Month: June 2020

MAAP #122: Amazon Deforestation 2019

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Table 1. Amazon 2019 primary forest loss for 2019 (red) compared to 2018 (orange). Data: Hansen/UMD/Google/USGS/NASA, MAAP.

Newly released data for 2019 reveals the loss of over 1.7 million hectares (4.3 million acres) of primary Amazon forest in our 5 country study area (Bolivia, Brazil, Colombia, Ecuador, and Peru).* That is twice the size of Yellowstone National Park.

Table 1 shows 2019 deforestation (red) in relation to 2018 (orange).

Primary forest loss in the Brazilian Amazon (1.29 million hectares) was over 3.5 times higher than the other four countries combined, with a slight increase in 2019 relative to 2018. Many of these areas were cleared in the first half of the year and then burned in August, generating international attention.

Primary forest loss rose sharply in the Bolivian Amazon (222,834 hectares), largely due to uncontrolled fires escaping into the dry forests of the southern Amazon.

Primary forest loss rose slightly in the Peruvian Amazon (161,625 hectares) despite a relatively successful crackdown on illegal gold mining, pointing to small-scale agriculture (and cattle) as the main driver.

On the positive side, primary forest loss decreased in the Colombian Amazon (91,400 hectares) following a major spike following the 2016 peace accords (between the government and FARC). It is worth noting, however, that we have now documented the loss of 444,000 hectares (over a million acres) of primary forest in the Colombian Amazon in the past four years since the peace agreement (see Annex).

*Two important points about the data. First, we use annual forest loss from the University of Maryland to have a consistent source across all five countries. Second, we applied a filter to only include loss of primary forest (see Methodology).

2019 Deforestation Hotspots Map

The Base Map below shows the major 2019 deforestation hotspots across the Amazon.

2019 deforestation hotspots across the Amazon. Data: Hansen/UMD/Google/USGS/NASA, MAAP.

Many of the major deforestation hotspots were in Brazil. Early in the year, in March, there were uncontrolled fires up north in the state of Roraima. Further south, along the Trans-Amazonian Highway, much of the deforestation occurred in the first half of the year, followed by the high profile fires starting in late July. Note that many of these fires were burning recently deforested areas, and were not uncontrolled forest fires (MAAP #113).

The Brazilian Amazon also experienced escalating gold mining deforestation in indigenous territories (MAAP #116).

Bolivia also had an intense 2019 fire season. Unlike Brazil, many were uncontrolled fires, particularly in the Beni grasslands and Chiquitano dry forests of the southern Bolivian Amazon (MAAP #108).

In Peru, although illegal gold mining deforestation decreased (MAAP #121), small-scale agriculture (including cattle) continues to be a major driver in the central Amazon (MAAP #112) and an emerging driver in the south.

In Colombia, there is an “arc of deforestation” in the northwestern Amazon. This arc includes four protected areas (Tinigua, Chiribiquete and Macarena National Parks, and Nukak National Reserve) and two Indigenous Reserves (Resguardos Indígenas Nukak-Maku and Llanos del Yari-Yaguara II) experiencing substantial deforestation (MAAP #120). One of the main deforestation drivers in the region is conversion to pasture for land grabbing or cattle ranching.

Annex – Colombia peace accord trend

Annex 1. Deforestation of primary forest in the Colombian Amazon, 2015-20. Data: Hansen/UMD/Google/USGS/NASA, UMD/GLAD. *Until May 2020

Methodology

The baseline forest loss data presented in this report were generated by the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland (Hansen et al 2013) and presented by Global Forest Watch. Our study area is strictly what is highlighted in the Base Map.

For our estimate of primary forest loss, we used the annual “forest cover loss” data with density >30% of the “tree cover” from the year 2001. Then we intersected the forest cover loss data with the additional dataset “primary humid tropical forests” as of 2001 (Turubanova et al 2018). For more details on this part of the methodology, see the Technical Blog from Global Forest Watch (Goldman and Weisse 2019).

For boundaries, we used the biogeographical limit (as defined by RAISG) for all countries except Bolivia, where we used the Amazon watershed limit (see Base Map).

All data were processed under the geographical coordinate system WGS 1984. To calculate the areas in metric units, the projection was: Peru and Ecuador UTM 18 South, Bolivia UTM 20 South, Colombia MAGNA-Bogotá, and Brazil Eckert IV.

Lastly, to identify the deforestation hotspots, we conducted a kernel density estimate. This type of analysis calculates the magnitude per unit area of a particular phenomenon, in this case forest cover loss. We conducted this analysis using the Kernel Density tool from Spatial Analyst Tool Box of ArcGIS. We used the following parameters:

Search Radius: 15000 layer units (meters)
Kernel Density Function: Quartic kernel function
Cell Size in the map: 200 x 200 meters (4 hectares)
Everything else was left to the default setting.

For the Base Map, we used the following concentration percentages: Medium: 7%-10%; High: 11%-20%; Very High: >20%.

References

Goldman L, Weisse M (2019) Explicación de la Actualización de Datos de 2018 de Global Forest Watch. https://blog.globalforestwatch.org/data-and-research/blog-tecnico-explicacion-de-la-actualizacion-de-datos-de-2018-de-global-forest-watch

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest.

Turubanova S., Potapov P., Tyukavina, A., and Hansen M. (2018) Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environmental Research Letters  https://doi.org/10.1088/1748-9326/aacd1c 

Acknowledgements

We thank G. Palacios for helpful comments to earlier versions of this report.

This work was supported by the following major funders: Norwegian Agency for Development Cooperation (NORAD), Gordon and Betty Moore Foundation, International Conservation Fund of Canada (ICFC), Metabolic Studio, Erol Foundation, MacArthur Foundation, and Global Forest Watch Small Grants Fund (WRI).

Citation

Finer M, Mamani N (2020) 2019 Amazon Deforestation. MAAP: 122.

MAAP #121: Reduction of Illegal Gold Mining in the Peruvian Amazon

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Base Map. Illegal gold mining deforestacion in the protected area buffer zones of the southern Peruvian Amazon, 2017-2019. Data: MAAP. Click to enlarge image.

Thanks to the support of the USAID, via the Prevent Project, dedicated to the prevention and combat of environmental crimes in the Amazon, we conducted a detailed analysis of recent illegal gold mining deforestation in the southern Peruvian Amazon.

The objective is to understand the trends from early 2017 to June 2020 (which includes the first part of the mandatory quarantine issued by the Peruvian government as of March 16, 2020 due to the coronavirus pandemic).

We focus on the buffer zones of two protected areas in the Madre de Dios region: Tambopata National Reserve and Bahuaja Sonene National Park (see Base Map).*

This area includes La Pampa, the current highest intensity illegal mining zone in the country. In February 2019, the Peruvian government launched Operation Mercury  to confront the illegality in La Pampa and surrounding areas.

The Base Map shows that gold mining deforestation in La Pampa decreased over 90% following Operation Mercury.

However, illegal gold mining does continue after Operation Mercury (including during the coronavirus state of emergency), but at lower rates. Thus, current snapshots may be misleading and recent context is important.

On the Base Map, the red arrows indicate the areas with the most recent illegal activity (click the image to enlarge). See below for more details.

Main Results

Table 1. Illegal gold mining deforestation before (yellow) and after (red) Operation Mercury in the buffer zones of Madre de Dios. Data: MAAP.

The Base Map and Table 1 illustrate the following key results:

  • In La Pampa, we documented mining deforestation of 173 hectares (428 acres) per month before Operation Mercury (January 2018 – February 2019). After the intervention, deforestation was reduced to 14 hectares (36 acres) per month (March 2019 – May 2020), a decrease of 92%.
    .
  • Upstream, in the Alto Malinowski, we documented the mining deforestation of 61 hectares (150 acres) per month before Operation Mercury. After the intervention, deforestation was reduced to 28 hectares (69 acres) per month, a decrease of 53%.
    .
  • Downstream, in the Apaylon area, we documented the mining deforestation of 2.9 hectares (7 acres) per month, before Operation Mercury. After the intervention, deforestation increased to 4 hectares (10 acres) per month, an increase of 41%. Apaylon is main area in the buffer zone where deforestation has increased.
    .
  • Within Tambopata National Reserve, we documented the mining deforestation of 6.5 hectares (16 acres) per month, before Operation Mercury. After the intervention, deforestation was reduced to 0.5 hectares (1.2 acres) per month, a decrease of 93%.
    .
  • Overall, illegal gold mining does continue in the buffer zones of Madre de Dios, but at lower rates than the previous two years. We documented the gold mining deforestation of 797 hectares (1,670 acres) after Operation Mercury.
    .
  • Regarding the speculation that illegal activity would increase during the coronavirus pandemic, we have not documented any major increase or surge in the buffer zones of Madre de Dios.* Illegal mining does continue, however, we documented the deforestation of 80 hectares (198 acres) during the quarantine.
    .

Reduction of 90% in La Pampa

The following images show the major decrease in gold mining deforestation in La Pampa after Operation Mercury. Image 1 shows the rapid deforestation before Operation Mercury, between January 2017 (left panel) and February 2019 (right panel). Image 2 shows how the deforestation decreased after Operation Mercury, between February 2019 (left panel) and May 2020 (right panel). The red dot represents a reference point between the images.

Image 1. Rapid gold mining deforestation in La Pampa before Operation Mercury, between January 2017 (left panel) and February 2019 (right panel). Data: Planet.
Image 2. Mining deforestation decreased in La Pampa after Operation Mercury, between February 2019 (left panel) and May 2020 (right panel). Data: Planet.

Displaced Miners?

Table 2. Deforestation by illegal gold mining before (yellow) and after (red) Operation Mercury in two other threatened areas. Data: MAAP.

There has also been speculation that the focus of Operation Mercury in La Pampa would lead to illegal miners moving to other areas.* Base Map 2 shows two of the most threatened areas: Camanti and Pariamanu.

These are the main results for these two areas:

  • In Camanti (located in the buffer zone of Amarakaeri Communal Reserve), we documented the gold mining deforestation of 13.3 hectares (33 acres) per month before Operation Mercury. After the intervention, deforestation was reduced to 6.1 hectares (15 acres) per month, a decrease of 54%.
    .
  • In Pariamanu, we documented  the mining deforestation of 2.5 hectares (6 acres) per month before Operation Mercury. After the intervention, it increased to 4.2 hectares (10 acres) per month, an increase of 70%.
    .
  • In summary, illegal gold mining continues in these two areas outside La Pampa. We documented the mining deforestation of 175 hectares (432 acres) after Operation Mercury (including 22 hectares during the pandemic). There is some evidence that miners are being displaced to Pariamanu, but there has not been a surge in Camanti.
Base Map 2. Main mining areas in the south of the Peruvian Amazon. Click to enlarge image.

Statement of the Peruvian Protected Area Agency (SERNANP)

El Servicio Nacional de Áreas Naturales Protegidas por el Estado (SERNANP) nos ha comunicado lo siguiente:

  • La actividad de control y vigilancia en la Reserva Nacional Tambopata es permanente y las autoridades (SERNANP, Policía Nacional del Perú, Fiscalías Especializadas en Materia Ambiental, y Marina de Guerra del Perú) continúan interviniendo a todas las actividades de minería ilegal, manteniendo el 100%.
  • Las zonas de amortiguamiento son espacios que están sujetos a la intervención de las autoridades de la Operación Mercurio (no del SERNANP). Se han realizado intervenciones continuas e interdicciones tanto en  las zonas indicadas en el reporte, como en Apaylon y Camanti.
    ,
  • Cabe mencionar que la Operación Mercurio, durante el 2019 y sobre todo en el 2020 (Incluyendo el período de cuarentena) ha ampliado sus operativos mas allá de la Pampa, lo cual explica porque en Camanti las cifras también se ha reducido.  En el segundo semestre de 2020 y en el 2021, se espera que los operativos es amplíen a otras zonas de Madre de Dios.

*Notes

Acknowledgments

We thank R. Segura, M. Castro, E. Ortiz, M. Silman, M. E. Gutierrez, S. Novoa, H. Balbuena, M. Allemant, and G. Palacios for their helpful comments on this report.

This report was conducted with technical assistance from USAID, via the Prevent project. Prevent is an initiative that, over the next 5 years, will work with the Government of Peru, civil society, and the private sector to prevent and combat environmental crimes in Loreto, Ucayali and Madre de Dios, in order to conserve the Peruvian Amazon.

This publication is made possible with the support of the American people through USAID. Its content is the sole responsibility of the authors and does not necessarily reflect the views of USAID or the US government.

Citation

Finer M, Mamani N (2020) Reduction of Illegal Gold Mining in the Peruvian Amazon. MAAP:

Fire Alert vs. Aerosol Emission Data

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Fire Alert vs. Aerosol Emission Data

This slider shows us how aerosol emission data allows users to prioritize hundreds (or thousands) of heat-based fire alerts. In other words, the aerosol data indicates just the fires that are  actually burning lots of biomass and putting out abundant smoke.

[twenty20 img1=”9170″ img2=”9169″ offset=”0.5″]

Amazon Fire Tracker 2020: Brazil #4 (June 17, 2020)

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As presented in MAAP #118, Amazon Conservation launched a real-time fire monitoring app that specializes in detection of elevated aerosol emissions in the smoke coming from burning Amazon fires. As detailed below, the app just detected the fourth major Amazon fire of 2020 on June 17. All four fires thus far have been in the state of Mato Grosso and burning recently deforested areas (see MAAP #113 for background).

Step 1. Detection of elevated emissions in the southeastern Brazilian Amazon (Mato Grosso).


Step 2. Zoom in on the emissions.

Step 3. Adjust the transparency to see the underlying fire alerts that indicate the exact location of the fires. Obtain coordinates of the source of the fires.

 

Step 4. Check the satellite imagery in Planet Explorer. Here is a high resolution Planet image showing the fire burning on June 17. Also see the slider below, comparing the the June 17 fires with a pre-fire image from June 10.

Imagery source: Planet.

[twenty20 img1=”9167″ img2=”9168″ width=”75%” offset=”0.5″]

Imagery source: Planet.

Step 5. Using Planet’s extensive imagery archive, we were able to determine that the fires were burning an area deforested in 2019 (and not a forest fire).

Coordinates: -10.45, -53.55

Annex – Fire Alert vs. Aerosol Emission Data

This slider shows us how aerosol emission data allows users to prioritize hundreds (or thousands) of heat-based fire alerts. In other words, the aerosol data indicates just the fires that are  actually burning lots of biomass and putting out abundant smoke.

[twenty20 img1=”9170″ img2=”9169″ offset=”0.5″]

References

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment.”
https://earthengine.google.com/faq/
Planet Team (2017). Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. https://api.planet.com

Acknowledgements

This work was supported by the following major funders: USAID/NASA (SERVIR), Global Forest Watch Small Grants Fund (WRI), Norwegian Agency for Development Cooperation (NORAD),  International Conservation Fund of Canada (ICFC), Metabolic Studio, and Erol Foundation.

Citation

Finer M, Villa L (2020) Amazon Fire Tracker 2020: Brazil #4 (June 17, 2020). MAAP.

MAAP: Amazon Fire Tracker #2 – Brazil, June 8 2020

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As presented in MAAP #118, Amazon Conservation launched a real-time fire monitoring app that specializes in detection of elevated aerosol emissions from burning Amazon fires. As detailed below, the app detected the second major 2020 fire on June 8, 2020 in Mato Grosso, Brazil.

Step 1. Detection of elevated emissions in the southeastern Brazilian Amazon (Mato Grosso).

 


Step 2. Zoom in on the emissions, adjust the transparency to see the underlying fire alerts that indicate the fire location.

 

Step 3. Zoom in again to see precisely the fire location and obtain coordinates.

Step 4. Check the satellite imagery archive in Planet Explorer. Here is a Landsat image (30 meter resolution) showing the fire burned around 3,000 hectares (7,400 acres) of an area deforested in July 2018. Note that MAAP #113 made the important discovery that most of the 2019 Brazilian Amazon fires were burning recently deforested areas (and not uncontrolled forest fires).

 

Coordinates

lat: -12.57, lon: -54.06

References

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment.”
https://earthengine.google.com/faq/

Acknowledgements

This work was supported by the following major funders: USAID/NASA (SERVIR), Global Forest Watch Small Grants Fund (WRI), Norwegian Agency for Development Cooperation (NORAD),  International Conservation Fund of Canada (ICFC), Metabolic Studio, and Erol Foundation.

Citation

Finer M, Villa L (2020) Amazon 2020 Fire Tracker #2 – Brazil, June 8. MAAP.

MAAP #120: Deforestation in the Colombian Amazon – 2020

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Table 1. Deforestation of primary forest in the Colombian Amazon, 2015-20. Data: Hansen/UMD/Google/USGS/NASA, UMD/GLAD. *Until May 2020

Here we present a first look at 2020 deforestation of primary forest in the Colombian Amazon, in relation to the new published annual data for 2019.*

This new data confirms that deforestation decreased in 2019 (91,400 hectares) after a peak in 2018 (153,900 hectares).

Table 1 shows the recent trend: a major deforestation spike following the 2016 peace agreement (between the Colombian government and the FARC) with a peak in 2018, followed by a major decrease in 2019.

In our first look at 2020, we estimate the deforestation of 76,200 hectares (188,295 acres) of primary forest through June.

Note that we have documented the deforestation of 444,000 hectares (over a million acres) of primary forest in the Colombian Amazon in the past four years since the peace agreement.

*Global Forest Watch recently released the annual forest loss data for 2019.

Deforestation Hotspots – 2020

Base Map. 2020 Deforestation hotspots in the Colombian Amazon. Data: UMD/GLAD.

The Base Map shows the 2020 deforestation hotspots.*

As in previous years, they are concentrated in an “arc of deforestation” in the northwest Colombian Amazon.

This arc includes four protected areas (Tinigua, Chiribiquete and Macarena National Parks, and Nukak National Reserve) that lost 0ver 7,700 hectares (19,000 acres) of primary forest in 2020 (see Table 2).

Tinigua National Park is the most impacted protected area with the deforestation of 5,100 hectares (12,600 acres). Note the rare occurrence of a major deforestation hotspot in the middle of a national park.

Chiribiquete National Park lost 510 hectares (1,260 acres) in the recently expanded sections of the park.

The arc of deforestation also includes two Indigenous Reserves (Resguardos Indígenas Nukak-Maku and Llanos del Yari-Yaguara II) that lost 4,000 hectares (9,885 acres) so far in 2020.

*To see detailed map of the 2019-20 primary forest deforestation in the Colombian Amazon, click here.

Deforestation in Protected Areas and Indigenous Lands – 2020

Below, we show 2020 examples within the arc of deforestation in the northwest Colombian Amazon.

Image 1 illustrates the extensive deforestation within Tinigua National Park over the last five years continuing in 2020.

Image 2 shows an example of deforestation within Chiribiquete National Park (western sector) between January (left panel) and April (right panel) of 2020.

Image 3 shows an example of deforestation within the Llanos del Yari-Yaguara II Indigenous Reserve between January (left panel) and April (right panel) of 2020.

Image 1. Extensive deforestation within Tinigua National Park over the last five years, continuing in 2020. Data: Hansen/UMD/Google/USGS/NASA, UMD/GLAD.
Image 2. Deforestation in Chirbiquete National Park (western sector) between January (left panel) and April (right panel) of 2020. Data: ESA, Planet, MAAP.
Image 3. Deforestation in Llanos del Yari-Yaguara II Indigenous Reserve. Data: ESA, Planet, MAAP.

Deforestation in Protected Areas, 2015-20

Table 2 shows the loss of primary forest in four protected areas located in the arc of deforestation arc in the northwestern Colombian Amazon, between 2015 and 2020.

Table 2. Primary forest loss in four protected areas in the northwestern Colombian Amazon, between 2015 and 2020. Data: Hansen/UMD/Google/USGS/NASA, UMD/GLAD.

Methodology

The data presented in this report were generated by the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland (Hansen et al 2013) and presented by Global Forest Watch. For the years 2015-18, we used annual forest loss data. For the years 2019-20, we used early warning alerts (GLAD alerts), and thus represent an estimate. Note that some forest loss detected early in the year may include events from late the preceding year.

Our study area is the Amazon biogeographical limit (not strict Amazon watershed) as highlighted in the Base Map.

Specifically, for our estimate of forest cover loss, we multiplied the annual “forest cover loss” data by the density percentage of the “tree cover” from the year 2001 (values >30%).

For our estimate of primary forest loss, we intersected the forest cover loss data with the additional dataset “primary humid tropical forests” as of 2001 (Turubanova et al 2018). For more details on this part of the methodology, see the Technical Blog from Global Forest Watch (Goldman and Weisse 2019).

All data were processed under the geographical coordinate system WGS 1984. To calculate the areas in metric units the UTM (Universal Transversal Mercator) projection was used: Colombia 18 North.

Lastly, to identify the deforestation hotspots, we conducted a kernel density estimate. This type of analysis calculates the magnitude per unit area of a particular phenomenon, in this case forest cover loss. We conducted this analysis using the Kernel Density tool from Spatial Analyst Tool Box of ArcGIS. We used the following parameters:

Search Radius: 15000 layer units (meters)
Kernel Density Function: Quartic kernel function
Cell Size in the map: 200 x 200 meters (4 hectares)
Everything else was left to the default setting.

For the Base Map, we used the following concentration percentages: Medium: 10%-20%; High: 21%-35%; Very High: >35%.

Acknowledgements

We thank R. Botero (FCDS), E. Ortiz (AAF), and G. Palacios for helpful comments to earlier versions of this report.

This work was supported by the following major funders: Norwegian Agency for Development Cooperation (NORAD), Gordon and Betty Moore Foundation, International Conservation Fund of Canada (ICFC), Metabolic Studio, Erol Foundation, MacArthur Foundation, and Global Forest Watch Small Grants Fund (WRI).

Citation

Finer M, Mamani N (2020) Deforestation in the Colombian Amazon – 2020. MAAP #120.