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Annual Deforestation Report of Brazil

2019


EXECUTIONMapBiomas

AUTHORSTasso Rezende de AzevedoMarcos Reis RosaJulia Zanin ShimboEduardo Velez MartinMagaly Gonzales de Oliveira

DATABASE ORGANIZATIONLeandro Leal ParenteLuiz Cortinhas Ferreira Neto Tasso Rezende de Azevedo

MAPS CONCEPTIONMarcos Reis Rosa

REVIEWLiuca YonahaBarbara Zimbres

ENGLISGH TRANSLATIONBarbara ZimbresCesar Guerreiro DinizJulia Zanin ShimboMarcelo Matsumoto Mario Barroso Ramos NetoRafaela Bergamo

EDITORIAL DESIGNThiago Oliveira Basso

INSTITUTIONS AND STAFF(veja a lista completa no anexo III)

CITATIONAnnual Deforestation Report of Brazil 2019 – São Paulo, SP – MapBiomas, 2020 – 49 pages.

http://alerta.mapbiomas.org


2019



SUMMARY

.

.12345.

Aknowledgments (4)

Executive Summary (5)

Introduction (5)

Objective and Scope (7)

Concepts (8)

Methods (10)

Results (15)

Annexes (36)

Brazilian DeforestationMonitoring Systems (37)

Methods Detailed Description (38)

MapBiomas Alert Institutions and Staff (47)

ACKNOWLEDGMENTS

To all co-creator institutions of MapBio-mas Alert, and to all the analysts who worked tirelessly to evaluate tens of thou-sands of deforestation alerts – especially those who coordinated the work on the bi-omes: Eduardo Vélez, Marcos Rosa, Diego Costa, Nerivaldo Afonso, Eduardo Rosa, Joaquim Pereira, Camila Balzani, Antonio Fonseca, Lana Teixeira, and Elaine Bar-bosa. All institutions and team member analysts are listed in Annex 3.

To the developers who created the tools that made it possible to execute the MapBiomas Alert, in particular: João Siqueira, Rafael Guerra, Leandro Leal, Luiz Cortinhas, Mateus Medeiros, and Sérgio Oliveira.

To the teams from INPE, IMAZON, and the University of Maryland for producing de-forestation detection systems, which are the fundamental raw material of MapBio-mas Alert, especially to the coordinators of these systems: Cláudio Almeida, Carlos Souza, and Matt Hansen.

To the government employees of IBAMA, ICMBio, Brazilian Forest Service, Public Prosecutor’s Office, TCU, INPE, and the SEMAs, who participated in the meetings of the Technical Committee of MapBio-mas Alert for the ideas, contributions, and even for pushing us to the limit of possibilities.

To our funders for their decisive support to make the MapBiomas project viable: Children’s Investment Fund Foundation (CIFF), Climate and Land Use Alliance (CLUA), Global Wildlife Conservation (GWC), Good Energies Foundation, Gor-don & Betty Moore Foundation, Norway’s International Climate and Forestry Initia-tive (NICFI), Arapyaú Institute, Climate and Society Institute (ICS), Humanize Institute, Walmart Foundation (US), and Wellspring Philanthropic Fund (WPC).

To SCCON/Planet for the partnership in building a customized platform to operate the selection of images for validation and refinement of deforestation alerts.

To Google for supporting the data process-ing and storage infrastructure that allows MapBiomas to truly operate as a network.

To IBAMA and the Brazilian Forest Ser-vice for providing web services granting access to the CAR and SINAFLOR data-bases, which are essential to produce customized reports.

To the Arapyaú Institute for the institu-tional, administrative, legal and financial support necessary to organize the Map-Biomas network, in particular Amanda Nunes, Emma Lima, Felipe Gasperi, Re-nata Piazzon, and Andrea Apponi.

LIST OF ABBREVIATIONS

ABEMA – Brazilian Association of State Environmental Entities

ANA – National Water Agency

ANAMMA – National Association of Municipal Environmental Bodies

APA – Environmental Protection Area.

API – Application Programming Interface

APNE – Northeast Plants Association

APP – Permanent Preservation Area

ASV – Vegetation Suppression Authorization

CAR –Rural Environmental Registry.

CIFF – Children’s Investment Fund Foundation

CLUA – Climate and Land Use Alliance

CNUC – National Register of Conservation Units

CRQ – Remnant Quilombola Communities

DETER – Real Time Deforestation Detection System

FEPAM – Rio Grande do Sul State Environmental Protection Foundation

Flona – National Forest

Funai – National Indian Foundation

GEE – Google Earth Engine

GLAD – Global Land Analysis and Discovery at the University of Maryland

GWC – Global Wildlife Conservation

ha – hectares

IBAMA – Brazilian Institute of the Environment and Renewable Natural Resources

IBGE – Brazilian Institute of Geography and Statistics

ICMBio – Chico Mendes Institute for Biodiversity Conservation

ICS – Instituto Clima and Society

ICV –Centro de Vida Institute

ID – Unique Identifier of an Alert

IMAZON – Institute of People and Environment of the Amazon

INCRA – National Institute of Colonization and Agrarian Reform

INPE – National Institute for Space Research

IPAM –Amazon Environmental Research Institute

ISA – Socioambiental Institute

JAXA – Japanese Aerospace Exploration Agency

JICA – Japan International Cooperation Agency

JJFAST – Forest Early Warning System in the Tropics

LAPIG/UFG – Laboratory of Image Processing and Geoprocessing at the Federal University of Goiás

MMA – Ministry of the Environment

MODIS – Moderate–Resolution Imaging Spectroradiometer

NICFI – Norway’s International Climate and Forest Initiative

PA – Settlement Project

PMFS – Sustainable Forest Management Plan

PRODES – Amazon Deforestation Monitoring Program

PRODES Cerrado – Deforestation Monitoring Program on Cerrado

QGIS – software Quantum GIS

RESEX – Extractive Reserve

RL – Legal Reserve

SAD – IMAZON Deforestation Alert System

SAD Caatinga – Deforestation Alert System for the Caatinga biome

SCCON – Santiago & Cintra Consultoria

SEMA – State Secretariat for the Environment

SFB – Brazilian Forest Service

SIAD – Integrated Deforestation Alert System

SIGEF – Land Management System

SINAFLOR – National System for the Control of the Origin of Forest Products

SIPAM/SAR – Integrated Deforestation Alert System with orbital radar

SIRAD-X – Deforestation monitoring system of the Xingu + Network

SIVAM – Amazon Surveillance System

TCU – Federal Audit Court

TI – Indigenous Reserves

TNC – The Nature Conservancy

UC – Conservation Units

UEFS –Feira de Santana State University

UF – Federation Unit

UFRGS – Federal University of Rio Grande do Sul

US – United States of America

WRI – World Resources Institute

Annual Deforestation Report of Brazil — 2019 5

EXECUTIVE SUMARYThis report analyzes the Brazilian defor-estation alerts, which have been validat-ed and refined by the MapBiomas Alert project based on high-resolution satellite imagery for the year 2019.

As part of the multi-institutional Map- Biomes initiative (MapBiomas.org), in-volving universities, NGOs, and technol-ogy companies, the MapBiomas Alert project aims to develop a system for the validation and refinement of alerts of deforestation, degradation, and regen-eration of native vegetation based on high-resolution images.

This publication corresponds to the first Annual Deforestation Report produced in Brazil, covering all the Brazilian bi-omes. In this version, the alerts gener-ated by DETER (INPE’s Real-Time Defor-estation Detection System, which covers the Amazon and Cerrado biomes), SAD (Imazon’s Deforestation Alert System, for the Amazon), and GLAD (Global Land Analysis and Discovery, from the Univer-sity Maryland, covering all the remaining biomes) were used as a reference to lo-cate deforestation with daily high-res-olution (3 meters) satellite images. For each validated and refined alert, a report was generated which contained images from before and after the deforestation event, as well as possible intersections between the alerts and areas from the Rural Environmental Registry (CAR), the

SINAFLOR (National System for the Con-trol of the Origin of Forest Products), the National Registry of Conservation Units (CNUC), and other geographic bound-aries (e.g., biomes, states, river basins). Also, the recent annual land use land cover history (2012 to 2018) acquired from the MapBiomas Brazil project was also presented in the reports.

In total, 56,867 alerts were identified, validated, and refined across the Brazil-ian territory, resulting in 1,218,708 hect-ares (12,187 km2) of deforestation. Out of all alerts, 83% (63% of the area) are in the Amazon biome, with a total area of 770 thousand ha. The Cerrado biome is next with 13% of the alerts (33.5% of the area), totaling 408.6 thousand ha, followed by the Pantanal with 16.5 thousand ha, the Atlantic Forest with 10.6 thousand ha, Caatinga with 12.1 thousand ha, and Pampa with 642 ha.

Incidence of alerts and total deforested area by biome (2019)

BIOME ALERT INCIDENCE

DEFORESTED AREA (HA)

Amazon 47,269 770,148

Caatinga 523 12,153

Cerrado 7,402 408,646

Atl. Forest 1,390 10.598

Pampa 68 642

Pantanal 215 16,521

BRAZIL 56,867 1,218,708

Source: MapBiomas Alert.

https://mapbiomas.org/


The Amazon and the Cerrado together represented 96.7% of the deforested area detected in 2019. These are the two best-monitored biomes in Brazil, presenting continuous deforestation monitoring systems with methodologi-cal approaches adapted for the respec-tive regions. The other biomes use data from GLAD, a global monitoring system without adaptation to specific condi-tions. As a result, the number of alerts and the areas identified by MapBiomas Alert are a conservative estimate, still representing an underestimation of the total area deforested.

The states with the highest numbers of deforestation events are Pará (18,500), Acre (9,300), Amazonas (7,000), Ron-donia (5,300), and Mato Grosso (4,7 thousand). In terms of total deforested area, the most prominant are Pará (299 thousand ha), Mato Grosso (202 thou-sand ha), and Amazonas (126 thousand ha). Together, these three latter states accounted for more than half of the deforestation detected in the country in 2019.

Of the total deforested areas, 11.1% of the alerts (12% of the area) overlap entire-ly or partially with Conservation Units (UCs, in Portuguese); 5.9% (3.6% of the area) with Indigenous Reserves (TI, in Portuguese); and 65% (77% in area) with rural properties included in the Rural Environmental Registry.

Over 38% of the alerts (55% of the area) have some degree of overlap with Per-manent Preservation Areas (APP), Legal Reserves, or headwaters as declared in the CAR, which are legally protected by the Forest Code.

Over 99% of validated deforestation alerts (96% in area) do not have the au-thorization to suppress native vegetation registered in the SINAFLOR system – National System for the Control of Origin of Forest Products.

When crossed with rural properties with a suppression license, which respected the Legal Reserve, APP, and headwater restriction zones, and which do not over-lap protected areas (UC and TI), only 105 of the 56,867 alerts, or 0.2% (0.5% of the total area), are identified as legally compliant. These indexes point to a level of irregularity of the deforestation in Brazil above 99%.

For the year 2019, more than 76 thou-sand reports were produced with analy-ses for each deforestation alert, identify-ing their overlaps with different spatial features, as well as their suppression licenses, when they do exist. All alerts and reports are freely and publicly avail-able in the MapBiomas Alert platform (alerta.mapbiomas.org).

This is a contribution from the Map-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..


1. INTRODUCTION

Brazil has a long tradition of monitoring deforestation. At the end of the 1980s, INPE established the Amazon Defor-estation Monitoring Program (PRODES) and, shortly after that, the map of the Atlantic Forest Remnants, as a partner-ship between INPE and the SOS Mata Atlântica Foundation. In 2004, INPE in-troduced DETER (Real-Time Deforesta-tion Detection System), a new tool with monthly information on deforestation in the Amazon.

Recently, DETER was expanded to the Cerrado biome. Since 2006, IMAZON’s SAD (Deforestation Alert System) is also in operation, covering the Amazon bi-ome. Currently, there are at least nine systems, national and international, that monitor deforestation in Brazil cov-ering different biomes and with varying frequencies and spatial resolutions.

Monitoring is central to taking action to control deforestation and restrict it to areas that have not been authorized through the proper licensing process.

Although monitoring has been around for a long time, actions are still limited, both with annual and monthly data, ei-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

fines, civil actions, and embargoes rep-rimanded less than 1% of the Amazon’s illegal deforestation.

Another problem is that continuous and consistent monitoring of deforestation is concentrated only in three biomes (Amazon, Cerrado, and the Atlantic For-est), while in the other three (Pantanal, Pampa, and Caatinga), including the Coastal Zone, there is still no such type of control.

The MapBiomas Alert initiative emerged at the end of 2018 with the objective of adding value to the existing defor-estation monitoring systems in Brazil. It ensures that each deforestation alert can be verified, validated, and refined with high spatial resolution satellite im-agery, improving the spatial precision of the alert, and determining its degree of legal regularity.

This report is the first in a series, which seeks to be annually released, consoli-dating and analyzing information on all deforestation in the Brazilian biomes, which has been detected by the multi-ple systems available and processed by MapBiomas Alert.


2. OBJECTIVEAND SCOPEThe purpose of this report is to present a consolidated overview of the defor-estation alerts detected in all Brazilian biomes throughout the year of 2019, and which have been validated and refined with high-resolution images by the Map-Biomas Alert Project.

This is the first Annual Deforestation Report produced in Brazil, covering all the Brazilian biomes.

It is worth clarifying that the deforesta-tion data processed and analyzed in this report are limited to the regions of the Brazilian territory where alerts of deforestation issued by the DETER, SAD, and GLAD monitoring systems are available.

3. CONCEPTS

DEFORESTATION IS THE COMPLETE OR ALMOST COMPLETE SUPPRESSION OF THE NATIVE VEGETATION EXISTING IN A CERTAIN AREA

The suppression of isolated trees or of a plot while maintaining a remnant vegetation does not constitute defor-estation. These cases constitute cutting down of an isolated tree, selective log-ging, or burning which may result from agricultural practices in contact with the borders of native vegetation, forest management, or degradation. These cases are therefore not detected as a deforestation alert.

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.


Deforestation: Primary or Secondary

– primary deforestation refers to the

deforestation of primary forests and native

vegetation, while secondary deforestation

refers to the suppression of secondary

vegetation. This report mainly addresses

primary deforestation, given that the

alert systems used are concentrated in

areas of primary vegetation. Nonetheless,

areas of secondary deforestation,

whenever verified, are also included

in the MapBiomas Alert data.

Gross or net deforestation – gross

deforestation considers the loss of native

vegetation cover alone. On the other hand,

the concept of net deforestation or net

loss refers to the deforestation already

discounting the area in which vegetational

regeneration has occurred. In this report,

only gross deforestation is addressed.

Deforestation alert and Deforested area

– a deforestation alert refers to an event

indicative of deforestation in a specific

place. Deforested area is the actual area

affected by the suppression of native

vegetation. MapBiomas Alert identifies

the deforested areas, using as a starting

point the deforestation alerts generated

by the monitoring systems available,

such as DETER, SAD, and GLAD.

Date of Detection and Deforestation

Ocurrence – the date of detection refers

to the moment in which the deforestation

was detected or verified. The date

of occurrence refers to the period in

which the deforestation actual took

place (always a date prior to detection).

This report addresses areas with

deforestation detected in the year 2019.

Rate of deforestation and deforestation

observed area – the observed area is

the spatial extent quantified directly

by the comparison of satellite images

from different dates (before and after

deforestation). The rate of deforestation

uses the information of the observed

area to estimate the deforestation

that occured in all of the territory,

including areas that could not be

observed. MapBiomas Alert works only

with the concept of observed area.

Deforestation Speed – refers to the ratio

between total deforested area and the

number of days that elapsed between

the start and the end of deforestation,

usually expressed in hectares or km2 per

day. In MapBiomas Alert, the speed is

always underestimated, as the calculation

is done in an approximate way, based

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.


4. METHODIn Brazil, data are available from at least nine deforestation monitoring systems. In Annex 1, we present a description of each of these systems, including nation-al and international initiatives.

In this report, we analyzed deforestation alerts detected by three monitoring 1 systems1 operating in Brazil in 2019:

DETER/INPE for the Amazon

and the Cerrado.

SAD/IMAZON for the Amazon.

GLAD/University of Maryland for

the Pampa, Pantanal, Caatinga,

and the Atlantic Forest.

These three systems were selected to ensure coverage over all biomes, and because they present similar spatial resolutions and produced data at least at a monthly frequency during 2019.

4.1 STEP DESCRIPTION

The detailed method description is available in Annex 2. Below, we present a brief and simplified explanation of the process applied for validating and refin-ing the deforestation alerts (Figure 1).

1 In addition, at the begging of the project, alerts generated from

SIPAM/SAR radar imagery produced by the Brazilian Armed

Forces, within the scope of the Amazon Surveillance System,

were tested. The data, however, are not public and, after a test

phase, access was ceased.

Each alert generated by the three se-lected systems is inserted into the da-tabase and goes through a process of aggregation, validation, and refine-ment on the MapBiomas Alert platform. This process is based on the analysis of satellite imagery (PlanetScope), at a 3-meter spatial resolution, and in-cludes the following steps:

Collection and Aggregation – in this

step, all of the detected deforestation

alerts in each month by the DETER/

INPE, SAD/Imazon, and GLAD/UMD

systems are downloaded from the

respective servers, and overlapping

alert polygons are aggregated. In this

process, a unique identifier (ID) is

attributed to each alert, which will be

permanent until the end of the validation,

refinement, and publication process.

Validation – includes two steps. The

first is done in an automated way, by

excluding alerts that overlap areas

mapped as forestry or agriculture by

the MapBiomas land cover collections,

or alerts that have been detected in

previous validations. After this process,

the remaining alerts are evaluated with

the support of PlanetScope high-resolution

images (3 meters) with high temporal

frequency (daily, and if possible weekly).

In this step, alerts that constitute cases

of false positives are discarded, and

the reason for discarding are recorded

(e.g. overlap with forestry, issues due to

seasonality, etc). The validation step is


concluded by selecting the best pair of

satellite images to represent the moments

before and after the deforestation event.

Refinement – alerts that have passed

the validation step go through a polygon

refinement process, which delineates

more precisely the area that has been

deforested, based on the high-resolution

images. The generation of a refined

polygon is done in an automated way,

supported by a supervised classification

algorithm (Random Forest) running on

the Google Earth Engine platform. The

only manual action in this step is to

collect training samples that represent

the deforested and non-deforested

area in the high-resolution images.

Auditing – each refined alert goes

through an auditing process carried

out by a technical supervisor for each

biome. In this step, the need to re-do

any of the steps before final publication

of the refined alerts is evaluated.

The first 20,000 published alerts did

not include the auditing process,

which was implemented later.

Intersection with territorial and

administrative boundaries – the final

refined alert polygons are crossed with

land tenure and fiscalization databases,

including the limits of Indigenous

Reserves (TI, in Portuguese), Protected

Areas (UC), rural settlements, areas

registered in the Environmental Rural

Registry (CAR), areas overlapping self-

declared Permanent Preservation Areas

(APP) and Legal Reserves (RL), as well

as areas under embargo, and areas

possessing suppression licenses or forest

management plans, according to the

IBAMA’s Sinaflor system. Alerts are also

crossed with maps of municipalities,

states, biomes, and river basins.

This information qualifies the alerts and

allows the generation of well-grounded

technical reports containing relevant

information to institutional users.

Publication – the final phase consists

of publishing all alerts and their

respective reports onto a web platform,

where each alert can be viewed and

filtered by territorial features (e.g.

states, municipalities, protected

areas) or administrative boundaries

(e.g. authorized or unauthorized

deforestation). The platform also allows

access to essential alert statistics (for

example, number and area of the alerts,

average deforestation speed, size class,

etc). Data can also be accessed by

machine–to–machine communication

services (API, WebServices, and

Plugin), or be downloaded.

4.2 METHOD LIMITATIONS

The MapBiomas Alert method has some limitations that must be taken into account:

A Omission of alerts – alerts are refined

based on the existence of a previously

captured alert by a third-party

deforestation detection system. The

possible omissions of these systems in

detecting deforestation also affect the alerts

evaluated by MapBiomas Alert.


It is worth noting that deforestation

monitoring systems have a minimum

detection area. For example, alerts smaller

than 6.25 hectares are not detected by

DETER Amazon, and smaller than 1 hectare

are not detected by DETER Cerrado. This

problem is particularly important in the

case of the Caatinga biome, where the only

detection system in operation (GLAD) is not

adapted for native vegetation suppression

in the semi-arid environment, presenting a

very high degree of omission. To overcome

this obstacle, the MapBiomas team in the

Caatinga developed the SAD Caatinga,

which will start operating in 2020.

B Underestimated Speed of Deforestation

– when validating and refining an alert, a

search is made for a pair of good quality

satellite images from before and after

deforestation. The “before” image is the

most recent one available, up to 12 months

before detection, while the “after” image

is the closest to the end of deforestation,

depending on good visual quality. Cloud

presence can increase the period between

before and after images in days, weeks,

and even months. This does not alter the

claim that deforestation occurred in the

period between the two images, but it

does affect the calculation of the average

speed at which deforestation occurred.

C Automatic Polygon Delineation – the

polygons that define the alerts after

refinement are established by an

automatic classification of the changing

area between two images, where the

native vegetation has been suppressed.

In some cases, the polygon can appear

too detailed. This happens because small

areas with previous signs of change

or small remnant clusters of trees are

discounted from the deforestation area.

D Non-woody vegetation detection –

detection of the suppression of non-forest

vegetation, such as grasslands, has

limitations due to the detection capability

of the original monitoring system, which

focuses on identifying forest suppression.

However, when suppression of non-forest

vegetation occurs in the alert area or

adjacent to it, the use of high-resolution

images allows its capturing during the

alert refinement phase. This way, most of

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

INTERSECTIONSVALIDATIONAGGREGATION PUBLICATIONREFINEMENT AND AUDITING

PRE- VALIDATION

PlanetScope Monthly Mosaic Sentinel Images

MapBiomas Maps

PlanetScope Asset (3 m)

Validation and Refinement

(machine-learning)

TI, UC, INCRA, CAR (RL and APP), Mun., State, Biome, Basins, Embargoes,

PMFlor

Open-Access Web-Platform (reports for IBAMA, MPF,

OEMA’s)

DETER-B

SAD

GLAD


the deforestation in non-woody vegetation

detected in 2019 was mapped occasionally,

always by observing the areas surrounding

the woody vegetation alerts. The current

system therefore still underestimates the

suppression of native non-forest vegetation.

E Up to Date Official Databases – We use

official databases for spatial information

(e.g. Funai for Indigenous Reserves, MMA

for Protected Areas, SICAR/Forest Service

for CAR), and administrative information

(e.g. Sinaflor/Ibama for licenses of

vegetation suppression, forest management

plans, and embargoes). In case these

databases are not up to date, this will also

affect the information that constitutes the

reports produced by MapBiomas Alert.

4.3 DIFFERENCES FROM ANNUAL OFFICIAL DATA

Deforestation data from MapBiomas Alert should be used with caution when compared to official deforestation data (PRODES Amazon, PRODES Cerrado, and Atlas of Atlantic Forest Remnants), as they have some important differenc-es (Table 1, below).

Minimum Mapped Area – PRODES

systems detect deforestation areas

larger than 6.25 ha. The Atlantic Forest

Atlas detects areas over 3 ha. PRODES

Cerrado, areas with more than 1 ha.

MapBiomas Alert takes into account

all observed areas larger than 0.3 ha.

Area Calculation – PRODES estimates

the total area deforested from the area

actually observed. Thus, by extrapolation,

it estimates the total area deforested

considering areas that could not be

observed due to cloud cover issues. In the

case of MapBiomas Alert, only the sum

of the observed areas of deforestation is

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).

Image capture period – PRODES Amazon

and PRODES Cerrado use images from

July to September to observe deforested

areas in regions often covered by clouds.

The Atlas uses images from July to

November. MapBiomas Alert can consult

daily images of July 2018, to identify the

best cloudless images from before the

2019 detections, and up to December

31st , 2019, seeking for cloud-free images

after the 2019 deforestation event.


Spatial Coverage – PRODES Amazon

considers the Legal Amazon region,

including the entire Amazon biome and

forests neighboring the Cerrado biome.

PRODES Cerrado considers the Cerrado

biome, according to the 2006 IBGE biome

map at a scale of 1:5,000,000, excluding

the areas overlapping the Legal Amazon.

The Atlantic Forest Atlas considers the

area defined in the Atlantic Forest Law

(Bill nº 11.428 from 2006), revised to a

scale of 1:1,000,000, which includes the

Atlantic Forest biome and forest enclaves

in the Northeast. MapBiomas Alert

considers deforestation throughout the

national territory and qualifies the biome

according to the map of biomes produced

by IBGE in 2019, at a scale of 1:250,000.

Mapped Vegetation Types – PRODES

Amazon detects deforestation in primary

forest (or forests classified as such existing

since 1988). PRODES Cerrado detects the

deforestation of primary forest, savannas,

and grasslands (or classified as such

since 2000). The Atlas of Atlantic Forest

Remnants detects deforestation in primary

Atlantic Forest formations (or classified

as such since 1985). MapBiomas Alert

detects deforestation in primary forests,

savannas, or in regenerating secondary

vegetation. Some alerts, mainly in the

Pampa and Pantanal biomes, may

also include grasslands converted to

human use adjacent to deforestation

alerts of forests and savannas.

.

Chart 1. Differences between data from official deforestation systems and the MapBiomas Alert project.

FEATURE PRODES AMAZON

PRODES CERRADO

ATLAS OF ATLANTIC

FOREST

MAPBIOMAS ALERTA

Minimum area 6.25 ha 1 ha 3 ha 0.3 ha

Area estimationEstimates the

deforestation rate, considering non-

observed area.

Sum of the observed area.



Analisys period August 2018 toJuly 2019

August 2018 toJuly 2019

October 2018 to September 2019

January 2018 to December 2019

Image capture period

July 2018 toSeptember 2019 June to Setember 2019 July 2018 to

November 2019July 2018 to

December 2019

Spatial coverage Brazilian Legal Amazon

Cerrado biome limits at a scale of

1:5,000,000, excluding areas of overlap with

the Legal Amazon.

The Atlantic Forest area as defined by law.

IBGE Biome limts at a scale of 1:250,000.

Type of vegetation

mapped

Primary or forest vegetation since 1988

(excluding cerrado and non-forest areas

in 1988).

Existing vegetation in 2000.

Primary or existing vegetation in 1985.

Primary vegetation, and may include

secondary vegetation.


5. RESULTS

5.1. NUMBER OF ALERTS ORIGINALLY GENERATED

The five detection systems consid-ered by MapBiomas Alert generated 168,134 deforestation alerts, which were detected in 2019. In Table 1, are indicated the number of alerts per bi-ome, after crossing the alerts with the biome boundary map defined by IBGE at the scale of 1:250,000, published in 2019. Some alerts by DETER-CERRADO can be found in the Amazon and the Caatinga biomes, since this new ver-sion of the IBGE’s boundary map altered the previous biome limits, which had been published in 2004 at the scale of 1:5,000,000, and which were used by the detection systems.

5.2. ALERT CONSOLIDATION, VALIDATION, AND REFINEMENTS

The alerts derived from the detection systems were consolidated and validat-ed, taking into consideration the over-lap of areas monitored by more than one system (e.g. SAD and DETER in the Amazon), as well as occasional intersec-tions between alerts. Next, alerts that could not be validated due to the lack of imagery, or that constituted a false positive (e.g. areas of forestry harvest) were discarded.

The whole process resulted in the vali-dation and refinement of 56,867 alerts, which together comprise 1,218,708 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 FOREST

PAMPA PANTANAL TOTAL

DETER-Cerrado

162 643 12,659 6 – 110 13,580

DETERB-Amazon

53,681 – 99 – – 2 53,782

GLAD 115 908 863 14,655 746 2,258 19,545

SAD 77,394 – 319 – – – 77,713

SIPAM-SAR

3,514 – – – – – 3,514

134.866 1,551 13,940 14,661 746 2,370 168,134


5.3. PROFILE OF THE VALIDATED AND REFINED ALERTS IN 2019

A. ALERTS BY BIOMETable 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 comprise 96% of all the alerts and 96.7% of the total deforested area in 2019. Figure 3 presents the spatial distribution of the alerts in the Brazilian biomes.

Figure 2. Participation of biomes in the number and total area of deforestation alerts in 2019.

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

DEFORESTED AREA33.5%63.2% 3.3%

NUMBER OF ALERTS13.0%83.1% 3,9%

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

NUMBER OF ALERTS

% OF ALERTS


DEFORESTED 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%).

500 km

N

Alerts Amazon Caatinga Cerrado Atlantic Forest Pampa Pantanal


B. ALERT SIZETable 3 presents the average alert area (hectares) by biome. The Pantanal pres-ents the greatest mean deforested area per alert, with 77 ha, followed by the Cerrado, with 55 ha. The Pampa and the Atlantic Forest present the smallest mean area per alert, which can be ex-plained by the high degree of landscape fragmentation and the smaller sizes of the rural properties in these biomes.

Table 3. Mean and maximum size of the alerts by biome in (ha).

BIOME MEAN MAXAmazon 16 4,451

Caatinga 23 707

Cerrado 55 2,377

Atl. Forest 8 123

Pampa 9 113

Pantanal 77 1,997

BRAZIL 21 4,451

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).

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

Altamira (PA), image before the deforestation

event, with an area equivalent to 4,500 soccer fields.


Areas smaller than 25 ha represent 83% of the alerts, but only 27% of the total deforested area. Areas larger than 100 ha represent 3.7% of the alerts but cor-respond to 44% of the total deforested area (Figure 5).

NUMBER OF ALERTS

< 1 ha

> 100 ha

50 a 100 ha

25 a 50 ha

10 a 25 ha

5 a 10 ha

1 a 5 ha

2,724

11,843

11,423

4,918

2,643

2,119

21,200


< 1 ha

> 100 ha

50 a 100 ha

25 a 50 ha

10 a 25 ha

5 a 10 ha

1 a 5 ha

1,972

88,400

180,030

171,026

183,468

58,099

539,318

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

C. DEFORESTATION SPEED

The following table presents indicators of the speed of deforestation. In 2019, in average 156 new deforestation events were detected and validated per day, with a mean estimated deforestation speed of 0.28 hectare per day for each event.

In 2019, in average 3,339 ha per day or 139 ha per hour were deforested in Brazil. The speed of deforestation of an alert is calculated by dividing the deforested area by the number of days between images from before and after the deforestation event. This speed is always underestimated, since it is not always possible to obtain a good image from the precise days at the beginning or at the end of the suppression, espe-cially in those periods and regions with high cloud cover. It is, however, a good approximation of the speed at which these events take place.

The mean maximum speed for a single event was reached in an area of 1,148 hectares in the Jaborandi municipality (state of Bahia). This area was deforest-ed between May 8th and 27th, 2019, with an average speed of 60 ha per day (Figure 6).


Table 4. Indicators of deforestation speed by biome in 2019.

MEAN SPEED PER ALERT

HA /ALERTA /DAY

MAXIMUN SPEED

HA /ALERTA /DAY

MEAN NUMBER OF

ALERTS PER DAY

DEFORESTED AREA PER

DAYHA

DEFORESTED AREA PER

HOURHA

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).


D. ALERTS BY STATE

All of the Brazilian states presented deforestation alerts in 2019, includ-ing the Federal District (Table 5). The northeastern states that comprise the Caatinga biome presented the lowest numbers of alerts and smallest areas of deforestation, which can reflect the limitations of the current systems in detecting suppression in the semi-arid.

Nearly a third of the deforestation events were located in the state of Pará (32.6%). Five Amazonian states (Pará, Acre, Ama-zonas, Rondônia, and Mato Grosso) were responsible for 78.8% of the detected alerts, and for 66% of the total deforested area. Ten states went over the mark of a thousand alerts detected in 2019.

Three states had a mean deforestation speed over 1 ha a day per alert: Tocantins and Piauí (1.19), and Bahia (1.06).

Table 5. Profile of the validated alerts by state.

STATE NUMBEROF ALERTS

AREA (HA)

NUMBER %

AREA %

MEAN SPEED

HA/ALERT/DAY

Acre 9,302 57,891 16.4% 4.8% 0.06

Alagoas 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

Ceará 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

Mato Grosso do Sul 407 28,069 0.7% 2.3% 0.85

Minas Gerais 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

Pernambuco 15 134 0,0% 0.0% 0.08

Piauí 600 41,776 1.1% 3.4% 1.19

Rio de Janeiro 21 125 0.0% 0.0% 0.09

Rio Grande do Norte 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

Roraima 2,138 24,001 3.8% 2.0% 0.10

Santa Catarina 130 494 0.2% 0.0% 0.08

São Paulo 54 369 0.1% 0.0% 0.08

Sergipe 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


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

500 km

NStates – v of Alerts

3 – 500 501 – 1,000 1,001 – 2,500 2,501 – 10,000 10,001 – 18,802

500 km

N

States – Deforested area lower than 10 km2

50 km2

500 km2

1,000 km2

more than 1,000 km2


E. ALERTS BY MUNICIPALITY

Out of the 5,570 Brazilian municipali-ties, 1,734 (31%) presented at least one deforestation event detected and vali-dated in 2019 (Figure 8). Out of those, 50 municipalities were responsible for 44% of the alerts and 50% of the total area of deforestation in Brazil (Table 6).

Among the ten municipalities that de-forested the most in 2019, four are in Pará, three are in Amazonas, one is in Bahia, one is in Mato Grosso, and one is in Rondônia. The Altamira municipality (Pará) had by far the largest deforested 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.

M U N I C I PA L I T Y ALERTS AREA (HA)Altamira (PA) 1,261 54,169

São Félix do Xingu (PA)

1,716 39,680

Porto Velho (RO) 1,254 35,523

Lábrea (AM) 730 32,492

Apuí (AM) 682 22,050

F. do Rio Preto (BA) 77 21,801

Novo Progresso (PA) 478 20,807

Itaituba (PA) 1,290 19,789

N. Aripuanã (AM) 293 18,241

Colniza (MT) 585 17,709

Aripuanã (MT) 432 15,596

Pacajá (PA) 1,121 13,400

Boca do Acre (AM) 801 13,031

Nova Mamoré (RO) 529 12,642

Portel (PA) 705 11,542

Uruará (PA) 691 11,309

N. Bandeirantes (MT) 283 9,985

Candeias Jamari (RO) 415 9,874

S. José Porfírio (PA) 630 9,566

Balsas (MA) 89 9,518

Jacareacanga (PA) 471 9,118

Rurópolis (PA) 570 9,045

Feijó (AC) 1,654 8,787

N. Repartimento (PA) 816 8,772

Placas (PA) 571 8,760

Cujubim (RO) 339 8,188

Humaitá (AM) 455 8,173

Baixa Grande do Ribeiro (PI)

38 8,147

Jaborandi (BA) 30 8,059

Trairão (PA) 280 7,875

Sena Madureira (AC) 1,124 7,729

Anapu (PA) 794 7,725

Uruçuí (PI) 17 7,257

Apiacás (MT) 191 7,124

Machadinho D’oeste (RO) 298 6,337

Manicoré (AM) 213 6,292

Rio Branco (AC) 667 6,223

Corumbá (MS) 54 6,133

Paranã (TO) 74 6,018

Juara (MT) 94 5,779

Rorainópolis (RR) 404 5,746

Tarauacá (AC) 1,223 5,744

Canutama (AM) 266 5,650

Seringueiras (RO) 109 5,503

Barreiras (BA) 44 5,392

Porto Murtinho (MS) 57 5,367

Marcelândia (MT) 56 5,227

Cocalinho (MT) 40 5,091

Costa Marques (RO) 200 4,891

Paranatinga (MT) 66 4,666

25,277 603,540

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


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.

States – Number of Alerts 2019

3 - 500 501 - 1,000 1,001 - 2,500 2,501 -10,000 10,001-18,802 500 km

N

Municipalities – Deforested area 2019

lower than 10 km2

50 km2

500 km2

1,000 km2

more than 1,000 km2


F. ALERTS IN PROTECTED AREAS

Out of the 1,453 protected areas (UC, in Portuguese) registered in the Nacio-nal Registry of Conservation Units, 226 (16%) presented at least one event of deforestation detected in 2019.

The highest number of alerts and defor-ested areas within protected areas were located in the Amazon, representing 12% of the total alerts, and 13% of the deforested area in the biome. The Pan-tanal biome, on the other hand, did not

present any deforestation alerts within protected areas (Table 7).

The deforestation that took place within protected areas represented 11% of the total alerts, and 12% of the total deforest-ed area in 2019. When diregarding the category ‘APA’, in which private proper-ties with rural activities are allowed, the total deforested area within protected areas drops to 5.1% of the total deforesta-tion in Brazil (Table 8). The deforestation rate, considering all protected areas in 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 % AREAAmazon 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).


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 deforestation alerts, 22 had more than 1,000 hectares deforested, and were distributed in eight states: Pará, Bahia, Tocantins, Rondônia, Acre, Mato Grosso, Maranhão, and Goiás (Figure 10).

The largest deforested area was located in the Triunfo do Xingu APA. However, the protected area with the highest num-ber of alerts was the Chico Mendes RE-SEX in Acre with 1,197 events (Table 9).


Figure 10. Deforested area by protected area in 2019.

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

500 km

NUC – Deforested area

lower than 5 km2

20 km2

50 km2

100 km2

more than 100 km2


G. ALERTS IN INDIGENOUS RESERVES

Out of the total 573 Indigenous Terri-tories in Brazil (in their various phases of legal recognition and demarcation, including interdiction ordinance), 213 (37%) had at least one event of defor-estation in 2019.

The deforestation rate was of 0.037% of the total area occupied by the In-digenous Reserves. The deforestation events that took place within indigenous reserves represented 5.9% of the total alerts detected, and 3.6% of the total deforested area in 2019 (Table 10).

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

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


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%

500 km

NTI – Deforested area

lower than 5 km2

20 km2

50 km2

100 km2

more than 100 km2


Out of the total 213 indigenous reserves with deforestation, 20 presented over 250 hectares of deforested areas. These are located in five states: Pará, Rondônia, Maranhão, Mato Grosso, and Roraima (Figure 11).

The largest deforested areas were lo-cated in the reserves Apytereua (8,939 ha), Cachoeira Seca (8,478 ha), and Itu-na-Itata (4,235 ha), all in the state of Pará. Apuytereua and Cachoeira Seca also presented the highest number of alerts in 2019, with 479 and 408, respec-tively (Table 11).

H. ALERTS IN RURAL SETTLEMENTS

Among the 9,374 rural settlements regis-tered in the INCRA database, including those within sustainable use conserva-tion units (e.g. Flona and Resex), 1,320 (14%) had at least one deforestation alert detected and validated in 2019.

The deforestation that took place within rural settlements represented 43% of the alerts, and 19.9% of the total deforested 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

Munduruku 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

Arara do Rio Branco MT 11 546

Paresi MT 2 540

Igarap RO 42 451

Kayabi MT 8 392

Yanomami RR 154 389

Bacurizinho MA 5 343

WedezN MT 4 276

Sagarana RO 3 260

1,945 36,833


Out of the total 1,320 settlements pre-senting deforestation in 2019 (Figure 12), 38 had a deforested area over 1,000 ha. The rural settlement PA Rio Juma,

in the Apuí municipality (Amazonas), was the one with the largest deforested area, presenting 18,161 ha of vegetation suppressed in 2019 (Figure 12).

Table 12. Alerts with total or partial overlap with rural settlements in each biome in 2019.


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%

Figure 12. Deforested areas in rural settlements in 2019.

500 km

NSettlements – Deforested area

lower than 2 km2

5 km2

25 km2

50 km2

more than 50 km2


Table 13. List of the rural settlements with largest deforested areas in 2019.

S E T T L E M E N T 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


I. ALERTS IN QUILOMBOLA TERRITORIES

Out of the total 2,775 legally recognized Remnant Quilombola Communities (CRQ, in Portuguese), only 47 (1.3%) had at least one deforestation alert detected

and validated in 2019. Deforestation in CRQs represent 0.2% of the alerts and 0.1% of the total deforested area in 2019.

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

NUMBER AREA (HA) % NUMBER % AREAAmazon 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, Flexinha, Carrazedo

PA 16 51

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, Panpelonia, Teofilo

PA 7 14

Benfica MA 4 12

Parateca e Pau Darco BA 1 11

Quilombola de Jesus RO 1 10

83 1,382


Figure13. Deforestation areas within the Barra do Aroeira Quilombola Community

(state of Tocantins).

The quilombola territory with the larg-est deforested area was the Barra do Aroeira, in Lagoa do Tocantins (state of Tocantins) in the Cerrado biome, with 602 ha deforested in 2019 (Table 15 and Figure 13).

J. ALERTS IN PRIVATE PROPERTIES (CAR)

Out of the total 5,669,375 properties reg-istered in the Rural Environmental Reg-istry (CAR, in Portuguese), deforestation events were detected totally or partially within 49,784 (0.9%), or 42,637 (0.7%) when considering only intersections over 1,000 m2 or 0.1 ha (Table 16).

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 TOTALAmazon 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 from 1 to 1,208 ha, when considering intersections over 0.1 ha. The Chico 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 than 0.1 ha (Figure 14).

The property with the greatest number of deforestation alerts, located in Pará, presented 217 alerts. The property with the largest deforested area, on the other hand, was located in the state of Ama-zonas, with 9,410 ha deforested in 2019 (Table 17).

Within private properties registered in the CAR, 21,693 alerts overlapped en-tirely or partially with legally restricted areas, such as Legal Reserves (RL, in Portuguese), Permanent Preservation Areas (APP, in Portuguese), and head-waters (Table 18).


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

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)

CA R 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

500 km

N

Alerts with CAR Alerts 2019 Amazon Caatinga Cerrado Atlantic Forest Pampa Pantanal


We highlight that the data source for private properties is the System of the Rural Environmental Registry (SICAR, in Portuguese), managed by the Bra-zilian Forest Service. Occasional un-synchronized registers between state systems and the SICAR were not taken into account.

K. ALERTS IN RURAL PROPERTIES UNDER EMBARGOS

We identified 13,565 alerts that overlap properties with at least one embargoed area due to environmental offenses (Table 19). These embargoes may have been put in place before or after the de-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 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.


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%

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


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%


L. ALERTS IN AREAS WITH AUTHORIZED SUPPRESSION AND FOREST MANAGEMENT

Deforestation in Brazil can only be conducted legally, upon a Vegetation Suppression Authorization (ASV, in Por-tuguese), which can be issued by the government at the federal and state, and occasionally the municipal, lev-els. The authorizations are linked to the properties’ registers in the CAR system since 2018, when it became a rule to

register all ASV issued by the states in the SINAFLOR system. Only the states of Mato Grosso and Para do not present an integrated databse with the SINAFLOR. The ASV data was accessed through the geoservice of the SINAFLOR/IBAMA sys-tem, and the data from Mato Grosso and Pará were obtained from the states’ respective environmental agencies.

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

NUMBER AREA (HA) % NUMBER % AREAAmazon 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%

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

NUMBER AREA (HA) % NUMBER % AREAAmazon 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%

Another type of authorization are the sustainable forest management plans (PMFS, in Portuguese), which are pres-ent especially in the Amazon and the Caatinga. In the Amazon, this modality does not allow the clear-cutting of the forest, only selective logging and the extraction of non-timber forest products.

In the Caatinga, on the other hand, the management method can include clear-cutting of the vegetation in rows, which can be identified as deforestation at first glance. Only 0.6% of the alerts detected (333) in 2019 are within rural properties with ASVs (Table 20).


M. DEGREE OF LEGAL COMPLIANCE

In Table 23, we identified the number and area of the alerts which overlap en-tirely or partially with areas with some kind of legal restriction for suppression, such as protected areas, indigenous re-serves, RL, APP, and headwaters. Nearly half of the total number of alerts, and

62% of the detected alert area in 2019 present irregularities. The data for RL, APP, and headwaters used in the analy-sis was obtained from the CAR database, and these areas are self-declared by the property owners.

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


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%

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

NUMEBR AREA (HA) % NUMBER % AREAAmazon 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%

By crossing the alerts which did not overlap legal restriction zones with the suppression authorization database, we identified that only 105 alerts, in a total of 5.499 ha, met the criteria for legal com-pliance. These represent 0.2% of the total of alerts, and 0.5% of the total deforested area in 2019. It is possible, therefore, to

state that over 99% of the deforestation alerts detected in 2019, after intersection with the oficial databases, were identified as presenting irregularities, ranging from overlap with protected areas or with le-gally restricted zones, up to the absence of legal authorization for suppression of the vegetation.


ANNEX I DESCRIPTION OFTHE DEFORESTATION MONITORING SYSTEMSIN BRAZIL

Table 1 shows the deforestation mon-itoring systems in operation in Brazil in 2019.

Table 1. Deforestation monitoring systems in operation in Brazil.

SYSTEM AUTHOR SCOPE CHARACTERISTICS REFERENCES

DETER Amazon INPE Forests in the Legal Amazon

It uses MODIS images (with spatial resolution of 250 m) to fortnightly map total suppression of the forest, forest degradation for deforestation, and forest fire scars. May also include areas with logging activities.

Shimabukuro et al. 2012; Diniz et al. 2015;

http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/deter

DETER Cerrado INPECerrado biome,

except areas covered by DETER

Amazonia

It uses images from the CBERS-4 / WFI satellite (with spatial resolution of 64 m) to generate daily alerts of deforestation and to map suppression of forests, savannas, and grasslands in the biome, considering a minimum mapping area of 3 ha.

http://cerrado.obt.inpe.br

SAD IMAZON Forests in the Amazon biome

It uses images from the Landsat and Sentinel satellites (with spatial resolution of 20 to 30 m) to detect deforestation in primary forests in the Amazon.

Souza Jr et al. 2009; Fonseca et al. 2018;

https://imazon.org.br/categorias/sad-alerta/

GLAD University of Maryland

Forests in the world’s tropical

region

It globally monitors the weekly loss and gain of tropical forests since 2015, using Landsat images.

Hansen et al. 2013;

https://glad.umd.edu

SIRAD-X ISA Xingu riverbasin

Based on radar images from the Sentinel satellite, it produces bimonthly deforestation data since the beginning of 2018.

https://xingumais.org.br/siradx

SIAD - Monitoramento Sistemático dos Desmatamentos

no Biome Cerrado

LAPIG/UFG Cerrado biomeAnnual deforestation mapping of the Cerrado biome since 2003 using MODIS, Landsat, and CBERS imagery.

Rocha et al. 2012;

https://www.lapig.iesa.ufg.br/lapig/index.php/produtos/14-menu-principal/projetos/38-siad-cerrado

Atlas Mata Atlântica

SOS Mata Atlântica e INPE

The Atlantic Forest area as defined by law

It annually monitors the deforestation of the Atlantic Forest since 1985, using Landsat imagery.

https://www.sosma.org.br/iniciativa/atlas-da-mata-atlantica/

SIPAMSARSIVAM/

Ministério da Defesa

Priority areas in the Amazon

Based on radar images, it produces weekly deforestation data every year in the rainy months between October and April for IBAMA’s priority areas. Data is not public.

http://www.sipam.gov.br/assuntos/projeto-amazonia-sar

JJFAST JICA Tropical forests

It uses ALOS-2 images from JAXA to monitor deforestation in tropical forests in 77 countries every 1.5 months, including in the rainy season.

https://www.eorc.jaxa.jp/jjfast/

http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/deter%20








https://glad.umd.edu



https://www.lapig.iesa.ufg.br/lapig/index.php/produtos/14-menu-principal/projetos/38-siad-cerrad















There are other local initiatives which also monitor deforestation, specific to states and municipalities. These sys-tems include:

“de Olho na Floresta” – Pará State

Government - it was in operation between

2017 and 2018, based on Planet images

with 3-m spatial resolution and weekly

data. It ceased operation in early 2019.

<https:// deolhonafloresta.sccon.com.br/>

“Olho Verde” – system operated by

the Rio de Janeiro state government

using high resolution images. It is

not available to the public. <http://

www. inea.rj.gov.br/olho-no-verde/>

Weekly deforestation alert system –

from the Mato Grosso environmental

agency, usig the same technology

from “de Olho na Floresta”. Begun

operating in mid-2019. <https://

alertas.sccon.com.br/matogrosso>

Weekly deforestation alert system

– from the Maranhão environmental

agency, usig the same technology

from “de Olho na Floresta”. Begun

operating in 2020. <https:// alertas.

sccon.com.br/maranhao/>

ANNEX IIFULLDESCRIPTION OF THE MAPBIOMAS ALERT METHOD

OVERVIEW

The process of validating and refining deforestation alerts includes automated and manuals stages executed by ana-lysts with knowledge and experience in remote sensing, geoprocessing, and the dynamics of deforestation in each Brazilian biome.

In the automated stages, the polygons of the aggregated alerts considered to be false positives, and those that overlap areas previously mapped as agriculture or forestry are discarded. In the manual 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 based on Planet high-resolution images (occasionally Sentinel-2). These sam-ples are then processed with supervised classification algorithms to generate the polygons that accurately define the re-fined alerts. The data processing and storage is entirely performed on the Goo-gle Cloud Platform, Google Cloud Stor-age, and Google Earth Engine platforms.


Each validated and refined alert is au-dited by a technical supervisor of the corresponding biome, and then submit-ted to a geoprocessing phase, in which alerts will be crossed with the limits of private properties from the Rural En-vironmental Registry (CAR), as well as other territorial and spatial limits (pro-tected areas, indigenous reserves, rural settlement areas, areas under embargo, areas possessing suppression licenses, etc.). This information is relevant for the user institutions, and complements the reports gerenated for each alert.

The alerts and their respective reports are published on the MapBiomas Alert platform, where it is possible to visualize all alerts, filter by territorial feature (e.g. states, municipalities, protected areas) or administrative features (e.g. property with or without a license for vegetation suppression). In the platform, the user can also access essential alert statistics (e.g. number and area of alerts, average speed of deforestation, size class). Data can also be accessed by machine com-munication services (API, WebServices, Plugin) or be downloaded.

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

alerta.mapbiomas.org

API Services

Reports

DASHBOARD AND WEBSERVICES

1.1 Automatic screening1.2 Alert aggregation 2.1 Alert pre-validation2.2 Image selection2.3 Image ingestion4.1 Sample collection4.2 Random forest classification 6.1 Simplification criteria7.1 GIS Server setup8.1 Alert auditing

10.1 Spatial analysis

Figure 1. General flowchart of the processing of deforestation alerts in the

MapBiomas Alert system.

WORKSPACE

4 Pre-approved

6 Refined

7 Preparing

images

5 Rejected

4.14.2

6.1

7.1

8 Audit

10 Approved

9 Revision

8.1

10.1

SCCON PLATFORM

2 Pre-analysis

2.12.3

2.2

GOOGLE INFRASTRUCTURE

Google Cloud Bucket Storage

Google Earth Engine

3 Dismissed

DATA INGESTOR

1 New

1.1

1.2

TERRITORIAL

LIMITS

CAR

DETER-B

SAD

GLAD

REFERENCE

LULC MAPS

MAPBIOMAS

11 Published

WORKSPACE

SCCON PLATFORM


DATA INGESTOR


STAGES

1. COLLECTION AND AGGREGATION

This stage includes the acquisition and ingestion of the original alerts (DETER, SAD, and GLAD), as well as of the ancil-lary data, into our database (Figure 2).

1.1. ALERTS OF DEFORASTATION OR NATURAL VEGETATION SUPPRESSION

The sources of the alerts used can change according to the availability for each biome (Table 1): for the Amazon, 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.

Table 1. Alert sources used in MapBiomas Alert.

BIOME SYSTEM SOURCE ACCESS ANDFREQUENCY

AmazonDETER-B Amazon INPE

http://terraBrazilis.dpi.inpe.br/file-delivery/

download/deter-amz/shape

SAD IMAZON Manual e mensal

Cerrado DETER Cerrado INPEhttp://terraBrazilis.

dpi.inpe.br/file-delivery/download/deter-cerrado/shape

Caatinga

GLAD Alerts Universtity of Maryland

Exportação do GEE (https://code.

earthengine.google.com/6413a8b49c8ed06 69894d69c160ee454)

Atlantic Forest

Pantanal

Pampa

1.1 Automatic screening

1.2 Alert aggregation

DATA INGESTOR

1 New

1.1

1.2

TERRITORIAL

LIMITS

CAR

DETER-B

SAD

GLAD

REFERENCE

LULC MAPS

MAPBIOMAS

DATA INGESTOR

WORKSPACE


SCCON PLATFORM

http://terrabrasilis.dpi.inpe.br/file-delivery/download/deter-amz/shape%20




http://terrabrasilis.dpi.inpe.br/file-delivery/download/deter-cerrado/shape




https://code.earthengine.google.com/6413a8b49c8ed0669894d69c160ee454






1.2. ANCILLARY DATA

Restricted areas (Ibama) Deforestation

Licenses and Forest Management Plans

Sinaflor/Ibama

SEMA/MT

SEMAS/PA

Rural Environmental Registry (SICAR):

Rural properties, Legal

reserves, APPs, headwaters

Other territorial limits

Rural Settlements (INCRA)

Water Basins Level 1 and 2

Biome limits in 2019 (IBGE)

Federation states – UF (IBGE)

Rural Properties (Sigef ) (Pending)

Cities (IBGE)

Indigenous Reserves – TI (Funai)

Quilombola territories (INCRA)

Protected Areas – UC (CNUC/MMA)

1.3. REFERENCE MAPS OF LAND COVER AND LAND USE

MapBiomas col.4 – 2019

Forestry areas (FEPAM/Rio Grande

do Sul and Paraná states)

2. ALERT VALIDATION

In this stage, deforestation alerts are selected and classified as valid (Figure 3), considering the characteristics of the alert systems in each biome, and the respective classes of native vegetation observed in the MapBiomas Brazil land cover maps (Table 2). At this stage, false positive alerts are automatically dis-carded (GLAD alerts in the Amazon and alerts over forestry and anthropogenic classes, according to the most recent map by MapBiomas).

Figure 3. Alert validation stage.

2.1 Alert pre-validation

2.2 Image selection

2.3 Image ingestion

SCCON PLATFORM

2 Pre-analysis

2.12.3

2.2

GOOGLE INFRASTRUCTURE

Google Cloud Bucket Storage

Google Earth Engine

3 Dismissed

WORKSPACE


DATA INGESTOR

SCCON PLATFORM


2.1. ALERT PRE-VALIDATION

In the pre-validation process, alerts are overlaid with land cover and land use databases to remove false positives, such as:

Alerts in areas of agriculture or pasture

in the 2018 MapBiomas map;

Alerts in forestry areas in the

2018 MapBiomas map;

Alerts in wetlands in the Pantanal biome.

2.2. ALERT VALIDATION, SELECTION, AND THE ACTIVATION OF HIGH-RESOLUTION IMAGES

In this step, analysts identify and re-move false positives, by the visual inter-pretation of satellite images. The visual inspection is done using Sentinel imag-es and geo-services for visualization of monthly Planet mosaics.

At this stage, we seek to identify whether an alert actually is deforestation and when the suppression of the vegeta-tion occurred. Whenever alerts are not validated, the reasons for rejection are recorded, which may include:

Duplicate: several very close polygons

can be grouped with a single larger alert

(the smaller polygons overlapping the

larger one are discarded as duplicates);

Forestry: the alert was detected as a

result of forestry harvest activity (for

example, pine or eucalyptus harvest);

Seasonality: the alert is a false positive

generated over natural vegetation that

has seasonal variation in its spectral

signature (drought or moisture);

Agriculture: the alert is a false positive

generated in an agricultural area;

Relief Shadow: the alert is a false positive

generated by relief shadow variation;

Fire scar: the alert is a false

positive generated by fire;

Cloud effect: the alert is a false positive

generated by atmospheric contamination

in the original images (clouds or shadows);

Degradation: the alert was generated by

capturing a process of forest degradation;

Already changed: the alert was

generated in an area that was already

suppressed before the detection date.

Table 2. Classes of native vegetation considered in each alert system used.

BIOME SYSTEM CLASSES OF NATIVE VEGETATION COVER

Amazon DETER-B-Amazon and SAD Forest

Cerrado DETER Cerrado Forest; savanna; grassland

Caatinga

GLAD Alerts

Forest; savanna; grassland

Atlantic Forest Forest; savanna; grassland

Pantanal Forest; non-forest natural wetland; grassland

Pampa Forest; savanna; grassland; other non-forest classes


As a next step, the analysts select areas around each alert considered valid, and activate the visualization of high-reso-lution images (Planet) for the further refinement of the alert polygon. Analysts then identify a pair of images, from be-fore and after the deforestation event (‘before’ and ‘after’ images).

The activation and visualization of Plan-et images are made via web services, through an API and online platform developed by the Planet representa-tive in Brazil. The activated and cropped images are stored on the Google Cloud Storage platform with all spectral bands

(blue, green, red, and near infrared), in addition to the unusable data mask (Unusable Data Mask–UDM), and their respective metadata.

2.3. IMAGE INGESTION ON THEGOOGLE EARTH ENGINE PLATFORM

In this step, the activated ‘before’ and ‘after’ Planet images are ingested onto the Google Earth Engine (GEE) platform via the Python API. Images are stored in Google Cloud Storage, which has a native integration with GEE, on which alerts will be refined.

3. ALERT POLYGON REFINMENT

The following steps make up the polygon refinement stage carried out by analysts in the GEE platform environment, named Alerts Workspace (Figure 4):

Collection of deforestation and

non-deforestation samples

within the region of interest;

Supervised classification using

the selected samples and the

Random Forest algorithm;

Simplification and fine-tuning of the

geometry of the polygon resulting from the

classification of the deforestation alert;

Export of the refined alert and respective

‘before’ and ‘after’ images to the

MapBiomas Alert platform (Figure 5).

Figure 4. Alert refinement stage.

4.1 Sample collection

4.2 Random forest classification

6.1 Simplification criteria

7.1 GIS Server setup

4 Pre-approved

6 Refined

7 Preparing

images

5 Rejected

4.14.2

6.1

7.1

WORKSPACE


DATA INGESTOR

SCCON PLATFORM

WORKSPACE


Figure 5. Example of a pair of Planet images before and after deforestation, and the refined polygon of a 2019 alert (ID 6177).

Figure 6. Auditing stage

.

4. AUDITING

Each refined alert goes through an au-dit process to assess the possible need to re-do some steps before publication (Figure 6).

The first 20,000 alerts published in 2019 did not include the audit process, which was later implemented.

8 Audit

9 Revision

8.1

WORKSPACE


DATA INGESTOR

SCCON PLATFORM

WORKSPACE


5. SPATIAL ANALYSIS— GEOPROCESSING

Once the alerts are validated and ap-proved, several spatial analyses are car-ried out to overlay the alert polygons with the territorial information layers acquired in Step 1: Rural settlements, TI, UC, CAR data (limits of private prop-erties, RL, APP, headwaters), Forest Management Plans, embargoed areas, and areas with vegetation suppression licenses (Figure 7).

The spatial features and proportion of overlap between alerts and the territorial information are included in the reports of each alert, together with information on the classes of land cover from the MapBiomas map (native vegetation, for-estry, land use classes, and other vege-tated classes), as well as the location of the alert on the property and its state or municipality.

Figure 7. Geoprocessing stage.

6. PUBLICATION AND ACCESS

6.1. DASHBOARD PUBLICATION

All alerts with an area greater than or equal to 0.3 hectares are published on the MapBiomas Alert online platform, where it is possible to view each alert 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

alert ID, or by geographic coordinates (Figure 8). On the platform, it is also pos-sible to access the report with essential statistics of the alerts.

WORKSPACE


DATA INGESTOR

SCCON PLATFORM 10

Approved

10.1WORKSPACE8.1 Alert auditing


Each alert can be viewed together with dated images from before and after de-forestation or suppression, and with a link to the related reports. Institutional users registered in the platform can assign actions to alerts and prepare customized reports for different public agencies (e.g. IBAMA, ICMBio, SFB, pub-lic ministries, and state environmental agencies).

6.2. PUBLICATION OF REPORTS

For each rural property identified in the CAR database, that intercepts a refined alert larger than or equal to 0.1 ha, a report is produced containing: (i) the CAR registry number, (ii) the source of the alert, (iii) images from before and after deforestation, (iv) the location of the property and of the alert within the property, (v) the location of the alert and property in the federation state, (vi) over-lay territorial information, (vii) existence

of an embargo, management plan, or suppression license on the property, (viii) history of land cover of the area in previous years (based on the Map-Biomas Collection), and (ix) descriptive specification of the alert. In the case of alerts that do not intercept CAR prop-erties, a simplified report is generated without items (i) and (iv).

6.3 ACCESS VIA SERVICE APIS

In addition to the dashboard, MapBio-mas Alert data can be accessed via the Application Programming Interface (API), available for integration with systems of the user institutions.

6.4. OTHER ACCESS

The data can also be accessed by down-loading shapefiles and alert reports di-rectly or via a QGIS plugin.

Figure 8. Alert publishing stage.

alerta.mapbiomas.org

API Services

Reports


WORKSPACE

DATA INGESTOR

SCCON PLATFORM 11

Published



ANNEX IIIWHO IS WHO IN MAPBIOMAS ALERTMAPBIOMAS ALERT IS FORMED BY:

COORDINATION BY BIOMES

Amazon – Amazon Institute of People

and the Environment (IMAZON)

in partnership with the Federal

University of Goiás (LAPIG/UFG)

Caatinga – Feira de Santana State

University (UEFS), Geodatin,

and the Association of Plants

from the Northeast (APNE)

Cerrado – Amazon Environmental

Research Institute (IPAM)

Atlantic Forest – SOS Mata Atlantica

Foundation and ArcPlan

Pampa – Federal University of

Rio Grande do Sul (UFRGS)

Pantanal – SOS Pantanal

Institute and ArcPlan

TECHNOLOGY AND SYSTEM PARTNERS

Google

EcoStage

Solved

LAPIG/UFG

COORDINATION

Tasso Azevedo (General)

Marcos Rosa (Technical)

Julia Shimbo (Scientific)

FINANCING

Children’s Investment Fund

Foundation (CIFF)

Climate and Land Use Alliance (CLUA)

Global Wildlife Conservation (GWC)

Good Energies Foundation

Gordon & Betty Moore Foundation

Norway’s International Climate

and Forest Initiative (NICFI)

Arapyaú Institute

Institute for Climate and Society e (ICS)

Humanize Institute

Oak Foundation

Wellspring Philanthropic Fund (WPC)

Walmart Foundation (in US)

INSTITUTIONAL PARTNERS

Arapyaú Institute

The Nature Conservancy (TNC)

TECHNICAL COOPERATION AGREEMENTS

ABEMA – Brazilian Association of

State Environment Authorities

ANAMMA – National Association of

Municipal Environmental Agencies

MMA – Ministry of Environment

IBAMA – Brazilian Institute of

Environment and Renewable

Natural Resources

SFB – Brazilian Forest Service

Public Ministry of the state of Paraná

TECHNICAL PARTNERS

Instituto Centro de Vida (ICV)

Instituto Socioambiental (ISA)


TECHNICAL ADVISORY COMMITTEE (INFORMAL AND CONSULTATIVE)OF MAPBIOMAS ALERT

IBAMA

Public Ministry

ICMBio

INPE

IMAZON

WRI/University

of Maryland

Brazilian Audit

Office (TCU)

Brazilian Forest

Service (SFB)

MAPBIOMAS ALERT TEAM

Amazon: Antonio Fonseca

Carlos Souza Jr

Dalton Cardoso

Julia Ribeiro

Marcelo Justino

Raíssa Paixão

Amazon/LAPIG: Amanda Falcão

Carmem Costa

Elaine Barbosa da Silva

Gabriela Gonçalves

Hyohanna Lopes

Lana Teixiera

Luan Rodrigues

Mário Dornelas

Murilo Azevedo

Nathália Vaz

Nathaly Brito

Nicole Barbosa

Rayssa Oliveira

Stefane Lemes

Tamires Ádila

Thais Cristine

Caatinga: Diego Costa

Nerivaldo Afonso

Rafael Franca Rocha

Rodrigo Vasconcelos

Soltan Galano

Washington Rocha

Cerrado: Ane Alencar

Camila Balzani

Felipe Lenti

Isabel Castro

João Paulo Ribeiro

Joaquim Raposo

Júlia Moura

Julia Shimbo

Vera Arruda

Victoria Varela

Atlantic Forest and Pantanal:

Eduardo Rosa

Fernanado Paternost

Jaqueline Freitas

Marcos Rosa

Viviane Mazin

Pampa: Allan de Oliveira

Eduardo Vélez

Heinrich Hasenack

Juliano Schirmbeck

Vanessa Ioriati

Adriel Fernandes

Developers: Cesar Diniz

Evandro Carrijo

João Siqueira

Kaio Max

Leandro Parente

Leonardo Momente

Lilian Guimarães

Lucas Rocha

Luiz Cortinhas

Mateus Medeiros

Rafael Guerra

Rafael Nai

Sergio Oliveira

Vinicius Mesquita

Management and communication:

Amanda Coutinho

Emma Lima

Julia Shimbo

Liuca Yohana

Magaly Oliveira

Visit alerta.mapbiomas.org/team to know more about the MapBiomas Alert team.

http://alerta.mapbiomas.org/team


2019

mapbiomas.org

http://mapbiomas.org

annual deforestation report of brazil annual deforestation ... · magaly gonzales de oliveira ......

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