MAAP Synthesis #2: Patterns and Drivers of Deforestation in the Peruvian Amazon

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

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

Our major findings include:

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

MAAP Synthesis #2: Patterns And Drivers Of Deforestation In The Peruvian Amazon

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

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

Our major findings include:

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

 


Deforestation Trends

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

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

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

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

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


Deforestation Patterns

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

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

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

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

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

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

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


Deforestation Drivers

Image 3. Data: MAAP, SERNANP.
Image 3. Data: MAAP, SERNANP.

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

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

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

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

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

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

 


Small/Medium-scale Agriculture

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

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

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

 


Large-scale Agriculture

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

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

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

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

 


Cattle Pasture

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

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

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

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

 


Gold Mining

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

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

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

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

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

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

 


Roads

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


Logging Roads

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

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

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

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

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

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


Coca

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


Hydroelectric Dams

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


Hydrocarbon (Oil & Gas)

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


Methodology

MAAP methodology has 4 major components:

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

Endnotes

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

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

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

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

Environmental Investigation Agency EIA (2015) Deforestation by Definition.

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

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

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

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

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

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

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

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

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


Citation

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

MAAP #48: Oil Palm Deforestation in The Central Peruvian Amazon

In MAAP #26, we presented a 2015 Deforestation Hotspots map for the Peruvian Amazon, which showed that the highest concentration of deforestation is located in the central Amazon region.

Here, we zoom in on one of these hotspots, located in the northern Huanuco region along its border with San Martin (see Inset E of Image 48a).*

Image 48a. Data: UMD/GLAD
Image 48a. Data: UMD/GLAD

We found that the main deforestation driver in this hotspot was the establishment of small- and medium-scale oil palm plantations.**

*Note that we analyzed the hotspots in Insets A-D in MAAP #26 and MAAP #37.

** We defined small-scale as less than 5 hectares, medium-scale as 5-50 hectares, and large-scale as greater than 50 hectares

MAAP #41: Confirming Large-Scale Oil Palm Deforestation in The Peruvian Amazon

In the previous MAAP #4, we documented the deforestation of 6,464 hectares (15,970 acres) between 2011 and 2015 associated with a large-scale oil palm project in the central Peruvian Amazon (Ucayali region) operated by the company Plantaciones de Pucallpa. In addition, we found that the majority of this deforestation occurred in primary forests,1 although there was also clearing of secondary vegetation.

In December 2015, the Native Community of Santa Clara de Uchunya presented an official complaint to the Roundtable on Sustainable Palm Oil (RSPO) against Plantaciones de Pucallpa, a member of the roundtable. An important component of the complaint centers on the deforestation described above, however the company has repeatedly denied causing it.

To better understand the deforestation in question, we compare three high-resolution satellite images: 1) July 2010, the most recent high-resolution, color image prior to the start of large-scale deforestation in May 2012; 2) June 2012, a black and white image from the time period when large-scale deforestation began; 3) September 2015, color image showing the established oil palm plantation.

Image 41a shows a base map of the project area in July 2010 (left panel), June 2012 (center panel), and September 2015 (right panel). We indicate areas of primary forest and secondary vegetation,2 recently deforested areas, and oil palm plantation. The images show that large-scale deforestation had begun by June 2012, and by 2015 there was a complete transformation of primary forest and secondary vegetation to large-scale oil palm plantationInsets A-F show the areas detailed in the zooms below. Click on images to enlarge.

Image 41a. Data: Digital Globe (Nextview), MAAP
Image 41a. Data: Digital Globe (Nextview), MAAP

[separator] Zoom A: Primary Forest

Images 41b-i show the zooms of the areas (Insets A – D) in which installation of the oil palm plantation replaced primary forest. The images show primary forest in July 2010 (left panel) and June 2012 (center panel) replaced by oil palm plantation in September 2015 (right panel). Note that in Inset D (Images 41h-i), recently cleared trees can seen as the large-scale deforestation was just starting at that time3.

Image 41b. Data: Digital Globe (Nextview)
Image 41b. Data: Digital Globe (Nextview)
Image 41c. Data: Digital Globe (Nextview)
Image 41c. Data: Digital Globe (Nextview)

Zoom B: Primary Forest

Image 41d. Data: Digital Globe (Nextview)
Image 41d. Data: Digital Globe (Nextview)
Image 41e. Data: Digital Globe (Nextview)
Image 41e. Data: Digital Globe (Nextview)

Zoom C: Primary Forest

Image 41f. Data: Digital Globe (Nextview)
Image 41f. Data: Digital Globe (Nextview)
Image 41g. Data: Digital Globe (Nextview)
Image 41g. Data: Digital Globe (Nextview)

Zoom D: Primary Forest

Image 41h. Data: Digital Globe (Nextview)
Image 41h. Data: Digital Globe (Nextview)
Image 41i. Data: Digital Globe (Nextview)
Image 41i. Data: Digital Globe (Nextview)

Zoom E: Secondary Vegetation

Images 41j-m show the zooms of the areas (Insets E – F) in which the oil palm plantation replaced secondary vegetation. The images show secondary vegetation in July 2010 (left panel) and June 2012 (center panel) replaced by oil palm plantation in September 2015 (right panel).

Image 41j. Data: Digital Globe (Nextview)
Image 41j. Data: Digital Globe (Nextview)
Image 41k. Data: Digital Globe (Nextview)
Image 41k. Data: Digital Globe (Nextview)

Zoom F: Secondary Vegetation

Image 41l. Data: Digital Globe (Nextview)
Image 41l. Data: Digital Globe (Nextview)
Image 41m. Data: Digital Globe (Nextview)
Image 41m. Data: Digital Globe (Nextview)

Notes

We define primary forest as an area that, from the first available Landsat image (in this case 1990), was characterized by a forest cover of closed and dense canopy. This definition is consistent with the official definition of the new Forest Law: “Forest with original vegetation characterized by the abundance of mature trees with superior or dominant species canopy, which has evolved naturally.”

2 Primary and secondary forest classifications come from the analysis published in MAAP #4

3 Analysis of additional satellite imagery reveals that the large-scale clearing started between May and June 2012.


Citation

Finer M, Cruz C, Novoa S (2016) Confirming Deforestation for Oil Palm by the company Plantations of Pucallpa. MAAP: 41


MAAP #32: Large-Scale vs. Small-Scale Deforestation in the Peruvian Amazon

In the previous MAAP #25 and MAAP #26, we illustrated deforestation hotspots in the Peruvian Amazon for the periods 2012-2014 and 2015*, respectively. Here in MAAP #32, we present a complementary analysis based on the size of deforestation events.

Graph 32a shows the comparative results of deforestation patterns between 2013 and 2015, indicating that:
Small-scale (< 5 hectares) accounted for the vast majority of deforestation events (70-80%) each year.
Medium-scale (5-50 hectares) accounted for approximately 20% of the deforestation events each year.
Large-scale (> 50 hectares) deforestation was variable. In 2013, the year with the most activity of new cacao and oil palm plantations, it accounted for 8% of the deforestation events. In 2015 it was only 1%.

In summary, small- and medium-scale deforestation events represent more than 90% of the total and a constant threat, while large-scale deforestation events represents a latent threat. As described below, large-scale projects can quickly cause massive deforestation events, and should therefore remain a high priority.

Graph 32a. Data: PNCB/MINAM, UMD/GLAD
Graph 32a. Data: PNCB/MINAM, UMD/GLAD

*We have increased our deforestation estimate for 2015 to 163,238 hectares (403,370 acres), the second highest on record (behind only 2014). This estimate is based on GLAD alerts, produced by University of Maryland, Google, and Global Forest Watch.


Base Map

Image 32a shows, in graphic form, the deforestation patterns described above for 2013 (left panel) and 2015 (right panel). Further below, we show zooms for three key zones in the north, central, and south, respectively.

Image 32a. Data: PNCB/MINAM, UMD/GLAD
Image 32a. Data: PNCB/MINAM, UMD/GLAD

Northern Peruvian Amazon

Image 32b shows a zoom of the northern Peruvian Amazon for 2013 (left panel) and 2015 (right panel). In general, there is a pattern of small-scale deforestation along the rivers of Loreto. Additionally, in 2013, there were large-scale deforestation events for a cacao project located to the southeast of the city of Iquitos (see MAAP #27 for more details) and for oil palm plantations along the border of Loreto and San Martin regions (see MAAP #16 for more details). In 2015, the expansion of deforestation continued in these areas, but at a medium-scale.

Image 32b. Data: PNCB/MINAM, UMD/GLAD
Image 32b. Data: PNCB/MINAM, UMD/GLAD

Central Peruvian Amazon

Image 32c shows a zoom of the central Peruvian Amazon for 2013 (left panel) and 2015 (right panel). In general, there is a concentration of small- and medium-scale deforestation between northwest Ucayali and southeast Huánuco. Additionally, in 2013, there is large-scale deforestation for two new oil palm plantations located northeast of the city of Pucallpa (see MAAP #4 for more details).

Image 32c. Data: PNCB/MINAM, UMD/GLAD
Image 32c. Data: PNCB/MINAM, UMD/GLAD

Southern Peruvian Amazon

Image 32d shows a zoom of the southern Peruvian Amazon for 2013 (left panel) and 2015 (right panel). In general, there is a pattern of small- and medium-scale deforestation along the Interoceanic highway in Madre de Dios. Additionally, there is the persistence of large-scale deforestation in southern Madre de Dios related to illegal gold mining (see MAAP #12 for more details).

Image 32d. Data: PNCB/MINAM, UMD/GLAD
Image 32d. Data: PNCB/MINAM, UMD/GLAD

Citation

Finer M, Novoa S (2016) Large-scale vs. Small-scale Deforestation in the Peruvian Amazon. MAAP: 32.


MAAP #32: Large-scale Vs. Small-scale Deforestation In The Peruvian Amazon

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Img1_Graph32 A Deforestation
Graph 32a. Data: PNCB/MINAM, UMD/GLAD

In the previous MAAP #25 and MAAP #26, we illustrated deforestation hotspots in the Peruvian Amazon for the periods 2012-2014 and 2015*, respectively. Here in MAAP #32, we present a complementary analysis based on the size of deforestation events.

Graph 32a shows the comparative results of deforestation patterns between 2013 and 2015, indicating that:

Small-scale (< 5 hectares / <12 acres) accounted for the vast majority of deforestation events (70-80%) each year.

Medium-scale (5-50 hectares / 12 – 120 acres) accounted for approximately 20% of the deforestation events each year.

Large-scale (> 50 hectares / < 120 acres) deforestation was variable. In 2013, the year with the most activity of new cacao and oil palm plantations, it accounted for 8% of the deforestation events. In 2015 it was only 1%.

In summary, small- and medium-scale deforestation events represent more than 90% of the total and a constant threat, while large-scale deforestation events represents a latent threat. As described below, large-scale projects can quickly cause massive deforestation events, and should therefore remain a high priority.

*We have increased our deforestation estimate for 2015 to 163,238 hectares (403,370 acres), the second highest on record (behind only 2014). This estimate is based on GLAD alerts, produced by University of Maryland, Google, and Global Forest Watch.

Base Map

Image 32a shows, in graphic form, the deforestation patterns described above for 2013 (left panel) and 2015 (right panel). Further below, we show zooms for three key zones in the north, central, and south, respectively.

Img2_BaseMap_Img_32A
Image 32a. Data: PNCB/MINAM, UMD/GLAD

Northern Peruvian Amazon

Image 32b shows a zoom of the northern Peruvian Amazon for 2013 (left panel) and 2015 (right panel). In general, there is a pattern of small-scale deforestation along the rivers of Loreto. Additionally, in 2013, there were large-scale deforestation events for a cacao project located to the southeast of the city of Iquitos (see MAAP #27 for more details) and for oil palm plantations along the border of Loreto and San Martin regions (see MAAP #16 for more details). In 2015, the expansion of deforestation continued in these areas, but at a medium-scale.

Img3_Northern_Peruvian_Amazon_Img32B
Image 32b. Data: PNCB/MINAM, UMD/GLAD

Central Peruvian Amazon

Image 32c shows a zoom of the central Peruvian Amazon for 2013 (left panel) and 2015 (right panel). In general, there is a concentration of small- and medium-scale deforestation between northwest Ucayali and southeast Huánuco. Additionally, in 2013, there is large-scale deforestation for two new oil palm plantations located northeast of the city of Pucallpa (see MAAP #4 for more details).

FtImg_Img4_Central_Peruvian_Amazon_Img32C
Image 32c. Data: PNCB/MINAM, UMD/GLAD

Southern Peruvian Amazon

Image 32d shows a zoom of the southern Peruvian Amazon for 2013 (left panel) and 2015 (right panel). In general, there is a pattern of small- and medium-scale deforestation along the Interoceanic highway in Madre de Dios. Additionally, there is the persistence of large-scale deforestation in southern Madre de Dios related to illegal gold mining (see MAAP #12 for more details).

Img5_Southern_Peruvian_Amazon_Img32D
Image 32d. Data: PNCB/MINAM, UMD/GLAD

Citation

Finer M, Novoa S (2016) Large-scale vs. Small-scale Deforestation in the Peruvian Amazon. MAAP: 32.

MAAP #25: Deforestation Hotspots in the Peruvian Amazon, 2012-2014

Deforestation continues to increase in the Peruvian Amazon. According to the latest information from the Peruvian Environment Ministry1, 2014 had the highest annual forest loss on record since 2000 (177,500 hectares, or 438,600 acres per year). 2013 and 2012 had the third and fourth highest annual forest loss totals, respectively (behind only 2009).

 

Source: PNCB/MINAM
Source: PNCB/MINAM

To better understand where this deforestation is concentrated, we conducted kernel density estimation. This type of analysis calculates the magnitude per unit area of a particular phenomenon (in this case, forest loss).

Image 25a shows the kernel density map for forest loss in the Peruvian Amazon between 2012 and 2014 and reveals that recent deforestation is concentrated in a number of “hotspots” in the departments of Loreto, San Martin, Ucyali, Huanuco, and Madre de Dios.

Insets A-D highlight four areas with high densities of forest loss described in previous MAAP articles. We are currently studying the other high density deforestation areas not included in the insets.

Image 25a. Kernel density map for forest loss in the Peruvian Amazon between 2012 and 2014. Data: PNCB/MINAM, Hansen/UMD/Google/USGS/NASA.
Image 25a. Kernel density map for forest loss in the Peruvian Amazon between 2012 and 2014. Data: PNCB/MINAM, Hansen/UMD/Google/USGS/NASA.

Inset A: Cacao in Loreto

Image 25b. Deforestation for cacao in northern Peru between December 2012 (left panel) and September 2013 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA
Image 25b. Deforestation for cacao in northern Peru between December 2012 (left panel) and September 2013 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA

Inset A (from Image 25a) indicates the deforestation of over 2,000 hectares (4,940 acres) on property owned by the company United Cacao (through its wholly owned Peruvian subsidiary, Cacao del Peru Norte) near the town of Tamshiyacu in the department of Loreto. MAAP #9 demonstrated that much of this deforestation took place at the expense of primary forest. Image 25b highlights this area, showing the forest loss between December 2012 (left panel) and September 2013 (center panel; the pinkish areas indicate recently cleared forests). The right panel shows the cumulative deforestation between 2012 and 2014. See MAAP #9 and MAAP #2 for more details.


Inset B: Oil Palm in Loreto/San Martin

Image 25c. Deforestation for oil palm in northern Peru between September 2011 (left panel) and September 2014 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA
Image 25c. Deforestation for oil palm in northern Peru between September 2011 (left panel) and September 2014 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA

Inset B (from Image 25a) indicates expanding deforestation within and around two large-scale oil palm plantations along the Loreto-San Martin border. Image 25c highlights this area, showing the forest loss between Setpember 2011 (left panel) and September 2014 (center panel). The right panel shows the cumulative deforestation between 2012 and 2014 (6,363 hectares, or 15,700 acres). See MAAP #16 for more details.


Inset C: Oil Palm in Ucayali

Image 25d. Deforestation for oil palm in central Peru between September 2011 (left panel) and September 2013 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA
Image 25d. Deforestation for oil palm in central Peru between September 2011 (left panel) and September 2013 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA

Inset C (from Image 25a) indicates the deforestation of 9,400 hectares (23,200 acres) of primary forest for two large-scale oil palm plantations in the department of Ucayali. Image 25d highlights this area, showing the forest loss between September 2011 (left panel) and September 2013 (center panel; the pinkish-black areas indicate recently cleared forests). The right panel shows the cumulative deforestation between 2012 and 2014. See MAAP #4 for more details.


Inset D: Gold Mining in Madre de Dios

Image 25e. Deforestation for gold mining in southern Peru between September 2011 (left panel) and September 2014 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA
Image 25e. Deforestation for gold mining in southern Peru between September 2011 (left panel) and September 2014 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA

Inset D (from Image 25a) indicates the extensive illegal gold mining deforestation in the buffer zone of Tambopata National Reserve in the department of Madre de Dios. Image 25e highlights this area, showing the forest loss between September 2011 (left panel) and September 2014 (center panel; the lighter areas indicate recently cleared forests). The right panel shows the cumulative deforestation between 2012 and 2014 (4,738 hectares, or 11,700 acres). See MAAP #1 for more details.

It is important to emphasize that in this case, extensive deforestation continued in 2015. See MAAP #12 and MAAP #24 for more details.


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: Quadratic

Cell Size in the map: 200 x 200 meters (4 hectares)

Everything else was left to the default setting.


References

1MINAGRI-SERFOR/MINAM-PNCB (2015) Compartiendo una visión para la prevención, control y sanción de la deforestación y tala ilegal.


Citation

Finer M, Snelgrove C, Novoa S (2015) Deforestation Hotspots in the Peruvian Amazon, 2012-2014. MAAP: 25.


MAAP #25: Deforestation Hotspots In The Peruvian Amazon, 2012-2014

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Forest Loss in the Peruvian Amazon
Source: PNCB/MINAM

Deforestation continues to increase in the Peruvian Amazon. According to the latest information from the Peruvian Environment Ministry1, 2014 had the highest annual forest loss on record since 2000 (177,500 hectares, or 438,600 acres per year). 2013 and 2012 had the third and fourth highest annual forest loss totals, respectively (behind only 2009).

 

 

 

 

 

 

 


 

Image 25a. Kernel density map for forest loss in the Peruvian Amazon between 2012 and 2014. Data: PNCB/MINAM, Hansen/UMD/Google/USGS/NASA.
Image 25a. Kernel density map for forest loss in the Peruvian Amazon between 2012 and 2014. Data: PNCB/MINAM, Hansen/UMD/Google/USGS/NASA.

To better understand where this deforestation is concentrated, we conducted kernel density estimation. This type of analysis calculates the magnitude per unit area of a particular phenomenon (in this case, forest loss).

Image 25a shows the kernel density map for forest loss in the Peruvian Amazon between 2012 and 2014 and reveals that recent deforestation is concentrated in a number of “hotspots” in the departments of Loreto, San Martin, Ucyali, Huanuco, and Madre de Dios.

Insets A-D highlight four areas with high densities of forest loss described in previous MAAP articles. We are currently studying the other high density deforestation areas not included in the insets.


 

Inset A: Cacao in Loreto

Image 25b. Deforestation for cacao in northern Peru between December 2012 (left panel) and September 2013 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA
Image 25b. Deforestation for cacao in northern Peru between December 2012 (left panel) and September 2013 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA

Inset A (from Image 25a) indicates the deforestation of over 2,000 hectares (4,940 acres) on property owned by the company United Cacao (through its wholly owned Peruvian subsidiary, Cacao del Peru Norte) near the town of Tamshiyacu in the department of Loreto. MAAP #9 demonstrated that much of this deforestation took place at the expense of primary forest. Image 25b highlights this area, showing the forest loss between December 2012 (left panel) and September 2013 (center panel; the pinkish areas indicate recently cleared forests). The right panel shows the cumulative deforestation between 2012 and 2014. See MAAP #9 and MAAP #2 for more details.

 


Inset B: Oil Palm in Loreto/San Martin

Image 25c. Deforestation for oil palm in northern Peru between September 2011 (left panel) and September 2014 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA
Image 25c. Deforestation for oil palm in northern Peru between September 2011 (left panel) and September 2014 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA

Inset B (from Image 25a) indicates expanding deforestation within and around two large-scale oil palm plantations along the Loreto-San Martin border. Image 25c highlights this area, showing the forest loss between Setpember 2011 (left panel) and September 2014 (center panel). The right panel shows the cumulative deforestation between 2012 and 2014 (6,363 hectares, or 15,700 acres). See MAAP #16 for more details.

 


Inset C: Oil Palm in Ucayali

Image 25d. Deforestation for oil palm in central Peru between September 2011 (left panel) and September 2013 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA
Image 25d. Deforestation for oil palm in central Peru between September 2011 (left panel) and September 2013 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA

Inset C (from Image 25a) indicates the deforestation of 9,400 hectares (23,200 acres) of primary forest for two large-scale oil palm plantations in the department of Ucayali. Image 25d highlights this area, showing the forest loss between September 2011 (left panel) and September 2013 (center panel; the pinkish-black areas indicate recently cleared forests). The right panel shows the cumulative deforestation between 2012 and 2014. See MAAP #4 for more details.

 


Inset D: Gold Mining in Madre de Dios

Image 25e. Deforestation for gold mining in southern Peru between September 2011 (left panel) and September 2014 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA
Image 25e. Deforestation for gold mining in southern Peru between September 2011 (left panel) and September 2014 (center panel) and cumulative 2012-14 (right panel). Data: USGS, PNCB/MINAM, Hansen/UMD/Google/USGS/NASA

Inset D (from Image 25a) indicates the extensive illegal gold mining deforestation in the buffer zone of Tambopata National Reserve in the department of Madre de Dios. Image 25e highlights this area, showing the forest loss between September 2011 (left panel) and September 2014 (center panel; the lighter areas indicate recently cleared forests). The right panel shows the cumulative deforestation between 2012 and 2014 (4,738 hectares, or 11,700 acres). See MAAP #1 for more details.

It is important to emphasize that in this case, extensive deforestation continued in 2015. See MAAP #12 and MAAP #24 for more details.


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: Quadratic

Cell Size in the map: 200 x 200 meters (4 hectares / 9.88 acres)

Everything else was left to the default setting.


References

1MINAGRI-SERFOR/MINAM-PNCB (2015) Compartiendo una visión para la prevención, control y sanción de la deforestación y tala ilegal.

 


Citation

Finer M, Snelgrove C, Novoa S (2015) Deforestation Hotspots in the Peruvian Amazon, 2012-2014. MAAP: 25.

MAAP #16: Oil Palm-Driven Deforestation in The Peruvian Amazon (Part 2: Shanusi)

In MAAP #4 we described the major deforestation caused by two new large-scale oil palm projects in the central Peruvian Amazon (Nueva Requena, Ucayali region).

Here in MAAP #16, we describe the major deforestation related to two other oil palm projects, Palmas del Shanusi and Palmas del Oriente, in the northern Peruvian Amazon (regions Loreto and San Martin). These projects (operated by Grupo Palmas, an agriculture company owned by Grupo Romero) cover 10,029 hectares.

Image 16a. Deforestation within and around the two large-scale oil palm projects Palmas del Shanusi and Oriente. Data: PNCB, USGS, Grupo Palmas.
Image 16a. Deforestation within and around the two large-scale oil palm projects Palmas del Shanusi and Oriente. Data: PNCB, USGS, Grupo Palmas.

Image 16a shows the extensive forest clearing within and around Palmas del Shanusi and Oriente. The 2000-2014 forest loss data comes from the Peruvian government (PNCB-MINAM/SERFOR-MINAGRI) and the 2015 data comes from our analysis of Landsat imagery using CLASlite forest monitoring software.

Within the two projects, we documented that Grupo Palmas cleared 6,974 hectares of primary forest between 2006 and 2011 (see Images 16a and 16b). This represents 70% of the projects’ area (Peruvian law requires the conservation of 30% of an agricultural project area’s forest cover). Thus, a key issue is that the Peruvian legal framework, under certain conditions, allows the clearing of thousands of hectares of primary forest for large-scale agriculuture projects (see the report Deforestation by Definition by the Environmental Investigation Agency for more details).

We defined primary forest as an area characterized by dense, closed-canopy coverage from the earliest available Landsat image (in this case 1994) until immediately prior to plantation installation.

Importantly, we also documented the clearing of an additional 9,840 hectares of primary forest immediately surrounding the projects (see Images 16a and 16b). There was clearing of more than a thousand hectares each year between 2010 and 2013, followed by another thousand hectares between 2014 and 2015. Analysis of high-resolution imagery confirms that much of this additional clearing resulted in large-scale model oil palm plantations.

In total, we documented the clearing of over 16,800 hectares of primary forest for large-scale oil palm plantations within and around Palmas del Shanusi and Oriente. It is important to note that there has now been more forest clearing outside than inside the original projects, an important lesson for other new agricultural areas such as Tamshiyacu.

Image 16b. Primary forest cleared within and around Grupo Palmas projects.
Image 16b. Primary forest cleared within and around Grupo Palmas projects.

High Resolution Zooms

Following is a series of high resolution zooms showing examples of forest clearing within and around Palmas del Shanusi and Oriente. Image 16c is the reference map indicating the location of the various zooms (Images 16d – 16g). Zooms 16d and 16e show the same area before (left panel) and after (right panel) forest clearing. Zooms 16f and 16g show areas of recent forest clearing.

Image 16c. Reference Map. Data: USGS.
Image 16c. Reference Map. Data: USGS.
Image 16d. High-resolution zoom A; deforestation outside the Grupo Palmas project. Data: Google Earth, WorldView-2 from Digital Globe (NextView).
Image 16d. High-resolution zoom A; deforestation outside the Grupo Palmas project. Data: Google Earth, WorldView-2 from Digital Globe (NextView).
Image 16e. High-resolution zoom B; forest clearing within the Grupo Palmas project. Data: Google Earth, WorldView-2 from Digital Globe (NextView).
Image 16e. High-resolution zoom B; forest clearing within the Grupo Palmas project. Data: Google Earth, WorldView-2 from Digital Globe (NextView).
Image 16f. High-resolution zoom C. Data: Google Earth, WorldView-2 from Digital Globe (NextView).
Image 16f. High-resolution zoom C. Data: Google Earth, WorldView-2 from Digital Globe (NextView).
Image 16g. High-resolution zoom D. Data: Google Earth, WorldView-2 from Digital Globe (NextView).
Image 16g. High-resolution zoom D. Data: Google Earth, WorldView-2 from Digital Globe (NextView).

References

This work builds off of information presented in the following publication: Environmental Investigation Agency. Deforestation by Definition. 2015. Washington, DC. Link: http://eia-global.org/news-media/deforestation-by-definition


Citation

Finer M, Novoa S (2015) Oil Palm-driven Deforestation in the Peruvian Amazon (Part 2: Shanusi) MAAP: Image #16. Link: https://maaproject.org/2015/10/image16-shanusi/

MAAP Synthesis #1: Patterns and Drivers of Deforestation in the Peruvian Amazon

We present a preliminary analysis of current patterns and drivers of deforestation in the Peruvian Amazon. This analysis is largely based on the first 15 articles published on MAAP between April and September 2015, but also incorporates information from other relevant sources. We describe this analysis as preliminary because as MAAP research continues, we will be able to improve and refine our synthesis in subsequent editions.

Image S1a. Recent patterns and drivers of deforestation in the Peruvian Amazon. Numbers indicate relevant MAAP article. Data: SERNANP, IBC, MINAM-PNCB/MINAGRI-SERFOR, MAAP.
Image S1a. Recent patterns and drivers of deforestation in the Peruvian Amazon. Numbers indicate relevant MAAP article. Data: SERNANP, IBC, MINAM-PNCB/MINAGRI-SERFOR, MAAP.

Introduction & Summary of Key Results

Image S1a illustrates recent (2000 – 2013) patterns of deforestation in the Peruvian Amazon based on data from the Peruvian Ministries of Environment[i] and Agriculture[ii]. These two Ministries have documented a total forest loss of around 1.65 million hectares (ha) in the Peruvian Amazon between 2001 and 2014, with an increasing trend in recent years (2014 had the highest forest loss on record with 177,571 ha)[iii],[iv]. Another recent report by the Peruvian government stated that the majority (75%) of the Amazonian deforestation is due to small-scale clearings related to agriculture and livestock activities, usually near roads or rivers[v].

Building off of that historical and annual information, our goal at MAAP is to monitor deforestation in near real-time. Since April 2015, we have published numerous articles analyzing areas in the northern, central, and southern Peruvian Amazon. In this initial analysis, we have found that three of the most important drivers of deforestation are large-scale oil palm (and cacao) plantations, gold mining, and coca cultivation. We also found a growing network of logging roads that contribute to forest degradation. Image S1a displays the general geographic distribution of these drivers of deforestation and degradation.

We estimate that around 30,000 hectares of primary forest was cleared since 2000 for large-scale oil palm and cacao plantations. Cacao has recently joined oil palm as a deforestation driver due to the arrival of the company United Cacao and their implementation of the large-scale agro-industrial model in place of traditional small-scale plantations on previously degraded lands.

Gold mining has directly caused the deforestation of over 43,000 ha since 2000, mostly in the region of Madre de Dios. In recent years, this deforestation has been concentrated in the Tambopata National Reserve buffer zone.

Although coca cultivation is reportedly declining in Peru, we found that it remains a major driver of deforestation, particularly within and around remote protected areas. For example, we documented 143 ha of coca related deforestation within the Sierra del Divisor Reserved Zone, and an additional 2,638 ha related to shifting agricultural cultivation, which includes coca, within and around Bahuaja Sonene National Park.

We also documented a recent expansion of logging roads in the central Peruvian Amazon. This finding is significant because it is difficult to detect selective logging in satellite imagery, but now we can at least detect the roads that indicate that selective logging is taking place in a given area.

We identified some important geographic patterns related to the four drivers described above. For example, large-scale oil palm (and cacao) are concentrated in the northern Peruvian Amazon, while gold mining deforestation has largely been in the south. Coca-driven deforestation appears to be particularly problematic in the southern Peruvian Amazon, but also exists in the north. The construction of new logging roads is currently most active in the central Peruvian Amazon.

The documented deforestation is caused by both illegal and legal means. For the former, there is extensive deforestation from illegal gold mining and coca cultivation. Regarding the latter, oil palm and cacao companies are exploiting loopholes in the Peruvian legal framework that facilitate large-scale deforestation for agricultural projects.


Large-scale Agriculture (Oil Palm and Cacao)

Image S1b. Large-scale agriculture deforestation in the northern Peruvian Amazon. Numbers indicate relevant MAAP article. Data: SERNANP, IBC, MINAM-PNCB/MINAGRI-SERFOR, MAAP.
Image S1b. Large-scale agriculture deforestation in the northern Peruvian Amazon. Numbers indicate relevant MAAP article. Data: SERNANP, IBC, MINAM-PNCB/MINAGRI-SERFOR, MAAP.

Image S1b illustrates that large-scale agriculture (namely oil palm and cacao) is an important cause of deforestation in northern Peru.

Importantly, several oil palm and cacao companies are changing the production model in Peru from small-scale to large-scale agro-industrial. For example, in a recent interview, United Cacao CEO Dennis Melka stated that his company is trying to replicate the agro-industrial model used by oil palm companies in Southeast Asia[vi].

This shift is noteworthy because large-scale plantations usually come at the expense of forests, while small-scale plantations are better able to take advantage of previously cleared lands[vii]. We estimate that over 30,000 hectares of primary forest was cleared since 2000 for large-scale oil palm and cacao plantations (see below). Much less primary forest, around 575 ha, was cleared for small-scale oil palm (we have yet to evaluate small-scale cacao).

Note that we emphasize the clearing of primary forest. We conducted an additional analysis to determine whether oil palm (both small and large-scale) and cacao (just large-scale) plantations were originally sited on lands with primary forest, secondary forest, or already deforested. We defined primary forest as an area that from the earliest available Landsat, in this case 1990, was characterized by dense closed canopy forest cover.

The following is a concise breakdown of how we calculated the 30,000 ha of primary forest loss from large-scale plantations.

MAAP articles #2, #9, and #13 demonstrated that 2,276 ha of primary forest was cleared by United Cacao between May 2013 and September 2015 outside of the town of Tamshiyacu in the northern Peruvian Amazon (Loreto region).

MAAP article #4 detailed the deforestation of 9,400 ha of primary forest (plus an additional 2,350 ha of secondary forest) between 2011 and 2015 for two large-scale oil palm projects near the town of Nueva Requena in the central Peruvian Amazon (Department of Ucayali).

In addition, yet unpublished MAAP analysis shows that in Palmas de Shanusi/Oriente (oil palm projects operated by the company Grupo Palmas), 6,974 ha of primary forest were cleared between 2006 and 2011, although the legally mandated 30% forest cover reserves were maintained. An additional 8,225 ha of primary forest was cleared in areas immediately surrounding the concessions.

Finally, although not yet published on MAAP, we also documented nearly 3,500 ha of primary forest loss in other large-scale oil palm projects in San Martin and Ucayali regions.

It is important to emphasize that several oil palm and cacao companies are exploiting various loopholes in the Peruvian legal framework that facilitate large-scale deforestation for agricultural projects[viii]. In fact, these companies argue that according to Peruvian law, they are engaged in legal “forest clearing”, not illegal “deforestation”[ix].


Gold Mining

Image S1c. Gold mining deforestation in the Peruvian Amazon. Numbers indicate relevant MAAP article. Data: SERNANP, IBC, MINAM-PNCB/MINAGRI-SERFOR, MAAP.
Image S1c. Gold mining deforestation in the Peruvian Amazon. Numbers indicate relevant MAAP article. Data: SERNANP, IBC, MINAM-PNCB/MINAGRI-SERFOR, MAAP.

Image S1c illustrates that gold mining-driven deforestation is largely concentrated in the southern Peruvian Amazon, particularly in the region of Madre de Dios and adjacent Cusco.

According to the scientific literature, gold mining deforestation in Madre de Dios increased from 10,000 ha in 2000 to 50,000 ha in 2012[x]. MAAP articles #1, #5, and #12 documented the deforestation of an additional 2,774 ha between 2013 and 2015 in two gold mining hotspots (La Pampa and Upper Malinowski), both of which are located within the buffer zone of the Tambopata National Reserve. In addition, MAAP #6 showed gold mining deforestation expanding from another Madre de Dios gold mining hotspot (Huepetuhe) into the tip of Amarakaeri Communal Reserve (11 ha).

Much of the Madre de Dios gold mining deforestation described above is illegal because it is occurring within and around protected areas where mining is not permitted under the government-led formalization process.

MAAP articles #6 and #14 detailed recent gold mining deforestation in the region of Cusco. Specifically, we documented the deforestation of 967 ha along the Nuciniscato River and its major tributaries since 2000 (with the vast majority occurring since 2010). Much of this deforestation appears to be linked to gold mining.

Thus, the total documented gold mining deforestation in Madre de Dios and adjacent Cusco is at least 53,750 ha[xi], over 80% of which has occurred since 2000. This total is an underestimate since we have not yet done detailed studies for 2013 – 2015 deforestation in all of the known gold mining zones in these two regions.

In addition, MAAP #7 showed two gold mining zones in the region of Ucayali (along the Sheshea and Abujao Rivers, respectively). Much of this deforestation occurred between 2000 and 2012.

Finally, there are also reports of extensive gold mining in northern Peru (the regions of Amazonas and Loreto) but we do not yet have data showing that it is causing deforestation.


Coca

Image S1d. Coca cultivation areas in the Peruvian Amazon. Numbers indicate relevant MAAP article. Data: UNODC 2014, MINAM-PNCB/MINAGRI-SERFOR, SERNANP, NatureServe.
Image S1d. Coca cultivation areas in the Peruvian Amazon. Numbers indicate relevant MAAP article. Data: UNODC 2014, MINAM-PNCB/MINAGRI-SERFOR, SERNANP, NatureServe.

Although the most recent report from the United Nations Office on Drugs and Crime (UNODC) indicates that overall coca cultivation is declining in Peru[xii], our research finds that it remains a major driver of deforestation in certain areas, particularly within and around several remote protected areas.

Image S1d displays the distribution of current coca-cultivation areas (in relation to protected areas) based on the data from the latest United Nations report. Of these areas, we have thus far focused on the three detailed below.

MAAP articles #7 and #8 show recent coca-related deforestation within the southern section of the Sierra del Divisor Reserved Zone. This area is particularly important because it is soon slated to be upgraded to a national park. Specifically, we documented coca-related deforestation of 130 ha between 2013 and 2014 within the southwestern section of the reserve, and, most recently, a new plantation of 13 ha during June 2015 within the southeast section.

MAAP article #10 revealed that shifting agricultural cultivation, that includes coca, is also a major issue within and around Bahuaja Sonene National Park, located in the southern Peruvian Amazon. Specifically, we found the recent deforestation of 538 hectares within the southern section of the Park, and an additional 2,100 hectares in the surrounding buffer zone. Much of this deforestation is likely linked to coca cultivation since the latest United Nations report indicates these areas contain high coca plantation densities.

MAAP article #14 documents the deforestation of 477 ha along the Nojonunta River in Cusco since 2000 (with a major peak since 2010). Much of this deforestation is likely linked to coca cultivation since the latest United Nations report indicates these areas contain medium to high coca plantation densities. 


Logging Roads

Image S1e. Logging roads in the Peruvian Amazon. Numbers indicate relevant MAAP article. Data: SERNANP, IBC, MINAM-PNCB/MINAGRI-SERFOR, MINAGRI, MAAP.
Image S1e. Logging roads in the Peruvian Amazon. Numbers indicate relevant MAAP article. Data: SERNANP, IBC, MINAM-PNCB/MINAGRI-SERFOR, MINAGRI, MAAP.

One of the major advances discovered in this work is the ability to identify the expansion of new logging roads. This advance is important because it is extremely difficult to detect illegal logging in satellite imagery because loggers in the Amazon often selectively cut high value species and do not produce large clearings. But now, although it remains difficult to detect the actual selective logging, we can detect the roads that indicate that selective logging is taking place in that area.

Image S1e illustrates the likely logging roads that we have recently detected. Of these areas, we have thus far focused on the two detailed below.

MAAP article #3 shows the rapid proliferation of two new road networks in the northern Peruvian Amazon (Loreto region). Most notably, it highlights the construction of 148 km of new roads, possibly illegal logging roads, through mostly primary forest between 2013 and 2014. One of the roads is within the buffer zone of the Cordillera Azul National Park.

In addition, MAAP article #7 shows the expansion of new logging roads near both the southern and northwestern sections of the Sierra del Divisor Reserved Zone. In both cases, the expansion is very recent (between 2013 and 2015).

 

[i] National Program of Forest Conservation for the Mitigation of Climate Change – PNCB.

[ii] Servicio Nacional Forestal y de Fauna Silvestre – SERFOR

[iii] MINAGRI-SERFOR/MINAM-PNCB (2015) Compartiendo una visión para la prevención, control y sanción de la deforestación y tala ilegal.

[iv] Note that some of the documented forest loss may come from natural causes, such as landslides or meandering rivers.

[v] MINAM (2013) Fondo Cooperativo Para El Carbono de los Bosques (FCPF) Plantilla de Propuesta para la Fase de Preparación para REDD+ (Readiness Plan Proposal – RPP). Link: http://www.minam.gob.pe/cambioclimatico/wp-content/uploads/sites/11/2014/03/R-PP-Per%C3%BA-Final-Dec-2013-RESALTADO_FINAL_PUBLICADA-FCPF_24-febrero.pdf

[vi] NF Joan (2015) United Cacao replicates Southeast Asia’s plantation model in Peru, says CEO Melka. The Edge Singapore.Link: http://www.unitedcacao.com/images/media-articles/20150713-the-edge-united-cacao.pdf

[vii] Gutiérrez-Vélez VH, DeFries R, Pinedo-Vásquez M, et al. (2011) High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon. Environ. Res. Lett., 6, 044029. Link: http://iopscience.iop.org/article/10.1088/1748-9326/6/4/044029/pdf

[viii] Environmental Investigation Agency (2015) Deforestation by Definition. Washington, DC. Link: http://eia-global.org/news-media/deforestation-by-definition

[ix] Tello Pereyra R (2015) Situacion legal, judicial, y administrativa de  Cacao del Peru Norte SAC. Link: https://www.youtube.com/watch?v=p_YIe70u1oA

[x] Asner GP, Llactayo W, Tupayachia R, Ráez Luna E (2013) PNAS 110 (46) 18454-18459. Link: http://www.pnas.org/content/110/46/18454.abstract

[xi] That is, 50,000 ha from the literature and 3,750 ha from MAAP analysis.

[xii] UNODC (2015) Monitoreo de cultivos ilícitos Perú 2014. Link: https://www.unodc.org/documents/crop-monitoring/Peru/Peru_Informe_monitoreo_coca_2014_web.pdf


Citation

Finer M, Novoa S (2015) Patterns and Drivers of Deforestation in the Peruvian Amazon. MAAP Synthesis #1. Link: https://maaproject.org/2015/09/maap-synthesis1/