S.M. de Jong

A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment

Abstract Various regions in Europe have suffered from severe flooding over the last decennium. Earth observation techniques can contribute toward more accurate flood hazard modelling and they can be used to assess damage to residential properties, infrastructure and agricultural crops. For this study, detailed land cover maps were created by using IKONOS-2 high spatial resolution satellite imagery. The IKONOS-2 image was first divided into segments and the land cover was classified by using spectral, spatial and contextual information with an overall classification accuracy of 74%. In spite of the high spatial resolution of the image, classes such as residential areas and roads are still fairly difficult to identify. The IKONOS-2-derived land cover map was used as input for the flood simulation model LISFLOOD-FP to produce a Manning roughness factor map of inundated areas. This map provides a more accurate spatial distribution of Manning’s roughness factor than maps derived from land cover datasets such as the EU CORINE land cover dataset. CORINE-derived roughness maps provide only averaged, lumped values of roughness factors for each mapping unit and are hence less accurate. Next, a method to produce a property damage map after flooding is presented. The detailed land cover map, water depth estimates resulting from the LISFLOOD-FP model, and known relations between water depth and property damage yielded a map of estimated property damage for the 1995 flood which affected the villages of Itteren and Borgharen in the southern part of The Netherlands. Such a map is useful information for decision makers and insurance companies.

Imaging Spectrometry: basic principles and prospective applications

Acknowledgements About the Editors Contributors Introduction Part I: Basic principles of imaging spectrometry 1. Basic physics of spectrometry 2. Imaging spectrometry: Basic analytical techniques Part II: Prospective applications of imaging spectrometry 3. Imaging spectrometry for surveying and modelling land degradation 4. Field and imaging spectrometry for identification and mapping of expansive soils 5. Imaging spectrometry and vegetation science 6. Imaging spectrometry for agricultural applications 7. Imaging spectrometry and geological applications 8. Imaging spectrometry and petroleum geology 9. Imaging spectrometry for urban applications 10. Imaging spectrometry in the Thermal Infrared 11. Imaging spectrometry of water Acronyms Index References

Regional assessment of soil erosion using the distributed model SEMMED and remotely sensed data

The soil erosion model for Mediterranean regions SEMMED is presented and used to produce regional maps of simulated soil loss for two Mediterranean test sites: one in southern . France and one in Sicily. The model demonstrates the integrated use of 1 multi-temporal Landsat .

Improving the results of spectral unmixing of Landsat thematic mapper imagery by enhancing the orthogonality of end-members

Spectral unmixing is a technique that has been developed to derive fractions of spectrally pure materials that contribute to observed spectral reflectance characteristics of a mixture through a inverse least-squares deconvolution using end-member spectra. This technique has been shown to be very successful when applied to high spectral resolution imaging or non-imaging data where subtle diagnostic absorption features largely determine the spectral characteristics of the data. A large and vastly growing number of papers where spectral unmixing is applied to analyse low resolution image data (e.g. Landsat Thematic Mapper (TM), NOAA AVHRR, etc.) often to derive abundances of different materials as input parameters for models (i.e. land degradation models, crop growth models, hydrologic models, etc.) has evolved throughout recent years. This justifies efforts put into the quality assessment of these abundance estimates. In this paper we evaluate the effect of end-member redundancy on the deconvolution of spec...

Evaluation of the Surface Energy Balance System (SEBS) applied to ASTER imagery with flux-measurements at the SPARC 2004 site (Barrax, Spain)

Accurate quantification of the amount and spatial variation of evapotranspiration is important in a wide range of disciplines. Remote sensing based surface energy balance models have been developed to estimate turbulent surface energy fluxes at different scales. The objective of this study is to evaluate the Surface Energy Balance System (SEBS) model on a landscape scale, using tower-based flux measurements at different land cover units during an overpass of the ASTER sensor over the SPARC 2004 experimental site in Barrax (Spain). A sensitivity analysis has been performed in order to investigate to which variable the sensible heat flux is most sensitive. Taking into account their estimation errors, the aerodynamic parameters ( hc, z0M andd0) can cause large deviations in the modelling of sensible heat flux. The effect of replacement of empirical derivation of these aerodynamic parameters in the model by field estimates or literature values is investigated by testing two scenarios: the Empirical Scenario in which empirical equations are used to derive aerodynamic parameters and the Field Scenario in which values from field measurements or literature are used to replace the empirical calculations of the Empirical Scenario. In the case of a homogeneous land cover in the footprints of the measurements, the Field Scenario only resulted in a small improvement, compared to the Empirical Scenario. The Field Scenario can even worsen the result in the case of heterogeneous footprints, by creating sharp borders related to the land cover map. In both scenarios modelled fluxes correspond Correspondence to: J. van der Kwast (hans.vanderkwast@vito.be) better with flux measurements over uniform land cover compared to cases where different land covers are mixed in the measurement footprint. Furthermore SEBS underestimates sensible heat flux especially over dry and sparsely vegetated areas, which is common in single-source models.

Above‐ground biomass assessment of Mediterranean forests using airborne imaging spectrometry: the DAIS Peyne experiment

In July of 1997, various experimental flights were carried out with the Digital Airborne Imaging Spectrometer (DAIS7915). DAIS7915, or DAIS for short, is a European airborne imaging spectrometer and is maintained and operated by the German Aerospace Centre (DLR) at Oberpfaffenhofen. One of the 1997 experimental sites was the Peyne catchment in southern France. The objectives of the experimental flight were to evaluate the technical performance of DAIS and to assess the feasibility of mapping the above‐ground biomass of the Mediterranean mixed oak forest. Field campaigns were organized to collect data for image calibration and for image interpretation and analyses. The technical performance of DAIS in 1997 was reasonable for visible and near‐infrared wavelengths. The images in short‐wave infrared showed severe striping due to aircraft engine vibrations. Signal‐to‐noise ratios were modest. Field biomass estimates at 83 locations were used to analyse the image spectra. Various well‐known spectral indices and a multiple regression analyses were tested for mapping above‐ground biomass. The multiple regression method yielded five spectral bands for biomass prediction. Based on the spatial distribution of the regression residuals, it was possible to indicate the reliability of the biomass prediction as a function of location and as a function of biomass level using geostatistics.

The benefits of using remotely sensed soil moisture in parameter identification of large‐scale hydrological models

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land-surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10–30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas.

Understanding precipitation patterns and land use interaction in Tibet using harmonic analysis of SPOT VGT‐S10 NDVI time series

Time series analysis of Normalized Difference Vegetation Index (NDVI) imagery is a powerful tool in studying land use and precipitation interaction in data‐scarce and inaccessible areas. The Fast Fourier Transform (FFT) was applied to the annual time series of 36 average dekadal NDVI images. The dekadal annual average pattern was calculated from 189 NDVI images from April 1998 to June 2003 acquired with the VEGETATION instruments of the SPOT‐4 and SPOT‐5 satellites in Tibet. It is shown that the first two harmonic terms of a Fourier series suffice to distinguish between land use classes. The results indicate that the highest biomass production occurs before the monsoon peak. Regression analysis with 15 meteorological stations has shown that the total amount of precipitation during the growing season shows the strongest relation with the sum of the amplitudes of the first two harmonic terms (R 2 = 0.72). Inter‐annual NDVI variation based on Fourier‐transformed time series was studied and it was shown that, early in the season, the expected NDVI behaviour of the up‐coming season could be forecast; if linked to food production this might provide a robust early warning system. The most important conclusion from this work is that harmonic time series analysis yields more reliable results than ordinary time series analysis.

Estimating spatial patterns of rainfall interception from remotely sensed vegetation indices and spectral mixture analysis

Rainfall interception by vegetation is an important factor in the water balance. Consequently, rainfall interception should also be an important factor in models simulating processes such as evaporation, transpiration, surface runoff, soil erosion, and crop growth. In practice, however, it is difficult to make quantitative assessments of the spatial and temporal distribution of rainfall interception loss at the catchment level, for instance, and to make these values available as model input. In this paper, we present a novel method using earth observation images to estimate local quantitative values of rainfall interception loss. Leaf Area Index (LAI) and fractional vegetation cover per grid cell are important process variables for rainfall interception. These two variables are estimated from images using spectral vegetation indices and using spectral mixture analysis, respectively. Relations between canopy storage capacity and LAI exist for several plant species and vegetation types, but limited data are found on crops, and more research is needed in this field. The new method is explained and illustrated for a study area in southern France with a variety of land‐cover types. It is found to be a valuable and practical approach to quantitatively assess spatial patterns of interception loss for given rainfall events.

Calibrating a large-extent high-resolution coupled groundwater-land surface model using soil moisture and discharge data

We explore the possibility of using remotely sensed soil moisture data and in situ discharge observations to calibrate a large-extent hydrological model. The model used is PCR-GLOBWB-MOD, which is a physically based and fully coupled groundwater-land surface model operating at a daily basis and having a resolution of 30 arc sec (about 1 km at the equator). As a test bed, we use the combined Rhine-Meuse basin (total area: about 200,000 km2), where there are 4250 point-scale observed groundwater head time series that are used to verify the model results. Calibration is performed by simulating 3045 model runs with varying parameter values affecting groundwater head dynamics. The simulation results of all runs are evaluated against the remotely sensed soil moisture time series of SWI (Soil Water Index) and field discharge data. The former is derived from European Remote Sensing scatterometers and provides estimates of the first meter profile soil moisture content at 30 arc min resolution (50 km at the equator). From the evaluation of these runs, we then introduce a stepwise calibration approach that considers stream discharge first, then soil moisture, and finally verify the resulting simulation to groundwater head observations. Our results indicate that the remotely sensed soil moisture data can be used for the calibration of upper soil hydraulic conductivities determining simulated groundwater recharge of the model. However, discharge data should be included to obtain full calibration of the coupled model, specifically to constrain aquifer transmissivities and runoff-infiltration partitioning processes. The stepwise approach introduced in this study, using both discharge and soil moisture data, can calibrate both discharge and soil moisture, as well as predicting groundwater head dynamics with acceptable accuracy. As our approach to parameterize and calibrate the model uses globally available data sets only, it opens up the possibility to set up large-extent coupled groundwater-land surface models in other basins or even globally. Key Points Soil moisture data can be used to calibrate upper soil conductivities Yet, discharge data should be included to fully calibrate the coupled model The combined calibration approach reproduces groundwater head dynamics well ©2013. American Geophysical Union. All Rights Reserved.