Guidelines to Temporal and Spatial Assessment of Collections
Collection assessment
What are the improvements and limitations of the current collection compared to the two previous collections?
Collection assessment
* Priority analysis
What do the spatiotemporal patterns of mapped area and accuracy analysis throughout data processing tell us about the methodological steps and data quality?
Collection assessment
* Priority analysis
Collection assessment
* Priority analysis
General Recommendations
INNOVATIONS, GAINS AND COMMITMENTS
Savanna - COLLECTION 8.0
Methodological workflow
1a. Overview*
Savanna Team
Present a flowchart of the methods and highlight the innovations in relation to the previous collection
1b. Users’ comments and demands and MapBiomas’ team response
Atlantic Forest Team
Inclusion of the mapping of MG rock fields following a request by email.
Present examples of improvements motivated by users comments and demands
2. Comparisons between collections
What are the improvements and limitations of the current collection compared to the two previous collections
C6.0: 83
C5.0: 83.8
C4.1: 82.9
C3.1: 81.4
Média
C6.0: 74.9
C5.0: 74.8
C4.1: 72.6
C3.1: 71.5
Reduced accuracy in C6 compared to C5, changes of classes and legend levels (eg. Silviculture)
Not comparable
(igual L3 na C6)
Increase in accuracy throughout the collections, but with lower accuracy at the beginning of the series
2a. Global accuracy comparison*
Atlantic Forest Team
Compare the global accuracy throughout the time series between collections in different legend levels
Collection 8 - Level 1
Better global accuracy than other col. : 84.7%
Allocation errors: 14.5% (11.8% Col. 7.1)
Quantity errors 0.8% (3.9% Col. 7.1)
Collection 8 - Level 3
Lower global accuracy than col. 7.1: 76.1%
Allocation errors: 13.3% (15.6% Col. 7.1)
Quantity errors 10.6% (8.1% Col. 7.1)
Savanna Team
2a. Global accuracy comparison*
Compare the global accuracy throughout the time series between collections in different legend levels
Highlights of the 7.1 collection
Global Accuracy
Level 3
col 7.1 < col 5 e 6
col 7.1 > col 7
All collections above 85% accuracy
Accuracy at the beginning of the series is lower, but with a variation between 82% and 87% considering ALL classes at level 3
Atlantic Forest Team
2a. Global accuracy comparison*
Compare the global accuracy throughout the time series between collections in different legend levels
2a. Global accuracy comparison
Compare the global accuracy of regions or tiles throughout the time series between collections**
Savanna Team
** Script available in analysis 3C; Regions may be substituted by tiles (“cartas/ super cartas”) available at projects/mapbiomas-workspace/AUXILIAR/cim-world-1-250000
Increase of the anthropic areas in Col. 8
Mosaic +4,2 Mha
Pasture +5,4 Mha
Col 7.1
Col 8.0
Savanna mapped as “mosaic of uses” and pasture
2b. Mapped area comparison using Sankey Diagram*
Savanna Team
Analysis of changes in pixels’ class assignment between collections: proportion of pixels that changed classes and new classes
https://sites.google.com/alumni.usp.br/mapbiomas-tools/sankey_71e8?authuser=0
Considering only pixels that changed class
Same as previous analysis, but excluding stable pixels in order to better visualize the proportion of change to each new class in the current collection
2b. Mapped area comparison using Sankey Diagram*
Floriano (PI)
Blue = Collection 8,
Red = Collection 7.1
Green = Both Collections
Mosaic of Uses
Mosaic of uses areas that became pasture in Collection 8
Savanna Team
2c. Mapped class agreement (between current and previous collections)
COLLECTION 7.1 - COLLECTION 8.0 (2021)
Present a map showing which pixels are mapped in a specific class in both current and previous collection or in each of them
Agreements por Classe 2018
Savanna Formation
2c. Mapped class agreement (between current and previous collections)
Quantify the area of specific classes in both current and previous collection or in each of them throughout the time series
Caatinga Team
INNOVATIONS, GAINS AND COMMITMENTS
Savanna - COLLECTION 8.0
Collection 7.1
Region 30 (2021)
2c. Mapped class agreement (between current and previous collections)
Savanna Team
Collection 8
Present examples of improvements and challenges in mapping specific classes
Decrease in commission errors of grasslands and savanna formations in non vegetated areas
Decrease in commission errors of native vegetation in relief shades
Silviculture
Collection 6 assessment
Collection 5
Collection 6
2c. Mapped class agreement (between current and previous collections)
Agriculture Team
Present examples of improvements and challenges in mapping specific classes
Agriculture and forestry map overlay between three collections, grouping temporary crops, perennial crops and forestry.
Comparison of C6, C7.1 and C8
2020
C6
C7
C8
Agreements por Classe 2018
2c. Mapped class agreement (between current collection and the previous two)
Present a map overlay of the current collection and the previous two for a specific class
Agriculture Team
Agriculture and forestry map overlay between three collections, grouping temporary crops, perennial crops and forestry (“level 3”).
Comparison of C6, C7.1 and C8
2020
1985
C6
C7
C8
Agriculture Team
Agreements por Classe 2018
2c. Mapped class agreement (between current collection and the previous two)
Based on the previous map, quantify the proportion of common area between the three collections by regions/biomes
Overlapping area between collections - sum of the entire time series
STABILITY BETWEEN COLLECTIONS
COLLECTIONS 6.0, 7.1 and 8.0 (2019)
Collection 6
Collection 7.1
Collection 8
Number of classes
Forest
Forest
Forest
1 - Concordant
5 –
Very discordant
Forest
Grassland
Agriculture
Grassland
Forest
Forest
Grassland
Grassland
Forest
2 – Recent concordance
3 – Recent discordance
Forest
Grassland
Forest
4 - Discordant
Legenda
HIgher uncertainty classification
1
2
2
2
3
2c. Mapped class agreement (between current collection and the previous two)
Savanna Team
Considering the current and the previous two collections, map and quantify the occurrence of pixels’ classifications disagreement between collections using agreement categories
Pampa Team
Agreements por Classe 2018
2c. Mapped class agreement (between current collection and the previous two)
Based on the previous map, quantify the proportion of common area between the three collections
Collections 7.1, 8 and 9
91,6%
Col 7.1
Col 8
Col 9
Venn Diagram
0,2%
3%
4,1%
1,1%
91,6%
Mean values:
17,8 Mha
Concordant
4,1%
0,8 Mha
Recent Concordant
0,2%
0,05 Mha
Very discordant
1,1%
0,2 Mha
Discordant
3%
0,6 Mha
Recent Discordance
2c. Mapped class agreement (between current collection and the previous two)
Considering the current and the previous two collections, map and quantify the occurrence of pixels’ classifications disagreement between collections using agreement categories
Col 7
Col 6
Col 5
Pampa Team
Con
Concordant
Recent concordance
Recent discordance
Discordant
Very discordant
STABILITY BETWEEN COLLECTIONS
COLLECTIONS 6.0, 7.1 and 8.0 (2019)
Nova Colinas (MA)
Considering 2019 for all collections
Savanna Team
2c. Mapped class agreement (between current collection and the previous two)
Considering the current and the previous two collections, map and quantify the occurrence of pixels’ classifications disagreement between collections using agreement categories
Between the last 3 collections, 16,8% agree on two or no collections, of which 0,8% do not agree in any pair of collections (class 5)
It is important to improve the class 3 - 6,7% ( recent discordance)
2c. Mapped class agreement (between current collection and the previous two)
Savanna Team
STABILITY BETWEEN COLLECTIONS
COLLECTIONS 6.0, 7.1 and 8.0 (2019)
Year 2019
Considering the current and the previous two collections, map and quantify the occurrence of pixels’ classifications disagreement between collections using agreement categories
4,7%
23,8%
2c. Mapped class agreement (between current collection and the previous two)
Pampa Team
Agriculture
Wetland
Same as previous analysis, but quantifying the area in each agreement/disagreement category throughout the time series by class
Diferença de Área Col 7.1 vs Col 8.0�Vegetação Nativa (3, 4, 11 e 12)
MATOPIBA: Maiores mudanças de áreas naturais em relação à Col 7.1
2d. Spatial comparison of the mapped area of each class by tiles or regions
Savanna Team
Area difference Col 7.1 vs Col 8
Native vegetation (3,4,11 and 12)
MATOPIBA: greaters changes in natural areas compared to Col. 7.1
Provide a quantification by tiles or regions of increase or decrease of each class’ area between collections
2d. Spatial comparison of the mapped area of each class by tiles or regions
Urban Areas Team
Provide a quantification by charts of agreement or disagreement of each class’ area between collections
1985
2020
disagreement
agreement
% of the area
% of the area
2d. Spatial comparison of the mapped area of each class by tiles or regions (by states)
COMPARISON BETWEEN MAPPED AREAS IN COLLECTIONS 7 & 8
More areas in Col. 7
More areas in Col. 8
Stability
MT -59.65204, -14.84975
Only Col.8
Only Col.7
Both collections
0
500km
- Commission error adjustment in Mato Grosso areas
-24.01
29.52
11.48
0.8
1.29
0.23
0.12
0.05
6.88
0.14
-0.9
0.3
9e-3
92e-2
0.32
1.19
89e-2
0.18
71e-2
0.67
34e-2
-2e-7
0
0
0
0
0
Area in Mha
Mining Team
Compare the mapped area between collections by states or regions
Forest Formations
Savanna Formations
Grasslands
Area(ha)
2021�Collection 7.1: 28.052.538 ha
Collection 8.0: 24.826.613 ha
2021�Collection 7.1: 60.513.568 ha
Collection 8.0: 56.558.461 ha
2021�Collection 7.1: 10.496.488 ha
Collection 8.0: 7.636.620 ha
-3.225.925 ha
-3.955.107 ha
-2.859.868 ha
COMPARISON OF MAPPED AREAS (Col 7.1 and Col 8.0)
2e. Comparison of the mapped area of each class throughout the time series
Savanna Team
Compare the total mapped area of each class throughout the time series between collections
4,3 Mha
3,2 Mha
2,6 Mha
Pampa Team
2e. Comparison of the mapped area of each class throughout the time series (emphasizing the difference between classes)
Compare the total mapped area of each class throughout the time series between collections
2e. Comparison of the mapped area of each class throughout the time series (by region)
?
A
A#
R3 - Campanha (oeste)
R7 - Zona Costeira
R5 - Fronteira Oeste
R2 - Campanha (leste)
Regions of Pampa BR
R1 -Serra do Sudeste
Pampa Team
Compare the total mapped area of each class throughout the time series between collections
3 – Forest Formation
Pampa Team
2e. Comparison of the mapped area of each class throughout the time series (by region)
Area difference Col 7.1-Col 6
Compare the total mapped area of each class throughout the time series between collections
Urban area comparison between Col. 5, Col. 7.1, Col.8 and IBGE (2019)
Tiles not mapped in MapBiomas collections that have small urbanized area.
Attention: tiles with worsening coincidence with IBGE throughout the collections and more than 20,000 ha of Urbanized Area (following IBGE).
Compare the class mapped area between collections considering a reference data
Urban Areas Team
2e. Comparison of the mapped area of each class throughout the time series (by tiles, using reference data)
2f. Omission and commission errors*
Pampa Team
Errors - Grasslands
Present charts or maps that compare omission and commission errors for each class between collections throughout the time series
Col 7.1
Col 8
Errors by class: how much, with what class, when?
139
Mean number
of points
55
25
1200
677
29*
49
Pampa Team
2f. Omission and commission errors*
Present charts or maps that compare omission and commission errors for each class between collections throughout the time series
Desmatamento
Avg. 321mil ha
Avg. 447mil ha
Atlantic Forest Team
2g. Comparison of deforestation and secondary vegetation
Compare the suppression of primary and secondary vegetation throughout the time series between collections
In 2021, 26% of natural vegetation is Secondary Vegetation in collection 7.1
26%
74%
23%
77%
COLEÇÃO 7.1
COLEÇÃO 8
In 2021, 23% of natural vegetation is Secondary Vegetation in collection 8
Redução da contribuição da vegetação secundária na área total de floresta da MA
Atlantic Forest Team
2g. Comparison of deforestation and secondary vegetation
Compare secondary vegetation area throughout the time series between collections
3. Current collection
What do the spatiotemporal patterns of mapped area and accuracy analysis throughout data processing tell us about the methodological steps and data quality?
https://public.flourish.studio/visualisation/15089325/
3a. Impact of spatial and temporal filters and themes integration*
Indonesia Team
Quantify the impact of filters on the mapped area of each class throughout the historical series
3a. Impact of spatial and temporal filters and themes integration*
Coastal Zone Team
Remote Sens. 2019, 11, 808; doi:10.3390/rs11070808
Show examples of the effects of filters application
Map which pixels were originated by filters, the integration phase or original classification
Buriti Bravo (PI)
Incidence filter (green) influence in the final classification
Mambaí (GO)
3a. Impact of spatial and temporal filters and themes integration*
Savanna Team
COLLECTION 8.0
Of the entire Savanna classification, 9.7% of the pixels come from post-classification filters and 3.4% come from integration
PIXEL’s ORIGIN ANALYSIS
COLLECTION 8.0
Year: 2022
Savanna Team
3a. Impact of spatial and temporal filters and themes integration*
Quantify the proportion of pixels originated by filters, the integration phase or original classification
Pre-filters
Post-filters
Integrated
NUMBER OF CLASSES PER PIXEL
NUMBER OF CLASSES PER PIXEL
NUMBER OF CLASSES PER PIXEL
In the pre-filters stage, the classification has up to 6 classes per pixel. Post-processing filters have a significant effect on the number of classes per pixel.
After integration, 50% of pixels have only 1 class
Show the effect of filters application on the number of classes per pixel throughout the time series
3a. Impact of spatial and temporal filters and themes integration
Savanna Team
2022
Classification
Filtered Classification
Show the effects of applying filters on classes using a Sankey Diagram
3a. Impact of spatial and temporal filters and themes integration
Pampa Team
Compare the effects of filters application between regions and throughout the time series
3a. Impact of spatial and temporal filters and themes integration
Savanna Team
Cartogram of the most active filter, by region
sum of the series 1985-2022
https://github.com/mapbiomas-brazil/cerrado/tree/master/3-integration/pixelSource
Percentage change on the map, per year
*Considera .remap para as classes [3, 4, 11, 12, 21, 25, 33]
4%
4.6%
0.1%
4.5%
1.9%
3.7%
5.9%
0.1%
3.3%
1.2%
2.9%
3.3%
5%
0.1%
3.6%
5.3%
3.6%
5.4%
0.1%
3.2%
1.3%
3.2%
17% (∓1%/ano) do mapa alterado pelos filtros
3,2% (∓0,8%/ano) do mapa alterado pela integração (Nat -> Ant)
3a. Impact of spatial and temporal filters
The temporal filter is the most active in 68% of the regions
16% incidence (areas with dynamics of use/degradation of native vegetation without conversion - e.g. pasture)
16% integration (areas of older/consolidated use and transition with Atlantic Forest/Amazon)
5% spatial (areas with high heterogeneity and fragmentation of native vegetation)
IMPACT OF POST-CLASSIFICATION FILTERS COLLECTION 9
(1985 - 2023)
MEAN FILTER EFFECT BY CLASS
Grasslands and Agriculture are the classes most changed by filters
Área (Mha)
1,5%
1%
1%
1%
0,6%
0,4%
0,5%
3,1%
1,6%
2%
0,7%
0,3%
2,2%
Compare the effects of filters application considering the classes
3a. Impact of spatial and temporal filters and themes integration
Pampa Team
Compare the effects of filters application between classes and throughout the time series
3a. Impact of spatial and temporal filters and themes integration
Savanna Team
28% (∓10%/year) of water added by GT Água
Spatial Filter 4% (∓0.3%/year) Acts on fragment edges
8% (∓1.4%/ano) added by incidence filter
5% (∓1.6%/ano) added by transversal themes
6.6% (∓1.2%/ano) added by other filters
Frequency filter is the most active
7% (∓1%/year) - It avoids seasonal fluctuations and maintains the structural concept of the class
Temporal filter is the most active 9% (∓1%/ano)
It prevents false transitions to grassland and forest
Temporal filter is the most active 7% (∓1%/year) - It prevents false transitions to savanna
29% (∓3%/ano) adicionado pelos transversais
4% (∓0.9%/ano) of native vegetation added by post-integration filters
Water
Forest
Anthropogenic Use
Wetland
Savanna Formation
Grassland
Non vegetated
Present maps of global accuracy by regions or tiles
3b. Spatiotemporal variation of accuracy
Collection 5 – Global Accuracy
Global accuracy by legend level
Level 1
Level 2
Level 3
Global accuracy
Savanna Team
3b. Spatiotemporal variation of accuracy
Savanna Team
Present maps of global accuracy by regions or tiles throughout the time series
Median 81.9%
Max. median 83.7% - 2012
Min. median 80.1% - 1993
Global accuracy per year - legend level 3
Forest Formation
2020
2.839 points of forest mapped correctly as forest
Omission
Comission
Comission
Agreement
Omission
3b. Spatiotemporal variation of accuracy
Present maps of omission and commission errors for the main classes
Atlantic Forest Team
OMISSION ERRORS DISTRIBUTION MAPS
AMAZÔNIA
Savanna Formation
Densidades de pontos
Baixa
Alta
3b. Spatiotemporal variation of accuracy
Present heatmaps of omission and commission errors throughout the time series
Amazonia Team
Pixel size: 1 km
Kernel size: 100 km
H3
N of points
Omission
Comission
3b. Spatiotemporal variation of accuracy
Present heatmaps of omission and commission errors throughout the time series
Pasture Team
Atlantic Forest Team
3c. Temporal analysis: number of changes, number of classes and trajectories for each mapped class*
Present gif animations showing class changes throughout the time series
3c. Temporal analysis: number of changes, number of classes and trajectories for each mapped class*
Pasture Team
Pasture Mapping -
Collection 8 (1985 - 2022)
Present gif animations showing class changes throughout the time series
Areas of forest overestimated in the initial years of the series
Pantanal Team
3c. Temporal analysis: number of changes, number of classes and trajectories for each mapped class*
Based on maps of pixels trajectories categories, identify possible inconsistent spatiotemporal patterns
Grasslands
Trajectories assessment
Region 5 - increase the limit to avoid sudden changes in the classification
Region 24 - reduce the limit to avoid noise at the border of grasslands
Noise in the early years of the classification
3c. Temporal analysis: number of changes, number of classes and trajectories for each mapped class*
Atlantic Forest Team
Based on maps of pixels trajectories categories, identify possible inconsistent spatiotemporal patterns
Grasslands
Trajectories assessment
Increase the duration of the period of the moving window post-classification temporal filter in order to remove 2, 3 or 4 years of mosaic of uses or pasture between natural classes
Atlantic Forest Team
3c. Temporal analysis: number of changes, number of classes and trajectories for each mapped class*
Based on pixels trajectories examples, identify possible inconsistent temporal patterns and solutions
3c. Temporal analysis: number of changes, number of classes and trajectories for each mapped class*
Based on maps of pixels trajectories categories, identify possible inconsistent spatiotemporal patterns
Trajectory Toolkit
Forest formation
Savanna Formation
Grassland
Wetland
Perda média de primária
137 mil ha/ano
Perda média de primária
500 mil ha/ano
Perda média de primária
62 mil ha/ano
Perda média de primária
11 mil ha/ano
DEFORESTED AREA BY CLASS (1986 a 2021)
Área (ha)
Área (ha)
LOSS OF NATIVE VEGETATION IN THE SAVANNA
Diferença entre os dados chega a 283 mil ha (1997)
Área (ha)
3d. Deforestation and secondary vegetation by class and age
Compare the filtered (working group) and raw deforestation data, and between classes
Savanna Team
How old is the secondary vegetation when deforestation occurs?
SECONDARY VEGETATION LOSS IN THE SAVANNA (DEFORESTATION WORK GROUP)
In 2020, 31.5% (42 thousand ha) of second. veget. deforestation occurred in vegetation aged 0 to 5 years, an age that represents 68% of the second. veget.
Secondary vegetation over 20 years old accounted for 15% of deforestation
Área (ha)
MATOPIBA
100%
25%
30%
18%
28%
31%
31%
14%
8%
25%
15%
Área desmatada de Veg. secundária
31%
69%
30%
30%
15%
10%
17%
Foto: Rafael Coelho /IPAM
Savanna Team
3d. Deforestation and secondary vegetation by class and age
SECONDARY VEGETATION RECOVERY IN THE SAVANNA
Agriculture → Second. Veg.
Silviculture → Second. Veg.
Mosaic of uses → Secon. Veg.
Pasture → Sec. Veg.
In 2020, the Cerrado had 487 thousand hectares of recovery for secondary vegetation
Of this area, 70% (345 thousand ha) represents regrowth in pasture areas
Área (%)
Ganho médio de secundária: 500 mil ha/ano
Mean
16K ha/year
Mean
12K ha/year
Mean
248K ha/year
Mean
274 k ha/ano
Savanna Team
3d. Deforestation and secondary vegetation by class and age
4. Comparisons with reference map/data
Considering differences in classes definitions and spatial resolution, to what extent are the results in accordance with other data sources?
Terraclass (2013) classes: Natural, Natural non vegetated, Silviculture, Pasture, Agriculture, Mining, Urbanizaded Area and others
MapBiomas Col. 8 (2013)
Terraclass 2013
Concordância/Discordância
55,2%
44%
50,4%
48,9%
68,4%
31,6%
Natural Florestal: 418.789 km²
Natural ñ Florestal: 692.301 km²
Natural Florestal: 249.130 km²
Natural ñ Florestal: 751.177 km²
Veg. nativa
Antrópico
Veg. nativa
Antrópico
Savanna Team
4a. Total agreement and disagreement
Present a map of agreement/disagreement of current collection compared to a reference map for a given year
LabGeo/UFRGS 2015
Col. 8 2015
Discordância
4b. Understanding the disagreement
When a wall to wall reference map is available, identify the confusion between classes using a Sankey diagram
Pampa Team
Perennial crop not mapped by Glad
Large adherence between Glad and MapBiomas maps
Large adherence between Glad and MapBiomas maps
Omission of sugarcane by mapbiomas
A
B
D
A
B
C
D
4c. Identifying regions of over and underestimation*
Agriculture Team
Present a map showing which pixels are mapped in a certain class in both current collection and in a reference map or in each of them
Only GLAD mapped
Only MapBiomas C6 mapped
Glad and MapBiomas mapped
C
Agriculture
Analysis of Collection 6 compared with
COMPARISON WITH REFERENCE DATA
TERRACLASS Savanna - COLLECTION 8.0 (2020)
Mapping Differences
Terraclass vs Col 8.0�Natural Classes (3, 4, 11, 12 e 29)
Vegetation Classes (1, 2)
Difference of 113,572 km² (11%) in the native vegetation class (MapBiomas - Terraclass)
Largest area differences (MapBiomas - Terraclass)
+Red: Terraclass maps more native vegetation
Terraclass 2020
Collection 8 - 2020
4d. Quantitative comparisons of mapped area (by tiles or regions)*
Savanna Team
If the spatial resolution differs among current and reference classifications, you may present a map of class area difference between them by tiles or regions. Present total numbers and proportions in charts.
ESRI - GLC - ESA- GLAD - COlLECTION 9 (2020)
Legend
(MapBiomas x Others)
were also mapped
in other initiatives
(ESRI, GLC, ESA, GLAD)
Alta discordância entre as coleções
Percentage of extra pixels mapped by MapBiomas (class color) or not mapped (dark grey bar)
**O percentual considera todo o universo de pixels mapeados nas duas coleções comparadas. Assim, o percentual de acerto é: 100% - mapeou a mais - mapeou a menos
Mapeia mais ANV
66,7%
63,4%
71,1%
33,2%
92,5%
23,8%
only MB
mapeou a mais
mapeou a menos
only others
4d. Quantitative comparisons of mapped area*
Present the proportion of pixels mapped by MapBiomas only and by reference map only by class in charts.
ESRI - GLC - ESA- GLAD - COLLECTION 9 (2020)
Pampa Team
4d. Quantitative comparisons of mapped area (throughout the time series)*
Agriculture Team
Compare mapped area estimates between current classification and reference data throughout the time series
4e. Accuracy analysis
Coastal Zone Team
Remote Sens. 2019, 11, 808; doi:10.3390/rs11070808
Present accuracy estimates of current collection based on reference maps.
4e. Accuracy analysis (by states)
Agriculture Team
Present accuracy estimates of current collection based on reference maps.
TERRACLASS - Savanna - Temporary crops
Agriculture Team
4e. Accuracy analysis
Compare accuracy estimates between current collection and reference maps using the same set of test points
4f. Comparisons with proxy data
Urban areas Team
Compare mapped area with other variables data may provide clues of whether spatial and temporal patterns are consistent
LOSS OF NATIVE VEGETATION IN THE SAVANNA
(REFERENCE DATA)
Área (km²)
Dado | Abrangência temporal | Calendário | Média de Desmatamento Anual |
GT Desmat. | 1987 a 2021 | Civil | 8,2 mil km² |
PRODES | 2001 a 2023 | Civil | 13,5 mil km² |
DETER | 2017 a 2024 | Ano Referência | 5,5 mil km² |
MB Alerta | 2019 a 2023* | Civil | 6,5 mil km² |
SAD Cerrado | 2022 a 2023 | Civil | 9,3 mil km² |
IBAMA | 2008 a 2010 | Ano Referência | 7,1 mil km² |
Maior diferença
(GT Desmatamento e PRODES)
21 mil Km²
Os produtos indicam aumento do desmatamento nos últimos anos
*Coleção 8 e soma primário e secundário
* dados até 01/04/24
Anual mean (2001 à 2022)
GT: 6,4 mil km²�Prodes: 13,5 mil km²
4g. Comparisons of deforestation data
Compare deforestation data throughout the time series with other data sources, taking into account differences of deforestation definition, spatial resolution and analized images’ date
Savanna Team
5. Improvement challenges and planned innovations for the next collection
INNOVATIONS, GAINS AND COMMITMENTS
SAVANNA - COLLECTION 8.0
Region 33 (2021)
Collection 7.1
Collection 8
For improvement:
“Loss” of small forest fragments mapped in the collection 7.1 but not in the collection 8
Savanna Team
5. Improvement challenges and planned innovations for the next collection*
Present maps, charts, flowcharts and bullet points to show major challenges, planned innovations and new methodological tests
CONSIDERATIONS / NEXT STEPS
Savanna Team
5. Improvement challenges and planned innovations for the next collection*
Present maps, charts, flowcharts and bullet points to show major challenges, planned innovations and new methodological tests
Few stable areas
Main challenges for Collection 8
Pantanal Team
5. Improvement challenges and planned innovations for the next collection*
Present maps, charts, flowcharts and bullet points to show major challenges, planned innovations and new methodological tests
New regionalization proposal based on depositional systems and sub-basin
adjustments in sample balance for each region
Planned methodological innovations
Anual classification
Pantanal Team
5. Improvement challenges and planned innovations for the next collection*
Present maps, charts, flowcharts and bullet points to show major challenges, planned innovations and new methodological tests
Challenges and next steps
Agriculture Team
5. Improvement challenges and planned innovations for the next collection*
Present maps, charts, flowcharts and bullet points to show major challenges, planned innovations and new methodological tests
Reference Map vs Raw MapBiomas C6 results
Reference Map vs New Sampling Methodology Results
Cena 218-75
Perennial Crop
Coffee
MapBiomas C6
MapBiomas C6
New Methodology
New Methodology
Agriculture Team
5. Improvement challenges and planned innovations for the next collection*
Innovations Collection 8 - Flooded Areas (in testing phase)
Amazon Team
5. Improvement challenges and planned innovations for the next collection*
Available Scripts, Tools and Guides