RESULTS & RECOMMENDATIONS from. The Dragon Forest Projects

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1 RESULTS & RECOMMENDATIONS from The Dragon Forest Projects

2 Forest Fire Id Forest Change Monitoring Id PolInSAR Id The Forest Dragon 3 Id Forest Resources Research Id Forest Modelling Id

3 Progress Summary Geographical Coverage Biomass research: Genhe, Zunhua, Puer, Danling, Evo, Roda, Hardtwald Large area mapping: NE and SW China Fire research: Heilongjiang, An Ning Change monitoring: Guangxi

4 Progress Summary Methodology Major Results I o AGB and Land Cover products and change used for plausibility check, but large disrepancies discovered o Comparison of different 3D metrics in estimation of plot level forest inventory variables (spaceborne SAR, ALS, aerial photos)

5 Progress Summary Methodology Major Results II o Tomographic study of the potential of SAOCOM- CS/TangoSAT over boreal forest for forest height and biomass estimation and comparison with the BIOMASS mission o Biomass modelling by multisource data and forest type classification

6 Progress Summary Methodology Major Results III o Fire Released Power applied to estimate burned biomass (BB) from MODIS, HJ-1A and GF-1 o Deforestation mapping using GF-1 and first assessment of Sentinel-1 time series o Progress of Chinese airborne SAR campaigns (Xband InSAR, P-band PolSAR, PolInSAR) for forest height and AGB

7 Forest Fire (10350) - The statistical analysis of Chinese forest fires shows its FRP follows a power law distribution, being the power law parameters different for every vegetation cover - The FRP power law distribution is a good hypothesis to determine FRP and to estimate the Burned Biomass by means of the duration time, maximum and minimum FRP values and power law parameters - Burned biomass at sub-compartment level has been estimated by different spatial resolution satellite images for forest management

8 Forest Change Monitoring(10549) Multi-temporal Gao Fen 1 wide swath MS data used for forest change detection Deforestation map between to

9 PolInSAR (10609) One AGB estimation method using multi-track X-band airborne InSAR data was developed and validated Total accuracy=89.45% 33.7 ton/ha 45.3 ton/ha InSAR estimated(ton/ha) X-InSAR AGB 0 LiDAR AGB o SAOCOM CS & BIOMASS tomographic mode simulation - Resolution limitations - Repeat- vs single-pass insar modes 0 LiDAR estimated (ton/ha) Evaluated PolTomSAR for characterizing Boreal forests using SAOCOM CS & BIOMASS o Over a Boreal forest - SAOCOM CS simulations: good performance z g and z t retrieval - BIOMASS: reduced performance - structure: new processing techniques are needed SAOCOM CS BIOMASS

10 The Forest Dragon (10666) Comparison of ASAR-retrieved Growing Stock Volume changes and the respective GlobCover Land Cover Class changes reveal different dynamics. These are are the basis for plausibility checks.

11 Forest Resources Research (Project ID ) -The forest parameter retrieval from single Tandem-X InSAR/6 TerraSAR-X Radargrammetry Key parameters investigated: 1 Basal area 2 Stem volume 3 AGB 4 Mean DBH 5 Lorey s height -Forest height from Chian Airboren SAR system P-band PolSAR X-band PolInSAR Key conclusions: 1 InSAR results (1 pair) slightly better than stereo (6 pairs) 2 Forest inventories of wide areas are possible using SAR data combined with an accurate terrain model China Airborne SAR system P-band PolInSAR data Forest height map

12 Forest Modelling (10676) Classification of forest type with simulated S2 and Landsat 8 data Final map of tree type classification Most frequent class (left) Class frequency (right) -fequency (0-100) 100 all runs same result Sensor kappa OA pa_mixed pa_con pa_dec ua_mixed ua_con ua_dec S2_10 0,48 0,65 0,53 0,70 0,74 0,50 0,73 0,73 S2_20 0,55 0,70 0,58 0,77 0,77 0,57 0,74 0,78 S2_all 0,56 0,71 0,58 0,81 0,76 0,60 0,72 0,80 OLI 0,55 0,70 0,58 0,82 0,72 0,55 0,79 0,76

13 Recommendations Methodology: - Harmonisation of tomographic methodologies and education and training - Space-Time-Cubes: Understanding spatio-temporal patterns - Linking Fire Radiative Energy and burned biomass measurements from Sentinel-2 and GF-1/-4 - Multi-sensor data (optical-radar) for ecosystem services - 3D-Information retrieval for forest height and DTM from spaceand airborne SAR data - Near real-time deforestation detection using Sentinel-1/-2 and GF-1 - Investigating L-band decorrelation patterns over tropical forest ( L-band TropiScat )

14 Data Issues: - Sentinel-1 and -2 availabilities for China (acquisition strategy for Dragon-3: temporal repetition, acquisition modes, interferometry,..) - PALSAR-2: 3 rd party agreements for high resolution modes in addition to JAXA AOs (?) - TanDEM-X data Recommendations - Chinese satellite data: e.g. ZY-3 continuous along-track stereoscanner, Fengyun-3B VIRR noise lines

15 Recommendations Preparations for Future Space Systems: SAOCOM-CS/TangoSAT: full polarimetric, multibaseline L-Band (ESA constellation under study) for Pampas and boreal mapping BIOMASS Earth Explorer: P-band with focus tropical areas German Missions: EnMAP+TanDEM-L Chinese Satellites: Gao Fen 3 C-band polarimetric SAR

16 Dragon-4: Forest Projects Recommendations I Test sites according to established geographic locations from Dragon-3 The Dragon-Forest projects have mainly focussed on technical developments, for Dragon-4 we propose to closely link our activities to international programmes (GFOI, CEOS Carbon from Space, UN-REDD, UNFCCC, GEOBON, GEOSS, GCP, ICOS, IGCO, GOFC-GOLD, Future Earth, )

17 Dragon-4: Forest Projects Recommendations II Data exchange platform, data repository, WebGIS or similar for use within China and internationally for sustainability of products and scientific results. Could this acitivity be supported by ESA? To ESA: Consider the restricted funding when asking for broader activities!

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