Contributions of Medium Resolution Satellite Data to Technical Capabilities for RED

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Rachel Fields
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1 Contributions of Medium Resolution Satellite Data to Technical Capabilities for RED R. DeFries, University of Maryland D. Morton, University of Maryland M. Hansen, University of South Dakota Y. Shimabukuro, INPE

2 GOFC/GOLD RED WORKING GROUP CONCEPTUAL FRAMEWORK

3 Table 1. Utility of optical sensors at multiple resolutions for deforestation monitoring* Sensor resolution Examples of current sensors Utility for monitoring Very high (<5m) IKONOS, QuickBird Validation over small areas of results from coarser resolution analysis High (10-60m) Landsat, SPOT HRV, AWiFs LISS III, CBERS Primary tool to identify deforestation Cost Very high Low/medium (historical) to medium/high (recent) Medium ( m) MODIS, SPOT Vegetation Consistent global annual monitoring to identify large clearings (>10-20 ha) and locate hotspots for further analysis with high resolution Low or free * Data from optical sensors have been widely used for deforestation monitoring. Data from Lidar and Radar (ERS1/2 SAR, JERS-1, ENVISAT-ASAR and ALOS PALSAR) have been demonstrated to be useful in project studies, however, so far are not widely used operationally for tropical deforestation monitoring. (from DeFries et al., in press)

4 Contributions of Medium Resolution Data to RED ADVANTAGES: Free data, less data-intensive analysis Daily coverage helps with cloud problems CAN BE USED FOR: Rapidly identifying major areas of change Real-time deforestation monitoring for large clearings Determining fate of land use following deforestation to improve accuracy of emission estimates

5 Contribution #1: Real-time deforestation monitoring with MODIS data for clearings >25ha

Low : -3000 Goal to identify deforestation locations from MODIS data that is: -FAST: as automated # Sinop as possible -EASY: minimizes data storage and processing time -ACCURATE: minimizes false

6 Low : Goal to identify deforestation locations from MODIS data that is: -FAST: as automated # Sinop as possible -EASY: minimizes data storage and processing time -ACCURATE: minimizes false positives # Nova Mutum 10 Kilometers Landsat May 11, 2004 MODIS July, Kilometers Legend deforestation identified by MODIS NDVI 16 day composite through July 6 Value High : 9997 Low : Kilometers Legend NDVI 16 day composite through July 6 Value High : 9997

Field Validation in central Mato Grosso July, 2004 Goal to identify deforestation locations from MODIS data that is: # Sinop -FAST: as automated as possible -EASY: minimizes data storage

7 Field Validation in central Mato Grosso July, 2004 Goal to identify deforestation locations from MODIS data that is: # Sinop -FAST: as automated as possible -EASY: minimizes data storage and processing time -ACCURATE: minimizes false Nova Mutum positives # 200 Kilometers Legend deforestation identified by MODIS NDVI 16 day composite through July 6 Value High : 9997 Low : -3000

8 HIGH ACCURACY IN RAPID IDENTIFICATION OF LARGE DEFORESTATION EVENTS WITH MODIS DATA USING VERY SIMPLE METHODS Field Validation of Deforestation Clusters % Number of Clusters % 80% 70% 60% 50% 40% 30% 20% 10% Percent Observed # Correct % Correct to to to to 408 0% Cluster Size (Pixels) BUT NOT A SUBSTITUTE FOR PRECISE ESTIMATES OF AREA DEFORESTED FROM LANDSAT-LIKE ANALYSES (Morton et al., 2005, Earth Interactions)

9 50% of deforestation area in Brazil occurred in annual clearings >100ha Proportion of number of deforested polygons and deforested area in small (<100 ha), medium ( ha) and large (>1000 ha) by state for Data from State Deforested area ( ) (km2) a Percent of number of deforested polygons in size category Percent of deforested area in size category small medium large small medium large Acre < Amapá < Amazonas < Maranhão b < Mato Grosso < Pará < Rondônia < Roraima < Tocantins < Total < a from b 2001 data excluded from analysis of deforestation polygons because they contain spurious large clearings DATA FROM Y. SHIMABUKURO, INPE

10 (Morton et al., 2005, Earth Interactions) Annual Deforestation derived from MODIS with a few days work

11 Contribution #2: Capability to identify major areas of change 2001 MODIS % tree cover at 500m resolution Tree cover 0% 100%

12 Calculate annual metrics using 23 MODIS 16-day composites (MODIS Vegetation product MOD13Q1, 250 m res.) per year 23 MOD13 composites per year, with bands Red, NIR, NDVI 102 Annual Metrics Annual metrics (total 102): Min, max, mean, median of all Red, NIR and NDVI bands throughout one year Red/NIR/NDVI value at the annual min, max, median Red/NIR/NDVI Min, max, mean, median of the four darkest and four brightest values Min, max, mean, median of Red/NIR/NDVI at the four brightest and four darkest values of Red/NIR/NDVI

13 Procedure Ikonos scenes Tree / non-tree classification Aggregate to percent tree cover at Landsat resolution Classify Landsat scenes for percent tree cover Aggregate Landsat percent tree cover to MODIS resolution Classify Annual MODIS Metrics for percent tree cover for each year Analyze for changes from year to year

14 DEFORESTATION DYNAMICS IN THE SOUTHERN AMAZON DERIVED FROM AVHRR 8KM DATA 800 km Early 1980s Tree cover 0% Circa % (From DeFries et al., PNAS, 2002: Hansen and DeFries, Ecosystems, 2004) Circa 2000

15 Stratum 1 no change Stratum 2 Stratum 3 Stratum 4 intense change 500m MODIS % tree cover from multiple years to stratify change areas from 2000 to present Humid tropical forest biome Landsat samples

16 Matt Hansen, South Dakota State University

17 EXAMPLE OF REGIONAL 250m MODIS PERCENT TREE COVER PERCENT WOODY COVER ESTIMATED FOR EACH YEAR USING HIGH RESOLUTION TRAINING DATA AND MODIS- DERIVED METRICS IN REGRESSION TREE

Contribution #3: Determining fate of land use following deforestation PRIMARY SECONDARY Pasture Forest Cropland NDVI x 10,000 10000 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 2000-2003 Time

18 Contribution #3: Determining fate of land use following deforestation PRIMARY SECONDARY Pasture Forest Cropland NDVI x 10, Time Series, Quality flag adjusted 2003 Conversion Agriculture Forest Pasture B1 B4 B7 B10 B13 B16 B19 B22 B25 B28 B31 B34 B37 B40 B43 B46 B49 B52 B55 B58 B61 B64 B67 B70 B73 B76 B79 B82 16-day Periods ( to ) Area deforested (km2) Small (<25 ha) Pasture yr 2+ Pasture yr 1 Not in Production Crop yr 2+ Crop yr 1 Soy Price Brazilian Reais (R$) per 60 kg Soy (from Morton et al, 2006) Year of Deforestation 0

19 FATE OF DEFORESTATION IN MATO GROSSO : WHO ARE THE ACTORS? Cropland Expansion Deforestation Forest Not in Production 3,609 km 2 Cerrado Crop 5,770 km 2 Pasture Crop 5,930 km 2 Forest Crop 4,670-5,463 km 2 Forest Pasture 23,463 km 2 Forest Small (<25ha) 5,562 km 2 (Morton et al., PNAS, 2006)

20 Combustion completeness of deforestation fires is function of land management FOREST TO PASTURE FOREST TO CROPLAND FOREST TO REGROWTH PASTURE TO CROPLAND

21 DECAF MODEL TO ESTIMATE GROSS CARBON FLUXES FROM DEFORESTATION AND MAINTENANCE FIRES LAND USE FOLLOWING DEFORESTATION FROM MODIS MODIS ACTIVE FIRES CASA LANDSAT-BASED PRODES DEFORESTATION BURN SCARS FROM PASTURE MAINTENANCE FIRES C EMISSIONS FROM FIRE C t = A t A=burned area E=combustion completeness M=mortality rate D=detritus B=biomass F=fuelwood d b E d D t,d + E b M b B t,b + E F F

22 Percent of Clearings Carbon emissions from deforestation fires are function of land use type following deforestation Maximum Fire Days Crop Pasture NIP Conversions of forest to pasture and not yet in production (NYIP) exhibit lower-frequency fires than cropland conversions Percent contribution of trajectories by area for Forest clearing for Forest pastureclearing for cropland pasture maintenance savanna to cropland forest to cropland forest to pasture forest to regrowth Percent contribution of trajectories to emissions for Pasture Secondary maintenance clearing fires of pasture to cropland

Conclusions Price and ease of medium resolution data attractive for operational use Operational real-time monitoring system using MODIS data has been demonstrated Analysis of medium resolution data

23 Conclusions Price and ease of medium resolution data attractive for operational use Operational real-time monitoring system using MODIS data has been demonstrated Analysis of medium resolution data can help zoom to areas of major change Phenological signal helps determine combustion completeness and land use following deforestation Medium resolution data is complement not substitute to high resolution data

INPE s Brazilian Amazon Deforestation and Forest Degradation Program. Dalton M. Valeriano (dalton@dsr.inpe.br) Program Manager

INPE s Brazilian Amazon Deforestation and Forest Degradation Program Dalton M. Valeriano (dalton@dsr.inpe.br) Program Manager Summary PRODES Yearly deforestation inventory DETER - Daily detection of deforestation