KLAUS KDA2 - Landslides Progress Meeting 7 09 Jun KLAUS Progress Meeting 7

Structure Critical User Requirements System Architecture Implementation Test and Validation User Manual Future steps: – Performance Evaluation – TTO Delivered Items KLAUS Progress Meeting 7

KDA2- Landslides Development Status Users Identification User Requirements Collection URD -> Technical Specifications TS -> Software implementation / Delivery to users Users Service usage and validation Service delivery to ESA KLAUS Progress Meeting 7

Critical User Requirements [KDA2-L-UR 02] The KDA shall provide the areas with the high values of water retention [KDA2-L-UR 03] The KDA shall provide an indexing of soil moisture [KDA2-L-UR 07] The KDA shall provide a map of the risk of areas at risk of saturation. [KDA2-L-UR 09] The KDA shall make use of very high resolution satellite images to identify the features that could cause a variation of soil moisture. [KDA2-L-UR 10] The KDA should make use of SAR data to identify soil moisture [KDA2-L-UR 12] The KDA shall make use of the DEM of the area of interest KLAUS Progress Meeting 7

Landslide – Structure KLAUS Progress Meeting 7

Landslide – Types Lateral spread – J Rotational slide – A Translational slide – B, C Falls – D Topples – E Flows - slow movement – H, I Flows - fast movement – F, G KLAUS Progress Meeting 7

Landslide – Triggering factors Slope Lithology Soil water content Rainfall (mainly short duration and high intensity) KLAUS Progress Meeting 7

Retrieval of soil moisture Two models seem to be practical for our purposes: – Delta index (compares two images) (Thoma, D. P., M. S. Moran, R. Bryant, M. Rahman, C. D. Holifield-Collins, S. Skirvin, E. E. Sano, and K. Slocum (2006), “Comparison of four models to determine surface soil moisture from C-band radar imagery in a sparsely vegetated semiarid landscape”, Water Resour. Res., 42, W01418, doi: /2004WR ) – Integral Equation Model - IEM (physical model) Fung, Adrian, Zongqian Li, and K.S. Chen: „Backscattering from a Randomly Rough Dielectric Surface“, IEEE Transactions on Geoscience and Remote Sensing, Vol. 30, No. 2, March KLAUS Progress Meeting 7

Retrieval of soil moisture – Delta Index Image differencing method: – Compares two images – Observes backscatter differences due solely to changes in near-surface soil moisture – The difference between a reference (dry) and a changed (wetter) image can improve coefficients of determination for soil moisture and backscatter – Assumptions: same wavelength and viewing geometry (incidence angle and footprint), surface roughness and vegetation remain time-invariant KLAUS Progress Meeting 7

Retrieval of soil moisture – Delta Index Change from a more negative backscatter (dry image) to a more positive backscatter (wet image) In terms of soil moisture this is a change in the positive direction The higher the soil moisture the higher the free water content; this affects the dielectric constant KLAUS Progress Meeting 7

Retrieval of soil moisture – Delta Index Advantages – Easy to use – Variability in surface properties may be accounted for implicitly in the image pair – Empirical calibration may not be required – Remaining residual speckle is cancelled – More suitable in site-scale estimations Disadvantages – Two images needed with the same viewing geometry – Surface roughness and vegetation are considered to be time-invariant KLAUS Progress Meeting 7

Retrieval of soil moisture – Integral Equation Model (IEM) Physical method Radar scattering model (predicts backscatter from inputs including radar frequency, incidence angle, surface roughness and dielectric constant) Based on a single image KLAUS Progress Meeting 7

Retrieval of soil moisture – Integral Equation Model (IEM) Advantages – No limitation for surface roughness – More applicable to a wider range of roughness conditions – Model can be approximated (e.g. simplified) – Used by a lot of studies (more reference test cases) – Only one image (from the scene to be investigated) is necessary Disadvantages – Very complex to use – Function depends on lot of variables – Difficult to apply for large areas – Additional information (topography, soil type) may be required to optimize the performance KLAUS Progress Meeting 7

System Architecture Proposed Approach  Delta Index for soil moisture retrieval  KEO Modules  Use of the NEST libraries  pairs of HR SAR data (ASAR, PALSAR) KLAUS Progress Meeting 7

System Architecture KLAUS Progress Meeting 7

Implementation 1/2 Integration of the NEST Libraries with KEO: – Step 1: installation of the NEST environment on the KEO nodes  done by ESA – Step 2: implementation of KEO modules (CLIs) to invoke the NEST gpt interface  done implementing a generic NEST interface CLI KLAUS Progress Meeting 7

Implementation 2/2 Implemented KEO modules KLAUS Progress Meeting 7 Software IDKEO NameShort nameKEO Category KLAUS.KEO.KDA2.LS.1 NEST4B-0.4 GPT Interface v1.0 CLI NESTSignal/Image Processing KLAUS.KEO.KDA2.LS.2 SAR Delta Index BEAM-DIMAP v1.0 CLI Delta IndexSignal/Image Processing KLAUS.KEO.KDA2.LS.3Delta Index calculation chain for SAR data v1.0 FEP DI SARSignal/Image Processing

Interfaces 1/2 NEST /home/sistema/modules/nest/nest.sh IMAGES CONFIGFILE OUTPUTDIR [EXFILES] [OUTPUTNAME] – IMAGES: absolute path to the compressed image(s) (.zip); – CONFIGFILE: absolute path to the configuration file in XML format; – OUTPUTDIR: absolute path to directory where the elaboration result files shall be stored (provided by KAOS); – [EXFILES]: optional input parameter (.zip) to provide necessary external files if specified in the configuration file; – [OUTPUTNAME]: optional input parameter to specify the name of the output file (default is “output”) The output file is a zip file that contains the result(s) of the elaboration CONFIGFILE is a xml file that can be created with NEST, only with general changes to be provied (see the FD document for details) KLAUS Progress Meeting 7

Interfaces 2/2 Delta Index /home/sistema/modules/deltaindex/deltaindex.sh IMAGE BANDD BANDW OUTPUTDIR [OUTPUTNAME] – IMAGE: absolute path to the compressed BEAM-DIMAP image (.zip) – BANDD: band name of the dry image – BANDW: band name of the wet image – OUTPUTDIR: absolute path to directory where the elaboration result files shall be stored (provided by KAOS) – [OUTPUTNAME]: optional input parameter to specify the name of the output file (default is “output”) The output file is a zip file that contains the result(s) of the elaboration The configuration file is not provided since it is hardcoded into the module KLAUS Progress Meeting 7

FEP Architecture KLAUS Progress Meeting 7

FEP Interfaces KLAUS Progress Meeting 7 Input Parameters: – IMAGES: compressed NEST readable images (.zip) for dry and wet scene; – CONFIGURATION FILE: configuration file in XML format – DRY BAND NAME: band name of the dry image (default pattern: “Sigma0_POLARIZATION_dB_mst_DATE”, like sigma0_HH_dB_mst_18.Jul.2009) – WET BAND NAME: band name of the wet image (default pattern: “Sigma0_POLARIZATION_dB_slvNUMBER_DATE”, like Sigma0_HH_dB_slv1_02.Sep.2009) – [EXTERNAL FILES]: optional input parameter (.zip) to provide necessary external files if specified in the configuration file – [OUTPUT NAME]: optional input parameter to specify the name of the output file (default is “output”) The output file is a zip file that contains the result(s) of the elaboration Two different configuration files have been prepared for ASAR and PALSAR images

Advances with respect to the state of the art Inclusion of NEST libraries into KEO Creation of a generic NEST interface module Creation of a Delta Index module that can permit, once routine SAR images are available (e.g. Sentinel 1 data) to quickly detect changes on the soil surface nature (not limited to soil moisture) KLAUS Progress Meeting 7

Future Activities Performance Evaluation (31 July 2011) TTO (31 July 2011) KLAUS Progress Meeting 7

Delivered Items D5.2.1 KLAUS_D5.2.1_1.1_TN.doc Hydrological hazards applications requirements (including input data identification) Technical Note Issue 1.1 D5.2.2 KLAUS_D5.2.2_1.0_TN.doc Hydrological hazards applications – Data model Description Technical Note. Issue 1.0 D5.2.3 KLAUS_D5.2.3_1.0_FD.doc Hydrological hazards applications – KEO FEP and SSE Services description document. Issue 1.0 KLAUS_D5.2.3_1.0_UM.doc Hydrological hazards applications - KEO and SSE User Manual. Issue 1.0 KLAUS_D5.2.3_1.0_SP (KLAUS-SP-SSM-GS-0014) Hydrological hazards applications – KEO modules and SSE Services: – KLAUS.KEO.KDA2.LS.1NEST4B-0.4 GPT Interface v1.0 CLI – KLAUS.KEO.KDA2.LS.2SAR Delta Index BEAM-DIMAP v1.0 CLI – KLAUS.KEO.KDA2.LS.3Delta Index calculation chain for SAR data v1.0 FEP D5.2.5 KLAUS_D5.2.5_1.0_STP.doc Hydrological hazards applications - KEO and SSE Software Test Plan. Issue 1.0 KLAUS_D5.2.5_1.0_STR.doc Hydrological hazards applications- KEO and SSE Software Test Report. Issue KLAUS Progress Meeting 7