Zoom C1. Data- ACCA
Zoom C1. Data- ACCA

Zoom C2. Data- ACCA.
Zoom C2. Data- ACCA.

 

Google Earth Engine App

We present a new app, developed with Google Earth Engine, that allows an interactive visualization of the evolution of gold mining deforestation in La Pampa. The app allows the user to take advantage of Google’s powerful computers to compare (with a slider) different dates from a large archive of Sentinel-1 satellite images (see screenshot, below). Sentinel-1 is radar, so there are no clouds in the images.

https://luciovilla.users.earthengine.app/view/mining-monitoring-by-sar-sentinel-1

Screen shot of the app. Data- ESA, MAAP
Screen shot of the app. Data- ESA, MAAP

 

Notes 

*La Pampa is the sector located in the buffer zone of Tambopata National Reserve, delimited by the northern boundary of the reserve, the Malinowski River and the Interoceanic Highway.

Full study area of La Pampa (shaded). Data: ACCA, MAAP.

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, and Global Forest Watch Small Grants Fund (WRI).

Citation

Villa L, Finer M (2019) Major Reduction in Illegal Gold Mining from Peru’s Operation Mercury. MAAP: 104.

MAAP #100: Western Amazon – Deforestation Hotspots 2018 (A Regional Perspective)

For the 100th MAAP report, we present our first large-scale western Amazon analysis: Colombia, Peru, Ecuador, Bolivia, and western Brazil (see Base Map).

We use the new 2018 data for forest cover loss, generated by the  University of Maryland (Hansen et al 2013) and presented by Global Forest Watch.

These data indicate 2.5 million acres of forest cover loss in the western Amazon in 2018.*

We conducted an additional analysis that indicates, of this total, 1.9 million acres were primary forest.*

Base Map. Deforestation Hotspots in the western Amazon. Data: Hansen/UMD/Google/USGS/NASA, GFW, SERNANP, SNAP, SINAP, SERNAP, RAISG
Base Map. Deforestation Hotspots in the western Amazon. Data: Hansen/UMD/Google/USGS/NASA, GFW, SERNANP, SNAP, SINAP, SERNAP, RAISG

To identify deforestation hotspots consistently across this vast landscape, we conducted a kernel density analysis (see Methodology).

The Base Map shows the hotspots in yellow, orange and red, indicating areas with medium, high, and very high forest loss concentrations, respectively.

Next, we focus on five zones of interest (Zooms A-E) in Colombia, Brazil, Bolivia, and Peru. For all images, please click to enlarge.

*Forest Cover Loss: 5 acres per minute. Almost half (49%) occurred in Brazil, followed by Peru (20%), Colombia (20%), Bolivia (8%), and Ecuador (3%). see Annex.

**Primary Forest Loss: 3.5 acres per minute. Over half (53%) occurred in Brazil, followed by Peru (20%), Colombia (18%), Bolivia (7%), and Ecuador (2%). see Annex.

 


Colombia

The largest concentration of 2018 forest loss is in the northeast Colombian Amazon (494,000 acres). Out of this total, 11% (56,800 acres) occurred in national parks. National experts indicate that land grabbing has emerged as a leading direct driver of deforestation (Arenas 2018). See MAAP #97 for more information.

Zoom A shows the forest loss expanding towards western Chiribiquete National Park, including distinct deforestation in this protected area during 2018.

Zoom B shows the extensive 2018 deforestation (30,000 acres) within Tinigua National Park. A recent news report indicates that cattle ranching is one of the factors related to this deforestation.

Zoom A. Colombia-Chiribiquete. Data: Hansen/UMD/Google/USGS/NASA, SINAP, Planet, ESA
Zoom A. Colombia-Chiribiquete. Data: Hansen/UMD/Google/USGS/NASA, SINAP, Planet, ESA
Zoom B. Colombia – Tinigua. Data: Hansen/UMD/Google/USGS/NASA, SINAP, Planet, ESA
Zoom B. Colombia – Tinigua. Data: Hansen/UMD/Google/USGS/NASA, SINAP, Planet, ESA

 


Brazil (border with Bolivia)

Another important result is the contrast between northern Bolivia (Pando department) and adjacent side Brazil (states of Acre, Amazonas, and Rondônia). Zoom C shows several deforestation hotspots on the Brazilian side, while the Bolivian side is much more intact.

Zoom C. Brazil, Bolivia border. Data: Hansen/UMD/Google/USGS/NASA, ESA, RAISG
Zoom C. Brazil, Bolivia border. Data: Hansen/UMD/Google/USGS/NASA, ESA, RAISG

 


Bolivia

In Bolivia, the major forest loss hotspots are further south. Zoom D shows the recent deforestation (5,000 acres in 2018) due to agricultural activity associated with one of the first major Mennonite settlements in Beni department (Kopp 2015). The other Mennonite settlements are located further south.

Zoom D. Bolivia, Black River Mennonite settlement. Data: Hansen/UMD/Google/USGS/NASA, SERNAP, Planet
Zoom D. Bolivia, Black River Mennonite settlement. Data: Hansen/UMD/Google/USGS/NASA, SERNAP, Planet

Peru

The Hansen data indicates over 200,000 acres of forest loss during 2018 in the Peruvian Amazon. One of the most important deforestation drivers, especially in southern Peru, is gold mining. We estimate 23,000 acres of gold mining deforestation during 2018 in the southern Peruvian Amazon (see MAAP #96).

Zoom E shows the most emblematic case of gold mining deforestation: the area known as La Pampa.

It is important to emphasize, however, that in February 2019 the Peruvian government launched “Operation Mercury 2019” (Operación Mercurio 2019), a multi-sectoral and comprehensive mega-operation aimed at eradicating illegal mining and associated crime in La Pampa, as well as promote development in the region.

Zoom D. Peru – La Pampa. Data: Hansen/UMD/Google/USGS/NASA, SERNAP, Planet
Zoom D. Peru – La Pampa. Data: Hansen/UMD/Google/USGS/NASA, SERNAP, Planet

Annex

Annex. Forest cover and primary forest loss in the western Amazon.  Data: Hansen/UMD/Google/USGS/NASA, Global Forest Watch.
Annex. Forest cover and primary forest loss in the western Amazon.  Data: Hansen/UMD/Google/USGS/NASA, Global Forest Watch.

Methods

The 2018 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 presented in the Base Map: the areas within the Amazonian biogeographic boundary of the western Amazon.

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 2000 (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

Arenas M (2018) Acaparamiento de tierras: la herencia que recibe el nuevo gobierno de Colombia. Mongabay, 2 AGOSTO 2018. https://es.mongabay.com/2018/08/acaparamiento-de-tierras-colombia-estrategias-gobierno/

Goldman L, Weisse M (2019) Technical Blog: Global Forest Watch’s 2018 Data Update Explained. 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.

Kopp Ad (2015) Las colonias menonitas en Bolivia. Tierra. http://www.ftierra.org/index.php/publicacion/libro/147-las-colonias-menonitas-en-bolivia

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

We thank M. Terán (ACEAA), M. Weisse (GFW/WRI), A. Thieme (UMD), R. Catpo (ACCA) and A. Cóndor (ACCA) for helpful comments to this report.


Citation

Finer M, Mamani N (2019) Western Amazon – Deforestation Hotspots 2018 (a regional perspective). MAAP: 100.

MAAP #99: Detecting Illegal Logging In The Peruvian Amazon

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.

MAAP #98: Deforestation Hotspots In The Peruvian Amazon, 2018

Base Map. 2018 Deforestation Hotspots. Data: PNCB/MINAM, SERNANP
Base Map. 2018 Deforestation Hotspots. Data: PNCB/MINAM, SERNANP

Thanks to early warning forest loss alerts,* we are able to make an initial assessment of the 2018 deforestation hotspots in the Peruvian Amazon.

The Base Map highlights the medium (yellow) to high (red) hotspots. In this context, hotspots are the areas with the highest density of forest loss alerts.

Note that the most intense hotspots are concentrated in the southern Peruvian Amazon, particularly the Madre de Dios region. In previous years, intense hotspots were also concentrated in the central Peruvian Amazon.

Next, we focus on 5 hotspots of interest (Zooms A-E).

A. La Pampa (Madre de Dios)
B. Bahuaja Sonene National Park (surroundings) (Madre de Dios, Puno)
C. Iberia (Madre de Dios)
D. Organized Deforestation (Ucayali, Loreto)
E. Central Amazon (Ucayali, Huánuco)

*The data presented in this report is an estimate based on early warning data generated by the National Program of Forest Conservation for the Mitigation of Climate Change of the Ministry of the Environment of Peru (PNCB/MINAM). We also analyzed University of Maryland GLAD alerts, obtained from Global Forest Watch.

A. La Pampa (Madre de Dios)

Zoom A shows two important cases in the southern Peruvian Amazon (Madre de Dios region). First, gold mining deforestation south of the Interoceanic Highway in the area known as La Pampa. It is important to emphasize that the Peruvian government just started “Operation Mercury 2019” (Operación Mercurio 2019), a multi-sectoral and comprehensive mega-operation aimed at eradicating illegal mining and associated crime in La Pampa, as well as promote development in the region. Second, deforestation due to agricultural activity north of the highway. As in all the zoom maps below, pink indicates forest loss in 2018.

Zoom A. La Pampa. Data- PNCB:MINAM, SERNANP, ACCA, ESA
Zoom A. La Pampa. Data- PNCB:MINAM, SERNANP, ACCA, ESA

B. Bahuaja Sonene National Park (surroundings) (Madre de Dios, Puno)

Zoom B also shows two important cases in the southern Peruvian Amazon (regions of Madre de Dios and Puno), surrounding Bahuaja Sonone National Park. First, to the north of the park, is gold mining deforestation along the upper Malinowski River. The Peruvian protected areas agency (SERNANP) points out that they have limited the deforestation south of the river (direction towards the national park) due to their intensified patrols on that side. Second, to the south of the park, is non-mining (partly agricultural) deforestation.

Zoom B. Bahuaja Sonene (surroundings). Data- PNCB:MINAM, SERNANP, Planet
Zoom B. Bahuaja Sonene (surroundings). Data- PNCB:MINAM, SERNANP, Planet

 

C. Iberia (Madre de Dios)

Zoom C takes us to the other side of Madre de Dios, around the town of Iberia, near the border with Brazil and Bolivia. This area is experiencing extensive deforestation due to agricultural activity. There most intense deforestation is just of Iberia, where a religious community of farmers (Arca Pacahuara) is reportedly establishing large corn plantations (References 1-2). Much of the 2018 (and 2017) deforestation is occurring within forest concessions, where agriculture is not permitted.

Zoom C. Iberia. Data- PNCB:MINAM, SERNANP, Planet
Zoom C. Iberia. Data- PNCB:MINAM, SERNANP, Planet

 

D. Organized Deforestation (Ucayali, Loreto)

In 2018 we documented two similar cases in the central Peruvian Amazon. Both have similar forms of organized deforestation, characterized by what seems to be agricultural plots arranged along new access roads. Zoom D shows the Masisea case (left panel, zoom D1) and the Sarayaku case (right panel, zoom D2). See MAAP #92 for more information.

Zoom D. Organized deforestation. Data- PNCB:MINAM, SERNANP, ESA
Zoom D. Organized deforestation. Data- PNCB:MINAM, SERNANP, ESA

 

E. Central Amazon (Ucayali, Huánuco)

As in previous years, there was extensive deforestation in the central Peruvian Amazon (Ucayali and Huánuco regions). Zoom E shows an example: small and medium-scale deforestation surrounding a pair of large-scale oil palm plantations. Some of the recent deforestation is occurring within “Permanent Production Forests,” forestry-zoned areas where agriculture is not permitted. This area also corresponds to the proposed territorial title of the indigenous Shipibo community of Santa Clara de Uchunya (see here for more information).

Zoom E. Central Amazon. Data- PNCB:MINAM, SERNANP, ESA
Zoom E. Central Amazon. Data- PNCB:MINAM, SERNANP, ESA

 

Methodology

We conducted this analysis using the Kernel Density tool from Spatial Analyst Tool Box of ArcGIS, using 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.

The data presented in this report is an estimate based on early warning data generated by the National Program of Forest Conservation for the Mitigation of Climate Change of the Ministry of the Environment of Peru (PNCB/MINAM). We also analyzed University of Maryland GLAD alerts, obtained from Global Forest Watch.

 

References

1. CIFOR 2016

2. GOREMAD 2016

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

 

Citation

Finer M, Mamani N (2018) Deforestation Hotspots in the Peruvian Amazon, 2018. MAAP: 98.

MAAP #96: Gold Mining Deforestation At Record High Levels In Southern Peruvian Amazon

Gold mining deforestation has been at record high levels in both 2017 and 2018 in the southern Peruvian Amazon.

Based on an analysis of nearly 500 high-resolution satellite images (from Planet and DigitalGlobe), we estimate the deforestation of 18,440 hectares across southern Peru during these last two years. That is equivalent to 45,560 acres (or 34,400 American football fields) in just two years.

The Base Map highlights this recent deforestation, with 2017 in red and 2018 in pink. The Reference Map in Annex 1 shows our full study area.

Base Map. Gold mining deforestation in southern Peruvian Amazon. Data- USGS/NASA, MAAP, SERNANP
Base Map. Gold mining deforestation in southern Peruvian Amazon. Data- USGS/NASA, MAAP, SERNANP

2017 had the highest gold mining deforestation on record at the time: 9,160 hectares (22,635 acres). According to recent research led by CINCIA (Centro de Innovación Científica Amazónica), this was the highest annual total on record dating back to 1985*.

In 2018, we found the gold mining deforestation was even higher: 9,280 hectares (22,930 acres).

Thus, combined, 2017-18 had the highest two-year deforestation total on record: 18,440 hectares (45,565 acres).

Note the location of Zooms (A-C) shown in greater detail below. These zooms represent three of the most threatened areas: A) La Pampa, B) Upper Malinowski, and C) Camanti.

Click (or right click) to enlarge (or download) images.

*CINCIA reports 9,860 hectares of gold mining deforestation in 2017 (CINCIA 2018, Caballero Espejo et al 2018), an estimate even higher than ours.

 

Zoom A: La Pampa

Image A shows the gold mining deforestation of 1,685 hectares (4,164 acres) between 2017 (left panel) and 2018 (right panel) in an area known as La Pampa (Madre de Dios region). Red indicates the major deforestation fronts.

MAAP96Image A. La Pampa. Data- Planet, MAAP
MAAP96Image A. La Pampa. Data- Planet, MAAP

As seen in the Land Use Map below (Annex 2), most of the recent mining deforestation in La Pampa is clearly illegal, concentrated in reforestation concessions and the buffer zone of Tambopata National Reserve.

According to the web portal GEOCATMIN (Geological Information System and Mining Register), developed by INGEMMET (Geological Mining and Metallurgical Institute of Peru), all titled mining concessions in the area are currently “without mining activity.” None are in authorized Exploration or Exploitation phase. Most of the mining activity is outside these concessions and in areas not authorized for mining.

 

Zoom B: Upper Malinowski

Image B shows the gold mining deforestation of 760 hectares (1,878 acres) between 2017 (left panel) and 2018 (right panel) along the upper stretches of the Malinowski River in the Madre de Dios region. Red indicates the major deforestation fronts.

Image B. Upper Malinowski. Data- Planet, MAAP.jpg
Image B. Upper Malinowski. Data- Planet, MAAP.jpg

As seen in the Land Use Map below (Annex 2), the recent gold mining deforestation along the Upper Malinowski is advancing in the Kotsimba Native Community and within the buffer zone of Bahuaja Sonene National Park.

According to GEOCATMIN, all titled mining concessions in the area are currently “without mining activity.” None are in authorized Exploration or Exploitation phase. Most of the mining activity is outside these concessions and in areas not authorized for mining.

 

Zoom C: Camanti

Image 4 shows the gold mining deforestation of 335 hectares (828 acres) between 2016 (left panel) and 2018 (right panel) in the Camanti area of the Cusco region. Red indicates the major deforestation fronts. Note the increasing proximity of the mining to Amarakaeri Communal Reserve.

Image C. Camanti. Data- Planet, MAAP
Image C. Camanti. Data- Planet, MAAP

As seen in the Land Use Map below (Annex 2), the recent gold mining in the Camanti area is advancing in mining concessions that are “in process” of titling. According to GEOCATMIN, there are no titled concessions in the area that are in Exploration or Exploitation phase.

 

Annex 1: Reference Map

Annex 1 features a Reference Map of our full study area. The background is white to better indicate the mining deforestation areas. It also serves as a reference map with additional labels.

Reference Map. Gold mining deforestation in southern Peruvian Amazon. Data- MAAP, SERNANP
Reference Map. Gold mining deforestation in southern Peruvian Amazon. Data- MAAP, SERNANP

Annex 2: Land Use Map

Annex 2 features a Land Use Map with detailed data on mining concessions and other important land designations. The mining concession data comes from the web portal GEOCATMIN (Geological Information System and Mining Register), developed by INGEMMET (Geological Mining and Metallurgical Institute of Peru). We downloaded the data on January 2, 2019.

Land use Map. Data- INGEMMET, IBC, MINAGRI, SERNANP, Planet, UMD:GLAD, MINAM:PNCB
Land use Map. Data- INGEMMET, IBC, MINAGRI, SERNANP, Planet, UMD:GLAD, MINAM:PNCB

Methodology

We analyzed high-resolution satellite imagery (DigitalGlobe and Planet) for both 2017 and 2018 and digitized all new gold mining deforestation. Given the widespread mining across a large area, we also used automated forest loss alerts based on medium resolution Landsat imagery (PNCB/MINAM) to guide our analysis.

References

Centro de Innovación Científica Amazónica (CINCIA) (2018) Tres décadas de deforestación por minería aurífera en la Amazonía suroriental peruana. Resumen de Investigación No. 1.

Caballero Espejo et al. (2018) Deforestation and Forest Degradation Due to Gold Mining in the Peruvian Amazon: A 34-Year Perspective.  Remote Sens. 2018, 10 (12), 1903; https://doi.org/10.3390/rs10121903

Asner GP and Tupayachi R (2016) Environ. Res. Lett. 12 094004.

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

Acknowledgements

We thank the following colleagues for helpful comments: Miles Silman (Wake Forest Univ), Sidney Novoa (ACCA), Ronald Catpo (ACCA), Efrain Samochuallpa (ACCA), Daniela Pogliani (ACCA), Alfredo Cóndor (ACCA), and Lorena Durand (ACCA).

Citation

Finer M, Mamani N (2018) Gold Mining Deforestation at Record High Levels in Southern Peruvian Amazon. MAAP: 96.

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

Download a PDF of this Article

Satellite image of the deforestation produced by United Cacao. Source: DigitalGlobe (Nextview)
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 / 1,052,668 acres). 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 / 12.35).

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

By analyzing high-resolution satellite imagery, we have developed a methodology for identifying areas deforested 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. The 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: 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.
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.
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, and 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: 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 Peruvian Amazon during the last five years (2013-17) due to forest loss, especially deforestation from mining and agricultural activities. This finding reveals that forest loss represents nearly half (47%) of Peru’s annual carbon emissions, including from burning fossil fuels.

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

The breakdown of results are:

1.85 billion tons safeguarded in the Peruvian national protected areas system;

1.15 billion tons safeguarded in titled native community lands; and

309.7 million tons safeguarded in Territorial Reserves for indigenous peoples in voluntary isolation.


Citation

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

MAAP #95: Oil Palm Baseline for The Peruvian Amazon

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

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

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

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

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

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