Tag: MAPS

MAAP #39: Gold Mining Deforestation Within Tambopata National Reserve Exceeds 865 Acres

Posted on by dcadmin

Based on analysis of satellite imagery, we have documented that the deforestation due to illegal gold mining activities within Tambopata National Reserve (Madre de Dios region) now exceeds 350 hectares (872 acres) since the initial invasion in late 2015 (see Image 39a). Although the rate of deforestation has decreased since April, when the Peruvian government installed a permanent control post* in the area, it is clear that the deforestation continues to expand.  In the Image, we highlighted the most recent deforestation (June and July 2016) in red to emphasize the current fronts. Insets A and B indicate the areas detailed in the zooms below.

*A recent article in the New York Times highlighted the extreme difficulty faced by the Peruvian government in cracking down on the illegal mining. Yesterday, the leading Peruvian newspaper El Comercio reported that the control post has been abandoned due to lack of resources.

Image 39a. Data: Planet, SERNANP, MAAP
Image 39a. Data: Planet, SERNANP, MAAP

Zoom A

In the following images, we show high-resolution examples of the recent deforestation within the reserve. Image 39b shows the deforestation that occurred between May 30 (left panel) and June 20 (right panel), 2016 in the area indicated by Inset A. The red circles indicate primary zones of new deforestation between these dates.

Image 39b. Data: Planet, SERNANP
Image 39b. Data: Planet, SERNANP

Zoom B

Image 39c shows the deforestation between May 3 (left panel) and July 21 (right panel), 2016 in the area indicated by Inset B. The red circles indicate primary areas of new deforestation between these dates.

Image 39c. Data: Digital Globe (Nextview), SERNANP
Image 39c. Data: Digital Globe (Nextview), SERNANP

Citation

Novoa S, Finer M, Olexy T (2016) Gold Mining Deforestation within Tambopata National Reserve exceeds 350 Hectares. MAAP: #39

MAAP #38: United Cacao Deforestation in Area Classified As “Forest Production”

Posted on by dcadmin

The Peruvian Ministry of Agriculture and Irrigation (MINAGRI) recently issued a resolution approving the Update of the Soil and Optimum Land Use Suitability Studies for Areas in the Loreto Region. It is important to emphasize that “Optimum Land Use” (Capacidad de Uso Mayor in Spanish)  is not determined by forest cover, but the quantitative interpretation of the soil, climate, and topography.

This new resolution represents an important advance in forest management in Peru because, according to both the previous1 and current2 Forestry Law, if the Optimum Land Use of a particular area is classified as Forest Production or Protection, it is illegal to change the land use to agriculture and cause deforestation. Thus, it is only possible to request land use change if the area has been classified as “Agriculture” (Optimum Land Use Annual Crop, Permanent Crop, or Pasture).3

Here, we analyze the spatial data corresponding to the new resolution. In Image 38a, we show that 92.6% (2,200 hectares) of the deforestation4 associated with the United Cacao project occurred on areas with an Optimum Land Use classification of Forest Production5. This classification “groups the lands in which climatic, terrain and soil conditions are not favorable for intensive cultivation, permanent crops, nor pastures, but for the production of timber species.”

Image 38a. Data: MINAGRI 2016. Red lines indicate areas deforested by United Cacao between 2013 and 2016. Green indicates areas with Optimum Land Use classification of Forest Production, while the yellows indicate areas with Optimum Land Use classification of Agriculture.
Image 38a. Data: MINAGRI 2016. Red lines indicate areas deforested by United Cacao between 2013 and 2016. Green indicates areas with Optimum Land Use classification of Forest Production, while the yellows indicate areas with Optimum Land Use classification of Agriculture.

In addition, 3.8% of the deforestation occurred in areas with an Optimum Land Use classification of Pasture/Forestry, while the remaining 3.6% occurred in areas with classification of Pasture. However, it is important to emphasize that even in these areas with an agricultural classification, our analysis of satellite imagery found that they were actually covered with primary forest (see Image 38b).

In conclusion, the vast majority of deforestation caused by United Cacao occurred in areas classified as optimally suited for forest production, where changes in land-use and associated deforestation are not permitted.

Imagen 38b. Data: Landsat/NASA/USGS
Imagen 38b. Data: Landsat/NASA/USGS

Notes

1Ley 27308 Articulo 7. Decreto Supremo 014-2001-AG, Reglamento de la Ley Forestal y de Fauna Silvestre, Art. 36.

2 LEY FORESTAL Y DE FAUNA SILVESTRE (LEY Nº 29763), Artículo 37

3 Decreto Legislativo No. 653, Ley de Promocion de las Inversiones en el Sector Agrario (1991)

4 See MAAP #35 for more information regarding this deforestation.

5 Specifically, this area is classified as F2s: Tierras Aptas para Producción Forestal (Símbolo F), Clase – Calidad Agrológica Media (Símbolo F2),  Subclase – Limitación por Suelo (Símbolo “s”)

Citation

Finer M, Novoa S, Cruz C (2016) United Cacao deforestation in area classified as “Forest Production.” MAAP: 38.

MAAP #37 Deforestation Hotspot in The Central Peruvian Amazon Driven By Cattle Pasture

Posted on by dcadmin

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.

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

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

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.

Image 37b. Data: PNCB/MINAM, UMD/GLAD, MTC
Image 37b. Data: PNCB/MINAM, UMD/GLAD, MTC
Image 37c. Data: WorldView of Digital Globe (NextView).
Image 37c. Data: WorldView of Digital Globe (NextView).

Inset D Hotspot

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.

Image 37d. Data: PNCB/MINAM, UMD/GLAD, MTC
Image 37d. Data: PNCB/MINAM, UMD/GLAD, MTC
Image 37e. Data: WorldView of Digital Globe (NextView).
Image 37e. Data: WorldView of Digital Globe (NextView).
Image 37f. Data: WorldView of Digital Globe (NextView).
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 #35: Confirming Amazon Deforestation by United Cacao in 2013 [High Res View]

Posted on by dcadmin

To date, we have published 4 MAAP articles* tracking deforestation by the company United Cacao in the northern Peruvian Amazon (outside the town of Tamshiyacu in the Loreto region). In these articles, based on analysis of satellite imagery, we have documented the deforestation of 2,380 hectares (5,880 acres) related to this project.

The company, however, continues to deny this deforestation**. In general, their main response seems to be that the land in question had been deforested for previous agricultural projects prior to their arrival in 2013.

Here in MAAP #35, we show definitively that this assertion simply does not match the satellite evidence. This article is based on analysis of recently-acquired satellite images from early 2013, the time period that the cacao project began. These images show, in extremely high resolution, the large-scale deforestation of primary forest in the project area between March and September 2013.*** Click each image to enlarge.

It is important to resolve the deforestation-related issues because the company has plans to expand its agricultural land bank in the coming years. Please see this recent statement from the Peruvian Forestry Service (SERFOR) for details on the legal aspect of this case.

As a reference, at the end of the article there is a graphic (Image 35l) illustrating the difference (as seen in high-resolution imagery) between primary forest, secondary vegetation, agricultural areas, and deforested areas.

New Evidence of Large-Scale Deforestation in 2013

We recently obtained high-resolution satellite imagery from March 25, 2013, immediately before the beginning of the deforestation for the cacao project. Image 35a shows the same exact project area between March (left panel) and September (right panel) 2013. In March, the project area is predominantly covered with primary forest*** and contains only a few scattered patches of previously disturbed land. In contrast, in September, the project area is clearly undergoing a large-scale deforestation event (1,100 hectares at that time).

Image 35a. Data: Airbus, Digital Globe (Nextview)
Image 35a. Data: Airbus, Digital Globe (Nextview)

Zoom A

In the following series of images, we show zooms of the areas indicated by Insets A-E in Image 35a. Each image shows the same exact area within the cacao project between March (left panel) and September (right panel) 2013. In all images, one can clearly see intact forest in March followed by large-scale deforestation in September.

Image 35b. Data: Airbus, Digital Globe (Nextview)
Image 35b. Data: Airbus, Digital Globe (Nextview)
Image 35c. Data: Airbus, Digital Globe (Nextview)
Image 35c. Data: Airbus, Digital Globe (Nextview)

Zoom B

Image 35d. Data: Airbus, Digital Globe (Nextview)
Image 35d. Data: Airbus, Digital Globe (Nextview)
Image 35e. Data: Airbus, Digital Globe (Nextview)
Image 35e. Data: Airbus, Digital Globe (Nextview)

Zoom C

Image 35f. Data: Airbus, Digital Globe (Nextview)
Image 35f. Data: Airbus, Digital Globe (Nextview)
Image 35g. Data: Airbus, Digital Globe (Nextview)
Image 35g. Data: Airbus, Digital Globe (Nextview)

Zoom D

Image 35h. Data: Airbus, Digital Globe (Nextview)
Image 35h. Data: Airbus, Digital Globe (Nextview)
Image 35i. Data: Airbus, Digital Globe (Nextview)
Image 35i. Data: Airbus, Digital Globe (Nextview)

Zoom E

Image 35j. Data: Airbus, Digital Globe (Nextview)
Image 35j. Data: Airbus, Digital Globe (Nextview)
Image 35k. Data: Airbus, Digital Globe (Nextview)
Image 35k. Data: Airbus, Digital Globe (Nextview)

Reference Graphic

Finally, for reference, Image 35l illustrates the difference (as seen in high-resolution imagery) between primary forest, secondary vegetation, agricultural areas, and deforested areas.

References

*MAAP #27, MAAP #13, MAAP #9, MAAP #2

**See articles in Directors Talk, La Region, y The Guardian

***see MAAP #9 for details on our time-series analysis dating back to 1985 that revealed that the vast majority of the project area is primary forest

Citation

Finer M, Cruz C, Novoa S (2016) Confirming Amazon Deforestation by United Cacao in 2013 [High Res View].  MAAP: 35.

MAAP #36: New Gold Mining Frontier in The Northern Peruvian Amazon

Posted on by dcadmin

In several previous MAAP articles, we have detailed gold mining deforestation in the southern Peruvian Amazon. Here, we provide evidence of the first known case of gold mining deforestation in northern Peru.

A recent news article published by the Peruvian organization DAR reported that gold mining activity continues to increase in the Santiago River (see Image 36a), located in the Amazonas region of the northern Peruvian Amazon. The article also mentions that this gold mining activity is no longer restricted to the river, but is now entering the forest. There are mining concessions in the area, but according to a recent article published in The Guardian, the miners are not operating legally with permission from the concessionaire.

Here, we show the first satellite images that confirm that the mining activity is indeed causing deforestation along the Santiago River (see below). Click each image to enlarge.

Image 36a. Credit: DAR, April 2016
Image 36a. Credit: DAR, April 2016

Satellite Images of Gold Mining Deforestation in Northern Peru

Image 36b shows a high-resolution image of the newly deforested area due to mining activity along the Santiago River (see yellow circle). The total forest loss to date is 8 hectares (20 acres).

Image 36b. Data: Planet Labs
Image 36b. Data: Planet Labs

Image 36c shows that the deforestation occurred between August 2014 (left panel) and August 2015 (right panel).

Image 36c. Data: USGS/NASA
Image 36c. Data: USGS/NASA

Citation

Finer M, Novoa S (2016) Gold Mining Deforestation in the Northern Peruvian Amazon. MAAP: 36.

MAAP #34: New Dams on The Madeira River in Brazil Cause Forest Flooding

Posted on by dcadmin

The Amazon lowlands have been connected to the Andes Mountains for millions of years by only six major rivers: the Caqueta, Madeira, Maranon, Napo, Putumayo, and Ucayali* (see Image 34a). This intimate connection allows rich Andean nutrients to fuel the Amazon floodplain and enables long-distance catfish migration between feeding grounds in the lowlands and spawning grounds in the highlands.

Image 34a. Data: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo
Image 34a. Data: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo

However, one of these six major Andean tributaries has recently been dammed on its main channel: the Madeira River in western Brazil (See Inset A). The Santo Antônio dam was completed in 2011, followed by the upstream Jirau dam in 2013.

Note in Image 34a that these dams are are located downstream of the Madre de Dios River in southern Peru. Thus, major ecological impacts — such as blocking the route of migratory catfish**— are also very relevant to Peru.

Here in MAAP #34, we describe the forest loss—over 36,100 hectares—associated with the flooding caused by these two dams (with a focus on the Jirau dam).

Zoom A: Forest Loss due to Flooding

Image 34b shows the forest loss due to flooding immediately upstream of the Jirau dam. As of 2015, the total flooded area for both dams is 36,139 hectares (89,301 acres). Major flooding was first detected in 2010, rose substantially in 2011-12, and peaked in 2014.

According to Fearnside 2014, although much of the forest along the Madeira is seasonally flooded, it dies when permanently flooded.*** Therefore, the flooded area is an appropriate measure of forest loss.

Further below, we show a series of satellite images of the areas indicated by Inset B (see Images 34c-e) and Inset C (see Image 34f).

Image 34b. Flooding-related forest loss along the Upper Madeira River. Data: USGS, CLASlite, Hansen/UMD/Google/USGS/NASA.
Image 34b. Flooding-related forest loss along the Upper Madeira River. Data: USGS, CLASlite, Hansen/UMD/Google/USGS/NASA.

Zoom B: Flooding Immediately Upstream Jirau Dam

Image 34c shows the flooding immediately upstream of the Jirau dam between 2011 (left panel) and 2015 (right panel). The red dot is a point of reference that indicates the same place in both images. Below, we show high-resolution images of the areas indicated by Insets B1 and B2.

Image 34c shows the flooding immediately upstream of the Jirau dam between 2011(left panel) and 2015 (right panel).
Image 34c shows the flooding immediately upstream of the Jirau dam between 2011(left panel) and 2015 (right panel).

Zooms B1 and B2: Jirau Dam and Flooding

Image 34d shows a high-resolution view of the Jirau dam in July 2015. Image 34e shows a high-resolution view of a portion of the flooded area immediately upstream of the Jirau dam in August 2015. The red dot is a point of reference that indicates the same place in both panels.

Image 34d. High-resolution view of the Jirau dam. Data: WorldView-2 from Digital Globe (NextView).
Image 34d. High-resolution view of the Jirau dam. Data: WorldView-2 from Digital Globe (NextView).
Image 34e: High-resolution view of flooded area immediately upstream of the Jirau dam. Data: WorldView-2 from Digital Globe (NextView).
Image 34e: High-resolution view of flooded area immediately upstream of the Jirau dam. Data: WorldView-2 from Digital Globe (NextView).

Zoom C: Flooding Further Upstream of Jirau Dam

Image 34f shows the flooding further upstream of the Jirau dam between 2011 (left panel) and 2015 (right panel). The red dot is a point of reference that indicates the same point in both images.

Image 34e: High-resolution view of flooded area immediately upstream of the Jirau dam. Data: WorldView-2 from Digital Globe (NextView).
Image 34e: High-resolution view of flooded area immediately upstream of the Jirau dam. Data: WorldView-2 from Digital Globe (NextView).

References

*Finer M, Jenkins CN (2012) Proliferation of Hydroelectric Dams in the Andean Amazon and Implications for Andes-Amazon Connectivity. PLOS ONE: 7(4): e35126. Link: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0035126

**Duponchelle F et al (2016) Trans-Amazonian natal homing in giant catfish. J. Appl. Ecol. http://doi.org/bd45

***Fearnside PM (2014) Impacts of Brazil’s Madeira River dams: Unlearned lessons for hydroelectric development in Amazonia. Environmental Science & Policy 38: 164-172.

Citation

Finer M, Olexy T (2015) New Dams on the Madeira River (Brazil) Cause Forest Flooding. MAAP: 34.

MAAP #33: Illegal Gold Mining Alters Course of Malinowski River (Border of Tambopata National Reserve)

Posted on by dcadmin

In MAAP #30, we described the illegal gold mining invasion of Tambopata National Reserve, an important protected area in the southern Peruvian Amazon (department of Madre de Dios). Here in MAAP #33, we show that illegal gold mining is also altering the course of the Malinowski River, which forms the natural boundary of the Reserve. Image 33a shows the two areas where we have documented a total artificial deviation (cutting) of 4.4 km (2.7 miles) of the river (see details below).

Image 33a. Data: Planet Labs, SERNANP
Image 33a. Data: Planet Labs, SERNANP

Zoom A: A Recent Deviation of the Malinowski River

Image 33b shows the final stage of the deviation of the Malinowski River between March 31 (left panel) and May 3 (right panel) of this year in the area indicated by Inset A in Image 33a. The new flow of the river is clearly seen in the right panel, cutting a 1.7 km stretch of the previous course.

Image 33b. Data: Planet Labs, Digital Globe (Nextview)
Image 33b. Data: Planet Labs, Digital Globe (Nextview)

Image 33c shows with greater precision how the Malinowski river was diverted in this area between April and May 2016. The red arrow indicates the exact same place across time in the three images.

Image 33c. Data: Digital Globe (Nextview)
Image 33c. Data: Digital Globe (Nextview)

Zoom B: An Earlier Deviation of the Malinowski River

In February 2016, Peruvian specialists presented how mining activity had recently changed the natural course of the Malinowski river in the area indicated in Inset B*. Image 33d shows the progressive change: from the increase in mining activity along the normal course of the river in June 2013 (left panel), to the new stretch of riverbed in June 2015 (center panel), and finally to the expansion of mining activity along the previous course (right panel). The red dot indicates the same place over time in the three images. A total of 2.7 km was cut from the previous river course.

Image 33d. Data: Digital Globe (Nextview), Planet Labs
Image 33d. Data: Digital Globe (Nextview), Planet Labs

Ecological Impacts

According to Dr. Carlos Cañas**, coordinator of the Amazon Waters Initiative for Wildlife Conservation Society in Peru, the deviation of the natural course of the Malinowski River will have significant ecological impacts, including:

  • Although the Malinowski River’s course has natural movement, the changes documented in MAAP #33 definitely represent an artificial alteration caused by mining activity.
  • These artificial changes are altering the course of the Malinowski from one that is “narrow and defined” to one that is “wide and scattered.” This change impacts the river’s flood patterns by changing the intensity, timing, and frequency of flooding along the river’s banks. This implies an effect on the migratory behavior of many species of fish downstream, which receive and interpret signals from the river to guide vital functions like feeding and reproduction.
  • The river’s new wider course also causes the velocity of water downstream to decrease, which will lead to increased levels of sediment in the discharge zone of the largest tributary, the Tambopata. Given the nature of the Tambopata, this could provide the almost-permanent damming of the Malinowski, as greater volume of the Tambopata means more sedimentation at the mouth of the river. Among other things, this could hinder the entry of fish to their feeding zones.
  • As seen in Image 33d, fish access to certain areas will be interrupted by the blockade and closure of channels. Also, the connection between the floodable forest and the river channel is completely altered, if not interrupted, in this section of the river. Many fish species that eat fruit or vegetation from the adjacent forest depend on this seasonal connection for food.
  • The Malinowski River, since it is a tributary of the Tambopata River, has natural areas that are crucial to the reproduction of many local species. Its tributary streams represent habitats that differ from the main river and harbor an incredible variety of fish and invertebrates that contribute to the biodiversity of the river basin. These streams have little sediment, and are thus highly transparent. Mining will destroy or drastically alter these environments, severely impacting this biodiversity.

Referencias

*Villa L., Campos L. G., Pino I. M. (01 de febrero de 2016). Primer Sistema de Alerta Temprana de Geoinformación (SAT-GI) para Áreas Naturales Protegidas del Perú: Reserva Nacional Tambopata y el Ámbito de Madre de Dios del Parque Nacional Bahuaja Sonene. Reporte Nº 001-2016.

** Cañas CM, Waylen PR (2011) Modelling production of migratory catfish larvae (Pimelodidae) on the basis of regional hydroclimatology features of the Madre de Dios Basin in southeastern Peru. Hydrol. Process. DOI: 10.1002/hyp.8192.

**Cañas CM, Pine WE (2011) DOCUMENTATION OF THE TEMPORAL AND SPATIAL PATTERNS OF PIMELODIDAE CATFISH SPAWNING AND LARVAE DISPERSION IN THE MADRE DE DIOS RIVER
(PERU): INSIGHTS FOR CONSERVATION IN THE ANDEAN-AMAZON HEADWATERS. River Res. Applic. 27: 602–611.

Citation

Finer M, Novoa S (2016)  Illegal Gold Mining Alters the Course of the Malinowski River (border of Tambopata National Reserve). MAAP: 33.

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

Posted on by dcadmin

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

Posted on by Ana Folhadella

Download PDF of this article 

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.

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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.

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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).

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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).

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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 #31: Deforestation Continues Expansion in La Pampa (Buffer Zone Of Tambopata National Reserve)

Posted on by dcadmin

Illegal gold mining deforestation continues to expand in La Pampa, an area located in the buffer zone of Tambopata National Reserve in the Madre de Dios region. Here, we present a series of high-resolution (0.5 m) images that clearly illustrate this expansion. Image 31a shows the large, expanding mass of deforestation in La Pampa (as of November 2015) in relation to the Tambopata National Reserve and its buffer zone. Insets A and B indicate the high-resolution zoom areas, where further below we show the rapid deforestation of 76 hectares (188 acres) between November 2015 and April 2016.

Image 31a. Data: WorldView-2 of Digital Globe (NextView).
Image 31a. Data: WorldView-2 of Digital Globe (NextView).

Zoom A: Rapid Advance of Deforestation

Image 31b shows the expansion of deforestation (28 hectares) between November 2015 (left panel) and April 2016 (right panel) in the eastern section of La Pampa. The red dot indicates the exact same point in both images across time.

Image 31b. Data: WorldView-2 of Digital Globe (NextView).
Image 31b. Data: WorldView-2 of Digital Globe (NextView).

Zoom B: Formation of a Large Camp

Image 31c shows the formation of a large mining camp between November 2015 (left panel) and April 2016 (right panel) in the eastern section La Pampa. The red dot indicates the exact same point in both images across time. The image also shows the deforestation of 48 hectares around the camp.

Image 31c. Data: WorldView-2 of Digital Globe (NextView).
Image 31c. Data: WorldView-2 of Digital Globe (NextView).

Citation

Finer M, Olexy T (2016) Deforestation Continues Expansion in La Pampa (buffer zone of Tambopata National Reserve). MAAP: 31.