MAAP #26: Deforestation Hotspots in the Peruvian Amazon, 2015

Thanks to the newly launched GLAD alerts (developed by the University of Maryland and Google1, and presented by Global Forest Watch), we now have weekly access to high-resolution forest loss data across Peru. Here in MAAP #26, we analyze the first batch of this data to better understand deforestation patterns in the Peruvian Amazon in 2015. In the coming weeks and months, we will use this map as a base for investigating major hotspots of forest loss in the country.

According to the GLAD alert data, total estimated forest loss in Peru in 2015 was 158,658 hectares (392,050 acres). If confirmed, that represents the second highest total on record, behind only 2014 (177,500 hectares).

To better understand where the GLAD alert data was concentrated in 2015, we conducted kernel density estimation, a type of analysis that calculates the magnitude per unit area of a particular phenomenon (in this case, forest loss). Image 26a shows the kernel density map for forest loss in the Peruvian Amazon in 2015. It reveals that recent deforestation was concentrated in a number of hotspots in the departments of Huánuco, Madre de Dios, and Ucayali.

Note that in MAAP #25, we conducted this same type of analysis for 2012 – 2014 forest loss data. Thus, with this latest analysis we can see how deforestation trends shifted in 2015.

Insets A and B highlight an area in central Peru (department of Ucayali) where deforestation intensified in 2015. See below for high-resolution images showing the deforestation in these areas. In the coming weeks and months, we will be publishing additional articles highlighting other key 2015 deforestation hotspots.

Image 26a. Kernel density map for forest loss in the Peruvian Amazon in 2015. Data: Hansen et al 2016 (ERL).
Image 26a. Kernel density map for forest loss in the Peruvian Amazon in 2015. Data: Hansen et al 2016 (ERL).

Inset A

Image 26b shows detailed deforestation information for the area indicated in Inset A (from Image 26a). Note the extensive 2015 deforestation just to the west of two large-scale oil palm plantations (201 hectares, see pink areas).

Image 26b. 2000-15 deforestation for area in Inset A. Data: Hansen et al 2016 (ERL), PNCB/MINAM, Hansen/UMD/Google/USGS/NASA, USGS (Landsat 8)
Image 26b. 2000-15 deforestation for area in Inset A. Data: Hansen et al 2016 (ERL), PNCB/MINAM, Hansen/UMD/Google/USGS/NASA, USGS (Landsat 8)

Further below, Image 26c shows a high-resolution satellite image of the area in Inset A1 before (left panel) and after (right panel) the recent deforestation events.

Image 26c. High-resolution view of area in Inset A1 before (left panel) and after (right panel) recent deforestation events. Data: WorldView-2 de Digital Globe (NextView).
Image 26c. High-resolution view of area in Inset A1 before (left panel) and after (right panel) recent deforestation events. Data: WorldView-2 de Digital Globe (NextView).

 


Inset B

Image 26d shows detailed deforestation information for the area indicated in Inset B (from Image 26a). Note the extensive 2015 deforestation along the Aguaytia River (164 hectares, see pink areas). Recent deforestation (2012-14) appears to be associated with agricultural and logging activities.

Image 26d. 2000-15 deforestation for area in Inset B from Image Xa. Data: Hansen et al 2016 (ERL), PNCB/MINAM, Hansen/UMD/Google/USGS/NASA, USGS (Landsat 8)
Image 26d. 2000-15 deforestation for area in Inset B from Image Xa. Data: Hansen et al 2016 (ERL), PNCB/MINAM, Hansen/UMD/Google/USGS/NASA, USGS (Landsat 8)

Further below, Image 26e shows a high-resolution satellite image of the area in Inset B1 before (left panel) and after (right panel) the recent deforestation events.

Image 26e. High-resolution view of area in Inset B1 before (left panel) and after (right panel) recent deforestation events. Data: WorldView-2 de Digital Globe (NextView).
Image 26e. High-resolution view of area in Inset B1 before (left panel) and after (right panel) recent deforestation events. Data: WorldView-2 de Digital Globe (NextView).

Methodology

We conducted this analysis using the Kernel Density  tool from Spatial Analyst Tool Box of ArcGis 10.1 software. Our goal was to emphasize local concentrations of deforestation in the raw data while still representing overarching patterns of deforestation between 2012 and 2014. We accomplished this 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.


Reference

1 Hansen, M.C., A. Krylov, A. Tyukavina, P.V. Potapov, S. Turubanova, B. Zutta, S. Ifo, B. Margono, F. Stolle, and R. Moore. Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, in press. Accessed through Global Forest Watch on March 2, 2016. www.globalforestwatch.org


Citation

Finer M, Novoa S, Snelgrove C (2015) 2015 Deforestation Hotspots in the Peruvian Amazon. MAAP: 26.

MAAP #23: Increasing Deforestation Along Lower Las Piedras River (Madre De Dios, Peru)

The Las Piedras River in the southern Peruvian Amazon (department of Madre de Dios) is increasingly recognized for its outstanding wildlife (for example, see this video by naturalist and explorer Paul Rosolie, and this trailer for the upcoming film Uncharted Amazon). As seen in Image 23a, its headwaters are born in the Alto Purus National Park, but the lower Las Piedras is surrounded by a mix of different types of forestry concessions (logging, Brazil nut harvesting, ecotourism, and reforestation).

Here in MAAP #23, we document the growing deforestation on the lower Las Piedras River in the area surrounding the community of Lucerna (see red box in Image 23a for context).

Image 23a. Las Piedras River and surrounding designations. Data: MINAGRI, IBC, SERNANP.
Image 23a. Las Piedras River and surrounding designations. Data: MINAGRI, IBC, SERNANP.

Deforestation Analysis

Image 23b shows our deforestation analysis for an area along the lower Las Piedras River near the community of Lucerna (see red box in Image 23a for context). We found a sharp increase in deforestation starting in 2012. In the 11 years between 2000 and 2011, we detected the deforestation of 88 hectares (218 acres). In contrast, in the 4 years between 2012 and 2015, we detected the deforestation of 472 hectares (1,166 acres). 2015 had the highest deforestation total with 155 hectares (383 acres).

Image 23b. Lower Las Piedras River deforestation analysis. Data: MINAGRI, CLASlite, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA.
Image 23b. Lower Las Piedras River deforestation analysis. Data: MINAGRI, CLASlite, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA.

Note that the Las Piedras Amazon Center (LPAC) Ecotourism Concession represents an effective barrier to deforestation. However, note that two other, less active, ecotourism concessions are experiencing extensive deforestation. The 4,460 hectare LPAC concession (which was created in 2007 and transferred to ARCAmazon in March 2015) hosts an active tourist lodge, research center,  and Forest Ranger Protection Program, which employs local people to patrol the area while monitoring wildlife and human impacts.


Image 23c. Recent Landsat image showing deforestation along lower Las Piedras. Data: USGS,MINAGRI.
Image 23c. Recent Landsat image showing deforestation along lower Las Piedras. Data: USGS,MINAGRI.

Image 23c shows a very recent (December 2015) Landsat image of the deforestation highlighted in Image 23b. The pinkish-red areas indicate the most recently cleared forests. We have received information indicating that much of this new deforestation is associated with cacao plantations. Cacao is of course used to produce chocolate.


Citation

Finer M, Pena N (2015) Increasing Deforestation along lower Las Piedras River (Madre de Dios, Peru). MAAP #23


MAAP #18: Proliferation of Logging Roads in The Peruvian Amazon

MAAP articles #3 and #15 detailed the construction of several new logging roads in the central Peruvian Amazon. Here in MAAP 18, we provide a more comprehensive analysis of the proliferation of logging roads in this section of the Amazon. In Image 18a, we show a high resolution example of a new logging road in this area with active construction during 2015 (see Inset A1 in Image 18c for more context).

Image 18a. New logging road in the Peruvian Amazon. Data: WorldView-2 of Digital Globe (NextView).
Image 18a. New logging road in the Peruvian Amazon. Data: WorldView-2 of Digital Globe (NextView).

Image 18b illustrates the location of all identified logging roads in the central Peruvian Amazon (southern Loreto and northern Ucayali). Most of these roads are located along the Ucayali River and its headwater tributaries. The left panel highlights just the logging roads, while the right panel also includes protected areas, native communities, and logging concessions.

Image 18b. Logging roads in the central Peruvian Amazon. Data: SERNANP, IBC, USGS, MINAGRI.
Image 18b. Logging roads in the central Peruvian Amazon. Data: SERNANP, IBC, USGS, MINAGRI.

In Image 18b, we documented the construction of 1,134 km of logging roads between 2013 and 2015 in the central Peruvian Amazon. Of this total, 538 km is in the matrix of logging concessions and native communities in southern Ucayali, 226.1 km is in undesignated areas in southern Loreto, 210 km is in the buffer zone of Cordillera Azul National Park, and 159 km is around the new Sierra del Divisor National Park.

Note that the buffer zone of Cordillera Azul National Park and surroundings of Sierra del Divisor National Park contain logging concessions and native communities, thus the responsibility of forest authority is the regional government.

Determining the legality of these roads is complex. As the right panel highlights, many of these roads are near logging concessions and native communities, whom may have obtained the rights for logging from the relevant forestry authority (in many cases, the regional government).

Below, we focus on the logging roads in the northern section of Image 18b (see Inset A).

Zoom A: Logging Roads in Southern Loreto/Northern Ucayali

 

Image 18c. Logging roads in southern Loreto/northern Ucayali. Data: SERNANP, IBC, USGS, MINAGRI.
Image 18c. Logging roads in southern Loreto/northern Ucayali. Data: SERNANP, IBC, USGS, MINAGRI.

Image 18c is a zoom of the logging roads shown in the northern section of Image 18a (Inset A), located in southern Loreto and northern Ucayali. It shows five primary areas of interest. Both Insets A1 and A2 correspond to new roads within the southeast buffer zone of the Cordillera Azul National Park with active construction in 2015 (see below for more details).

Insets A3, A4, and A5 correspond to roads with active construction between 2013 and 2015 that have already been featured on MAAP. Inset 3 includes a logging road in the northeast sector of the buffer zone of Cordillera Azul National Park (see MAAP #3 for more details). Insets 3 and 5 show logging roads around the new Sierra del Divisor National Park (see MAAP #15 and MAAP #7 for more details).

Zoom A1: Logging Roads in Nuevo Irazola

Image 18d provides more details about a new logging road with very recent construction within the southeast buffer zone of Cordillera Azul National Park (See Inset A1 in Image 18C for context). This road has grown 68 km between 2013 and 2015, with more than half of this construction occurring over the past year. According to information obtained from the forestry department within the Regional Government of Ucayali (PRMRFFS), the native community of Nuevo Irazola made a logging permission request for industrial and/or commercial use and prepared an Annual Operating Plan. However, a high-resolution (0.5 m) image shows a recent stretch of the road exceeds the area requested for forestry activities (see Image 18d).

Image 18d. High-resolution image of a new forest road in the southeast buffer zone of Cordillera Azul National Park. Data: WorldView-2 of Digital Globe (NextView).
Image 18d. High-resolution image of a new forest road in the southeast buffer zone of Cordillera Azul National Park. Data: WorldView-2 of Digital Globe (NextView).

Zoom A2: Rapid Expansion of a Logging Road

 

Image 18e. Time series of a forest road in the southeast buffer zone of Cordillera Azul National Park. Data: USGS.
Image 18e. Time series of a forest road in the southeast buffer zone of Cordillera Azul National Park. Data: USGS.

Image 18e illustrates the rapid expansion of another forest road located in the southeast section of the Cordillera Azul National Park buffer zone (See Inset A2 in Image 18C for context). We documented the construction of 29.1 km during the six weeks between September 10 (left panel) and October 20 (right panel), a rate of nearly five kilometers per week. The legality of this road is currently unknown, but note that it is extending in the direction of a forestry concession.

Citation

Novoa S, Fuentes MT, Finer M, Pena N, Julca J (2015) Proliferation of Logging Roads in the Peruvian Amazon. MAAP #18.

Note: MAAP #18 is a collaborative effort between Amazon Conservation Association (ACA), Conservación Amazónica (ACCA), and the Centro de Conservación Investigación y Manejo de Áreas Naturales (CIMA).

Image #11: Importance of Protected Areas in The Peruvian Amazon

The Peruvian national protected areas system, known as SINANPE, is critically important to Amazon conservation efforts in the country.

There are currently 46 protected areas in the Peruvian Amazon under national or regional administration*. In total, these areas cover 19.5 million hectares and include a wide variety of designations, including areas of indirect use (those with strict protection, such as National Parks) and direct use (those that allow the exploitation of natural resources, such as National Reserves) under national administration and Regional Conservation Areas under regional administration.

Here, MAAP #11 presents a deforestation analysis that demonstrates the effectiveness of protected areas in relation to the surrounding landscape in the Peruvian Amazon.

Image 11a. Recent forest loss in relation to protected areas in the Peruvian Amazon. Data: SERNANP, PNCB-MINAM/SERFOR-MINAGRI, NatureServe.
Image 11a. Recent forest loss in relation to protected areas in the Peruvian Amazon. Data: SERNANP,

Key Results

Image 11a shows recent (2000 – 2013) forest loss patterns in relation to the current national protected area system in the Peruvian Amazon (Image 11b shows the same, but with zooms of the northern, central, and southern regions, respectively). Note that some of the documented forest loss surely comes from natural causes, such as landslides or meandering rivers.

Across all protected areas administered nationally (such as National Parks and National Reserves), we found that deforestation was significantly lower starting at 2 km within their boundaries compared to outside them (see Images 11b and 11c).

The rate of deforestation outside of protected areas is more than twice of that within them (within the 5 km buffer zone study area, see below).

Image 11b. Regional zooms (north, central, south) of recent forest loss in relation to protected areas. Data: SERNANP, PNCB-MINAM/SERFOR-MINAGRI, NatureServe.
Image 11b. Regional zooms (north, central, south) of recent forest loss in relation to protected areas. Data: SERNANP, PNCB-MINAM/SERFOR-MINAGRI, NatureServe.

Deforestation Analysis – Methods

We conducted a basic analysis of all protected areas administered nationally (National Park, National Sanctuary, Historic Sanctuary, National Reserve, Protection Forest, Communal Reserve, and Reserved Zone) to estimate their relative effectiveness in controlling deforestation in relation to the surrounding landscape. The forest loss data comes from the National Program of Forest Conservation for the Mitigation of Climate Change (PNCB) of the Ministry of the Environment of Peru. This deforestation analysis had two key components.

Image 11c. Illustration of spatial intervals for deforestation analysis.
Image 11c. Illustration of spatial intervals for deforestation analysis.

First, we compared recent forest loss within versus outside each protected area at four different spatial intervals: 1 km, 2 km, 3 km, and 5 km (see Image 11c). In other words, starting at the boundary line for each area, we created a 1 km buffer both inside and outside the area and compared the relative (forest loss/area *100) deforestation. We then repeated this analysis for the other intervals. The establishment of these intervals areas is based on the assumption that the closer to the limits of each protected area, deforestation could be more related to anthropogenic activities in surrounding areas, which is expected to reduce the effect of natural losses due to changes in the courses of rivers and landslides in unstable areas.

Second, we controlled for protected area creation date. If an area was created prior to 2000, such as Manu National Park created in 1973, we used the complete 2000-2013 PNCB forest loss dataset. If an area was created after 2000, such as Alto Purus National Park created in 2004, we used just the forest loss dataset for the years following its creation (in this case, 2005-2013).

This analysis was designed to show general patterns, not be a definitive evaluation of the effectiveness of protected areas. A more complete evaluation could control for additional variables (such as slope, elevation, climate, distance to population centers, etc…).

Deforestation Analysis – Results

 

Image 11d. Results of deforestation analysis.
Image 11d. Results of deforestation analysis.

Across all protected areas administered nationally, we found that deforestation was significantly lower starting at 2 km within their boundaries compared to outside them (p < 0.05) (see Image 11d). The significance level increased by an order of magnitude between 3 and 5 km. We didn’t detect a significant difference between 1 km within and outside the protected area boundaries.

On average, we found that 0.5% of the area within protected areas experienced forest loss between 2000-2013, while outside the protected areas was nearly 1.2%. In other words, the rate of deforestation outside of protected areas is more than twice of that within them. Furthermore, as mentioned earlier, some forest loss within the protected areas surely comes from natural causes, such as landslides or meandering rivers.

Related Studies

As noted above, this analysis was designed to show general patterns, not be a definitive evaluation of the effectiveness of protected areas. Several other recent studies have pointed out the importance of controlling for additional variables.

In a study focused on the Brazilian Amazon, Pfaff et al (PLOS ONE 2015) found that is important to control for the location of protected areas, which is often in more isolated areas with lower deforestation pressures.

Specifically regarding the Peruvian Amazon, a study by the research organization Resources for the Future (2014) found that “the average protected area reduces forest cover change”. This study rigorously controlled for a number of key variables (such as elevation, slope, climate, and distance to cities), but used older and more limited forest loss and protected areas data.

*This total of 46 protected areas includes: a) all the categories considered part of SINANPE (including Reserved Zones and Regional Conservation Areas) except for Private Conservation Areas, and b) all areas that are totally or partially located in the Amazon basin.

SERNANP Response

In response to this article, SERNANP (the Peruvian protected areas agency) issued this statement:

Actualmente el SERNANP viene realizando una verificación en campo por parte del personal guardaparque de las Áreas Naturales Protegidas durante sus acciones de patrullaje merced a la información de pérdida de bosque proporcionada por el Ministerio del Ambiente, periodo 2013-2014, a fin de determinar si el cambio de la cobertura se debe a causas naturales o antrópicas. Esto podrá complementar el análisis desarrollado por ACCA.

Es importante señalar, que el SERNANP viene aplicando el enfoque ecosistémico en la planificación y gestión de las Áreas Naturales Protegidas, en este sentido desarrolla acciones que permiten evitar la deforestación al interior de estos espacios protegidos, pero a su vez nos proponemos que en su entorno se desarrollen actividades compatibles con la conservación que eviten el fraccionamiento del hábitat y permitan la sostenibilidad de la conservación de las Áreas Naturales Protegidas a futuro.

En este sentido, considerando de vital importancia generar alianzas con las entidades que toman decisiones en el territorio fuera de estos espacios, hemos establecido a nivel nacional un trabajo conjunto con los Gobiernos Regionales a fin de integrar las Áreas Naturales Protegidas dentro de corredores de conservación con otras modalidades de conservación que se impulsan a través de sus sistemas regionales de conservación. Con ello, se esperaría detener el fraccionamiento de hábitat alrededor de las Áreas Naturales Protegidas, lo que podría conllevar a su insostenibilidad a futuro. Al respecto, es preciso mencionar que los Sistemas Regionales de Conservación cuentan con un espacio de coordinación donde se reúnen las principales instituciones que gestionan territorio y en la cual se discuten las iniciativas de desarrollo social y económico para que se realicen en armonía con la conservación de la biodiversidad del país, el SERNANP forma parte de estos espacios a nivel nacional.


Citation

Finer M, Novoa S (2015) Importance of Protected Areas in the Peruvian Amazon. MAAP: Image #11. Link: https://maaproject.org/2015/08/image-11-protected-areas

MAAP #9: Confirming Forest Clearing for Cacao in Tamshiyacu (Loreto, Peru) Came from Primary Forest

Recall that in Image #2 we documented the rapid clearing of 2,126 hectares of primary forest between May 2013 and August 2014 for a new cacao project outside of the town of Tamshiyacu in the northern Peruvian Amazon (Department of Loreto).

However, the company that carried out the forest clearing (United Cacao, through its wholly-owned subsidiary in Peru, Cacao del Peru Norte) has responded “that this area had been used for farming since the late 1990s, and thus it was not primary forest…There was no high-conservation-value forest on that land (Cannon JC, 2015, mongabay.com).” In addition, the company’s website states that “The site was heavily logged of all tropical hardwoods in the 1980s.”

Here, in Image #9, we 1) publish new high-resolution (33 cm) satellite imagery that shows how the cacao project is expanding into dense, closed-canopy forest and 2) detail exactly how we determined that the vast majority of the clearing indeed came from primary forest. These findings are critically important because the company has major expansion plans.

Image of the Week 9a. Mosaic of very high-resolution (33 cm) images of the United Cacao plantation near Tamshiyacu, Peru, in June 2015. Colors indicate insets. Data: WorldView-3 from Digital Globe (NextView).
Image of the Week 9a. Mosaic of very high-resolution (33 cm) images of the United Cacao plantation near Tamshiyacu, Peru, in June 2015. Colors indicate insets. Data: WorldView-3 from Digital Globe (NextView).

Key Results:

We obtained very high-resolution (33 cm) satellite imagery taken over the United Cacao plantation in June 2015 (see Image 9a). In this imagery, one can clearly see that the cacao project is embedded and expanding into dense, closed-canopy forest.

We analyzed a series of satellite (Landsat) images dating back to 1985 to determine that, prior to the arrival of United Cacao in 2013, the project area 1) had NOT been used for major farming activities, 2) was NOT heavily logged of all tropical hardwoods, and 3) was dominated (98%) by primary forest (see Image 9b). In fact, by analyzing spectral signatures in the Landsat images, we confirm that the area cleared by United Cacao in 2013 was dominated by primary forest (see Image 9c).

We show data from the Carnegie Airborne Observatory showing that the majority of the United Cacao project area had the highest possible value of carbon (over 150 tons per hectare) immediately prior to the forest clearing in 2013.

Finally, we present information indicating that the current documented forest clearing of 2,126 hectares may soon double or triple.

Landsat Time-series

 

Image 9b. Landsat time-series (1985-2012) of the future United Cacao plantation area (indicated by black box) prior to arrival of the company. Data: USGS
Image 9b. Landsat time-series (1985-2012) of the future United Cacao plantation area (indicated by black box) prior to arrival of the company. Data: USGS

Image 9b displays a series of Landsat images dating back to 1985 showing that, prior to the arrival of United Cacao, the area was dominated (nearly 98%) by primary forest and NOT used for major agriculture activities or heavily logged of all tropical hardwoods.

In these Landsat images, dark green indicates forest cover, light green indicates secondary vegetation, pink indicates exposed ground (and is therefore a key indicator of recent forest clearing), and the scattered white and black spots indicate clouds and their shade.

In 1985, the future cacao project area (indicated by black box) was completely covered by forest with no signs of clearing, major logging, or farming. By 1995, there were a few scattered areas of cleared forest in the center of the future project area. By 2005, there was a slight expansion of these cleared areas in the center of the future project area. By 2012, immediately before the start of forest clearing, the future project area appeared much the same: a few scattered areas of cleared forest in the center, but the vast majority of the area was primary forest.

We defined primary forest as an area that from the earliest available image (in this case, from 1985) was characterized by dense closed-canopy coverage and experienced no major clearing events.

NDVI Analysis

 

Image 9c. NDVI analysis of the United Cacao plantation area prior to arrival of the company. Letters indicate significance (i.e., “a” values are significantly different than “b” values). Data: USGS.
Image 9c. NDVI analysis of the United Cacao plantation area prior to arrival of the company. Letters indicate significance (i.e., “a” values are significantly different than “b” values). Data: USGS.

To further investigate the issue of primary forest, we used the Landsat imagery to conduct an NDVI (Normalized Difference Vegetation Index) analysis. NDVI is a common index of photosynthetic activity, or “greenness,” based on the fact that different surfaces (primary forest, secondary forest, water, bare ground, etc…) reflect light (visible and near-infrared) differently.

As seen in Image 9c, we obtained NDVI measurements across four different years (1985, 1995, 2005, and 2012) for 100 random points from each of three different areas: 1) area cleared by United Cacao in 2013 (orange dots), 2) nearby protected area that is proxy for primary forest (yellow dots), and 3) disturbed area along a major river that is proxy for secondary forest (purple dots).

For all four years, we found that the NDVI values for the area cleared by United Cacao in 2013 were similar to those of the nearby protected area (in fact, these values were nearly identical in 1985 and 1995), but significantly different than the disturbed area along the major river. In other words, the forest cleared by United Cacao was nearly identical to our proxy for primary forest and significantly different than our proxy for secondary forest. Thus, we conclude that United Cacao cleared over 2,000 hectares of primary forest in 2013.

Carbon Data Tells the Same Story

 

Image 9d. High-resolution carbon map of United Cacao plantation area (indicated by black box) prior to forest clearing. Data: Asner et al (2014) The high-resolution carbon geography of Peru. Berkeley, CA: Minuteman Press.
Image 9d. High-resolution carbon map of United Cacao plantation area (indicated by black box) prior to forest clearing. Data: Asner et al (2014) The high-resolution carbon geography of Peru. Berkeley, CA: Minuteman Press.

The Carnegie Airborne Observatory, led by Dr. Greg Asner, and the Peruvian Ministry of Environment, recently produced a high-resolution carbon geography of Peru. Interestingly, they mapped the carbon content of the United Cacao plantation area immediately prior to the forest clearing.

As seen in Image 9d, the vast majority of the United Cacao project area had the highest possible value of carbon (over 150 tons per hectare) immediately prior to the forest clearing in 2013. The only exceptions were the scattered previously cleared areas identified in Image 9b.

According to Asner, “The carbon levels were extremely high, indicating that they were large, intact forests that we normally picture when we think of primary Amazon forest.”

More Forest Clearing Coming…

 

Image 9e. Project area map from the United Cacao website.
Image 9e. Project area map from the United Cacao website.

According to its website, United Cacao owns around 3,250 hectares near Tamshiyacu, and this total may soon increase to 4,000 hectares. In addition, the company has started an initiative with local farmers that may include an additional 3,250 hectares.

Thus, the current documented forest clearing of 2,126 hectares may soon double or triple.

Finally, it is worth mentioning that we detected a sawmill within the project area. This discovery raises the question, Has the company obtained the necessary permits for this activity?

Image 9f. A sawmill detected within the cacao project area. Inset: The pink dot indicates location of sawmill within the project area. Data: WorldView-3 de Digital Globe (NextView).
Image 9f. A sawmill detected within the cacao project area. Inset: The pink dot indicates location of sawmill within the project area. Data: WorldView-3 de Digital Globe (NextView).

Citation

Finer M, Novoa S (2015) Demonstrating that Forest Clearing for Cacao in Tamshiyacu (Loreto, Peru) came from Primary Forest. MAAP: Image #8. Link: https://maaproject.org/2015/06/image-9-cacao-tamshiyacu/