Comparing Lidar, InSAR, RapidEye optical, and global Landsat and ALOS PALSAR maps for forest area estimation

Transcription

1 Comparing Lidar, InSAR, RapidEye optical, and global Landsat and ALOS PALSAR maps for forest area estimation Erik Næsset, Hans O. Ørka, Ole M. Bollandsås, Endre H. Hansen, Ernest Mauya, Terje Gobakken (NMBU, Norway) Svein Solberg (NFLI, Norway) Eliakimu Zahabu, Rogers Malimbwi (SUA, Tanzania) Nurdin Chamuya (TFSA, Tanzania) Håkan Olsson (SLU, Sweden)

2 Introduction Remotely sensed data can improve precision of parameters such as estimated mean biomass per hectare or estimated forest area beyond the precision that can be obtained by use of field data alone Quantification of the contribution of remotely sensed data to improve precision is important information to inform investment decisions in future inventory and monitoring systems

3 Objectives Quantify and compare precision of estimates of forest area in miombo woodlands by using data from 1. Airborne Lidar 2. TandemX InSAR 3. RapidEye high-resolution optical imageries 4. Global Landsat maps 5. Global ALOS PALSAR maps Fundamental requirement: Use estimators that satisfy the IPCC requirements and thus are valid for reporting to UNFCCC: 1. We should use unbiased estimators 2. We should quantify the precision (variance) of the estimates

4 Datasets Wall-to-wall Lidar RapidEye high-resolution optical TandemX Insar

5 Study area and design

6 Field data Protection forest forest Production forest Biomass=26.0 Agriculture 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

7 Residuals in Lidar biomass models Biomass=133.5 t/ha Lidar=70.7 t/ha

8 Residuals in Lidar biomass models Common sources of prediction errors: 1. Large trees located close to the border (inside or outside) with crown inside or outside. Means to reduce error: increase plot size 2. Small trees located close to the border which are measured by the Lidar but not in field (concentric circles) Means to reduce error: avoid sampling on plot Biomass=192.3 t/ha Lidar=100.6 t/ha

9 Forest/non-forest maps produced from remote sensing models

10 Estimated forest area (hectares) and relative efficiency Relative efficiency: RE = V field V RS

11 Forest/non-forest maps produced from Landsat global maps Global Landsat forest maps: A: >10 tree cover B: modelled probability of forest C: probability of forest >0.5

12 Results for forest area estimates based on Global forest maps

13 A discussion on forest definition and model-assisted estimation Forest definition: Marrakesh Accords (COP 7, 2001): Forest is a minimum area of land of hectares with tree crown cover (or equivalent stocking level) of more than per cent with trees with the potential to reach a minimum height of 2-5 metres at maturity in situ. A forest may consist either of closed forest formations where trees of various storeys and undergrowth cover a high proportion of the ground or open forest. Young natural stands and all plantations which have yet to reach a crown density of per cent or tree height of 2-5 metres are included under forest, as are areas normally forming part of the forest area which are temporarily unstocked as a result of human intervention such as harvesting or natural causes but which are expected to revert to forest.

14 A discussion on forest definition and model-assisted estimation Forest definition, Tanzania: Global Forest Resources Assessment of the Food and Agricultural Organization of the United Nations; Land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use

15 A discussion on forest definition and model-assisted estimation Does it matter if we use field plot size (e.g. 707 m 2 in NFI) or 0.5- ha units in estimation of forest area? What about segments patches of land >/< 0.5 ha? Two issues involved here: 1. Definition: what do we mean by forest and importance of a rigorous definition that is followed in field (0.5 ha). Spatial unit is clearly of greater importance in spatially fragmented forests 2. Estimation: does choice of size of spatial unit matter (assuming that a rigorous definition has been followed)? a. Pure field-based estimation: probably no consequence b. Remote sensing-based estimation: most likely an effect of choice of spatial unit (how do we treat the remotely sensed data?)

16 A discussion on forest definition and model-assisted estimation Definition 0.5 ha NFI 707 m 2 Forest/non-forest attribute carried by NFI plot but defined and recorded for 0.5 ha

17 Cases four different approaches to produce forest maps Train classifier with plot and pixel-data Train classifier with plot and pixel-data A B C Classify and estimate with pixel-data Train classifier with 0.5 ha plot and aggregated pixel-data (0.5 ha) Classify and estimate with aggregated pixeldata (0.5 ha)

18 Cases four different approaches to produce forest maps D Train classifier with segment (aggregated pixel data) NFI 707 m 2 Classify and estimate with segment (aggregated pixel data)