Using global datasets for biomass and forest area estimation: Miombo forests in Tanzania
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1 Using global datasets for biomass and forest area estimation: Miombo forests in Tanzania Erik Næsset, Terje Gobakken, Hans Ole Ørka (NMBU, Norway)
2 Objectives Quantify and compare precision of estimates of biomass and forest area in miombo woodlands (90% of Tanzania) by using data from 1. RapidEye high-resolution optical imageries 2. Global Landsat maps 3. Global ALOS PALSAR maps Fundamental requirement: Use estimators that satisfy the IPCC requirements and thus are valid for reporting to UNFCCC: 1. Use unbiased estimators 2. We should quantify the precision (variance) of the estimates
3 Estimation: We will demonstrate two different approaches to obtain reference samples for modeling/classification and estimation: 1. Field data (NFI) 2. Manual interpretation of aerial photography..and illustrate potential consequences for forest area estimates
4 DEPARTMENT OF ECOLOGY AND NATURAL RESOURCE MANAGEMENT Study areas Photo Photo Photo Field Photo Photo Each block ca ha
5 Block E: design of field survey (biomass and forest area)
6 Field data 88 NFI plots Biomass measurements in all land use classes Forest definition: - >5 m - >10% cover - >0.5 ha
7 DEPARTMENT OF ECOLOGY AND NATURAL RESOURCE MANAGEMENT Field data Protection forest Protection forest Production forest Production forest Agriculture Agriculture Biomass=26.0 t/ha Biomass=48.9 t/ha Biomass=57.9 t/ha Biomass=43.8 t/ha Biomass=112.0 t/ha Biomass=40.2 t/ha
8 Residuals in Lidar biomass models edge problems (700 m 2 plots) Biomass=133.5 t/ha Lidar=70.7 t/ha
9 Biomass maps produced from remote sensing biomass models A=RapidEye high-resolution optical imageries B=Global Landsat maps C=Global ALOS PALSAR maps
10 Estimated mean biomass (Mg/ha) and 95% confidence interval
11 Relative efficiency of remote sensing-assisted estimates of biomass Relative efficiency: RE = VV field VV RS
12 Forest/non-forest maps produced from remote sensing models A=RapidEye high-resolution optical imageries B=Global Landsat maps C=Global ALOS PALSAR maps
13 Estimated forest area (hectares) and relative efficiency
14 Forest/non-forest maps produced from global Landsat maps Global Landsat forest maps: A: >10 tree cover B: modelled probability of forest C: probability of forest >0.5
15 Results for forest area estimates based on Global forest maps No local calibration
16 Blocks A-E: design of manual image interpretation (forest area) A: n=105 B: n=104 C: n=69 E: n=105 D: n=69
17 Examples of initial segmentation to get patches >0.5 ha Landsat RapidEye
18 Relative efficiency of forest area estimates across blocks A-E
19 Relative efficiency of forest area estimates for blocks E
20 Prediction maps of probability of forest Photo calibration Ground/NFI calibration Landsat RapidEye
21 Prediction maps of probability of forest Photo calibration Ground/NFI calibration Landsat ha (32 450, ) ha (18 050, ) RapidEye ha (32 000, ) ha (17 130, ) Agriculture Biomass=112.0 t/ha
22 Conclusions 1. Global map products (classified and mosaics) can improve precision of estimates of forest area in open and fragmented forests, but less so for biomass 2. Local calibration of global maps seems essential to improve precision 3. Reference samples seems to be needed to ensure unbiased estimators 4. Consistency with forest definition can be hard to achieve when acquiring reference samples by image interpretation 5. Distinguishing between land use and land cover in remote sensing is a non-trivial issue with potentially large implications for forest area estimates
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