Pietro Alessandro Brivio

Integration of remote sensing data and GIS for accurate mapping of flooded areas

This paper describes a synergetic use of satellite radar images and ancillary information to detect flooded areas at their peak and evaluates its potential with mapping. The procedure was tested on the catastrophic flood that occurred in Regione Piemonte in Italy in November 1994. Two ERS-1 synthetic aperture radar (SAR) images were processed, one acquired one month before the flood and the other acquired three days after the event. Visual interpretation and two different thresholding techniques were performed. The flood map derived shows only a small fraction (20%) of the actually flooded lands because of the time delay between the flood peak and the satellite overpass. To overcome this limitation, the authors developed a new procedure to estimate the flooded area at the peak time by integrating the flooded area from SAR imagery with digital topographic data from a GIS technique. This method allowed inundated areas covering 96.7% of the flooded area officially recorded by the local government to be mapped. The proposed procedure is suitable for mapping flooded areas even when satellite data are acquired some days after the event, thus overcoming the constraint of temporal resolution in the application of SAR imagery in hydrology.

A fuzzy set-based accuracy assessment of soft classification

Despite the sizable achievements obtained, the use of soft classifiers is still limited by the lack of well-assessed and adequate methods for evaluating the accuracy of their outputs. This paper proposes a new method that uses the fuzzy set theory to extend the applicability of the traditional error matrix method to the evaluation of soft classifiers. It is designed to cope with those situations in which classification and/or reference data are expressed in multimembership form and the grades of membership represent diAerent levels of approximation to intrinsically vague classes. ” 1999 Elsevier Science B.V. All rights reserved.

DETECTING CHLOROPHYLL, SECCHI DISK DEPTH AND SURFACE TEMPERATURE IN A SUB-ALPINE LAKE USING LANDSAT IMAGERY

Some bio-physical parameters, such as chlorophyll a concentration, Secchi disk depth and water surface temperature were mapped in the sub-alpine Lake Iseo (Italy) using Landsat Thematic Mapper (TM) data acquired on the 7 March 1997. In order to adequately investigate the water-leaving radiance, TM data were atmospherically corrected using a partially image-based method, and the atmospheric transmittance was measured in synchrony with the satellite passage. An empirical approach of relating atmospherically corrected TM spectral reflectance values to in situ measurements, collected during the satellite data acquisition, was used. The models developed were used to map the chlorophyll concentration and Secchi disk depth throughout the lake. Both models gave high determination coefficients (R2 = 0.99 for chlorophyll and R2 = 0.85 for the Secchi disk) and the spatial distribution of chlorophyll concentration and Secchi disk depth was mapped with contour intervals of 1 mg/m3 and 1 m, respectively. A scene-independent procedure was used to derive the surface temperature of the lake from the TM data with a root mean square error of 0.3 degrees C.

Multi-year monitoring of rice crop phenology through time series analysis of MODIS images

Precise phenological calendars, for each cultivated species and variety, are necessary both to highlight anomalous agronomic situations and to feed crop models. This study, conducted in the Italian rice area, focuses on the evaluation of the contribution of remote sensing satellite data to providing phenological information on rice cropping systems. A time series of 5 years (2001–2005) of the Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composites was analysed with the TIMESAT program in order to automatically retrieve key phenological information such as the start of season (emergence), peak (heading) and end of season (maturity). The procedure involved two steps: (1) interpolation and smoothing of MODIS NDVI temporal profile and (2) the analysis of a temporal signal for the extraction of the phenological metrics. The remote sensing estimates were evaluated using information regarding cultivated variety, sowing dates, management and production directly acquired from rice farmers. A good correlation (R 2 = 0.92, n = 24) has been observed between estimates derived from satellites and estimates produced with the traditional Growing Degree Days (GDD) method based on thermal unit accumulation. Improved estimates of the maturity stage were obtained using a procedure that integrates satellite and GDD methods; however its application requires spatially distributed information on the cultivated varieties. Satellite derived maps of the retrieved phenological parameters showed an intra-seasonal pattern related to different cultivated varieties. Inter-seasonal analysis allowed the anomalous behaviour of the year 2003 to be highlighted, characterized by rapid growth at the beginning of the spring and an early senescence. The results confirm the potential of remotely sensed data for the monitoring of crop status and for the forcing of crop models in a spatially distributed way.

Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images

The distribution of phytoplankton chlorophyll concentration in Lake Garda (Italy) was estimated using Landsat Thematic Mapper (TM) data acquired at two different times, February 1992 and March 1993. To investigate the waterleaving radiance adequately, the contribution of the atmospheric path radiance reaching the sensor should be removed. In this work a completely image-based atmospheric correction method was applied by means of an inversion technique based on a simplified radiative transfer code (RTC). A semi-empirical approach of relating atmospherically corrected TM spectral reflectances to in situ measurements through regression analysis was used. Limnological parameters were measured near to the TM images dates; some of the in situ measurements were used to define algorithms relating chlorophyll concentration measurements to water surface reflectance and the others too were used to validate the results of the predictive model. The models developed, which performed better (r2 = 0.818) when concentrations were higher than > 3.0 mg m3, were used to map chlorophyll concentration throughout the lake. Spatial distribution maps of chlorophyll concentration and concentration changes were produced with contour intervals of 1 mg m3.

Comparison of the multilayer perceptron with neuro-fuzzy techniques in the estimation of cover class mixture in remotely sensed data

Mixed pixels are a major source of inconvenience in the classification of remotely sensed data. This paper compares MLP with so-called neuro-fuzzy algorithms in the estimation of pixel component cover classes. Two neuro-fuzzy networks are selected from the literature as representatives of soft classifiers featuring different combinations of fuzzy set-theoretic principles with neural network learning mechanisms. These networks are: 1) the fuzzy multilayer perceptron (FMLP) and 2) a two-stage hybrid (TSH) learning neural network whose unsupervised first stage consists of the fully self-organizing simplified adaptive resonance theory (FOSART) clustering model, FMLP, TSH, and MLP are compared on CLASSITEST, a standard set of synthetic images where per-pixel proportions of cover class mixtures are known a priori. Results are assessed by means of evaluation tools specifically developed for the comparison of soft classifiers. Experimental results show that classification accuracies of FMLP and TSH are comparable, whereas TSH is faster to train than FMLP. On the other hand, FMLP and TSW outperform MLP when little prior knowledge is available for training the network, i.e., when no fuzzy training sites, describing intermediate label assignments, are available.

Comparative analysis of normalised difference spectral indices derived from MODIS for detecting surface water in flooded rice cropping systems

Identifying managed flooding in paddy fields is commonly used in remote sensing to detect rice. Such flooding, followed by rapid vegetation growth, is a reliable indicator to discriminate rice. Spectral indices (SIs) are often used to perform this task. However, little work has been done on determining which spectral combination in the form of Normalised Difference Spectral Indices (NDSIs) is most appropriate for surface water detection or which thresholds are most robust to separate water from other surfaces in operational contexts. To address this, we conducted analyses on satellite and field spectral data from an agronomic experiment as well as on real farming situations with different soil and plant conditions. Firstly, we review and select NDSIs proposed in the literature, including a new combination of visible and shortwave infrared bands. Secondly, we analyse spectroradiometric field data and satellite data to evaluate mixed pixel effects. Thirdly, we analyse MODIS data and Landsat data at four sites in Europe and Asia to assess NDSI performance in real-world conditions. Finally, we test the performance of the NDSIs on MODIS temporal profiles in the four sites. We also compared the NDSIs against a combined index previously used for agronomic flood detection. Analyses suggest that NDSIs using MODIS bands 4 and 7, 1 and 7, 4 and 6 or 1 and 6 perform best. A common threshold for each NDSI across all sites was more appropriate than locally adaptive thresholds. In general, NDSIs that use band 7 have a negligible increase in Commission Error over those that use band 6 but are more sensitive to water presence in mixed land cover conditions typical of moderate spatial resolution analyses. The best performing NDSI is comparable to the combined index but with less variability in performance across sites, suggesting a more succinct and robust flood detection method.

The satellite derived optical information for the comparative assessment of lacustrine water quality

Abstract The objective of this research is to define the support that can be derived from satellite optical remote sensing to monitor the trophic status of lake waters, even in the absence of corroborating in situ measurements. An analysis was conducted on Lake Garda (Italy), where sub-basins showing different water quality conditions can be delineated. For this purpose, two Landsat-TM images, taken in April and August 1985, at specific seasonal situations of the limnological cycle were used. Image processing provided radiance-derived quantities, such as reflectance and chromaticity, in the form of digital transects drawn from the margin of the lake to cross the pelagic environment. In order to make the multi-temporal analysis possible, a method for compensating the differential haze effect between the two images, based on the darkest object technique was adopted. Results demonstrated the suitability of satellite remote sensing observations as a fast and relatively low cost effective tool for early and expeditious assessment of both the spatial and temporal variability of lake water quality conditions.

A sampling method for the retrospective validation of global burned area products

This work presents a design-based validation and calibration scheme for the Global Burned Area 2000 (GBA2000) products. The objective of such a scheme is to assess the margins of uncertainty associated with the burned area products and to estimate calibration coefficients needed to convert burned pixel counts into areal estimates. As the validation of GBA2000 was performed long after 2000, and given the fact that burned areas are a predominantly nonpermanent land cover change, the reference data are obtained from a set of Landsat-7 Enhanced Thematic Mapper Plus high-resolution remotely sensed data. A stratified sampling scheme is presented, specifically designed for the retrospective validation of burned area data; the scheme is based on combining information from two low-resolution burned area products (GBA2000 itself and Globscar). The resulting stratification has been applied to the whole global GBA2000 dataset, and preliminary validation results are reported for Africa. The conclusions highlight the limits of a retrospective validation exercise, and summarize some of the open issues in the validation of global burned area maps

Validation of satellite data for quality assurance in lake monitoring applications

The operational application of remote sensing technologies to lake water quality monitoring requires products derived from remote sensing to be quantitatively self-consistent and have a certified accuracy. Fundamental elements in this quality assurance framework are sensor radiometric calibration and atmospheric correction models, which are briefly discussed in the paper. In order to evaluate the accuracy of present operational techniques to retrieve basic parameters from satellite data, such as water-leaving radiance and reflectance, an experiment was organised in the frame of SAtellite remote sensing for Lake MONitoring (SALMON), a European Union co-funded research project. A series of ship-based radiometric and atmospheric measuring campaigns were conducted on Lake Iseo and Lake Garda (Italy) together with limnological sampling. Four Landsat-5 Thematic Mapper (TM) scenes were acquired during different seasons and simultaneous in situ measurements were made. After the radiometric calibration procedure, satellite digital images were processed by applying two entirely image-based atmospheric correction models. These models account for the effects of both additive scattering and multiplicative transmittance effects in the atmosphere on the at-satellite measured signal. The results achieved using these procedures were evaluated by comparing satellite-based estimates with in situ measurements of water reflectance. The root mean square difference between Landsat TM-derived reflectance values and ground measurements was close to 0.010 reflectance for each TM spectral band. Such image-based correction models, requiring no in situ field measurements during the satellite overpass, constitute a valid method of lake water monitoring.