Fire in the Amazon Forest and Cerrado biome · Objective: •to map the spatial distribution and...

Mapping Fire Scars in the Ecotone of Amazon Forest - Cerrado biome using Remote Sensing: a

study case in Mato Grosso, Brazil.

Gabriel Antunes Daldegan

PhD Student, Geography Department, UCSB

• Why is this important: Use of Fire in Deforestation, Greenhouse Gases emissions, Climate Change.

• Previous Studies: • INPE (Setzer, Shimabukuro) UMD (Justice, Schroeder), IMAZON (Souza Jr), SDSU

(Roy).• Landsat - Fire Scars • ASTER, AVHRR, GOES,MODIS, VIIRS, – Active Fire

• Climatology: Dry Season – June, July, August.

• Geographical Variation: • Transition between the two major Brazilian biomes (variation of physiognomies,

soil,climate, occupation)• Agricultural Frontier – Human Occupation / Deforestation arc (Cattle, Soybean,

Mining);

Problem:

Objective:

• to map the spatial distribution and the temporal permanence of fire

scars in the study area by identifying and delimitating burned areas

present on Landsat 5TM scenes - Path 226, Rows 68 and 69 - from

June, July and August of 2005.

Methods:• Identify annual driest months;

• Search for Landsat 5TM imagery that have higher probability to show fire scars in the area;

• Consolidate the satellite imagery database (Georrectify, DN to Radiance, Reflectance Retrieval, Haze Correction);

• Classification (Decision Tree with two classes -Fire Scar/ No Fire- 800 pixels average),• 3x3 median filter,• Export to Shapefile;

• Visual interpretation and edition of the polygons representing burned areas;

• Analysis (Spatial-Temporal Patterns);

Methods:

• Dataset: 6 LANDSAT 5TM scenes from 2005.

Image -Path/Row

Date

226/68

6/8/2005

7/10/2005

8/27/2005

226/69

6/8/2005

7/10/2005

8/11/2005

Methods:

Decision Tree 226 68 08 June 2005

Classification tree:

snip.tree(tree = treeJun08, nodes = 3L)

Variables actually used in tree construction:

[1] "V5" "V7" "V6" "V3"

Number of terminal nodes: 9

Residual mean deviance: 0.1779 = 285.6 / 1605

Misclassification error rate: 0.02788 = 45 / 1614

Results:

Results:

Image -

Path/RowDate

Classification -

ha

Visual Edition -

ha

Difference -

ha%

226/68

6/8/2005 99,144.44 43,212.85 55,931.59 43.59

7/10/2005 147,659.22 68,851.27 78,807.95 46.63

8/27/2005 158,486.13 121,728.06 36,758.07 76.81

226/69

6/8/2005 45,820.98 28,567.05 17,253.93 62.34

7/10/2005 91,356.93 40,420.74 50,936.19 44.24

8/11/2005 109,984.68 50,407.30 59,577.38 45.83

Results: Confusions

Results:

Image -

Path/RowPolygons - Count

Fire Scars Total

Area Mapped- ha

226/68 2,175 112,428.36

226/69 2,564 119,395.11

Total 4,739 231,823.47

Results:

Image -

Path/Row

Fire Scar

June-July

ha

%

Fire Scar

July-

August

ha

%

Fire Scar

June-

August

ha

%

226/68 32,116.43 74.32 50,431.56 73.2530,286.92 70.09

226/69 17,477.89 61.18 23,928.03 59.2016,753.22 58.65

Validation:

Total Burned Area Mapped -ha

231,823.47

Burned Area Overlappedwith Active Fire-ha 146,300.46

% 63.11

Conclusions

• Decision tree approach has over mapped fire scars• About 47% of the mapped area were discarded;

• The principal source of confusion was agriculture bare soil;

• About 66% of fire scars could still be detected after 1 month and 64% after two months;

• The median of the polygons size is 2.16 hectares, which could indicate that the majority of fire were managed fires and did not spread to wider areas.

Future Studies

• Further classifications to figure out patterns,• Other sites,

• Other dates,

• Cross the results with active fire and fire scars maps(MODIS, VIIRS and other studies)

• Cross the results with Land Cover Use.

Acknowledgments

• Prof. Dr. Dar Roberts – Department of Geography, UCSB.

• CAPES – Science Without Borders Fellowship.

• Jack and Laura Dangermond – Dangermond Travel Fellowship.

• SCGIS

THANK YOU!