International Journal of Remote Sensing

Fowler, M. J. F.

doi: 10.1080/01431160151144297pmid: N/A

Kiiveri, H. T.; Caccetta, P.; Evans, F.

doi: 10.1080/01431160151144305pmid: N/A

Causal or conditional probability networks (CPNs) are shown to provide a natural framework for combining a time sequence of classified satellite images with other maps for environmental monitoring. The key features of CPNs are described by way of application to an example involving the monitoring of salinization of farmland over time using satellite images and an ancillary dataset derived from a digital terrain model. It is shown that CPNs can be used to improve mapping accuracies by incorporating knowledge about the spatial and temporal variation of the map classes of interest. The methods provide a practical solution to the challenging problem of mapping and monitoring salt in farmland. The representation and propagation of uncertainty within this framework is discussed, as well as the spatial and temporal prediction of images and maps.

Lovejoy, S.; Schertzer, D.; Tessier, Y.; Gaonac'h, H.

doi: 10.1080/01431160151144314pmid: N/A

We argue that geophysical and geographical fields are generally characterised by wide range scaling implying systematic, strong (power law) resolution dependencies when they are remotely sensed. The corresponding geometric structures are fractal sets; the corresponding fields are multifractals. Mathematically, multifractals are measures that are singular with respect to the standard Lebesgue measures; therefore, they are outside the scope of many of the methods of classical geostatistics. Because the resolution of a measurement is generally (due to technical constraints) much larger than the inner scale of the variability/scaling, the observations will be fundamentally observer dependent; hence, standard remote sensing algorithms that do not explicitly take this dependence into account will depend on subjective resolution-dependent parameters. We argue that, on the contrary, the resolution dependence must be systematically removed so that scale-invariant algorithms independent of the observer can be produced. We illustrate these ideas in various ways with the help of eight-channel, 7 m resolution remote ocean colour data (from the MIES II sensor) over the St Lawrence estuary. First, we show that the data is indeed multiscaling over nearly four orders of magnitude in scale, and we quantify this using universal multifractal parameters. With the help of conditional multifractal statistics, we then show how to use multifractals in various practical ways such as for extrapolating from one resolution to another or from one location to another, or to correcting biases introduced when studying extreme, rare phenomena. We also show how the scaling interrelationship of surrogate and in situ data can be handled using vector multifractals and examine the resolution dependence of principle components in dual wavelength analyses. Finally, we indicate why the standard ocean colour algorithms have hidden resolution dependencies, and we show how they can (at least in principle) be removed.

Del Frate, F.; Wang, L.-F.

doi: 10.1080/01431160151144323pmid: N/A

Inversion of biomass for sunflower fields using radar backscattering data has been carried out with neural network algorithms. An electromagnetic model is used to generate the scattering coefficients for training and testing of the net. The model is validated with experimental data obtained from the Montespertoli test site during the Remote Sensing Campaign Mac-Europe 91. The inversion results show that the neural network is capable of performing the retrieval with good accuracy. By optimizing the structural complexity of the net, a better inversion result is obtained.

Rahman, H.

doi: 10.1080/01431160151144332pmid: N/A

A quantitative approach has been made for the estimation of biophysical parameters of a vegetation canopy by the inversion of a vegetation canopy reflectance model. Model inversion has been done using a non-linear optimization scheme against directional reflectance data over the canopy. A quasi-Newton algorithm has been employed that searches the minimum of a function iteratively using the functional values only. The technique provides a reasonably good estimation of the biophysical parameters. A study has been conducted to quantify the error related to the estimation of biophysical parameters of vegetation with simulated satellite data corrected with improper values of atmospheric aerosol and water vapour contents. In the visible, atmospheric correction of satellite data with improper values of atmospheric aerosol content results in a modification of the amplitude and angular pattern of the directional reflectance for both low-density and high-density vegetation canopies. However, in the near-infrared, the atmospheric correction of data with improper values of aerosol and water vapour contents changes the amplitude of directional reflectance, but, no significant changes in angular pattern are noticed. This study indicates that parameter estimation can be significantly influenced by using improper values of both aerosol and water vapour contents during data correction in the visible and near-infrared regions of the solar spectrum. The estimation accuracy is higher for a low-density canopy than for a dense vegetation canopy. Retrievals of all the surface parameters are not equally affected by such improper atmospheric correction of data. Particularly, estimations of soil reflectance and leaf area index are significantly influenced by such improper correction for a high-density vegetation canopy. However, the accuracy of the retrieved parameter values is higher in the near-infrared than in the visible for both high-density and low-density canopies.

Tahnk, W. R.; Coakley, J. A.

doi: 10.1080/01431160151144341pmid: N/A

An analysis of the calibration coefficients used to describe sensor degradation in channels 1 and 2 of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-14 spacecraft is presented. The radiometrically stable permanent ice sheet of central Antarctica is used as a calibration target to characterize sensor performance. Published calibration coefficients and the coefficients imbedded in the NOAA Level 1b data stream for the period January 1995 to November 1998 are shown to be deficient in correcting for the degradation of the sensor with time since launch. Calibration formulae constructed from NOAA-9 reflectances are used to derive improved calibration coefficients for the AVHRR visible and near-infrared channels for NOAA-14. Channel 1 reflectances for the Greenland ice sheet derived using the new coefficients are consistent with those derived previously using NOAA-9 AVHRR. In addition, improved reference AVHRR channel 2 reflectances for Greenland are derived from NOAA-14 observations. It is recommended that the coefficients derived in this study be used to calibrate reflectances for NOAA-14 AVHRR channels 1 and 2.

Li, X.; Pichel, W.; Clemente-Colón, P.; Krasnopolsky, V.; Sapper, J.

doi: 10.1080/01431160151144350pmid: N/A

An interactive validation monitoring system is being used at the NOAA/NESDIS to validate the sea surface temperature (SST) derived from the NOAA-12 and NOAA-14 polar orbiting satellite AVHRR sensors for the NOAA CoastWatch program. In 1997, we validated the SST in coastal regions of the Gulf of Mexico, Southeast US and Northeast US and the lake surface temperatures in the Great Lakes every other month. The in situ

Small, C.

doi: 10.1080/01431160151144369pmid: N/A

The spatio-temporal distribution of vegetation is a fundamental component of the urban environment that can be quantified using multispectral imagery. However, spectral heterogeneity at scales comparable to sensor resolution limits the utility of conventional hard classification methods with multispectral reflectance data in urban areas. Spectral mixture models may provide a physically based solution to the problem of spectral heterogeneity. The objective of this study is to examine the applicability of linear spectral mixture models to the estimation of urban vegetation abundance using Landsat Thematic Mapper (TM) data. The inherent dimensionality of TM imagery of the New York City area suggests that urban reflectance measurements may be described by linear mixing between high albedo, low albedo and vegetative endmembers. A three-component linear mixing model provides stable, consistent estimates of vegetation fraction for both constrained and unconstrained inversions of three different endmember ensembles. Quantitative validation using vegetation abundance measurements derived from high-resolution (2 m) aerial photography shows agreement to within fractional abundances of 0.1 for vegetation fractions greater than 0.2. In contrast to the Normalised Difference Vegetation Index (NDVI), vegetation fraction estimates provide a physically based measure of areal vegetation abundance that may be more easily translated to constraints on physical quantities such as vegetative biomass and evapotranspiration.

Murakami, T.; Ogawa, S.; Ishitsuka, N.; Kumagai, K.; Saito, G.

doi: 10.1080/01431160151144378pmid: N/A

Nine scenes of SPOT/HRV data obtained in eight different months in 1997 were evaluated for crop discrimination in the Saga Plains, Japan. All images were atmospherically corrected with the 6S code. Annual Normalized Difference Vegetation Index (NDVI) profiles were generated to characterize seasonal trends in six cropping systems (rice, rice-winter cereal, soybean, soybean-winter cereal, lotus, and rush). The dataset of this study showed the unique temporal change patterns of NDVI for each cropping system. Separability analyses determined optimal scene combinations for the highest accuracy in classifying the cropping systems. The scene combinations for the accurate classification of cropping systems were obtained from three separability measurements (Euclidean spectral distance, divergence, and Jeffries-Matsushita distance). Kappa statistics were applied to evaluate the classification accuracies. The four-scene combination that was derived from April, June, July and September classified the cropping systems almost as well as those combinations including more scenes. A colour composition technique applied to the three-scene combination that showed the highest separability also discriminated each cropping system. Based on these results, we can request observations during specific time intervals considering local crop calendars and environmental conditions.

Lee, W-H.; Kudoh, J-I.; Makino, S.

doi: 10.1080/01431160151144387pmid: N/A

A new automatic cloud detection method to process the large data sets required for long-term trends in AVHRR time series images is described and tested over the Far East region. In the cloud detection method, a simple NDVI test was used to roughly separate land from ocean. After the NDVI test we carried out two tests: a visible or near-infrared test and an infrared test. Results from the cloud detection method over the Far East region are presented. In order to assess the cloud detection method, a comparison was carried out between the method presented and the Saunders and Kriebel daytime procedure using the N-Land database. We found that in general, the method presented has better performance than the Saunders and Kriebel procedure by about 0.5% to 10.5%.