Ferdinand Bonn

Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements

Estimating surface parameters by radar-image inversion requires the use of well-calibrated backscattering models. None of the existing models is capable of correctly simulating scatterometer or satellite radar data. We propose a semi-empirical calibration of the Integral Equation Model (IEM) backscattering model in order to better reproduce the radar backscattering coefficient over bare agricultural soils. As correlation length is not only the least accurate but also the most difficult to measure of the parameters required in the models, we propose that it be replaced by a calibration parameter that would be estimated empirically from experimental databases of radar images and field measurements. This calibration was carried out using a number of radar configurations with different incidence angles, polarization configurations, and radar frequencies. Using several databases, the relationship between the calibration parameter and the surface roughness was determined for each radar configuration. In addition, the effect of the correlation function shape on IEM performance was studied using the three correlation functions (exponential, fractal, and Gaussian). The calibrated version of the IEM was then validated using another independent set of experimental data. The results show good agreement between the backscattering coefficient provided by the radar systems and that simulated by the calibrated version of the IEM. This calibrated version of the IEM can be used in inversion procedures to retrieve surface roughness and/or moisture values from radar images.

Vegetation indices derived from remote sensing for an estimation of soil protection against water erosion

Abstract In order to regionalize an erosion model based on a soil loss equation, the type of agricultural landuse and the percentage of ground cover play a major role. A supervised classification of multidate SPOT imagery has been used for mapping the crops, and vegetation indices have been derived from spectral data of each crop class in order to evaluate the soil coverage associated with these classes, in a hilly environment of the Quebec Appalachians in Canada. The relation between ground coverage and vegetation indices for each crop has been obtained by photographic and radiometric measurements on the ground at ten days interval throughout the growing season. Similarity between ground and satellite observations is reasonably good. Results of the field campaign show that, generally, vegetation indices overestimate the ground coverage at the beginning of the growing season and underestimate it at the end, with the appearance of senescence. These data will be integrated into a GIS for spatial mapping and modelling of erosion.

Assessment of land‐cover changes related to shrimp aquaculture using remote sensing data: a case study in the Giao Thuy District, Vietnam

Shrimp culture is a sector of aquaculture that has a high potential for poverty alleviation and rural development in Vietnam. However, the development of this activity induces changes that potentially have negative impacts on the environment, one of which is wetland deterioration. This paper describes the use of a proposed change detection methodology in the assessment of mangrove forest alterations caused by aquaculture development, as well as the effectiveness of the measures taken to mitigate deforestation in the district of Giao Thuy, Vietnam, between 1986, 1992 and 2001. Geometric and radiometric corrections were applied to Landsat images prior to identifying changes through comparison of unsupervised classifications. Changes were afterwards validated using a thresholding method based on Tasselled Cap feature image differencing and a rule‐based feature selection matrix. The matrix is used to identify the feature that is most efficient at detecting the presence of change between given land‐cover classes. The proposed approach aims to minimize commission errors in the post‐classification change detection process. The results suggest that 63% of mangrove areas apparent in 1986 had been replaced by shrimp ponds in 2001. Between 1986 and 1992, 440 ha of adult mangrove trees had disappeared, whereas the mangrove extent increased by 441 ha between 1992 and 2001. This recovery is attributed to reforestation projects and conservation efforts that promoted natural regeneration.

Agricultural land-use patterns and soil erosion vulnerability of watershed units in Vietnam's northern highlands

Since the mid eighties, agricultural development and increased population growth in Vietnam’s northern highlands have modified land use patterns and thus, increased the runoff process and soil degradation induced by water erosion. In the last decade, Vietnamese literature has focused on the computation of soil losses over large areas. Most of these spatial and quantitative soil erosion studies do not consider the impact of agricultural land use diversity (spatial heterogeneity), particularly at the watershed scale, and the annual variability of seasonal landscape factors on soil erosion vulnerability and hence, landscape dynamics. We present an integrated approach combining field measurements and observations, GIS and modeling to determine the spatial and temporal dynamics of soil erosion vulnerability according to watershed units and hence, the impact of physical environment components and agricultural land use patterns on landscape evolution. Tables and graphics showing the cropping systems, the periods within a year, and the watershed units that are most vulnerable are presented. The double cultivation cycles for paddy rice fields not only imply two periods of land preparation and establishment that expose the soil surface to raindrop impacts, but also increased soil management practices that decrease the soil’s resistance to detachment. Despite the low levels of soil management practices for the shifting cultivation system, the near absence of soil conservation practices clearly increases their vulnerability. Hence, rainfed cropping systems, mainly soya and cassava, cultivated on sloping lands (hills and mountains) where soil erosion vulnerability is the highest represent the watershed units which are the most prone to soil loss.

Radiometric correction of C-band imagery for topographic effects in regions of moderate relief

The combined effects of topography, slope, look angle, and aspect on C-band synthetic-aperture radar (SAR) data on the radiometric quality of SAR images in a region of moderate relief are examined. A correction method was used to attenuate the change of illumination across the swath due to the antenna pattern. Ground data were integrated into the analysis using a digital terrain model (DTM). Correction functions based on the cosine of the incidence angle were applied to the thematic classes and to the grouped classes in order to reduce the effects related to topography. It was found that it is possible to eliminate part of the radiometric variations created by moderate topographic relief. After the corrections were applied, a reduction was noted of the variance in the radiometric values of the spectral signatures of the cover types, which ranged between 3.03% and 9.47%, depending on the correction function used. >

A theoretical review of different mathematical models of geometric corrections applied to remote sensing images

Abstract One of the objectives of remote sensing is to go beyond simple visual interpretation in order to provide the user with quantitative information for producing documents that conform to cartographic standards and for deriving digital data files compatible with geographical information systems (GIS). In this framework, rigorous geometrical correction is essential. Error sources which introduce geometrical image distortions are related to the platform vector (attitude, altitude, speed), the sensor (distortions, oblique viewing), and to the earth (rotation, earth curvature, ellipsoid, relief). Many methods can be applied for correcting each error separately or for globally correcting the image from all geometrical distortions. This theoretical review has two complementary parts. The first section deals with errors causing deformations on satellite images and related to the platform vector, the sensor and the earth, as well as the mathematical formulation for each error. In the second section, we discu...

A comparison of multi-polarization and multi-angular approaches for estimating bare soil surface roughness from spaceborne radar data

Soil surface roughness and moisture content both have a significant effect on microwave backscatter to the satellite. The purpose of this work is to evaluate the optimum sensor configuration for existing radar satellites to quantify soil surface roughness. A simulation study using theoretical and empirical models permits the estimation of the sensitivity of the backscatter coefficient to relative variations in soil parameters in terms of radar characteristics. Two different configurations for estimating surface roughness were tested, multi-polarization (co-polarizations) and multi-angular, and the results of the multi-angular configuration provided the best results. A normalized radar backscatter soil roughness index (NBRI) is presented for estimating soil roughness from a multi-angular approach using sensors such as RADARSAT-1. This index was tested using the geometric optics model (GOM) and RADARSAT data. Coefficients of determination of 99% and 83%, respectively, were obtained for each simulation.

Estimation of the moisture content of bare soil from RADARSAT-1 SAR using simple empirical models

Synthetic Aperture Radar (SAR) provides a remote sensing tool to estimate soil moisture. Mapping surface soil moisture from the grey level of SAR images is a demonstrated procedure, but several factors can interfere with the interpretation and must be taken into account. The most important factors are surface roughness and the radar configuration (frequency, polarization and incidence angle). This Letter evaluates the influence of these variables for estimation of bare soil moisture using RADARSAT-1 SAR data. First, the parameters of two linear backscatter models, the Ji and Champion models (Ji et al . 1995, Champion 1996), were tested and the constants recalculated. rms error based on the backscattering coefficient was reduced from 6.12 and 6.48 dB to 4.28 and 1.68 dB for the Ji and Champion models respectively. Secondly, a new model is proposed which had an rms error of only 1.21 dB. The results showed a marked increase in accuracy compared with the previous models.

Urbanization and Landsat MSS albedo change in the Windsor-Québec corridor since 1972

Abstract Quantitative determination of anthropogenic land use change from space observations has been carried out by comparing urban area expansion in the Montreal, Ottawa and Quebec regions, three representative centres of the economic heartland of the Windsor-Quebec corridor in eastern Canada. Land use monitoring using Landsat satellite data shows a marked process of urbanization since 1972 (Montreal: + 70 per cent, Quebec: +40 per cent and Ottawa: + 11 per cent). The total amount of rural and forest land converted to urban use is estimated. The induced mean ground Multispectral Scanner (MSS) albedo decrease, computed from the corrected satellite reflectance, is in the order of −0.001 to −0 005. This impact of urbanization is inferred to be similar over south-eastern Canada, using statistical data from governmental agencies. This change could be large enough to affect the regional climate since trends of past urban growth rates and extrapolation of the present rate to the end of the century are significant.

Settlement location and population density estimation in rugged terrain using information derived from Landsat ETM and SRTM data

It is useful to have a disaggregated population database at uniform grid units in disaster situations. This study presents a method for settlement location probability and population density estimations at a 90 m resolution for northern Iraq using the Shuttle Radar Topographic Mission (SRTM) digital terrain model and Landsat Enhanced Thematic Mapper satellite imagery. A spatial model each for calculating the probability of settlement location and for estimating population density is described. A randomly selected subset of field data (equivalent to 50%) is first analysed for statistical links between settlement location probability and population density; and various biophysical features which are extracted from Landsat or SRTM data. The model is calibrated using this subset. Settlement location probability is attributed to the distance from roads and water bodies and land cover. Population density can be estimated based upon land cover and topographic features. The Landsat data are processed using a segmentation and subsequent feature–based classification approach making this method robust to seasonal variations in imagery and therefore applicable to a time series of images regardless of acquisition date. The second half of the field data is used to validate the model. Results show a reasonable estimate of population numbers (r = 0.205, p<0.001) for both rural and urban settlements. Although there is a strong overall correlation between the results of this and the LandScan model (r = 0.464, p<0.001), this method performs better than the 1 km resolution LandScan grid for settlements with fewer than 1000 people, but is less accurate for estimating population numbers in urban areas (LandScan rural r = 0.181, p<0.001; LandScan urban r = 0.303, p<0.001). The correlation between true urban population numbers is superior to that of LandScan however when the 90 m grid values are summed using a filter which corresponds to the LandScan spatial resolution (r = 0.318, p<0.001).