SAR data for forestry and agriculture

Transcription

1 SAR data for forestry and agriculture Thuy Le Toan September 5, 2007 Lecture D3L3

2 Assets of radar data for vegetation monitoring? All weather capability to secure data acquisition during growing season Information content on : -biomass - vegetation structure - soil moisture

3 Forest biomass Rice monitoring Context Use of SAR data in studies related to the Carbon cycle

4 The terrestrial carbon balance Mass balance equation (Inventory approach) Δ C= Δ Above Ground Biomass + Δ Below Ground Biomass + Δ Litter + Δ Soil Carbon Process equation (Dynamic Vegetation Model Approach, Productivity Efficiency Approach) Δ C= Gross Primary Production- Autotrophic respiration- Heterotrophic respiration - Loss by disturbances (GPP and Ra: depend on biomass Rh and Loss by disturbances depend on past biomass)

5 Why a SAR for forest biomass? Austrian pine X band λ= 3 cm L band λ= 27 cm P band λ= 70 cm VHF λ > 3 m Radar signal related to biomass of leaves, branches, trunk, depending on the SAR frequency band, polarisation and incidence

6 Biomass vs Stem volume and height ( from Saatchi et al.) Biomass (tons/ha) Volume (m3/ha) Stem volume (m3) Height (m)

7 Sensitivity of SAR data to forest biomass -10 HV data -15 Sigma (db) -20 Landes forest France L - HV P - HV HF - VHF Above-ground biomass (tons/ha)

8 Biomass Grid-10 9 ha day-100s years Biomass information in terrestrial carbon modelling Forcing Test Dynamic Vegetation modelling Biomass Grid-10 9 ha day-10s years Input Light Use Efficiency modelling Carbon Budget Biomass ha years Input Landscape modelling (inventory) Biomass ha day-100s years Input Test Ecological studies

9 The Dynamic Vegetation Model (CTCD) ATMOSPHERIC CO 2 Photosynthesis GPP Fire Biophysics NPP Growth Mortality Thinning Litter Soil NBP Biomass Disturbance LEACHED

10 Research results to illustrate: Use of radar-retrieved biomass on temperate forests (local scale) for:. Local or regional carbon budget ( Kyoto protocol etc..). Forest management Map of biomass using L-band ESAR Comparison with output from Sheffield Dynamic Vegetation Model C flux prediction using SDVM Büdingen forest (Germany)

11 Mapping of biomass using L-band data Stand-wise inversion Büdingen forest, Land Hessen, Germany. Pixel-wise inversion < 12 t/ha > 12 t/ha - 24 t/ha > t/ha > 42 t/ha

12 Comparison biomass predicted by model and biomass by SAR Radar retrieved biomass (m3/ha) SDVGM estimated biomass (m3/ha) Spruce Douglas Oak Beech

13 Use of Dynamic Vegetation Model to predict Timber Volume for scenarios of climate (temperature, precipitation, CO 2 ) Timber volume up to 2020

14 Use of Dynamic Vegetation Model to predict Carbon fluxes Net Primary Productivity (red) and soil respiration (blue)

15 BIOMASS A P-band SAR for Carbon Assessment

16 The missing carbon sink The annual flux of C to the atmosphere , in which the unidentified sink is assigned to uptake by the land (Houghton, 1999).

17 Current status of global biomass information 1. Biomass from inventory data 2. Biomass from remote sensing -->no biomass information suitable for carbon cycle and Earth system models -> no consistent mapping of forest area, structure, and change

18 Uncertainty in estimates of biomass at regional scale Spatial distribution of biomass in the Amazon: Comparison of current methods (Houghton et al., 2001)

19 Carbon sources and sinks predicted by models LPJ SDGVM ( Hybrid, IBIS, LPJ, SDGVM, Triffid, Vecode)

20 90 E Biomass estimates: Comparison Model-EO Sheffield Dynamic Vegetation Model 100 E Siberia-I radar forest map 60 N N kg C m Le Toan et al., 2004 J. of Climatic Change

21 Mapping Disturbance and Recovery Yellowstone National Park 2003 Burn 1988 Burn ( from Saatchi et al.) Pine Beatle Disease

22 Biomass mapping: temperate conifer plantations Image optique P-band SAR Image Forêt de Nézer Le Toan et al., 1992, Beaudoin et al.,1994 CESBIO Biomasse (T/ha)

23 Biomass mapping in boreal forest Field Stem Biomass (tons/ha) P-band Algorithm R=0.954 Saatchi & Moghaddam SAR Predicted Stem Biomass Biomass map for BOREAS test site from P-Band SAR (Saatchi et Moghaddam, 2000)

24 Advanced techniques to measure tree height Intensity P-band Polarimetric interferometr Coherence Phase Garestier et al., 2006 September 5,2007 Lecture D3L3 BIOMASS SAR data proposal for agriculture and forestry Thuy Le Toan

25 Rice monitoring using SAR data

26 Methane global concentrations (SCIAMACHY):

27 Rice field: a major source of methane (Denier van der Gon, 1996)

28 Questions Role of rice fields in the terrestrial carbone cycle? (CH 4 & CO 2 ) Changes in Green House Gases (GHG) due to changes in land use and farming practices? Projection in the future and solution for mitigation?

29 Rice Monitoring in China ESA-China Dragon Project

30 LPJmL grid cell - crops - managed grasses - land use change Modelling approach The LPJ Dynamic Global Vegetation Model adapted to managed land (Bondeau et al., GCB, 2007) Crop specific parameterisation: - Phenology: sowing dates, maturity, determination of some varietal parameters - Carbon allocation to the yield-storage organ. C budget, H 2 0 & CO 2 Fluxes, Vegetation Composition Crop production

31 C model from Institute of Atmospheric Physics, CAS, Beijing Process level Understanding CH4MOD Crop-C Field measurements & literature reports Model validation GIS

32 LPJ Model inputs Farming practices: Cropping system (single-crop rice, double-crop rice, rice-wheat, rice-maize,etc) Crop calendar: sowing date, harvest dates for each crop variety Flooding and drainage calendar Which variety class (e.g. high-yield variety: harvest index) Sowing density (plant density) Fertilizer inputs calendar Residues management (straw taken out, burned, left over the field and flooded, ploughted into the soil?) Tillage Land use / land cover % crops cover (rice and other crops) within the grid cell History (land use change, needed for soil carbon building)

33 Approach: use of remote sensing & models Earth Observation Rice mapping Rice varieties LAI/FAPAR Crop calendar Watermanagement Biomass. Initiation Assimilation Run time control Testing Rice carbon Models

34 Ancillary& ground data Meteorological data ENVISAT ASAR & MERIS, VGT Remote Sensing Methods ENVISAT SCIAMACHY Methane Measurement Rice Parameters Rice Mapping Local & regional Rice and Biochemistry Models Yield estimation Methane & CO2 Fluxes Rice Production Rice in Carbon cycle

35 Remote Sensing results Development and validation of methods for mapping of paddy fields at local and regional scales using ASAR data Retrieval of rice biomass using ASAR Mapping of rice varieties using ASAR Determination of rice and other crop calendar using SPOT VGT Detection of mid season drainage using ASAR

36 ASAR for rice monitoring At C band, HH and VV: the dominant scattering mechanism is the double bounce vegetation-water HH>VV because of the stronger attenuation of VV by vertical stems HH and VV increases with the plant biomass.the increase is very important (up to 10 db during the growth season) (Le Toan et al., 1997). Water HH/VV is related to biomass

37 Basic principle of using C-band SAR for rice monitoring Scatterring mechanisms Backscattering coefficient (db) ERS Model Moist Biomass (g/m²) Backscattering coefficient (db) Volume-Surface Volume Scattering Days (days after sowing) Strong increase (>10 db) during the growth cycle Main scattering mechanism is volume-surface interaction Le Toan et al., 1997, Wang et al., 2004

38 Effect of Polarisation Effet of Incidence angle Backscattering Coefficient (db) RADARSAT (HH) ERS (VV) Solid line: modelling result Moist Biomass (g/m²) Backscattering Coefficient (db) -4-5 C-HH C-HH Solid line: modelling result Age (# days after sowing) Strong increase as a function of biomass, from transplanting to maturity HH>VV Smaller increase at higher incidence

39 date 1 date 2 date M 1 2. M Mapping algorithm Initial images. Calibration Registration M Multi image filtering 1 2. M Spatial filtering Geocoding 1 2. M Analysis, Retrieval Classification Calibrated coregistered Filtered Filtered geocoded Example of Chain developed using: Gamma ASAR (Gamma RS) Multi-image filtering (Quegan et al., 2000) Temporal change (Le Toan et al., 1997)

40 Rice monitoring using ENVISAT Jiangsu Province

41 Small field: powerful filtering Filtering using 20 images (2 polarisations, 10 dates)

42 HH Hongze (Jiangsu) VV

43 Rice mapping at a single date using HH/VV Magenta=HH, Green=VV 34km*38km yellow=rice, red=urban, black=other September 6th, 2004, Hongze area

44 Ground campaign for validation In situ mapping of 1km x 1km samples with DGPS Overall accuracy: 80-88%

45 Rice mapping using WS ASAR data Shuyang Suqian Funing 210 km * km pixel size : 75 m magenta : August 18green : October 27 HuaiAn Chuzhou Yancheng ASAR WSM region North of Qingjiang, Jiangsu province

46 Regional rice mapping Lianyungang Huaimu xin River Qiangwei River Guboshanh ou River Guanyun Xinqi River Jieyu River Guannian Guang river

47 Mapping of rice varieties Japonica rice Hybrid rice Dragon project CAAS, Nanjing

48 Mid-season drainage reduces methane emissions Continuous flooding 30 to 60% Li et al., JGR,2001 Mid-season drainage Wuxian, Jiangsu province

49 Mapping of mid season drainage in rice fields WSM VV Magenta: Green: Magenta: Green: Fields in magenta are flooded rice fields and fields in green are rice fields with temporary drainage that occurs approximately 3 weeks after transplantation (in July)

50 Retrieving of biomass 5 HH/VV vs Wet biomass of a test field in Gaoyou, 2004 Dates june HH/VV (db) tillering jointing heading ripening flowering 20-july 2-august 18-august 8-sept. 1 ready for harvest 24-sept oct. Wet biomass (kg/m²)

51 6 5 Hongze and Gaoyou - Beginning of Tillering to Flowering 2004 (Blue), 2005 (Magenta) Relation HH/VV and rice wet biomass y = 1,0266x - 0,1058 R 2 = 0, HH/VV ,5 1 1,5 2 2,5 3 3,5 4 4,5 5-1 Wet Biomass

52 Determining rice and other crop cycle and calendar using SPOT VGT (1 km, 10 days) Wheat Rice Rice Rice SPOT VGT-NDVI

53 Landuse/cover and number of crop/year using VGT 2005 water urban forest double-cropping farmland triple-cropping farmland

54 Transplanting date Light green: 1-10 June Green: June Dark green: June Transplanting date (min NDVI) 2005

55 Rice percentage 2004

56 Initial modelling result using LPJmL Rice monthly CH GgCH4 Jun Jul Aug Total annual CH 4 emission from Rice fields in China: 4.9 Tg CH

57 Modelling results by IAP IAP-CAS In June and August, the extreme high tropospheric CH 4 enrichment appeared over central China with the values of 9.2 ppb and 11.5 ppb.

58 IAP-CAS A peak increment of 11.9 ppb in July occurred in Sichuan

59 IAP-CAS In June and August, the extreme high tropospheric CH 4 enrichment appeared over central China

60

61

62

63