Annual Report of

Annual Deforestation Report of Brazil

2019 Annual Deforestation Report of Brazil

2019

EXECUTION MAPS CONCEPTION EDITORIAL DESIGN MapBiomas Marcos Reis Rosa Thiago Oliveira Basso REVIEW INSTITUTIONS AND STAFF AUTHORS Liuca Yonaha (veja a lista completa no anexo III) Tasso Rezende de Azevedo Barbara Zimbres Marcos Reis Rosa Julia Zanin Shimbo Eduardo Velez Martin ENGLISGH TRANSLATION Magaly Gonzales de Oliveira Barbara Zimbres CITATION Cesar Guerreiro Diniz Annual Deforestation Report of Brazil DATABASE ORGANIZATION Julia Zanin Shimbo 2019 – , SP – MapBiomas, Leandro Leal Parente Marcelo Matsumoto 2020 – 49 pages. Luiz Cortinhas Ferreira Neto Mario Barroso Ramos Neto Tasso Rezende de Azevedo Rafaela Bergamo http://alerta.mapbiomas.org SUMMARY

. Aknowledgments (4)

. Executive Summary (5)

1 Introduction (5)

2 Objective and Scope (7)

3 Concepts (8)

4 Methods (10)

5 Results (15)

. Annexes (36)

Brazilian Deforestation Monitoring Systems (37)

Methods Detailed Description (38)

MapBiomas Alert Institutions and Staff (47) ACKNOWLEDGMENTS LIST OF ABBREVIATIONS

To all co-creator institutions of MapBio- ABEMA – Brazilian Association of LAPIG/UFG – Laboratory of Image mas Alert, and to all the analysts who State Environmental Entities Processing and Geoprocessing at worked tirelessly to evaluate tens of thou- ANA – National Water Agency the Federal University of Goiás sands of deforestation alerts – especially MMA – Ministry of the Environment those who coordinated the work on the bi- ANAMMA – National Association of omes: Eduardo Vélez, Marcos Rosa, Diego Municipal Environmental Bodies MODIS – Moderate–Resolution Costa, Nerivaldo Afonso, Eduardo Rosa, APA – Environmental Protection Area. Imaging Spectroradiometer Joaquim Pereira, Camila Balzani, Antonio API – Application NICFI – Norway’s International Fonseca, Lana Teixeira, and Elaine Bar- Climate and Forest Initiative bosa. All institutions and team member Programming Interface analysts are listed in Annex 3. APNE – Northeast Plants Association PA – Settlement Project APP – Permanent Preservation Area PMFS – Sustainable Forest To the developers who created the tools Management Plan that made it possible to execute the ASV – Vegetation Suppression PRODES – Amazon Deforestation MapBiomas Alert, in particular: João Authorization Monitoring Program Siqueira, Rafael Guerra, Leandro Leal, CAR –Rural Environmental Registry. Luiz Cortinhas, Mateus Medeiros, and PRODES – Deforestation Sérgio Oliveira. CIFF – Children’s Investment Monitoring Program on Cerrado Fund Foundation QGIS – software Quantum GIS To the teams from INPE, IMAZON, and the CLUA – Climate and Land Use Alliance University of Maryland for producing de- RESEX – CNUC – National Register forestation detection systems, which are of Conservation Units RL – Legal Reserve the fundamental raw material of MapBio- SAD – IMAZON Deforestation mas Alert, especially to the coordinators CRQ – Remnant Quilombola Alert System of these systems: Cláudio Almeida, Carlos Communities Souza, and Matt Hansen. DETER – Real Time Deforestation SAD – Deforestation Alert Detection System System for the Caatinga biome To the government employees of IBAMA, SCCON – Santiago & Cintra Consultoria ICMBio, Brazilian Forest Service, Public FEPAM – State Prosecutor’s Office, TCU, INPE, and the Environmental Protection Foundation SEMA – State Secretariat SEMAs, who participated in the meetings Flona – National Forest for the Environment of the Technical Committee of MapBio- Funai – National Indian Foundation SFB – Brazilian Forest Service mas Alert for the ideas, contributions, SIAD – Integrated Deforestation and even for pushing us to the limit of GEE – Google Earth Engine Alert System possibilities. GLAD – Global Land Analysis and Discovery at the University of Maryland SIGEF – Land Management System To our funders for their decisive support GWC – Global Conservation SINAFLOR – National System for the to make the MapBiomas project viable: Control of the Origin of Forest Products Children’s Investment Fund Foundation ha – hectares (CIFF), Climate and Land Use Alliance SIPAM/SAR – Integrated Deforestation IBAMA – Brazilian Institute of (CLUA), Global Wildlife Conservation Alert System with orbital radar the Environment and Renewable (GWC), Good Energies Foundation, Gor- Natural Resources SIRAD-X – Deforestation monitoring don & Betty Moore Foundation, Norway’s system of the Xingu + Network International Climate and Initia- IBGE – Brazilian Institute of SIVAM – Amazon Surveillance System tive (NICFI), Arapyaú Institute, Climate Geography and Statistics and Society Institute (ICS), Humanize ICMBio – Chico Mendes Institute TCU – Federal Audit Court Institute, Walmart Foundation (US), and for Conservation TI – Indigenous Reserves Wellspring Philanthropic Fund (WPC). ICS – Instituto Clima and Society TNC – The Nature Conservancy To SCCON/Planet for the partnership in ICV –Centro de Vida Institute UC – Conservation Units building a customized platform to operate ID – Unique Identifier of an Alert the selection of images for validation and UEFS –Feira de Santana refinement of deforestation alerts. IMAZON – Institute of People and State University Environment of the Amazon UF – Federation Unit To Google for supporting the data process- INCRA – National Institute of UFRGS – Federal University ing and storage infrastructure that allows Colonization and Agrarian Reform of Rio Grande do Sul MapBiomas to truly operate as a network. INPE – National Institute US – United States of America for Space Research To IBAMA and the Brazilian Forest Ser- WRI – World Resources Institute vice for providing web services granting IPAM –Amazon Environmental access to the CAR and SINAFLOR data- Research Institute bases, which are essential to produce ISA – Socioambiental Institute customized reports. JAXA – Japanese Aerospace To the Arapyaú Institute for the institu- Exploration Agency tional, administrative, legal and financial support necessary to organize the Map- JICA – Japan International Biomas network, in particular Amanda Cooperation Agency Nunes, Emma Lima, Felipe Gasperi, Re- JJFAST – Forest Early Warning nata Piazzon, and Andrea Apponi. System in the Tropics EXECUTIVE

SUMARY

This report analyzes the Brazilian defor- SINAFLOR (National System for the Con- estation alerts, which have been validat- trol of the Origin of Forest Products), the ed and refined by theMapBiomas Alert National Registry of Conservation Units project based on high-resolution satellite (CNUC), and other geographic bound- imagery for the year 2019. aries (e.g., biomes, states, river basins). Also, the recent annual land use land As part of the multi-institutional Map- cover history (2012 to 2018) acquired Biomes initiative (MapBiomas.org), in- from the MapBiomas Brazil project was volving universities, NGOs, and technol- also presented in the reports. ogy companies, the MapBiomas Alert project aims to develop a system for the validation and refinement of alerts of In total, 56,867 alerts were identified, deforestation, degradation, and regen- validated, and refined across the Brazil- eration of native vegetation based on ian territory, resulting in 1,218,708 hect- 2 high-resolution images. ares (12,187 km ) of deforestation. Out of all alerts, 83% (63% of the area) are in the This publication corresponds to the first , with a total area of ​​770 Annual Deforestation Report produced thousand ha. The Cerrado biome is next in Brazil, covering all the Brazilian bi- with 13% of the alerts (33.5% of the area), omes. In this version, the alerts gener- totaling 408.6 thousand ha, followed by ated by DETER (INPE’s Real-Time Defor- the with 16.5 thousand ha, the estation Detection System, which covers with 10.6 thousand ha, the Amazon and Cerrado biomes), SAD Caatinga with 12.1 thousand ha, and (Imazon’s Deforestation Alert System, for Pampa with 642 ha. the Amazon), and GLAD (Global Land Incidence of alerts and total Analysis and Discovery, from the Univer- deforested area by biome (2019) sity Maryland, covering all the remaining BIOME ALERT DEFORESTED biomes) were used as a reference to lo- INCIDENCE AREA (HA) cate deforestation with daily high-res- Amazon 47,269 770,148 olution (3 meters) satellite images. For Caatinga 523 12,153 each validated and refined alert, a report Cerrado 7,402 408,646 was generated which contained images Atl. Forest 1,390 10.598 from before and after the deforestation Pampa 68 642 event, as well as possible intersections Pantanal 215 16,521 between the alerts and areas from the BRAZIL 56,867 1,218,708

Rural Environmental Registry (CAR), the Source: MapBiomas Alert.

Annual Deforestation Report of Brazil — 2019 5 The Amazon and the Cerrado together Over 38% of the alerts (55% of the area) represented 96.7% of the deforested have some degree of overlap with Per- area detected in 2019. These are the manent Preservation Areas (APP), Legal two best-monitored biomes in Brazil, Reserves, or headwaters as declared in presenting continuous deforestation the CAR, which are legally protected by monitoring systems with methodologi- the Forest Code. cal approaches adapted for the respec- tive regions. The other biomes use data Over 99% of validated deforestation from GLAD, a global monitoring system alerts (96% in area) do not have the au- without adaptation to specific condi- thorization to suppress native vegetation tions. As a result, the number of alerts registered in the SINAFLOR system – and the areas identified by MapBiomas National System for the Control of Origin Alert are a conservative estimate, still of Forest Products. representing an underestimation of the total area deforested. When crossed with rural properties with a suppression license, which respected The states with the highest numbers of the Legal Reserve, APP, and headwater deforestation events are Pará (18,500), restriction zones, and which do not over- (9,300), Amazonas (7,000), Ron- lap protected areas (UC and TI), only donia (5,300), and (4,7 105 of the 56,867 alerts, or 0.2% (0.5% of thousand). In terms of total deforested the total area), are identified as legally area, the most prominant are Pará (299 compliant. These indexes point to alevel thousand ha), Mato Grosso (202 thou- of irregularity of the deforestation in sand ha), and Amazonas (126 thousand Brazil above 99%. ha). Together, these three latter states accounted for more than half of the For the year 2019, more than 76 thou- deforestation detected in the country sand reports were produced with analy- in 2019. ses for each deforestation alert, identify- ing their overlaps with different spatial Of the total deforested areas, 11.1% of the features, as well as their suppression alerts (12% of the area) overlap entire- licenses, when they do exist. All alerts ly or partially with Conservation Units and reports are freely and publicly avail- (UCs, in Portuguese); 5.9% (3.6% of the able in the MapBiomas Alert platform area) with Indigenous Reserves (TI, in (alerta.mapbiomas.org). Portuguese); and 65% (77% in area) with rural properties included in the Rural This is a contribution from the Map- Environmental Registry. Biomas Project to support public and private institutions in the process of reducing deforestation and promoting the conservation and sustainable use of biodiversity in the Brazilian territory. .

Annual Deforestation Report of Brazil — 2019 6 1. INTRODUCTION

Brazil has a long tradition of monitoring fines, civil actions, and embargoes rep- deforestation. At the end of the 1980s, rimanded less than 1% of the Amazon’s INPE established the Amazon Defor- illegal deforestation. estation Monitoring Program (PRODES) and, shortly after that, the map of the Another problem is that continuous and Atlantic Forest Remnants, as a partner- consistent monitoring of deforestation ship between INPE and the SOS Mata is concentrated only in three biomes Atlântica Foundation. In 2004, INPE in- (Amazon, Cerrado, and the Atlantic For- troduced DETER (Real-Time Deforesta- est), while in the other three (Pantanal, tion Detection System), a new tool with Pampa, and Caatinga), including the monthly information on deforestation Coastal Zone, there is still no such type in the Amazon. of control.

Recently, DETER was expanded to the The MapBiomas Alert initiative emerged Cerrado biome. Since 2006, IMAZON’s at the end of 2018 with the objective SAD (Deforestation Alert System) is also of adding value to the existing defor- in operation, covering the Amazon bi- estation monitoring systems in Brazil. ome. Currently, there are at least nine It ensures that each deforestation alert systems, national and international, can be verified, validated, and refined that monitor deforestation in Brazil cov- with high spatial resolution satellite im- ering different biomes and with varying agery, improving the spatial precision of frequencies and spatial resolutions. the alert, and determining its degree of legal regularity. Monitoring is central to taking action to control deforestation and restrict it This report is the first in a series, which to areas that have not been authorized seeks to be annually released, consoli- through the proper licensing process. dating and analyzing information on all deforestation in the Brazilian biomes, Although monitoring has been around which has been detected by the multi- for a long time, actions are still limited, ple systems available and processed by both with annual and monthly data, ei- MapBiomas Alert. ther to prevent, control, or penalize ille- gal deforestation in all Brazilian biomes. According to IBAMA, between the years of 2005 and 2018, it is estimated that

Annual Deforestation Report of Brazil — 2019 7 2. OBJECTIVE 3. CONCEPTS AND SCOPE DEFORESTATION IS THE The purpose of this report is to present COMPLETE OR ALMOST a consolidated overview of the defor- COMPLETE SUPPRESSION estation alerts detected in all Brazilian OF THE NATIVE biomes throughout the year of 2019, and VEGETATION EXISTING which have been validated and refined IN A CERTAIN AREA with high-resolution images by the Map- Biomas Alert Project. The suppression of isolated trees or of a plot while maintaining a remnant This is the firstAnnual Deforestation vegetation does not constitute defor- Report produced in Brazil, covering all estation. These cases constitute cutting the Brazilian biomes. down of an isolated tree, selective log- ging, or burning which may result from It is worth clarifying that the deforesta- agricultural practices in contact with tion data processed and analyzed in the borders of native vegetation, forest this report are limited to the regions management, or degradation. These of the Brazilian territory where alerts cases are therefore not detected as a of deforestation issued by the DETER, deforestation alert. SAD, and GLAD monitoring systems are available. The definition of deforestation encom- passes a series of conditions that are clarified as following, in order to clearly qualify the data and analysis in this report.

Deforestation or suppression of native vegetation – deforestation is commonly associated with the idea of complete suppression of native forest only. In this report, the term deforestation refers to the broader understanding, which encompasses suppression of any and all native vegetation, including non- forest vegetation such as grasslands and savannas. Therefore, this report takes into account suppression of native vegetation.

Annual Deforestation Report of Brazil — 2019 8 Deforestation: Primary or Secondary Date of Detection and Deforestation – primary deforestation refers to the Ocurrence – the date of detection refers deforestation of primary forests and native to the moment in which the deforestation vegetation, while secondary deforestation was detected or verified. The date refers to the suppression of secondary of occurrence refers to the period in vegetation. This report mainly addresses which the deforestation actual took primary deforestation, given that the place (always a date prior to detection). alert systems used are concentrated in This report addresses areas with areas of primary vegetation. Nonetheless, deforestation detected in the year 2019. areas of secondary deforestation, whenever verified, are also included Rate of deforestation and deforestation in the MapBiomas Alert data. observed area – the observed area is the spatial extent quantified directly Gross or net deforestation – gross by the comparison of satellite images deforestation considers the loss of native from different dates (before and after vegetation cover alone. On the other hand, deforestation). The rate of deforestation the concept of net deforestation or net uses the information of the observed loss refers to the deforestation already area to estimate the deforestation discounting the area in which vegetational that occured in all of the territory, regeneration has occurred. In this report, including areas that could not be only gross deforestation is addressed. observed. MapBiomas Alert works only with the concept of observed area. Deforestation alert and Deforested area – a deforestation alert refers to an event Deforestation Speed – refers to the ratio indicative of deforestation in a specific between total deforested area and the place. Deforested area is the actual area number of days that elapsed between affected by the suppression of native the start and the end of deforestation, vegetation. MapBiomas Alert identifies usually expressed in hectares or km2 per the deforested areas, using as a starting day. In MapBiomas Alert, the speed is point the deforestation alerts generated always underestimated, as the calculation by the monitoring systems available, is done in an approximate way, based such as DETER, SAD, and GLAD. on the dates of the satellite images available to document the moments before and after the deforestation event.

Deforestation and Degradation – deforestation addresses the complete suppression of native vegetation, while degradation refers to the partial removal of native vegetation. This report deals only with cases of deforestation.

Annual Deforestation Report of Brazil — 2019 9 4. METHOD

In Brazil, data are available from at least Each alert generated by the three se- nine deforestation monitoring systems. lected systems is inserted into the da- In Annex 1, we present a description of tabase and goes through a process of each of these systems, including nation- aggregation, validation, and refine- al and international initiatives. ment on the MapBiomas Alert platform. This process is based on the analysis In this report, we analyzed deforestation of satellite imagery (PlanetScope), at alerts detected by three monitoring 1 a 3-meter spatial resolution, and in- systems1 operating in Brazil in 2019: cludes the following steps:

DETER/INPE for the Amazon Collection and Aggregation – in this and the Cerrado. step, all of the detected deforestation SAD/IMAZON for the Amazon. alerts in each month by the DETER/ GLAD/University of Maryland for INPE, SAD/Imazon, and GLAD/UMD the Pampa, Pantanal, Caatinga, systems are downloaded from the and the Atlantic Forest. respective servers, and overlapping alert polygons are aggregated. In this These three systems were selected to process, a unique identifier (ID) is ensure coverage over all biomes, and attributed to each alert, which will be because they present similar spatial permanent until the end of the validation, resolutions and produced data at least refinement, and publication process. at a monthly frequency during 2019. Validation – includes two steps. The first is done in an automated way, by 4.1 STEP DESCRIPTION excluding alerts that overlap areas mapped as forestry or agriculture by the MapBiomas land cover collections,

The detailed method description is or alerts that have been detected in available in Annex 2. Below, we present previous validations. After this process, the remaining alerts are evaluated with a brief and simplified explanation of the the support of PlanetScope high-resolution process applied for validating and refin- images (3 meters) with high temporal ing the deforestation alerts (Figure 1). frequency (daily, and if possible weekly). In this step, alerts that constitute cases of false positives are discarded, and 1 In addition, at the begging of the project, alerts generated from the reason for discarding are recorded SIPAM/SAR radar imagery produced by the , within the scope of the Amazon Surveillance System, (e.g. overlap with forestry, issues due to were tested. The data, however, are not public and, after a test seasonality, etc). The validation step is phase, access was ceased.

Annual Deforestation Report of Brazil — 2019 10 concluded by selecting the best pair of possessing suppression licenses or forest satellite images to represent the moments management plans, according to the before and after the deforestation event. IBAMA’s Sinaflor system. Alerts are also crossed with maps of municipalities, Refinement – alerts that have passed states, biomes, and river basins. the validation step go through a polygon This information qualifies the alerts and refinement process, which delineates allows the generation of well-grounded more precisely the area that has been technical reports containing relevant deforested, based on the high-resolution information to institutional users. images. The generation of a refined polygon is done in an automated way, Publication – the final phase consists supported by a supervised classification of publishing all alerts and their algorithm (Random Forest) running on respective reports onto a web platform, the Google Earth Engine platform. The where each alert can be viewed and only manual action in this step is to filtered by territorial features (e.g. collect training samples that represent states, municipalities, protected the deforested and non-deforested areas) or administrative boundaries area in the high-resolution images. (e.g. authorized or unauthorized deforestation). The platform also allows Auditing – each refined alert goes access to essential alert statistics (for through an auditing process carried example, number and area of the alerts, out by a technical supervisor for each average deforestation speed, size class, biome. In this step, the need to re-do etc). Data can also be accessed by any of the steps before final publication machine–to–machine communication of the refined alerts is evaluated. services (API, WebServices, and The first 20,000 published alerts did Plugin), or be downloaded. not include the auditing process, which was implemented later. 4.2 METHOD LIMITATIONS Intersection with territorial and administrative boundaries – the final The MapBiomas Alert method has refined alert polygons are crossed with some limitations that must be taken land tenure and fiscalization databases, into account: including the limits of Indigenous

Reserves (TI, in Portuguese), Protected A Omission of alerts – alerts are refined Areas (UC), rural settlements, areas based on the existence of a previously registered in the Environmental Rural captured alert by a third-party Registry (CAR), areas overlapping self- deforestation detection system. The declared Permanent Preservation Areas possible omissions of these systems in (APP) and Legal Reserves (RL), as well detecting deforestation also affect the alerts as areas under embargo, and areas evaluated by MapBiomas Alert.

Annual Deforestation Report of Brazil — 2019 11 It is worth noting that deforestation claim that deforestation occurred in the monitoring systems have a minimum period between the two images, but it detection area. For example, alerts smaller does affect the calculation of the average than 6.25 hectares are not detected by speed at which deforestation occurred. DETER Amazon, and smaller than 1 hectare

are not detected by DETER Cerrado. This C Automatic Polygon Delineation – the problem is particularly important in the polygons that define the alerts after case of the Caatinga biome, where the only refinement are established by an detection system in operation (GLAD) is not automatic classification of the changing adapted for native vegetation suppression area between two images, where the in the semi-arid environment, presenting a native vegetation has been suppressed. very high degree of omission. To overcome In some cases, the polygon can appear this obstacle, the MapBiomas team in the too detailed. This happens because small Caatinga developed the SAD Caatinga, areas with previous signs of change which will start operating in 2020. or small remnant clusters of trees are discounted from the deforestation area.

B Underestimated Speed of Deforestation

– when validating and refining an alert, a D Non-woody vegetation detection – search is made for a pair of good quality detection of the suppression of non-forest satellite images from before and after vegetation, such as grasslands, has deforestation. The “before” image is the limitations due to the detection capability most recent one available, up to 12 months of the original monitoring system, which before detection, while the “after” image focuses on identifying forest suppression. is the closest to the end of deforestation, However, when suppression of non-forest depending on good visual quality. Cloud vegetation occurs in the alert area or presence can increase the period between adjacent to it, the use of high-resolution before and after images in days, weeks, images allows its capturing during the and even months. This does not alter the alert refinement phase. This way, most of

AGGREGATION PRE- VALIDATION REFINEMENT INTERSECTIONS PUBLICATION VALIDATION AND AUDITING

DETER-B

SAD

GLAD

PlanetScope PlanetScope Asset Validation and TI, UC, INCRA, Open-Access Web- Monthly Mosaic (3 m) Refinement CAR (RL and APP), Platform (reports Sentinel Images (machine-learning) Mun., State, Biome, for IBAMA, MPF, MapBiomas Maps Basins, Embargoes, OEMA’s) PMFlor

Figure 1. Process of aggregation, validation, refinement, intersections, and publication of the deforestation alerts in the MapBiomas Alert platform.

Annual Deforestation Report of Brazil — 2019 12 the deforestation in non-woody vegetation Minimum Mapped Area – PRODES detected in 2019 was mapped occasionally, systems detect deforestation areas always by observing the areas surrounding larger than 6.25 ha. The Atlantic Forest the woody vegetation alerts. The current Atlas detects areas over 3 ha. PRODES system therefore still underestimates the Cerrado, areas with more than 1 ha. suppression of native non-forest vegetation. MapBiomas Alert takes into account all observed areas larger than 0.3 ha.

E Up to Date Official Databases – We use official databases for spatial information (e.g. Funai for Indigenous Reserves, MMA Area Calculation – PRODES estimates for Protected Areas, SICAR/Forest Service the total area deforested from the area for CAR), and administrative information actually observed. Thus, by extrapolation, (e.g. Sinaflor/Ibama for licenses of it estimates the total area deforested vegetation suppression, forest management considering areas that could not be plans, and embargoes). In case these observed due to issues. In the databases are not up to date, this will also case of MapBiomas Alert, only the sum affect the information that constitutes the of the observed areas of deforestation is reports produced by MapBiomas Alert. accounted for, and no estimates are made to account for areas that were not observed.

Analysis Period – PRODES Amazon and PRODES Cerrado analyze the period from August 1st, 2018 to July 30th, 2019. The Atlantic Forest Atlas analyzes the period from October 1st, 2018 to September 30th, 2019. MapBiomas Alert publishes all alerts detected in the calendar year of 2019 (from January 1st to December 31st).

4.3 DIFFERENCES FROM Image capture period – PRODES Amazon and PRODES Cerrado use images from ANNUAL OFFICIAL DATA July to September to observe deforested areas in regions often covered by clouds. Deforestation data from MapBiomas The Atlas uses images from July to Alert should be used with caution when November. MapBiomas Alert can consult compared to official deforestation data daily images of July 2018, to identify the (PRODES Amazon, PRODES Cerrado, best cloudless images from before the and Atlas of Atlantic Forest Remnants), 2019 detections, and up to December as they have some important differenc- 31st , 2019, seeking for cloud-free images es (Table 1, below). after the 2019 deforestation event.

Annual Deforestation Report of Brazil — 2019 13 Chart 1. Differences between data from official deforestation systems and the MapBiomas Alert project.

FEATURE PRODES PRODES ATLAS OF MAPBIOMAS AMAZON CERRADO ATLANTIC ALERTA FOREST Minimum area 6.25 ha 1 ha 3 ha 0.3 ha Estimates the deforestation rate, Sum of the Sum of the Sum of the Area estimation considering non- observed area. observed area. observed area. observed area.

August 2018 to August 2018 to October 2018 to January 2018 to Analisys period July 2019 July 2019 September 2019 December 2019

Image capture July 2018 to June to Setember 2019 July 2018 to July 2018 to period September 2019 November 2019 December 2019

Cerrado biome limits at a scale of Spatial coverage Brazilian Legal 1:5,000,000, excluding The Atlantic Forest IBGE Biome limts at a Amazon areas of overlap with area as defined by law. scale of 1:250,000. the Legal Amazon.

Primary or forest Type of vegetation since 1988 Primary vegetation, vegetation (excluding cerrado Existing vegetation Primary or existing and may include and non-forest areas in 2000. vegetation in 1985. secondary vegetation. mapped in 1988).

Spatial Coverage – PRODES Amazon Mapped Vegetation Types – PRODES considers the Legal Amazon region, Amazon detects deforestation in primary including the entire Amazon biome and forest (or forests classified as such existing forests neighboring the Cerrado biome. since 1988). PRODES Cerrado detects the PRODES Cerrado considers the Cerrado deforestation of primary forest, savannas, biome, according to the 2006 IBGE biome and grasslands (or classified as such map at a scale of 1:5,000,000, excluding since 2000). The Atlas of Atlantic Forest the areas overlapping the Legal Amazon. Remnants detects deforestation in primary The Atlantic Forest Atlas considers the Atlantic Forest formations (or classified area defined in the Atlantic Forest Law as such since 1985). MapBiomas Alert (Bill nº 11.428 from 2006), revised to a detects deforestation in primary forests, scale of 1:1,000,000, which includes the savannas, or in regenerating secondary Atlantic Forest biome and forest enclaves vegetation. Some alerts, mainly in the in the Northeast. MapBiomas Alert Pampa and Pantanal biomes, may considers deforestation throughout the also include grasslands converted to national territory and qualifies the biome human use adjacent to deforestation according to the map of biomes produced alerts of forests and savannas. by IBGE in 2019, at a scale of 1:250,000.

.

Annual Deforestation Report of Brazil — 2019 14 5. RESULTS 5.2. ALERT 5.1. NUMBER OF ALERTS CONSOLIDATION, VALIDATION, ORIGINALLY GENERATED AND REFINEMENTS

The five detection systems consid- The alerts derived from the detection ered by MapBiomas Alert generated systems were consolidated and validat- 168,134 deforestation alerts, which ed, taking into consideration the over- were detected in 2019. In Table 1, are lap of areas monitored by more than indicated the number of alerts per bi- one system (e.g. SAD and DETER in the ome, after crossing the alerts with the Amazon), as well as occasional intersec- biome boundary map defined by IBGE tions between alerts. Next, alerts that at the scale of 1:250,000, published in could not be validated due to the lack 2019. Some alerts by DETER-CERRADO of imagery, or that constituted a false can be found in the Amazon and the positive (e.g. areas of forestry harvest) Caatinga biomes, since this new ver- were discarded. sion of the IBGE’s boundary map altered the previous biome limits, which had The whole process resulted in the vali- been published in 2004 at the scale of dation and refinement of 56,867 alerts, 1:5,000,000, and which were used by which together comprise 1,218,708 the detection systems. hectares over the six Brazilian biomes.

Table 1. Number of alerts generated by the detection systems by biome in 2019.

SYSTEM AMAZON CAATINGA CERRADO ATLANTIC PAMPA PANTANAL TOTAL FOREST DETER- 162 643 12,659 6 – 110 13,580 Cerrado DETERB- 53,681 – 99 – – 2 53,782 Amazon GLAD 115 908 863 14,655 746 2,258 19,545 SAD 77,394 – 319 – – – 77,713 SIPAM- 3,514 – – – – – 3,514 SAR 134.866 1,551 13,940 14,661 746 2,370 168,134

Annual Deforestation Report of Brazil — 2019 15 Figure 2. Participation of biomes in the number 5.3. PROFILE OF THE and total area of deforestation alerts in 2019. VALIDATED AND REFINED ALERTS IN 2019 NUMBER OF ALERTS 83.1% 3,9% 13.0% A. ALERTS BY BIOME Table 2 and Figure 2 present the quan- titative analysis of the alerts and their respective areas (hectares) by biome. The Amazon and Cerrado biomes together DEFORESTED AREA comprise 96% of all the alerts and 96.7% 63.2% 3.3% 33.5% of the total deforested area in 2019. Figure 3 presents the spatial distribution of the alerts in the Brazilian biomes.

Table 2. Validated and refined alerts by biome in 2019.

NUMBER OF % OF DEFORESTED DEFORESTED ALERTS ALERTS AREA (HA) AREA % Amazon 47,269 83.1% 770,148 63.2% Caatinga 523 0.9% 12,153 1.0% Cerrado 7,402 13.0% 408,646 33.5% Atl. Forest 1,390 2.4% 10,598 0.9% Pampa 68 0.1% 642 0.1% Pantanal 215 0.4% 16,521 1.4% BRAZIL 56,867 1,218,708

Although the Cerrado is responsible for only 13% of the number of alerts, its deforested area represents a third of the total (33.5%).

Alerts Amazon Caatinga N Cerrado Atlantic Forest Pampa 500 km Pantanal

Figure 3. Geographic distribution of the deforestation alerts validated and refined in the Brazilian biomes in 2019.

Annual Deforestation Report of Brazil — 2019 16 B. ALERT SIZE Table 3. Mean and maximum size of the alerts by biome in (ha). Table 3 presents the average alert area (hectares) by biome. The Pantanal pres- BIOME MEAN MAX ents the greatest mean deforested area Amazon 16 4,451 per alert, with 77 ha, followed by the Caatinga 23 707 Cerrado, with 55 ha. The Pampa and Cerrado 55 2,377 the Atlantic Forest present the smallest Atl. Forest 8 123 mean area per alert, which can be ex- Pampa 9 113 plained by the high degree of landscape Pantanal 77 1,997 fragmentation and the smaller sizes of BRAZIL 21 4,451 the rural properties in these biomes. The largest deforested area detected in 2019, covering 4,551 ha, was located in the Amazon, in the Altamira municipal- ity (state of Pará) (Figure 4). Altamira (PA), image before the deforestation event, with an area equivalent to 4,500 soccer fields.

Figure 4. Largest deforestation area detected in 2019 (Alert id 27847) in the Altamira municipality (state of Pará).

Annual Deforestation Report of Brazil — 2019 17 Areas smaller than 25 ha represent 83% C. DEFORESTATION SPEED of the alerts, but only 27% of the total deforested area. Areas larger than 100 The following table presents indicators ha represent 3.7% of the alerts but cor- of the speed of deforestation. In 2019, in respond to 44% of the total deforested average 156 new deforestation events area (Figure 5). were detected and validated per day, with a mean estimated deforestation speed of 0.28 hectare per day for each NUMBER OF ALERTS event.

< 1 ha 2,724 In 2019, in average 3,339 ha per day 21,200 1 a 5 ha or 139 ha per hour were deforested in Brazil. The speed of deforestation of 5 a 10 ha 11,843 an alert is calculated by dividing the

10 a 25 ha 11,423 deforested area by the number of days between images from before and after 25 a 50 ha 4,918 the deforestation event. This speed is always underestimated, since it is not 50 a 100 ha 2,643 always possible to obtain a good image > 100 ha 2,119 from the precise days at the beginning or at the end of the suppression, espe- cially in those periods and regions with DEFORESTED AREA (HA) high cloud cover. It is, however, a good approximation of the speed at which < 1 ha 1,972 these events take place.

1 a 5 ha 58,099 The mean maximum speed for a single 5 a 10 ha 88,400 event was reached in an area of 1,148 hectares in the Jaborandi municipality 10 a 25 ha 180,030 (state of ). This area was deforest- 25 a 50 ha 171,026 ed between May 8th and 27th, 2019, with an average speed of 60 ha per day 50 a 100 ha 183,468 (Figure 6). 539,318 > 100 ha

Figure 5. Distribution of the number of alerts and deforested area per size class in 2019.

Annual Deforestation Report of Brazil — 2019 18 Table 4. Indicators of deforestation speed by biome in 2019.

MEAN SPEED MAXIMUN MEAN DEFORESTED DEFORESTED PER ALERT SPEED NUMBER OF AREA PER AREA PER HA/ALERTA/DAY HA/ALERTA/DAY ALERTS DAY HOUR PER DAY HA HA Amazon 0.17 40 130 2,110.0 87.92 Caatinga 0.42 9 1 33.3 1.39 Cerrado 0.99 60 20 1,119.6 46.65 Atl. Forest 0.12 19 4 29.0 1.21 Pampa 0.12 2 0 1.8 0.07 Pantanal 0.87 13 1 45.3 1.89 BRAZIL 0.28 60 156 3,339 139

Figure 6: Alert (id 87545) with the highest mean deforestation speed, located in Jaborandi (state of Bahia).

Annual Deforestation Report of Brazil — 2019 19 D. ALERTS BY STATE

All of the Brazilian states presented Nearly a third of the deforestation events deforestation alerts in 2019, includ- were located in the state of Pará (32.6%). ing the Federal District (Table 5). The Five Amazonian states (Pará, Acre, Ama- northeastern states that comprise the zonas, Rondônia, and Mato Grosso) were Caatinga biome presented the lowest responsible for 78.8% of the detected numbers of alerts and smallest areas alerts, and for 66% of the total deforested of deforestation, which can reflect the area. Ten states went over the mark of a limitations of the current systems in thousand alerts detected in 2019. detecting suppression in the semi-arid. Three states had a mean deforestation speed over 1 ha a day per alert: and Piauí (1.19), and Bahia (1.06).

Table 5. Profile of the validated alerts by state.

STATE NUMBER AREA NUMBER AREA MEAN OF ALERTS (HA) % % SPEED HA/ALERT/DAY

Acre 9,302 57,891 16.4% 4.8% 0.06 6 59 0.0% 0.0% 0.07 Amapá 505 1,487 0.9% 0.1% 0.03 Amazonas 7,014 125,881 12.3% 10.3% 0.15 Bahia 1,227 66,753 2.2% 5.5% 1.06 29 845 0.1% 0.1% 0.27 Distrito Federal 4 96 0.0% 0.0% 0.27 Espírito Santo 19 107 0.0% 0.0% 0.09 Goiás 1,098 33,163 1.9% 2.7% 0.53 Maranhão 2,486 80,974 4.4% 6.6% 0.49 Mato Grosso 4,701 201,621 8.3% 16.5% 0.58 407 28,069 0.7% 2.3% 0.85 855 26,066 1.5% 2.1% 0.41

Pará 18,564 298,540 32.6% 24.5% 0.17

Paraíba 3 11 0.0% 0.0% 0.06 Paraná 265 2,197 0.5% 0.2% 0.10 15 134 0,0% 0.0% 0.08 Piauí 600 41,776 1.1% 3.4% 1.19 21 125 0.0% 0.0% 0.09 4 72 0.0% 0.0% 0.19 Rio Grande do Sul 222 1,155 0.4% 0.1% 0.10 Rondônia 5,255 122,507 9.2% 10.1% 0.22 2,138 24,001 3.8% 2.0% 0.10 130 494 0.2% 0.0% 0.08 São Paulo 54 369 0.1% 0.0% 0.08 15 257 0.0% 0.0% 0.19 Tocantins 1,928 104,056 3.4% 8.5% 1.19 BRAZIL 56,687 1,218,708 100% 100% 0.28

Annual Deforestation Report of Brazil — 2019 20 States – Deforested area lower than 10 km2 2 50 km N 500 km2 1,000 km2 more than 1,000 km2 500 km

States – v of Alerts 3 – 500 N 501 – 1,000 1,001 – 2,500 2,501 – 10,000 10,001 – 18,802 500 km

Figure 7. Deforested area and number of alerts by state in Brazil in 2019.

Annual Deforestation Report of Brazil — 2019 21 E. ALERTS BY MUNICIPALITY

Out of the 5,570 Brazilian municipali- Among the ten municipalities that de- ties, 1,734 (31%) presented at least one forested the most in 2019, four are in deforestation event detected and vali- Pará, three are in Amazonas, one is in dated in 2019 (Figure 8). Out of those, Bahia, one is in Mato Grosso, and one is 50 municipalities were responsible for in Rondônia. The Altamira municipality 44% of the alerts and 50% of the total (Pará) had by far the largest deforested area of deforestation in Brazil (Table 6). area detected in 2019, over 54,000 ha. On the other hand, São Félix do Xingu (Pará) was the municipality with the highest number of events, totalling 1,716 alerts.

Table 6. List of the 50 municipalities with highest deforestation rates in 2019 in Brazil.

MUNICIPALITY ALERTS AREA (HA) Altamira (PA) 1,261 54,169 Cujubim (RO) 339 8,188

São Félix do 1,716 39,680 Humaitá (AM) 455 8,173 Xingu (PA) Baixa Grande do Ribeiro 38 8,147 (RO) 1,254 35,523 (PI)

Lábrea (AM) 730 32,492 Jaborandi (BA) 30 8,059

Apuí (AM) 682 22,050 Trairão (PA) 280 7,875

F. do Rio Preto (BA) 77 21,801 Sena Madureira (AC) 1,124 7,729

Novo Progresso (PA) 478 20,807 Anapu (PA) 794 7,725

Itaituba (PA) 1,290 19,789 Uruçuí (PI) 17 7,257

N. Aripuanã (AM) 293 18,241 Apiacás (MT) 191 7,124

Colniza (MT) 585 17,709 Machadinho D’oeste (RO) 298 6,337

Aripuanã (MT) 432 15,596 Manicoré (AM) 213 6,292

Pacajá (PA) 1,121 13,400 Rio Branco (AC) 667 6,223

Boca do Acre (AM) 801 13,031 Corumbá (MS) 54 6,133

Nova Mamoré (RO) 529 12,642 Paranã (TO) 74 6,018

Portel (PA) 705 11,542 Juara (MT) 94 5,779

Uruará (PA) 691 11,309 Rorainópolis (RR) 404 5,746

N. Bandeirantes (MT) 283 9,985 Tarauacá (AC) 1,223 5,744

Candeias Jamari (RO) 415 9,874 Canutama (AM) 266 5,650

S. José Porfírio (PA) 630 9,566 Seringueiras (RO) 109 5,503

Balsas (MA) 89 9,518 Barreiras (BA) 44 5,392

Jacareacanga (PA) 471 9,118 Porto Murtinho (MS) 57 5,367

Rurópolis (PA) 570 9,045 Marcelândia (MT) 56 5,227

Feijó (AC) 1,654 8,787 Cocalinho (MT) 40 5,091

N. Repartimento (PA) 816 8,772 Costa Marques (RO) 200 4,891

Placas (PA) 571 8,760 Paranatinga (MT) 66 4,666 25,277 603,540

Annual Deforestation Report of Brazil — 2019 22 States – Number Municipalities – of Alerts 2019 Deforested area 2019

3 - 500 lower than 10 km2 N 501 - 1,000 50 km2 1,001 - 2,500 500 km2 2,501 -10,000 1,000 km2 10,001-18,802 500 km more than 1,000 km2

Figure 8. Deforested area and number of alerts by municipality in Brazil in 2019. . .

Figure 9. Deforestation alerts detected and validated in São Félix do Xingu in 2019.

Annual Deforestation Report of Brazil — 2019 23 F. ALERTS IN PROTECTED AREAS present any deforestation alerts within protected areas (Table 7). Out of the 1,453 protected areas (UC, in Portuguese) registered in the Nacio- The deforestation that took place within nal Registry of Conservation Units, 226 protected areas represented 11% of the (16%) presented at least one event of total alerts, and 12% of the total deforest- deforestation detected in 2019. ed area in 2019. When diregarding the category ‘APA’, in which private proper- The highest number of alerts and defor- ties with rural activities are allowed, the ested areas within protected areas were total deforested area within protected located in the Amazon, representing areas drops to 5.1% of the total deforesta- 12% of the total alerts, and 13% of the tion in Brazil (Table 8). The deforestation deforested area in the biome. The Pan- rate, considering all protected areas in tanal biome, on the other hand, did not Brazil, was 0.1% of the total area.

Table 7. Alerts with total or partial overlap with protected areas in each biome in 2019.

NUMBER AREA (HA) % NUMBER % AREA Amazon 5,711 100,483 12.1% 13.0% Caatinga 21 320 4.0% 2.6% Cerrado 452 44,069 6.1% 10.8% Atl. Forest 116 767 8.3% 7.2% Pampa 4 15,951 5.9% 2.5% Pantanal – – 0.0% 0.0% BRAZIL 6,304 145,655 11.1% 12.0%

Table 8. Alerts with total or partial overlap with protected areas in each biome in 2019, except APAs (Área de Proteção Ambiental, in Portuguese). NUMBER AREA (HA) % NUMBER % AREA Amazon 3,878 60,594 8.2% 7.9% Caatinga – – 0.0% 0.0% Cerrado 10 1,813 0.1% 0.4% Atl. Forest 13 71 0.9% 0.7% Pampa – – 0.0% 0.0% Pantanal – – 0.0% 0.0% BRAZIL 3,901 62,478 6.9% 5.1%

Out of the 226 protected areas with The largest deforested area was located deforestation alerts, 22 had more than in the Triunfo do Xingu APA. However, 1,000 hectares deforested, and were the protected area with the highest num- distributed in eight states: Pará, Bahia, ber of alerts was the Chico Mendes RE- Tocantins, Rondônia, Acre, Mato Grosso, SEX in Acre with 1,197 events (Table 9). Maranhão, and Goiás (Figure 10).

Annual Deforestation Report of Brazil — 2019 24 Table 9. List of the protected areas with the largest deforested areas in 2019.

PROTECTED AREAS STATE ALERTS AREA (HA) Area de Proteção Ambiental Triunfo do Xingu PA 540 30,360 Area de Proteção Ambiental do Rio Preto BA 67 13,449 Floresta Nacional do Jamanxim PA 162 10,099 Area de Proteção Ambiental Ilha do Bananal/Cantão TO 172 9,756 Reserva Extrativista Jaci-Paraná RO 212 8,970 Reserva Extrativista Chico Mendes AC 1,197 6,997 Floresta Nacional Altamira PA 62 6,259 Area de Proteção Ambiental do Tapajós PA 702 6,259 Area de Proteção Ambiental Bacia do Rio de Janeiro BA 22 5,053 Area de Proteção Amb. da Cabeceiras do Rio Cuiabá MT 28 4,299 Estação Ecológica da Terra do Meio PA 90 4,217 Area de Proteção Ambiental dos Morros Garapenses MA 26 2,410 Floresta Nacional de Bom Futuro RO 94 2,195 Area de Prot. Amb. das Nascentes do Rio Vermelho GO 9 2,045 Reserva Extrativista Rio Preto-Jacundá RO 37 1,821 Reserva Biológica Nascentes Serra do Cachimbo PA 22 1,538 Area de Proteção Ambiental Pouso Alto GO 42 1,495 Parque Estadual e Guajará-Mirim RO 68 1,370 Reserva Extrativista Guariba-Roosevelt MT 55 1,346 Floresta Nacional de Itaituba II PA 43 1,188 Area De Proteção Ambiental do Lago de Tucurui PA 127 1,061 Area de Proteção Ambiental Serra da Tabatinga TO 9 1,036 3,786 123,224

UC – Deforested area 2 lower than 5 km N 20 km2 50 km2 100 km2 more than 100 km2 500 km

Figure 10. Deforested area by protected area in 2019.

Annual Deforestation Report of Brazil — 2019 25 G. ALERTS IN INDIGENOUS RESERVES

Out of the total 573 Indigenous Terri- The deforestation rate was of 0.037% tories in Brazil (in their various phases of the total area occupied by the In- of legal recognition and demarcation, digenous Reserves. The deforestation including interdiction ordinance), 213 events that took place within indigenous (37%) had at least one event of defor- reserves represented 5.9% of the total estation in 2019. alerts detected, and 3.6% of the total deforested area in 2019 (Table 10).

Table 10. Alerts with total or partial overlap with Indigenous Reserves by biome in 2019.

NUMBER AREA (HA) % NUMBER % AREA Amazon 3,325 40,912 7.0% 5.3% Caatinga 2 12 0.4% 0.1% Cerrado 26 3,168 0.4% 0.8% Atl. Forest 14 187 1.0% 1.8% Pampa – – 0.0% 0.0% Pantanal 3 167 1.4% 1.0% BRAZIL 3,370 44,446 5.9% 3.6%

TI – Deforested area 2 lower than 5 km N 20 km2 50 km2 100 km2 more than 100 km2 500 km

Figure 11. Deforested area in indigenous reserves in Brazil in 2019.

Annual Deforestation Report of Brazil — 2019 26 Out of the total 213 indigenous reserves with deforestation, 20 presented over H. ALERTS IN RURAL SETTLEMENTS 250 hectares of deforested areas. These are located in five states: Pará, Rondônia, Maranhão, Mato Grosso, and Roraima Among the 9,374 rural settlements regis- (Figure 11). tered in the INCRA database, including those within sustainable use conserva- The largest deforested areas were lo- tion units (e.g. Flona and Resex), 1,320 cated in the reserves Apytereua (8,939 (14%) had at least one deforestation ha), Cachoeira Seca (8,478 ha), and Itu- alert detected and validated in 2019. na-Itata (4,235 ha), all in the state of Pará. Apuytereua and Cachoeira Seca The deforestation that took place within also presented the highest number of rural settlements represented 43% of the alerts in 2019, with 479 and 408, respec- alerts, and 19.9% of the total deforested tively (Table 11). area in 2019 (Table 12).

Table 11. List of the indigenous reserves with the largest deforested areas in 2019.

INDIGENOUS RESERVE STATE ALERTS AREA (HA) Apyterewa PA 479 8,939 Cachoeira Seca PA 408 8,478 Ituna/Itata PA 88 4,235 Trincheira Bacaja PA 234 3,724 PA 188 1,978 KayapT PA 209 1,240 Uru-Eu-Wau-Wau RO 41 1,165 P. Canela-ApAnjekra MA 4 986 Karipuna RO 55 952 Bakairi MT 2 697 Menkragnoti PA 7 654 Kawahiva do Rio Pardo MT 1 587 do Rio Branco MT 11 546 Paresi MT 2 540 Igarap RO 42 451 Kayabi MT 8 392 RR 154 389 Bacurizinho MA 5 343 WedezN MT 4 276 Sagarana RO 3 260 1,945 36,833

Annual Deforestation Report of Brazil — 2019 27 Table 12. Alerts with total or partial overlap with rural settlements in each biome in 2019.

NUMBER AREA (HA) % NUMBER % AREA Amazon 17,383 219,830 36.8% 28.5% Caatinga 69 1,213 13.2% 10.0% Cerrado 709 21,119 9.6% 5.2% Atl. Forest 32 342 2.3% 3.2% Pampa 1 4 1.5% 0.5% Pantanal 11 155 5.1% 0.9% BRAZIL 18,205 242,662 32.0% 19.9%

Out of the total 1,320 settlements pre- in the Apuí municipality (Amazonas), senting deforestation in 2019 (Figure was the one with the largest deforested 12), 38 had a deforested area over 1,000 area, presenting 18,161 ha of vegetation ha. The rural settlement PA Rio Juma, suppressed in 2019 (Figure 12).

Settlements – Deforested area 2 lower than 2 km N 5 km2 25 km2 50 km2 more than 50 km2 500 km

Figure 12. Deforested areas in rural settlements in 2019.

Annual Deforestation Report of Brazil — 2019 28 Table 13. List of the rural settlements with largest deforested areas in 2019.

SETTLEMENT ALERTS AREA (HA) Pa Rio Juma 572 18,161 Resex Rio Jaci-Parana 214 9.147 Reserva Extrativista Chico Mendes 1,178 6,814 Pds Liberdade I 200 6,701 Pa Acari 124 5,759 Pds Vale do Jamanxim 28 4,615 Pae Antimary 163 4,191 Paf Jequitibá 161 3,977 Pa Monte 86 3,712 Pa Juari 262 2,901 Resex Rio Preto Jacunda 63 2,368 Pds Terra Nossa 85 2,062 Pa Pombal 106 2,034 Pa Bom Jardim 120 2,029 Pa Tuere 194 1,927 Pa Jacaré 40 1,851 Pa Rio Gelado 151 1,799 Pa Nova Cotriguaçu 114 1,782 Pae Santa Quitéria 297 1,758 Pds Ademir Fredericce 45 1,651 Pds Realidade 67 1,573 Pa Surubim 101 1,454 Pa Moju I E Ii 147 1,405 Pa Santa Clara 12 1,331 Pds Divinópolis 52 1,319 Pa Margarida Alves 26 1,309 Pds Laranjal 23 1,281 Pa Paraíso 96 1,278 Pa Bom Princípio 28 1,272 Pds Itatá 180 1,240 Pa Terra Para Paz 61 1,224 Pa Jatapu 163 1,223 Pae Remanso 178 1,205 Pa Pilão Poente Ii E Iii 152 1,180 Pa Santo Antonio da Mata Azul 26 1,170 Pa Beira Rio 6 1,139 Pa Cidapar 1ª Parte 175 1,121 Pa Cujubim 70 1,067 5,766 108,031

Annual Deforestation Report of Brazil — 2019 29 I. ALERTS IN QUILOMBOLA TERRITORIES

Out of the total 2,775 legally recognized and validated in 2019. Deforestation in Remnant Quilombola Communities CRQs represent 0.2% of the alerts and (CRQ, in Portuguese), only 47 (1.3%) had 0.1% of the total deforested area in 2019. at least one deforestation alert detected

Table 14. Alerts with total or partial overlap with Quilombola Territories in each biome in 2019.

NUMBER AREA (HA) % NUMBER % AREA Amazon 73 438 0.2% 0.1% Caatinga 1 11 0.2% 0.1% Cerrado 37 994 0.5% 0.2% Atl. Forest 5 25 0.4% 0.2% Pampa – – 0.0% 0.0% Pantanal – – 0.0% 0.0% BRAZIL 116 1,467 0.2% 0.1%

Table 15. List of the Remnant Quilombola Communities with largest deforested areas in 2019.

QUILOMBOLA COMMUNITY STATE ALERTS AREA (HA) Barra a Aroeira TO 8 602 Alto Trombetas II - Area II PA 4 194 Mata Cavalo MT 2 121 Kalunga do Mimoso TO 4 100 Santa Rosa dos Pretos MA 8 75 Ariramba PA 7 66 Gurupa Mirim, Jocojo, PA 16 51 Flexinha, Carrazedo Piqui/Santa Maria MA 5 32 Campina de Pedra MT 1 27 Bailique Beira, Bailinque Centro, Pocao PA 4 18 Peruana PA 5 17 Santana e São Patrício MA 2 17 Gleba Jamary dos Pretos MA 4 15 Igarape Preto, Baixinha, PA 7 14 Panpelonia, Teofilo Benfica MA 4 12 Parateca e Pau Darco BA 1 11 Quilombola de Jesus RO 1 10 83 1,382

Annual Deforestation Report of Brazil — 2019 30 Table 16. Alerts with total or partial overlap with areas registered in the Rural Environmental Registry (CAR, in Portuguese) in 2019.

BIOME > 0,1 HA TOTAL Amazon 33,038 37,772 Caatinga 435 564 Cerrado 7,682 9,489 Atl. Forest 1,247 1,662 Pampa 59 79 Pantanal 176 218 BRAZIL 42,637 49,784

The number of alerts per property varied

Figure13. Deforestation areas within the from 1 to 1,208 ha, when considering Barra do Aroeira Quilombola Community intersections over 0.1 ha. The Chico (state of Tocantins). Mendes RESEX presented the greatest number of alerts. Disregarding rural set- tlements, 29,780 properties registered in the CAR presented deforestation alerts in 2019, considering intersections larger The quilombola territory with the larg- than 0.1 ha (Figure 14). est deforested area was the Barra do Aroeira, in Lagoa do Tocantins (state of The property with the greatest number Tocantins) in the Cerrado biome, with of deforestation alerts, located in Pará, 602 ha deforested in 2019 (Table 15 and presented 217 alerts. The property with Figure 13). the largest deforested area, on the other hand, was located in the state of Ama- J. ALERTS IN PRIVATE zonas, with 9,410 ha deforested in 2019 PROPERTIES (CAR) (Table 17).

Out of the total 5,669,375 properties reg- Within private properties registered in istered in the Rural Environmental Reg- the CAR, 21,693 alerts overlapped en- istry (CAR, in Portuguese), deforestation tirely or partially with legally restricted events were detected totally or partially areas, such as Legal Reserves (RL, in within 49,784 (0.9%), or 42,637 (0.7%) Portuguese), Permanent Preservation when considering only intersections Areas (APP, in Portuguese), and head- over 1,000 m2 or 0.1 ha (Table 16). waters (Table 18).

Annual Deforestation Report of Brazil — 2019 31 Table 17. Rural properties registered in the Rural Environmental Registry (CAR, in Portuguese) with the highest number of alerts in 2019 (does not include settlements)

CAR ALERTS AREA (HA) PA-1507805-E042DD14F51B46B98F9BC17B3C205E5C 217 1,356 PA-1500602-9B3BEBDD4CB94C7EAA3A6B5A49C8121D 204 1,208 AM-1302405-A6F760C244FF4EC096AD9D8859B6FEBF 171 9,410 PA-1500503-1B415D59863B470E9EFB4BE2F150E6D7 141 626 AC-1200609-16792F9DEC6E485E83A6339800AA91C5 132 465 PA-1505486-680A3BC9B3DA4AF2A3BD936CD6D891EA 76 957 PA-1505486-42001AC93D5E4FF18AE6D64CDE2B850B 76 957 RO-1100338-DBCC0049B8524732B25C3FE8D4993EC3 75 2,033 PA-1501576-2EF60C9D312A4C85AA16CFC77ACF1F4A 65 281 AC-1200302-D612FCFF0A904D6E86BD265A1BCE4F9C 57 278 AM-1300706-E0ABA0AC4DD64F679598F472078D8BC8 56 800 PA-1505486-D953349C25EE47C8AC3810D9CF10AB12 54 489 AC-1200302-84CB8ABCEB4F48B9AEC913D4195DD06E 50 283 MT-5106299-8DE586B64F1545FD8AA10C63BAF63FAA 47 1,680 PA-1505809-FAF7011D79B64B0D9119A4F7EEDB9270 47 858 AM-1300300-56F0937AB418445FA55E5A6E35F8CC0E 46 380 AM-1302405-39C41CABF0984DAD855B83A246F2B366 43 753 AC-1200302-8F6C518049EB4236962DBEBDA75E0FD6 41 330 PA-1508159-E2A870AFCD364F909E82270CDC44910E 41 194 AC-1200609-0C5F824186EE426EA6D1E19E4970A099 39 168 5,766 108,031

Alerts with CAR Alerts 2019 Amazon Caatinga N Cerrado Atlantic Forest Pampa 500 km Pantanal

Figure 14. Deforestation alerts overlapping properties registered in the Rural Environmental Registry (CAR, in Portuguese).

Annual Deforestation Report of Brazil — 2019 32 Table 18. Alerts with total ou partial overlap with Permanent Preservation Areas (APP, in Portuguese), Legal Reserves (RL, in Portuguese), or headwaters by biome in 2019.

NUMBER AREA (HA) % NUMBER % AREA Amazon 17,067 395,395 36.1% 51.3% Caatinga 145 4,120 27.7% 33.9% Cerrado 3,756 258,608 50.7% 63.3% Atl. Forest 614 5,266 44.2% 49.7% Pampa 32 458 47.1% 71.2% Pantanal 79 6,807 36.7% 41.2% BRAZIL 21,693 670,653 38% 55%

We highlight that the data source for K. ALERTS IN RURAL PROPERTIES private properties is the System of the UNDER EMBARGOS Rural Environmental Registry (SICAR, We identified 13,565 alerts that overlap in Portuguese), managed by the Bra- properties with at least one embargoed zilian Forest Service. Occasional un- area due to environmental offenses synchronized registers between state (Table 19). These embargoes may have systems and the SICAR were not taken been put in place before or after the de- into account. forestation detection. The embargoed areas considered by MapBiomas Alerta are the ones included in the SINAFLOR/ IBAMA system. Areas embargoed by state agencies, which are not synchro- nized with the Sinaflor, were not taken into account.

Table 19. Alerts with total or partial overlap with rural properties with embargoed areas by biome in 2019.

NUMBER AREA (HA) % NUMBER % AREA Amazon 12,925 328,655 27.3% 42.7% Caatinga 11 226 2.1% 1.9% Cerrado 544 38,567 7.3% 9.4% Atl. Forest 61 785 4.4% 7.4% Pampa – – 0.0% 0.0% Pantanal 24 3,835 11.2% 23.2% BRAZIL 13,565 372,069 23.9% 30.5%

Annual Deforestation Report of Brazil — 2019 33 L. ALERTS IN AREAS WITH AUTHORIZED SUPPRESSION AND FOREST MANAGEMENT

Deforestation in Brazil can only be register all ASV issued by the states in conducted legally, upon a Vegetation the SINAFLOR system. Only the states of Suppression Authorization (ASV, in Por- Mato Grosso and Para do not present an tuguese), which can be issued by the integrated databse with the SINAFLOR. government at the federal and state, The ASV data was accessed through the and occasionally the municipal, lev- geoservice of the SINAFLOR/IBAMA sys- els. The authorizations are linked to the tem, and the data from Mato Grosso properties’ registers in the CAR system and Pará were obtained from the states’ since 2018, when it became a rule to respective environmental agencies.

Table 20. Alerts in rural properties possessing a Vegetation Suppression Authorization (ASV, in Portuguese) in 2019.

NUMBER AREA (HA) % NUMBER % AREA Amazon 232 25,043 0.5% 3.3% Caatinga 3 24 0.6% 0.2% Cerrado 94 14,291 1.3% 3.5% Others (3) 4 81 1.0% 0.7% BRAZIL 333 39,439 0.6% 3.2%

Another type of authorization are the In the Caatinga, on the other hand, the sustainable forest management plans management method can include (PMFS, in Portuguese), which are pres- clear-cutting of the vegetation in rows, ent especially in the Amazon and the which can be identified as deforestation Caatinga. In the Amazon, this modality at first glance. Only 0.6% of the alerts does not allow the clear-cutting of the detected (333) in 2019 are within rural forest, only selective logging and the properties with ASVs (Table 20). extraction of non-timber forest products.

Table 21. Alerts in rural properties possessing a sustainable forest management license (PMFS, in Portuguese) in 2019.

NUMBER AREA (HA) % NUMBER % AREA Amazon 826 41,461 1.7% 5.4% Caatinga 3 72 0.6% 0.6% Cerrado 1 12 0.0% 0.0% Others (3) – – 0.0% 0.0% BRAZIL 830 41,456 1.5% 3.4%

Annual Deforestation Report of Brazil — 2019 34 M. DEGREE OF LEGAL COMPLIANCE

In Table 23, we identified the number and area of the alerts which overlap en- 62% of the detected alert area in 2019 tirely or partially with areas with some present irregularities. The data for RL, kind of legal restriction for suppression, APP, and headwaters used in the analy- such as protected areas, indigenous re- sis was obtained from the CAR database, serves, RL, APP, and headwaters. Nearly and these areas are self-declared by the half of the total number of alerts, and property owners.

Table 22. Alerts in areas overlapping legal restriction zones for deforestation (protected areas, indigenous reserves, RL, APP, and headwaters)

NUMBER AREA (HA) % NUMBER % AREA Amazon 23,461 474,974 49.6% 61.7% Caatinga 147 4,132 28.1% 34.0% Cerrado 3,779 260,687 51.1% 63.8% Atl. Forest 631 5,440 45.4% 51.3% Pampa 32 458 47.1% 71.2% Pantanal 82 6,974 38.1% 42.2% BRAZIL 28,132 752,664 49,5% 61.8%

By crossing the alerts which did not state that over 99% of the deforestation overlap legal restriction zones with the alerts detected in 2019, after intersection suppression authorization database, we with the oficial databases, were identified identified that only 105 alerts, in a total as presenting irregularities, ranging from of 5.499 ha, met the criteria for legal com- overlap with protected areas or with le- pliance. These represent 0.2% of the total gally restricted zones, up to the absence of alerts, and 0.5% of the total deforested of legal authorization for suppression of area in 2019. It is possible, therefore, to the vegetation.

Table 23. Alerts in properties possessing a Vegetation Suppression Authorization (ASV, in Portuguese) and not overlapping legal restriction zones (2019).

NUMEBR AREA (HA) % NUMBER % AREA Amazon 63 2,122 0.1% 0.3% Caatinga 2 20 0.4% 0.2% Cerrado 37 3,314 0.5% 0.8% Others (3) 3 43 0.6% 0.4% BRAZIL 105 5,499 0.2% 0.5%

Annual Deforestation Report of Brazil — 2019 35 Annual Deforestation Report of Brazil ANNEX I DESCRIPTION OF THE DEFORESTATION MONITORING SYSTEMS Table 1 shows the deforestation mon- itoring systems in operation in Brazil IN BRAZIL in 2019.

Table 1. Deforestation monitoring systems in operation in Brazil.

SYSTEM AUTHOR SCOPE CHARACTERISTICS REFERENCES It uses MODIS images (with Shimabukuro et al. spatial resolution of 250 m) to 2012; Diniz et al. 2015; fortnightly map total suppression DETER Amazon INPE Forests in the of the forest, forest degradation for http://www.obt.inpe. Legal Amazon deforestation, and forest fire scars. br/OBT/assuntos/ May also include areas with logging programas/amazonia/ activities. deter

It uses images from the CBERS-4 / WFI satellite (with spatial Cerrado biome, resolution of 64 m) to generate except areas daily alerts of deforestation and http://cerrado.obt. DETER Cerrado INPE covered by DETER to map suppression of forests, inpe.br Amazonia savannas, and grasslands in the biome, considering a minimum mapping area of 3 ha.

It uses images from the Landsat Souza Jr et al. 2009; and Sentinel satellites (with spatial Fonseca et al. 2018; SAD IMAZON Forests in the resolution of 20 to 30 m) to detect Amazon biome deforestation in primary forests in https://imazon.org.br/ the Amazon. categorias/sad-alerta/

Forests in the It globally monitors the weekly loss Hansen et al. 2013; GLAD University of world’s tropical and gain of tropical forests since Maryland region 2015, using Landsat images. https://glad.umd.edu

Based on radar images from the ISA Xingu river Sentinel satellite, it produces https://xingumais.org. SIRAD-X basin bimonthly deforestation data since br/siradx the beginning of 2018.

Rocha et al. 2012; SIAD - Monitoramento https://www.lapig.iesa. Annual deforestation mapping ufg.br/lapig/index. Sistemático dos LAPIG/UFG Cerrado biome of the Cerrado biome since 2003 php/produtos/14- Desmatamentos using MODIS, Landsat, and CBERS menu-principal/ no Biome imagery. projetos/38-siad- Cerrado cerrado

The Atlantic It annually monitors the https://www.sosma. Atlas Mata SOS Mata Forest area as deforestation of the Atlantic Forest org.br/iniciativa/atlas- Atlântica Atlântica e INPE defined by law since 1985, using Landsat imagery. da-mata-atlantica/

Based on radar images, it produces SIVAM/ weekly deforestation data every http://www.sipam. SIPAMSAR Ministério da Priority areas in year in the rainy months between gov.br/assuntos/ Defesa the Amazon October and April for IBAMA’s projeto-amazonia-sar priority areas. Data is not public.

It uses ALOS-2 images from JAXA to monitor deforestation in tropical JJFAST JICA Tropical forests forests in 77 countries every 1.5 https://www.eorc.jaxa. months, including in the rainy jp/jjfast/ season.

Annual Deforestation Report of Brazil — 2019 37 There are other local initiatives which Weekly deforestation alert system – also monitor deforestation, specific to from the Mato Grosso environmental states and municipalities. These sys- agency, usig the same technology tems include: from “de Olho na Floresta”. Begun operating in mid-2019.

“de Olho na Floresta” – Pará State alertas.sccon.com.br/matogrosso> Government - it was in operation between 2017 and 2018, based on Planet images Weekly deforestation alert system with 3-m spatial resolution and weekly – from the Maranhão environmental data. It ceased operation in early 2019. agency, usig the same technology from “de Olho na Floresta”. Begun operating in 2020.

“Olho Verde” – system operated by sccon.com.br/maranhao/> the Rio de Janeiro state government using high resolution images. It is not available to the public.

ANNEX II FULL In the automated stages, the polygons of the aggregated alerts considered to be DESCRIPTION OF false positives, and those that overlap THE MAPBIOMAS areas previously mapped as agriculture or forestry are discarded. In the manual ALERT METHOD stages, analysts identify the best images where it is possible to view deforesta- tion (closer dates to before and after the event), and collect training samples OVERVIEW based on Planet high-resolution images (occasionally Sentinel-2). These sam- The process of validating and refining ples are then processed with supervised deforestation alerts includes automated classification algorithms to generate the and manuals stages executed by ana- polygons that accurately define the re- lysts with knowledge and experience fined alerts. The data processing and in remote sensing, geoprocessing, and storage is entirely performed on the Goo- the dynamics of deforestation in each gle Cloud Platform, Google Cloud Stor- Brazilian biome. age, and Google Earth Engine platforms.

Annual Deforestation Report of Brazil — 2019 38 Each validated and refined alert is au- The alerts and their respective reports dited by a technical supervisor of the are published on the MapBiomas Alert corresponding biome, and then submit- platform, where it is possible to visualize ted to a geoprocessing phase, in which all alerts, filter by territorial feature (e.g. alerts will be crossed with the limits of states, municipalities, protected areas) private properties from the Rural En- or administrative features (e.g. property vironmental Registry (CAR), as well as with or without a license for vegetation other territorial and spatial limits (pro- suppression). In the platform, the user tected areas, indigenous reserves, rural can also access essential alert statistics settlement areas, areas under embargo, (e.g. number and area of alerts,​​ average areas possessing suppression licenses, speed of deforestation, size class). Data etc.). This information is relevant for the can also be accessed by machine com- user institutions, and complements the munication services (API, WebServices, reports gerenated for each alert. Plugin) or be downloaded.

The general flow of this process is il- lustrated in Figure 1, and the steps are presented below:

Figure 1. General flowchart of the processing of deforestation alerts in the MapBiomas Alert system. TERRITORIAL LIMITS

CAR DATA INGESTOR SCCON PLATFORM WORKSPACE DETER-B

SAD 1.1 DASHBOARD AND GLAD WEBSERVICES 1.2 5 1 3 REFERENCE Dismissed Rejected 6.1 LULC MAPS New MAPBIOMAS 7.1 2 4 6 DATA Pre-analysis Pre-approved Refined INGESTOR 2.2 4.2 7 9 2.1 4.1 Preparing Revision SCCON 2.3 images PLATFORM 8 WORKSPACE Audit 10 GOOGLE 8.1 INFRASTRUCTURE Approved 11 Published 10.1

1.1 Automatic screening Google Cloud Google Earth 1.2 Alert aggregation Bucket Storage Engine 2.1 Alert pre-validation 2.2 Image selection DASHBOARD AND 2.3 Image ingestion WEBSERVICES 4.1 Sample collection 4.2 Random forest classification alerta.mapbiomas.org 6.1 Simplification criteria 7.1 GIS Server setup API Services 8.1 Alert auditing 10.1 Spatial analysis Reports

Annual Deforestation Report of Brazil — 2019 39 STAGES

1. COLLECTION AND AGGREGATION 1.1. ALERTS OF DEFORASTATION OR NATURAL VEGETATION SUPPRESSION

This stage includes the acquisition and The sources of the alerts used can ingestion of the original alerts (DETER, change according to the availability for SAD, and GLAD), as well as of the ancil- each biome (Table 1): for the Amazon, lary data, into our database (Figure 2). DETER (INPE) and SAD (Imazon) are used; for the Cerrado, alerts from DE- TER Cerrado (INPE); and for the other biomes, in which DETER is not yet avail- able, alerts from GLAD (University of Maryland) are used.

Figure 2. Alert collection and aggregation stage.

DATA INGESTOR TERRITORIAL SCCON LIMITS PLATFORM WORKSPACE CAR DATA DASHBOARD AND WEBSERVICES INGESTOR DETER-B SAD 1.1

1.1 Automatic screening GLAD 1.2

REFERENCE 1 1.2 Alert aggregation LULC MAPS New

MAPBIOMAS

Table 1. Alert sources used in MapBiomas Alert.

BIOME SYSTEM SOURCE ACCESS AND FREQUENCY http://terraBrazilis.dpi. inpe.br/file-delivery/ DETER-B Amazon INPE download/deter-amz/ Amazon shape

SAD IMAZON Manual e mensal

http://terraBrazilis. dpi.inpe.br/file- Cerrado DETER Cerrado INPE delivery/download/ deter-cerrado/shape Caatinga Exportação do Atlantic Forest Universtity of GEE (https://code. GLAD Alerts Maryland earthengine.google. Pantanal com/6413a8b49c8ed06 69894d69c160ee454) Pampa

Annual Deforestation Report of Brazil — 2019 40 1.2. ANCILLARY DATA 1.3. REFERENCE MAPS OF LAND COVER AND LAND USE

Restricted areas (Ibama) Deforestation MapBiomas col.4 – 2019 Licenses and Forest Management Plans Forestry areas (FEPAM/Rio Grande Sinaflor/Ibama do Sul and Paraná states) SEMA/MT SEMAS/PA Rural Environmental Registry (SICAR): Rural properties, Legal 2. ALERT VALIDATION reserves, APPs, headwaters Other territorial limits In this stage, deforestation alerts are Rural Settlements (INCRA) selected and classified as valid (Figure Water Basins Level 1 and 2 3), considering the characteristics of the Biome limits in 2019 (IBGE) alert systems in each biome, and the Federation states – UF (IBGE) respective classes of native vegetation Rural Properties (Sigef) (Pending) observed in the MapBiomas Brazil land Cities (IBGE) cover maps (Table 2). At this stage, false Indigenous Reserves – TI (Funai) positive alerts are automatically dis- Quilombola territories (INCRA) carded (GLAD alerts in the Amazon and Protected Areas – UC (CNUC/MMA) alerts over forestry and anthropogenic classes, according to the most recent map by MapBiomas).

Figure 3. Alert validation stage.

3 Dismissed

DATA INGESTOR SCCON 2 PLATFORM WORKSPACE Pre-analysis 2.2 DASHBOARD AND WEBSERVICES 2.1 SCCON 2.3 PLATFORM

GOOGLE INFRASTRUCTURE

2.1 Alert pre-validation

2.2 Image selection Google Cloud Google Earth 2.3 Image ingestion Bucket Storage Engine

Annual Deforestation Report of Brazil — 2019 41 Table 2. Classes of native vegetation considered in each alert system used.

BIOME SYSTEM CLASSES OF NATIVE VEGETATION COVER DETER-B-Amazon Amazon and SAD Forest Cerrado DETER Cerrado Forest; savanna; grassland Caatinga Forest; savanna; grassland Atlantic Forest Forest; savanna; grassland GLAD Alerts Pantanal Forest; non-forest natural wetland; grassland Forest; savanna; grassland; Pampa other non-forest classes

2.1. ALERT PRE-VALIDATION Duplicate: several very close polygons can be grouped with a single larger alert In the pre-validation process, alerts (the smaller polygons overlapping the are overlaid with land cover and land larger one are discarded as duplicates); use databases to remove false positives, Forestry: the alert was detected as a such as: result of forestry harvest activity (for Alerts in areas of agriculture or pasture example, pine or eucalyptus harvest); in the 2018 MapBiomas map; Seasonality: the alert is a false positive Alerts in forestry areas in the generated over natural vegetation that 2018 MapBiomas map; has seasonal variation in its spectral Alerts in wetlands in the Pantanal biome. signature ( or moisture); Agriculture: the alert is a false positive generated in an agricultural area; 2.2. ALERT VALIDATION, SELECTION, AND THE Relief Shadow: the alert is a false positive ACTIVATION OF HIGH-RESOLUTION IMAGES generated by relief shadow variation; Fire scar: the alert is a false In this step, analysts identify and re- positive generated by fire; move false positives, by the visual inter- Cloud effect: the alert is a false positive pretation of satellite images. The visual generated by atmospheric contamination inspection is done using Sentinel imag- in the original images (clouds or shadows); es and geo-services for visualization of Degradation: the alert was generated by monthly Planet mosaics. capturing a process of forest degradation; At this stage, we seek to identify whether Already changed: the alert was an alert actually is deforestation and generated in an area that was already when the suppression of the vegeta- suppressed before the detection date. tion occurred. Whenever alerts are not validated, the reasons for rejection are recorded, which may include:

Annual Deforestation Report of Brazil — 2019 42 As a next step, the analysts select areas (blue, green, red, and near infrared), in around each alert considered valid, and addition to the unusable data mask activate the visualization of high-reso- (Unusable Data Mask–UDM), and their lution images (Planet) for the further respective metadata. refinement of the alert polygon. Analysts then identify a pair of images, from be- fore and after the deforestation event 2.3. IMAGE INGESTION ON THE (‘before’ and ‘after’ images). GOOGLE EARTH ENGINE PLATFORM

The activation and visualization of Plan- In this step, the activated ‘before’ and et images are made via web services, ‘after’ Planet images are ingested onto through an API and online platform the Google Earth Engine (GEE) platform developed by the Planet representa- via the Python API. Images are stored tive in Brazil. The activated and cropped in Google Cloud Storage, which has a images are stored on the Google Cloud native integration with GEE, on which Storage platform with all spectral bands alerts will be refined.

3. ALERT POLYGON Collection of deforestation and REFINMENT non-deforestation samples within the region of interest; Supervised classification using The following steps make up the polygon the selected samples and the refinement stage carried out by analysts Random Forest algorithm; in the GEE platform environment, named Simplification and fine-tuning of the Alerts Workspace (Figure 4): geometry of the polygon resulting from the classification of the deforestation alert; Export of the refined alert and respective Figure 4. Alert refinement stage. ‘before’ and ‘after’ images to the MapBiomas Alert platform (Figure 5).

DATA INGESTOR SCCON PLATFORM WORKSPACE

4.1 Sample collection DASHBOARD AND WEBSERVICES 5 Rejected 6.1 4.2 Random forest classification 7.1 4 6 6.1 Simplification criteria Pre-approved Refined 7.1 GIS Server setup 4.2 7 4.1 Preparing images WORKSPACE

Annual Deforestation Report of Brazil — 2019 43 Figure 5. Example of a pair of Planet images before and after deforestation, and the refined polygon of a 2019 alert (ID 6177).

. 4. AUDITING

Each refined alert goes through an au- The first 20,000 alerts published in 2019 dit process to assess the possible need did not include the audit process, which to re-do some steps before publication was later implemented. (Figure 6).

Figure 6. Auditing stage

WORKSPACE DATA INGESTOR 9 SCCON PLATFORM WORKSPACE Revision

DASHBOARD AND 8 WEBSERVICES Audit

8.1

Annual Deforestation Report of Brazil — 2019 44 5. SPATIAL ANALYSIS — GEOPROCESSING

Once the alerts are validated and ap- The spatial features and proportion of proved, several spatial analyses are car- overlap between alerts and the territorial ried out to overlay the alert polygons information are included in the reports with the territorial information layers of each alert, together with information acquired in Step 1: Rural settlements, on the classes of land cover from the TI, UC, CAR data (limits of private prop- MapBiomas map (native vegetation, for- erties, RL, APP, headwaters), Forest estry, land use classes, and other vege- Management Plans, embargoed areas, tated classes), as well as the location of and areas with vegetation suppression the alert on the property and its state or licenses (Figure 7). municipality.

Figure 7. Geoprocessing stage.

DATA INGESTOR SCCON PLATFORM WORKSPACE 10 Approved

DASHBOARD AND WEBSERVICES 8.1 Alert auditing WORKSPACE 10.1

6. PUBLICATION AND ACCESS

6.1. DASHBOARD PUBLICATION

All alerts with an area greater than or alert ID, or by geographic coordinates equal to 0.3 hectares are published on (Figure 8). On the platform, it is also pos- the MapBiomas Alert online platform, sible to access the report with essential where it is possible to view each alert statistics of the alerts. and its respective report. The user can also filter alerts by territorial cutout (bi- ome, state, municipality, UC, TI), by the CAR registry number, the suppression licensing status (authorized or not), by

Annual Deforestation Report of Brazil — 2019 45 Each alert can be viewed together with of an embargo, management plan, or dated images from before and after de- suppression license on the property, forestation or suppression, and with a (viii) history of land cover of the area link to the related reports. Institutional in previous years (based on the Map- users registered in the platform can Biomas Collection), and (ix) descriptive assign actions to alerts and prepare specification of the alert. In the case of customized reports for different public alerts that do not intercept CAR prop- agencies (e.g. IBAMA, ICMBio, SFB, pub- erties, a simplified report is generated lic ministries, and state environmental without items (i) and (iv). agencies). 6.3 ACCESS VIA SERVICE APIS 6.2. PUBLICATION OF REPORTS In addition to the dashboard, MapBio- For each rural property identified in the mas Alert data can be accessed via the CAR database, that intercepts a refined Application Programming Interface alert larger than or equal to 0.1 ha, a (API), available for integration with report is produced containing: (i) the systems of the user institutions. CAR registry number, (ii) the source of the alert, (iii) images from before and after deforestation, (iv) the location of 6.4. OTHER ACCESS the property and of the alert within the property, (v) the location of the alert and The data can also be accessed by down- property in the federation state, (vi) over- loading shapefiles and alert reports di- lay territorial information, (vii) existence rectly or via a QGIS plugin.

Figure 8. Alert publishing stage.

DATA INGESTOR SCCON PLATFORM 11 WORKSPACE Published

DASHBOARD AND WEBSERVICES

DASHBOARD AND WEBSERVICES

alerta.mapbiomas.org API Services Reports

Annual Deforestation Report of Brazil — 2019 46 ANNEX III COORDINATION WHO IS WHO Tasso Azevedo (General) Marcos Rosa (Technical) IN MAPBIOMAS Julia Shimbo (Scientific) ALERT FINANCING MAPBIOMAS ALERT Children’s Investment Fund IS FORMED BY: Foundation (CIFF) Climate and Land Use Alliance (CLUA)

Global Wildlife Conservation (GWC) COORDINATION BY BIOMES Good Energies Foundation Amazon – Amazon Institute of People Gordon & Betty Moore Foundation and the Environment (IMAZON) Norway’s International Climate in partnership with the Federal and Forest Initiative (NICFI) University of Goiás (LAPIG/UFG) Arapyaú Institute Caatinga – Feira de Santana State Institute for Climate and Society e (ICS) University (UEFS), Geodatin, Humanize Institute and the Association of Plants Oak Foundation from the Northeast (APNE) Wellspring Philanthropic Fund (WPC) Cerrado – Amazon Environmental Walmart Foundation (in US) Research Institute (IPAM) INSTITUTIONAL PARTNERS Atlantic Forest – SOS Mata Atlantica Arapyaú Institute Foundation and ArcPlan The Nature Conservancy (TNC) Pampa – Federal University of Rio Grande do Sul (UFRGS) TECHNICAL COOPERATION AGREEMENTS Pantanal – SOS Pantanal ABEMA – Brazilian Association of Institute and ArcPlan State Environment Authorities ANAMMA – National Association of TECHNOLOGY AND SYSTEM PARTNERS Municipal Environmental Agencies Google MMA – Ministry of Environment EcoStage IBAMA – Brazilian Institute of Solved Environment and Renewable LAPIG/UFG Natural Resources SFB – Brazilian Forest Service Public Ministry of the state of Paraná

TECHNICAL PARTNERS Instituto Centro de Vida (ICV) Instituto Socioambiental (ISA)

Annual Deforestation Report of Brazil — 2019 47 TECHNICAL ADVISORY Stefane Lemes Developers: COMMITTEE (INFORMAL Tamires Ádila AND CONSULTATIVE) Cesar Diniz OF MAPBIOMAS ALERT Thais Cristine Evandro Carrijo João Siqueira IBAMA Caatinga: Kaio Max Public Ministry Diego Costa Leandro Parente ICMBio Nerivaldo Afonso Leonardo Momente INPE Rafael Franca Rocha Lilian Guimarães IMAZON Rodrigo Vasconcelos Lucas Rocha WRI/University Soltan Galano Luiz Cortinhas of Maryland Washington Rocha Mateus Medeiros Brazilian Audit Rafael Guerra Office (TCU) Cerrado: Rafael Nai Brazilian Forest Ane Alencar Sergio Oliveira Service (SFB) Camila Balzani Vinicius Mesquita Felipe Lenti MAPBIOMAS ALERT TEAM Isabel Castro Management and João Paulo Ribeiro communication: Amazon: Joaquim Raposo Amanda Coutinho Antonio Fonseca Júlia Moura Emma Lima Carlos Souza Jr Julia Shimbo Julia Shimbo Dalton Cardoso Vera Arruda Liuca Yohana Julia Ribeiro Victoria Varela Magaly Oliveira Marcelo Justino Raíssa Paixão Atlantic Forest and Pantanal: Amazon/LAPIG: Eduardo Rosa Amanda Falcão Fernanado Paternost Carmem Costa Jaqueline Freitas Elaine Barbosa da Silva Marcos Rosa Gabriela Gonçalves Viviane Mazin Hyohanna Lopes Lana Teixiera Pampa: Luan Rodrigues Allan de Oliveira Mário Dornelas Eduardo Vélez Murilo Azevedo Heinrich Hasenack Nathália Vaz Juliano Schirmbeck Nathaly Brito Vanessa Ioriati

Nicole Barbosa Adriel Fernandes Visit alerta.mapbiomas.org/team to know more about the MapBiomas Rayssa Oliveira Alert team.

Annual Deforestation Report of Brazil — 2019 48 Annual Deforestation Report of Brazil

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