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Aboveground Carbon Emissions from Gold Mining in the Peruvian Amazon

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Aboveground Carbon Emissions from Gold Mining in the Peruvian Amazon Environmental Research Letters LETTER • OPEN ACCESS Aboveground carbon emissions from gold mining in the Peruvian Amazon To cite this article: Ovidiu Csillik and Gregory P Asner 2020 Environ. Res. Lett. 15 014006 View the article online for updates and enhancements. This content was downloaded from IP address 186.155.71.247 on 24/03/2020 at 22:52 Environ. Res. Lett. 15 (2020) 014006 https://doi.org/10.1088/1748-9326/ab639c LETTER Aboveground carbon emissions from gold mining in the Peruvian OPEN ACCESS Amazon RECEIVED 9 October 2019 Ovidiu Csillik and Gregory P Asner REVISED Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ, United States of America 5 December 2019 ACCEPTED FOR PUBLICATION E-mail: [email protected] 18 December 2019 Keywords: deep learning, forest degradation, gold mining, Madre de Dios, Planet Dove, REDD+, tropical forest PUBLISHED 14 January 2020 Original content from this Abstract work may be used under In the Peruvian Amazon, high biodiversity tropical forest is underlain by gold-enriched subsurface the terms of the Creative Commons Attribution 3.0 alluvium deposited from the Andes, which has generated a clash between short-term earnings for licence. miners and long-term environmental damage. Tropical forests sequester important amounts of Any further distribution of this work must maintain carbon, but deforestation and forest degradation continue to spread in Madre de Dios, releasing attribution to the fi author(s) and the title of carbon to the atmosphere. Updated spatially explicit quanti cation of aboveground carbon emissions the work, journal citation caused by gold mining is needed to further motivate conservation efforts and to understand the effects and DOI. of illegal mining on greenhouse gases. We used satellite remote sensing, airborne LiDAR, and deep learning models to create high-resolution, spatially explicit estimates of aboveground carbon stocks and emissions from gold mining in 2017 and 2018. For an area of ∼750 000 ha, we found high variations in aboveground carbon density (ACD) with mean ACD of 84.6 (±36.4 standard deviation) − − Mg C ha 1 and 83.9 (±36.0) Mg C ha 1 for 2017 and 2018, respectively. An alarming 1.12 Tg C of emissions occurred in a single year affecting 23,613 hectares, including in protected zones and their ecological buffers. Our methods and findings are preparatory steps for the creation of an automated, high-resolution forest carbon emission monitoring system that will track near real-time changes and will support actions to reduce the environmental impacts of gold mining and other destructive forest activities. 1. Introduction since 2011 (Caballero Espejo et al 2018). Multiple factors have contributed to this intensification, The Amazonian rainforest is under the chainsaw of including an increase in international gold prices, fast-growing economies and expanding populations. increased accessibility following paving of the Of these threats, gold mining poses one of the most Interoceanic Highway (Moreno-Brush et al 2016), the negatively impactful types of damage to the environ- lack of formalization of artisanal and small-scale ment. The Peruvian Amazon, the sixth largest produ- mining activities (Salo et al 2016), and the hope of high cer of gold with 155 metric tons removed in 2017 short-term earnings by mining and selling the gold (Ober 2018), harbors gold-rich alluvium deposited (Sanguinetti 2018), which also led to a migration of by erosion in layers of sediments in the lowland people from nearby regions to Madre de Dios (Asner ecosystems, including river floodplain forests (Asner and Tupayachi 2017). The three largest mines, Huepe- et al 2013). This attracted gold miners to the Madre tuhe, Delta-1, and Guacamayo (now called La Pampa), de Dios region of the Peruvian Amazon, which is used to dominate the landscape, with Huepetuhe responsible for 70% of Peru’sgoldproduction accounting for 76% of region-wide mining in 1999 (Swenson et al 2011). (Asner et al 2013). However, in 2012, small mining, Gold mining in Madre de Dios has proliferated as a clandestine operations accounted for 51% of total gold rush responsible for 100 000 hectares of defor- mining activities in Madre de Dios (Asner et al 2013). estation between 1984 and 2017, with 10% of defor- Most of the mining activities are unregulated, artisanal estation occurred only in 2017 and 53% occurred in nature, and most are illegal (Swenson et al 2011, © 2020 The Author(s). Published by IOP Publishing Ltd Environ. Res. Lett. 15 (2020) 014006 Asner et al 2013). The environmental damage pro- pillars for an automated monitoring system that will duced by artisanal miners is visible from space (Swen- help track the changes in near real-time and act son et al 2011, Asner et al 2013) and consists of large towards reducing the environmental impacts of gold areas of deforestation and forest degradation, bare soil mining activities. and water pools used in the mining process. Earth Observation satellites have become widely used to report deforestation and improve intervention 2. Data and methods (Finer et al 2018). Analyzing time series of free Landsat 2.1. Study area satellite images at 30 m spatial resolution, forest loss − Madre de Dios is a biodiversity hotspot lying at the caused by gold mining averaged 4437 ha yr 1 between foothills of the Andes, whose Holocene alluvium 1999 and 2016, with a total increase of estimated gold deposited important subsurface gold resources (Asner mining area of 40% between 2012 and 2016 (Asner et al 2013). Our study area is bounded by the Andes and Tupayachi 2017). Landsat data were also used to (S), Rio Madre de Dios (N), Rio Colorado (W) and the identify ∼15 500 ha of mining areas for the three lar- fl ( ) gest mines by 2009 (Swenson et al 2011) and found con uence on Inambari and Madre de Dios rivers E (fi ) that ∼65% of all artisanal small-scale mining occurred gure 1 . Seasonal tropical moist forests have excep- outside the legal mining concessions (Elmes et al tionally high biological diversity in our region of ( ) 2014), including protected areas, like the Tambopata interest Román-Dañobeytia et al 2015 with unique National Reserve and its buffer zone (Asner and functional, taxonomic and phylogenetic diversity. The Tupayachi 2017). Asner et al (2013) combined Landsat dry season spans between June and September, with data with airborne mapping and field surveys to find a July being the driest month. Intense mining activities 400% increase in gold mining between 1999 and 2012, are present in the Colorado, Puquiri, Inambari, ( with forest loss tripled in 2008. Aboveground carbon Malinowski, and Tambopata river catchments Asner ) stocks and emissions were mapped at 0.1 ha resolution and Tupayachi 2017 , around the Tambopata National ( ) over 4.3 million ha in Madre de Dios using satellite Reserve multiple use , the Bahuaja-Sonene National ( ) imaging, airborne LiDAR and field plots, totaling 395 Park strict protection and the Communal Reserve ( )( Tg C (million metric tons of carbon)(Asner et al Amarakaeri multiple use Alvarez-Berríos and ) 2010). Gold mining areas had the lowest mean carbon Mitchell Aide 2015 . More than 59 000 miners were − ( density (16.7 Mg C ha 1) when compared to forest estimated to perform illegal mining in 2015 Amazon ) ’ degradation and deforestation (35.6 and 27.8 Mg C Conservation Association 2015 , with the region s − ha 1, respectively), while deforestation caused by gold capital city of Puerto Maldonado acting as the hotspot mining and logging concessions accounted for carbon of mining-related activities, like gold shops or amal- − ( ) emissions of 0.42 Tg C yr 1 between 2006 and 2009 gam roasting Moreno-Brush et al 2016 . (Asner et al 2010). However, because land-use conver- sion in Madre de Dios happens quickly and mostly 2.2. Airborne LiDAR canopy height and LiDAR- illegally, high-spatial and temporal resolution satellite based ACD images are needed for ecosystem monitoring (Boyle We used airborne LiDAR data acquired in 2011 and et al 2014). 2013 by the Global Airborne Observatory (GAO; Planet Inc. satellite data address both challenges in formerly Carnegie Airborne Observatory)(Asner et al terms of spatial and temporal resolution, offering daily 2012). The flight campaigns covered extensive areas of 3.7 m spatial resolution Dove images with four spec- gold mining and rivers, as well as large portions of the tral bands (Planet Team 2018). Until now, Dove ima- floodplain, swamp and terra firme forests (Asner et al ges were mostly used in combination with other 2013). A 3-D LiDAR point cloud with a resolution of geospatial datasets to manually digitize land-use chan- 2 m was obtained, with an average-on-the-ground ges in the Peruvian Amazon region by conservation LiDAR points spacing of 4–8 shots per square meter projects like the Monitoring of the Andean Amazon (Asner et al 2014). To account for the different Project (MAAP)(Finer and Mamani 2018). Planet acquisition times for LiDAR and Planet Dove images, Dove images were successfully used to map coral reefs we removed the LiDAR patches that suffered changes (Asner et al 2017) or agricultural environments (Ara- through visual interpretation. Subtracting the digital gon et al 2018, Houborg and McCabe 2018), but were terrain model from the digital surface model, derived not used to assess gold mining effects using robust and from the last and first returns, respectively, resulted in automated approaches. a top-of-canopy height (TCH) model at 2 m spatial We developed high-resolution estimates of above- resolution and covering 164 563 hectares in our study ground carbon density (ACD) and emissions for the area of ∼750 000 ha. TCH was shown to be a very good region most affected by gold mining activities in estimator of ACD, and we transformed the TCH into Madre de Dios for 2017 and 2018 by combining hun- estimates of ACD using the equation proposed by dreds of Planet Dove images, Sentinel-1, topography Asner et al (2014), developed by correlating extensive and airborne LiDAR measurements using a deep field-based estimates of ACD with TCH at the national learning regression framework.
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