MAAP #77: Deforestation Hotspots in the Colombian Amazon, part 2

We present the second in a series of story maps investigating deforestation hotspots in the Colombian Amazon. Our goal is to identify the most critical hotspots (areas with the highest densities of deforestation) and use satellite imagery to identify the primary deforestation drivers.

The first report focused on a hotspot approaching Chiribiquete National Park in Caquetá department, and the  deforestation was largely driven by cattle pasture.

Here, we move to the south and focus on a hotspot surrounding La Paya National Park in Putumayo Department. We show high-resolution satellite imagery that reveals the major driver is again cattle pasture.

Please follow this link to view the Story Map: Deforestation Hotspots in the Colombian Amazon, part 2

 

This work reflects an important collaboration with our colleagues at Amazon Conservation Team, funded by the MacArthur Foundation.

MAAP Interactive: Deforestation Drivers in the Andean Amazon

Since its launch in April 2015, MAAP has published over 70 reports related to deforestation (and natural forest loss) in the Andean Amazon. We have thus far focused on Peru, with several reports in Colombia and Brazil as well.

These reports are meant to be case studies of the most important and urgent deforestation events. We often use forest loss alerts (known as GLAD) to guide us, and satellite imagery (from Planet and DigitalGlobe) to identify the deforestation driver.

Here we present an interactive map highlighting the drivers identified in all published MAAP reports. These drivers include gold mining, agriculture (e.g. oil palm and cacao), cattle pasture, roads, and dams (see icon legend below map). We also include natural causes such as floods and blowdowns (fire included under agriculture since most human caused). Furthermore, we highlight deforestation events within protected areas. Note that you can filter by driver by checking boxes of interest.

We hope the result is one of the most detailed and up-todate resources on patterns and drivers of deforestation in the Andean Amazon. Over the coming year we will continue to focus on Peru and Colombia, and begin to include Ecuador and Bolivia as well.

To view the interactive map, please visit:

MAAP Interactive: Deforestation Drivers in the Andean Amazon
https://www.maaproject.org/interactive/

For more information on patterns and drivers of deforestation in the Peruvian Amazon, see our latest Synthesis report 

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.

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.

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.

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. Click to enlarge.

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).
  1. 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
    (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).
  2. 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 #45: Threats to El Sira Communal Reserve in central Peruvian Amazon

esira_recovery_o_v2_en
Image 45a. Data: ESRI, SERNANP

El Sira Communal Reserve, located in the central Peruvian Amazon (regions of Pasco, Huánuco and Ucayali), aims to protect the biological diversity of the El Sira Mountain Range in benefit of the native communities of the area (Ashaninka, Yanesha, and Shipibo-Conibo indigenous groups).

This report presents an initial threat assessment for this large national protected area, which covers more than 615,000 hectares (1.5 million acres).

We identified 3 threatened sectors of the Reserve, as indicated in Image 45a (see Insets A-C).

We found that the principal drivers of deforestation in these three sectors are agriculture & cattle pasture (Insets A and C) and illegal gold mining (Inset B).

It is important to note that the deforestation for agriculture & cattle pasture continues to rapidly increase – 1,600 hectares (3,950 acres) since 2013 – while the deforestation for gold mining has been limited due to regular interventions by the Peruvian government.

Below, we show high-resolution satellite images of the recent deforestation in all three threatened sectors. Click each image to enlarge.

 

 

 

 

 

 

 

Inset A: Increasing Deforestation in the Northern Sector

esira_recovery_a_v3_en
Image 45b. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, USGS/NASA, SERNANP

Image 45b illustrates the increasing deforestation in the northern sector of the El Sira Communal Reserve.

We documented the deforestation of 285 hectares (700 acres) within the Reserve thus far in 2016 (as of late August). Over 90% of this loss comes from small-scale deforestation events (less than 5 hectares).

We calculated an additional deforestation of 1,320 hectares (3,260 acres) within the Reserve between 2013 and 2015.

Thus, we documented a total deforestation of 1,600 hectares (3,950 acres) within the northern sector of the Reserve since 2013.

Based on the analysis of high-resolution imagery, we found that the principal driver of this deforestation was agriculture & cattle pasture.

Note that this sector is near the deforestation hotspot described in MAAP #37, where we determined that cattle pasture was the principal driver of deforestation.

Insets A1 – A3 indicate the location of the high-resolution zooms described below.

 

 

 

Images 45c-45d show examples of deforestation between September 2015 (left panel) and August/September 2016 (right panel). The red circles indicate newly deforested areas in 2016. The yellow circles indicate areas deforested in 2015 and subsequently converted to cattle pasture in 2016.

esira_recovery_a1_m_v1_en
Image 45c. Data: Digital Globe (Next View), Planet
esira_recovery_a2_m_v1_en
Image 45d. Data: Digital Globe (Next View), Planet


Image 45e shows examples of deforestation between September 2015 (left panel) and August/September 2016 (right panel). The yellow circles indicate areas deforested in 2015 and subsequently converted to cattle pasture in 2016. The blue circles indicate recently burned areas (note the smoke in the right) panel. This type of annual burning pattern is characteristic of cattle-grazing areas.

esira_recovery_a3_m_v1_en
Image 45e. Data: Digital Globe (Next View), Planet

Inset B: Illegal Gold Mining Activity

esira_recovery_b_v3_en
Image 45f. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, USGS/NASA, SERNANP

Illegal gold mining currently threatens the upper Negro River, located in the northwest sector of the Reserve (see Inset B of Image 45a).

During 2015 and 2016, the Peruvian government has carried out several interventions against this illegal mining.

Image 45f shows the recent deforestation along the upper Negro River. Insets B1-B3 indicate the areas detailed below in high resolution.

 

 

 

 

 

 

 

 

Images 45g-h show recently deforested areas (indicated by yellow circles) between 2015 (left panel) and 2016 (right panel). These areas have been the target of recent government interventions; note that within the red circles the mining machinery has been eliminated between August and September 2016.

esira_recovery_b1_a_m_v1_en
Image 45g. Data: Digital Globe (NextView)
esira_recovery_b2_a_m_v1_en
Image 45h. Data: Digital Globe (NextView)


Image 45i shows a mining area abandoned between 2015 and 2016.

esira_recovery_b3_a_m_v1_en
Image 45i. Data: Digital Globe (NextView)

New Deforestation Zones

Image 45j shows the recent deforestation of 8.6 hectares (21 acres) between August (left panel) and September (right panel) 2016, within the area indicated by Inset B4 in Image 45f.

esira_recovery_b4_m_v1_en
Image 45j. Data: Digital Globe (Nextview)

Image 45k shows the recent deforestation of 12 hectares (30 acres) within a remote area located in the northeast sector of the Reserve (see Inset C in Image 45a for context).

esira_recovery_c_m_v1_en
Image 45j. Data: Planet

Citation

Novoa S, Finer M, Snelgrove C (2016) Threats to Peru’s El Sira Communal Reserve. MAAP: 45

MAAP #40: Early Warning Deforestation Alerts in the Peruvian Amazon

GLAD alerts are a powerful new tool to monitor forest loss in the Peruvian Amazon in near real-time. This early warning system, created by the GLAD (Global Land Analysis and Discovery) laboratory at the University of Maryland and supported by Global Forest Watch, was launched in March 2016 as the first Landsat-based (30-meter resolution) forest loss alert system (previous systems were based on lower-resolution imagery). The alerts are updated weekly and can be accessed through Global Forest Watch (Image 40a, left panel) or GeoBosques (Image 40a, right panel), a web portal operated by the Peruvian Ministry of Environment.

Imagen 41a. Datos: UMD/GLAD, WRI/GFW, PNCB/MINAM
Image 40a. Data: UMD/GLAD, WRI/GFW, PNCB/MINAM

In MAAP, we often combine these alerts with analysis of high-resolution satellite imagery (courtesy of the Planet Ambassador Program and Digital Globe NextView service) to better understand patterns and drivers of deforestation in near real-time. In this article, we highlight 3 examples of this type of innovative analysis from across the Peruvian Amazon:

Example 1: Logging Roads in central Peru (Ucayali)
Example 2: Invasion of Ecotourism Concessions in southern Peru (Madre de Dios)
Example 3: Buffer Zone of Cordillera Azul National Park (Loreto)

Example 1: Logging Roads in central Peru (Ucayali)

In the previous MAAP #18, we documented the proliferation of logging roads in the central Peruvian Amazon during 2015. In recent weeks, we have seen the start of rapid new logging road construction for 2016. Image 40b shows the linear forest loss associated with two new logging roads along the Tamaya river in the remote central Peruvian Amazon (Ucayali region). Red indicates the 2016 road construction (35.8 km). Insets A and B indicate the areas shown in the high-resolution zooms below.

Image 40b. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, MINAGRI
Image 40b. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, MINAGRI

The following images show, in high-resolution, the rapid construction of logging roads in 2016. Image 40c shows the construction of 16.1 km between March (left panel) and July (right panel) 2016 in the area indicated by Inset A. Image 40d shows the construction of 19.7 km between June (left panel) and July (right panel) 2016 in the area indicated by Inset B.

Image 40c. Data: Planet
Image 40c. Data: Planet
Image 40d. Data: Planet
Image 40d. Data: Planet

Example 2: Invasion of Ecotourism Concessions in southern Peru (Madre de Dios)

Image 40e shows the recent deforestation within two ecotourism concessions along the Las Piedras River in the Madre de Dios region. Red indicates the 2016 GLAD alerts (67.3 hectares). Note that the Las Piedras Amazon Center (LPAC) Ecotourism Concession represents an effective barrier against deforestation occurring in the surrounding concessions. According to local sources, the main drivers of deforestation in the area are related to the establishment of cacao plantations and cattle pasture (see s MAAP #23). Inset A indicates the areas shown in the high-resolution zoom below.

Image 40e. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, MINAGRI
Image 40e. Data: UMD/GLAD, Hansen/UMD/Google/USGS/NASA, MINAGRI

Image 40f shows high-resolution images of the area indicated by Inset A between April (left panel) and July (right panel) 2016. The yellow circles indicate areas of deforestation between these dates.

Image 40f. Data: Planet, DigitalGlobe (Nextview)
Image 40f. Data: Planet, DigitalGlobe (Nextview)

Example 3: Buffer Zone of Cordillera Azul National Park (Loreto)

Image 40g shows the recent deforestation within the western buffer zone of the Cordillera Azul National Park in the Loreto region. Red indicates the 2016 GLAD alerts (87.3 hectares). It is worth noting that this area is classified as Permanent Production Forest, not as an agricultural area.

Image 40g. Data: SERNANP, Landsat, UMD/GLAD, Hansen/UMD/Google/USGS/NASA
Image 40g. Data: SERNANP, Landsat, UMD/GLAD, Hansen/UMD/Google/USGS/NASA

Image 40h shows high-resolution images of the area indicated by Inset A between December 2015 (left panel), January 2016 (central panel), and July 2016 (right panel). The yellow circles indicate areas that were deforested between these dates. The driver of the deforestation appears to be the establishment of small-scale agricultural plantations.

Image 40h. Data: RapidEye/Planet, Digital Globe (Nextview)
Image 40h. Data: RapidEye/Planet, Digital Globe (Nextview)

Citation

Finer M, Novoa S, Goldthwait E (2016) Early Alerts of Deforestation in the Peruvian Amazon. MAAP: 40.

MAAP #37: Deforestation Hotspot in the central Peruvian Amazon driven by Cattle Pasture

Image 36a. Data: UMD/GLAD
Image 37a. Data: UMD/GLAD

In the previous MAAP #26, we presented a map of Deforestation Hotspots in the Peruvian Amazon during 2015*. This analysis showed that the highest concentration of deforestation is in the central Peruvian Amazon.

Here in MAAP #37, we focus on this region, as indicated by Image 37a. Specifically, we analyze the hotspots shown in Insets C and D, located in the eastern section of the department of Huanuco.

(Note that we previously described the hotspots indicated by Insets A and B, located in northwest Ucayali department, in MAAP #26).

For 2015, we calculated a total deforestation of 7,930 hectares (19,595 acres) in the area indicated by these two insets. The main deforestation driver is likely cattle pasture (see below). It is worth noting that the vast majority of the deforested area (87%) is outside of areas zoned for agriculture use.

We calculated an additional deforestation of 16,590 hectares (41,000 acres) in 2013 and 2014. Again, the vast majority of the forest loss appears to be outside areas zoned for agriculture use.

 

 

 

 

Deforestation Driver: Cattle Pasture

The predominant land use in the area is cattle pasture, so that is likely the leading driver of the documented deforestation.

We took a sample (1,500 hectares) of areas that were deforested in 2014, and found that 76% (1,140 hectares) were converted to cattle pasture in 2015. All sample areas were greater than 5 hectares and had available high-resolution imagery from September 2015. Based on an analysis of the imagery, we estimate that a similar amount of area was being cleared for pasture in 2015.

Below, we show a series of high-resolution images of this deforestation (click each image to enlarge).

Inset C Hotspot

Huanuco_zoomC_v5
Image 37b. Data: PNCB/MINAM, UMD/GLAD, MTC

Image 37b shows a detailed view of the deforestation inside the area indicated by Inset C.

In this area, we documented deforestation of 5,050 hectares in 2015. Of this total, 46% of the deforestation events were small-scale (<5 ha), 43% were medium-scale (5-50 ha), and 12% were large-scale (>50 ha).

We calculated an additional deforestation 0f 9,940 hectares in 2013 and 2014.

In Image 37c we show, in high resolution, an example of the recent deforestation in this area between August 2014 (left panel) and September 2015 (right panel). See Inset C1 for context.

Huanuco_C1_v5_DG
Image 37c. Data: WorldView of Digital Globe (NextView).

Inset D Hotspot

Huanuco_zoomD_v5
Image 37d. Data: PNCB/MINAM, UMD/GLAD, MTC

Image 37d shows a detailed view of the deforestation inside the area indicated by Inset D.

In this area, we documented deforestation of 2,883 hectares in 2015. Of this total, 44% of the deforestation events were small-scale (<5 ha), 51% were medium-scale (5-50 ha), and 6% were large-scale (>50 ha).

We calculated an additional deforestation of 6,650 hectares in 2013 and 2014.

In Images 37e – 37f, we show, in high resolution, two examples of the recent deforestation in this area between June (left panel) and September (right panel) of 2015. See Insets D1 and D2 for context.

Huanuco_D1_v3_DG
Image 37e. Data: WorldView of Digital Globe (NextView).
Huanuco_D2_v2_DG
Image 37f. Data: WorldView of Digital Globe (NextView).

References

* Based on the data from the GLAD alerts, produced by the University of Maryland, Google, and Global Forest Watch. http://www.globalforestwatch.org/map/5/-9.31/-75.01/PER/grayscale/umd_as_it_happens

*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. Environ. Res. Lett. 11: 034008.


Citation

Finer M, Novoa S, Cruz C, Peña N (2016) Deforestation Hotspot in the central Peruvian Amazon. MAAP: 37.

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.

Kernell_2015a_v1_en
Image 26a. Kernel density map for forest loss in the Peruvian Amazon in 2015. Data: Hansen et al 2016 (ERL).

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.

 

 

 

 

 

 

 

 

Inset A

MAAP_Coronel_Portillo_29a_v1_en
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 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).

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.

 

MAAP_Coronel_Portillo_29b_v1_m_en
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

MAAP_Coronel_Portillo_29d_v1_en
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 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.

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.

MAAP_Coronel_Portillo_29c_v1_m_en
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.