Remote Sensing and Modeling: A tool to provide the spatial information for biomass production potential
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1 Remote Sensing and Modeling: A tool to provide the spatial information for biomass production potential K. P. Günther, E. Borg, K. Wißkirchen, M. Schroedter-Homscheidt, B. Fichtelmann, J. Gehrung Folie 1 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
2 Introduction Biomass resource assessment today: 1. Regional or continental estimation of biomass (forestry and agriculture) is based on statistics including land cover maps -> EUROSTAT, FAOSTAT 2. Use of biomass is forced by the evaluation of restrictions e.g. technical and environmental constraints associated with - slope (-> digital elevation model) - soils (-> map) - wildlife habitat (-> map) and other site factors. 3. Currently, there is a lack of reliable, dynamic data delineating the location, quantity, and quality of available biomass (-> no differentiation with regard to energy plants ). Folie 2 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
3 Typical biomass potential map tons x 1000 No data < >100 Spatial resolution is limited by the resolution for statistics (in Europe NUTS) Folie 3 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
4 Goal for the future Biomass resource assessment tomorrow: 1. Use remote sensing data to derive high quality information on land cover / land use and on plant status parameter (leaf area index, temperature,..) 2. Feed a carbon model with the remote sensed products to determine the yearly carbon increase (Net Primary Productivity, NPP). 3. Find a relation for biomass ~ NPP. Folie 4 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
5 Land cover classification (LCC) Input parameter for carbon modeling PELCOM Land Cover based on remote sensing data -AVHRR - Scale: Legend: 1km x 1km 16 classes Reference: 1995 Folie 5 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
6 Land cover classification (LCC) Input parameter for carbon modeling CORINE Land Cover (CLC 2000) based on remote sensing data - LANDSAT, SPOT - Scale: 1 : 100,000 Legend: Reference year: > 100m x 100m 44 classes (Europe) 37 classes (Germany) Folie 6 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
7 Land cover classification (LCC) Approach for automatic classification R [ % ] λ [ nm ] Soil Vegetation, Clouds Soil, Snow t [ d ] Schematic presentation of the variation of spectral signature of a landscape object for a year, which is additionally disturbed by cloud cover and snow cover. Folie 7 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
8 First conclusion concerning LCC In the near future, land cover / land use mapping using remote sensing data will be performed in an automatic way by integrating multi-spectral and multi- temporal data sets. Basic tools are under development at DLR. Differentiation of agricultural plants is a challenge for the future and will be integrated. Keyword: energy plants for biomass use Folie 8 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
9 Plant status (Leaf Area Index LAI) Input parameter for carbon modeling NDVI: Normalized Difference Vegetation Index LAI: Leaf Area Index fpar: Fraction of absorbed PAR Flowchart of data processing for LAI time series Folie 9 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
10 Carbon modeling (BETHY - DLR) Water Budget Heat Transfer Radiation Transpiration Precipitation Evaporation CO 2 - Exchange Photosynthesis Soil Water Roots, water uptake Soil Heat Flux Folie 10 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
11 Carbon modeling (BETHY - DLR) Remote sensing data ECMWF data BETHY: Biosphere Energy Transfer Hydrology Model Wolfgang Knorr, MPI for Biogeochemistry Jena, 1997 Klaus Wißkirchen, DLR, 2005 Folie 11 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
12 Carbon modeling (BETHY - DLR) GPP: Gross Primary Productivity NPP: Net Primary Productivity NPP = GPP aut. Respiration Modelled daily cycle of NPP [kgc m -2 s -1 ] (red line) at Tharandt Folie 12 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
![Carbon modeling (BETHY - DLR) Regional simulation result of NPP [gc m -2 y -1 ] for the region of Tharandt, near Dresden /](/docs-images/78/77606768/images/13-1.jpg "Germany. Spatial resolution is 1.")
13 Carbon modeling (BETHY - DLR) Regional simulation result of NPP [gc m -2 y -1 ] for the region of Tharandt, near Dresden / Germany. Spatial resolution is 1.1 km Time: 1998 Folie 13 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
14 Validation of NEP GPP: Gross Primary Productivity NPP: Net Primary Productivity NEP: Netto Ecosystem Productivity NPP = GPP Aut. Respiration NEP = NPP Het. Respiration Folie 14 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
15 Combining NPP & crop statistics Crop statistics Potential of cereal straw and other energy plants Annual energy yield or potential, monitoring resources NPP (TgC/year/pixel) for one class Allocation parameter or model for energy yield or potential after Dieter and Englert, 2001 Folie 15 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
16 Straw potential derived from statistics and NPP (as distribution parameter) From: Julia Gehrung (DLR, University Karlsruhe), Diploma Thesis, 2006 Folie 16 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
17 Conclusion Remote sensing and carbon modeling delivers maps of Net Primary Productivity (NPP). Depending on the spatial resolution of the remote sensing data, local to continental maps of NPP will be available. Looking at energy plants for sustainable biomass use, land use maps are necessary indicating the location of energy plants as well as their plant status. Folie 17 > Biomass Assessment / Remote Sensing and Modelling > Gunther et al.
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