![\"\"](\"https:\/\/i0.wp.com\/rfmproductions.org\/wp-content\/uploads\/2023\/07\/maaproject.org-maap-187-amazon-deforestation-amp-fire-hotspots-2022-Peru-PFL-Fire2022-PanAmz-Eng-200dpi-v3.jpg?resize=791%2C1024&ssl=1\")
<\/p>\nWe present a detailed look at the major\u00a02022 Amazon forest loss hotspots<\/b>, based on the final annual data recently released by the University of Maryland (and featured on Global Forest Watch).<\/p>\n
This dataset is unique in that it is consistent across all nine countries of the Amazon, and distinguishes forest loss from fire, leaving the rest as a proxy for deforestation (but also includes natural loss).<\/p>\n
Thus, we are able to present both\u00a0deforestation and fire hotspots\u00a0<\/b>across the Amazon.<\/p>\n
The Base Map (see right) and Results Graph (see below) reveal several\u00a0key findings<\/b>:<\/p>\n
In 2022, we estimate the deforestation of\u00a01.98 million hectares\u00a0<\/b>(4.89 million acres). This represents a major\u00a021% increase\u00a0<\/b>from 2021, and is the\u00a0second highest on record<\/b>, behind only the peak in 2004.<\/p>\n
Below, we zoom in on the Peru with additional maps and analysis.<\/p>\n
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Peru Base Map, 2022. Deforestation and fire hotspots in the Peruvian Amazon. Data: UMD\/GLAD, ACA\/MAAP.<\/p>\n
In 2022, the Peruvian Amazon lost\u00a0144,682 hectares\u00a0<\/strong>of primary forest to deforestation. Fires directly impacted an additional 16,408 hectares.<\/p>\n Deforestation increased 6.7% from 2021, and was the 5th highest on record. Fire impact decreased from last year, but was still relatively high.<\/p>\n The deforestation was concentrated in the central and southern Amazon (Ucayali and Madre de Dios regions, respectively) (see Peru Base Map).<\/p>\n In the central Amazon, we highlight the rapid deforestation for a\u00a0new Mennonite colony\u00a0<\/strong>(see\u00a0MAAP #166<\/u>).<\/p>\n In the southern Amazon, gold mining deforestation continues to be an issue in indigenous communities and within the official Mining Corridor.<\/p>\n <\/p>\n The analysis was based on 30-meter resolution annual forest loss data produced by the University of Maryland and also presented by Global Forest Watch.<\/p>\n This data was complemented with the\u00a0Global Forest Loss due to fire\u00a0<\/u>dataset that is unique in terms of being consistent across the Amazon (in contrast to country specific estimates) and distinguishes forest loss caused directly by fire (note that virtually all Amazon fires are human-caused). The values included were \u2018medium\u2019 and \u2018high\u2019 confidence levels (code 3-4).<\/p>\n The remaining forest loss serves as a likely close proxy for deforestation, with the only remaining exception being natural events such as landslides, wind storms, and meandering rivers. The values used to estimate this category was \u2018low\u2019 certainty of forest loss due to fire (code 2), and forest loss due to other \u2018non-fire\u2019 drivers (code 1).<\/p>\n For the baseline, it was defined to establish areas with >30%\u00a0tree canopy density<\/u>\u00a0in 2000. Importantly, we applied a filter to calculate only primary forest loss by intersecting the forest cover loss data with the additional dataset \u201cprimary humid tropical forests\u201d as of 2001 (Turubanova et al 2018). For more details on this part of the methodology, see the\u00a0Technical Blog<\/u>\u00a0from Global Forest Watch (Goldman and Weisse 2019).<\/p>\n Our geographic range for the Amazon is a hybrid designed for maximum inclusion: biogeographic boundary (as defined by RAISG) for all countries, except for Bolivia and Peru, where we use the watershed boundary, and Brazil, where we use the Legal Amazon boundary.<\/p>\n 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 the Spatial Analyst Tool Box of ArcGIS. We used the following parameters:<\/p>\n Search Radius:<\/strong>\u00a015000 layer units (meters)<\/p>\n Kernel Density Function:<\/strong>\u00a0Quartic kernel function<\/p>\n Cell Size in the map:<\/strong>\u00a0200 x 200 meters (4 hectares)<\/p>\n Everything else was left to the default setting.<\/p>\n For the Base Map, we used the following concentration percentages:\u00a0High: 3-14%; Very High: >14%.<\/strong><\/p>\n We thank colleagues at Global Forest Watch (GFW), an initiative of the World Resources Institute (WRI) for comments and access to data.<\/p>\n This work was supported by Norad (Norwegian Agency for Development Cooperation) and ICFC (International Conservation Fund of Canada).<\/p>\n Finer M, Mamani N (2023) Amazon Deforestation & Fire Hotspots 2022. MAAP: 187<\/p>","protected":false},"excerpt":{"rendered":" We present a detailed look at the major\u00a02022 Amazon forest loss hotspots, based on the final annual data recently released by the University of Maryland (and featured on Global Forest Watch). This dataset is unique in that it is consistent across all nine countries of the Amazon, and distinguishes forest loss from fire, leaving the […]<\/p>\n","protected":false},"author":5,"featured_media":1698,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"give_campaign_id":0,"nf_dc_page":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1570","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"\n![\"\"](\"https:\/\/i0.wp.com\/rfmproductions.org\/wp-content\/uploads\/2023\/07\/maaproject.org-maap-187-amazon-deforestation-amp-fire-hotspots-2022-PFL-Peru-2002-2022-v2.jpg?resize=800%2C470&ssl=1\")
<\/p>\nMethodology<\/h2>\n
Acknowledgments<\/h2>\n
Citation<\/h2>\n