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is related to the dimensions of the pixels in a raster image. The pixel corresponds usually to area with dimensions ranging from 1 to 1000 meters. The spectral resolution refers to the wavelength width of the detected frequency bands. The radiometric resolution indicates the ability of an imaging system to discriminate slight differences in the energy of the detected radiation. The temporal resolution is gathered from the frequency of flyover by the satellite. Over the studied area, m sampling points are chosen either randomly or using a grid. Soil samples are A table of reflectance spectra of the collected soil samples and their corresponding class constitut he “database”. The reflectance spectrum of a soil sample depends mostly on the soil chemic omposition. Therefore, the reflectance spectra of soils belonging to different classes are expected to | ifferent. Unfortunately, the mathematical law expressing the relation between the reflectance spectru nd the chemical composition of the soil is not known. However, the data in the database conta nformation that can be used to build an empirical model of the spectrum-soil relationship. The proce hat leads to obtaining such empirical relation is called “modeling”. The model allows the prediction | he soil class using the reflectance data for arbitrary pixels of the satellite image in places where t ampling and chemical analysis were done. In this way, a map representing the distribution of the varion oil classes over the studied area is obtained.

Figure 2 is related to the dimensions of the pixels in a raster image. The pixel corresponds usually to area with dimensions ranging from 1 to 1000 meters. The spectral resolution refers to the wavelength width of the detected frequency bands. The radiometric resolution indicates the ability of an imaging system to discriminate slight differences in the energy of the detected radiation. The temporal resolution is gathered from the frequency of flyover by the satellite. Over the studied area, m sampling points are chosen either randomly or using a grid. Soil samples are A table of reflectance spectra of the collected soil samples and their corresponding class constitut he “database”. The reflectance spectrum of a soil sample depends mostly on the soil chemic omposition. Therefore, the reflectance spectra of soils belonging to different classes are expected to | ifferent. Unfortunately, the mathematical law expressing the relation between the reflectance spectru nd the chemical composition of the soil is not known. However, the data in the database conta nformation that can be used to build an empirical model of the spectrum-soil relationship. The proce hat leads to obtaining such empirical relation is called “modeling”. The model allows the prediction | he soil class using the reflectance data for arbitrary pixels of the satellite image in places where t ampling and chemical analysis were done. In this way, a map representing the distribution of the varion oil classes over the studied area is obtained.

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The concept of globalization is a technological unification of the world leading to a global information ociety. Such processes require several steps such as the design and development of well-organized nfrastructures able to solve global and regional problems [1]. To do this, the informatization and eoreferenciation of the world’s knowledge is required. This is the task of the “Digital Earth” project. In his framework, remote sensing and earth observation technologies contribute significantly to the progress f Digital Earth. Among the various tasks and objectives of remote sensing sciences is the monitoring of oil characteristics, deterioration, and use (e.g., cultivation or urbanization). Soil degradation and lesertification is a phenomenon that affects many countries, especially those with arid and semiarid egions. Common degradation processes include water stress, soil salinization, forest fires, overgrazing nd water erosion, etc. Such processes are often monitored using approaches based on various biophysica nd socioeconomic indicators. The evaluation of such indicators has recently been reported [2]. Other pproaches are based on the estimation of the vegetation cover fraction and/or the class of the soil by emote sensing using satellite imagery data (usually reflectance spectra). The scheme of the reflectance lata acquisition procedure using a satellite is given in Figure 1. Remote sensing estimation of soil class and land use/cover is a complex process that involves several steps. First, the image of the studied area is recorded by the satellite. The quality of satellite image is given by parameters such as spatial, spectral, radiometric, and temporal resolution. The spatial resolution
The concept of globalization is a technological unification of the world leading to a global information ociety. Such processes require several steps such as the design and development of well-organized nfrastructures able to solve global and regional problems [1]. To do this, the informatization and eoreferenciation of the world’s knowledge is required. This is the task of the “Digital Earth” project. In his framework, remote sensing and earth observation technologies contribute significantly to the progress f Digital Earth. Among the various tasks and objectives of remote sensing sciences is the monitoring of oil characteristics, deterioration, and use (e.g., cultivation or urbanization). Soil degradation and lesertification is a phenomenon that affects many countries, especially those with arid and semiarid egions. Common degradation processes include water stress, soil salinization, forest fires, overgrazing nd water erosion, etc. Such processes are often monitored using approaches based on various biophysica nd socioeconomic indicators. The evaluation of such indicators has recently been reported [2]. Other pproaches are based on the estimation of the vegetation cover fraction and/or the class of the soil by emote sensing using satellite imagery data (usually reflectance spectra). The scheme of the reflectance lata acquisition procedure using a satellite is given in Figure 1. Remote sensing estimation of soil class and land use/cover is a complex process that involves several steps. First, the image of the studied area is recorded by the satellite. The quality of satellite image is given by parameters such as spatial, spectral, radiometric, and temporal resolution. The spatial resolution
Table 1. Wavelengths and spatial resolution of satellite spectral bands used.
Table 1. Wavelengths and spatial resolution of satellite spectral bands used.
Satellite reflectance data concerning 100 locations randomly chosen in the El-Fayoum area (Figure 5) were recorded at nine wavelengths. The first two examples concern the use of reflectance data for land use/cover estimation. For this purpose, the locations were grouped into “vegetation” (V), “lake” (L) and “urban” (U) classes. The third example concerns the use of reflectance data for soil type classification. “urban” (U) classes. The third example concerns the use of reflectance data for soil type classification. 7. Examples
Satellite reflectance data concerning 100 locations randomly chosen in the El-Fayoum area (Figure 5) were recorded at nine wavelengths. The first two examples concern the use of reflectance data for land use/cover estimation. For this purpose, the locations were grouped into “vegetation” (V), “lake” (L) and “urban” (U) classes. The third example concerns the use of reflectance data for soil type classification. “urban” (U) classes. The third example concerns the use of reflectance data for soil type classification. 7. Examples
in the El-Fayoum Egyptian region. The method of the tangents gives two structural eigenvalues. The percentage of variance in data explained by each eigenvalue is given.
in the El-Fayoum Egyptian region. The method of the tangents gives two structural eigenvalues. The percentage of variance in data explained by each eigenvalue is given.
Figure 8. Representation of samples in the reduced bi-dimensional principal factor space.
Figure 8. Representation of samples in the reduced bi-dimensional principal factor space.
Figure 11. Distribution of samples in the reduced bi-dimensional factor space given by the first and second principal components (PC1 and PC2, respectively). The partial overlap of clusters for samples belonging to the U class with that for samples belonging to the V class is highlighted by the black ellipse. The arrows indicate possible outliers. The doubtful case is highlighted with a dotted circle. represent an urban area where vegetation is also present (e.g., a park in a city). is a partial overlap of the clusters for the classes U and V (highlighted by ellipse in Figure 11). This might mean that several samples represent “green” urban zones. In the same way, the samples belonging to the class L highlighted by red arrows in Figure 11 might be either “outliers” or samples representing “lake” areas where vegetation is abundant. The sample highlighted by the dotted circle in Figure 11 is quite far from the other samples belonging to the V class. However, this does not necessarily mean that the sample in the dotted circle represents an outlier. Its position in the scores plot indicates that it might represent an urban area where vegetation is also present (e.g., a park in a city).
Figure 11. Distribution of samples in the reduced bi-dimensional factor space given by the first and second principal components (PC1 and PC2, respectively). The partial overlap of clusters for samples belonging to the U class with that for samples belonging to the V class is highlighted by the black ellipse. The arrows indicate possible outliers. The doubtful case is highlighted with a dotted circle. represent an urban area where vegetation is also present (e.g., a park in a city). is a partial overlap of the clusters for the classes U and V (highlighted by ellipse in Figure 11). This might mean that several samples represent “green” urban zones. In the same way, the samples belonging to the class L highlighted by red arrows in Figure 11 might be either “outliers” or samples representing “lake” areas where vegetation is abundant. The sample highlighted by the dotted circle in Figure 11 is quite far from the other samples belonging to the V class. However, this does not necessarily mean that the sample in the dotted circle represents an outlier. Its position in the scores plot indicates that it might represent an urban area where vegetation is also present (e.g., a park in a city).
Figure 13. Distribution of samples in the reduced bi-dimensional factor space given by the first and second principal components (PC1 and PC2, respectively). The labels “F” and “P” refer to “fluvent” and “psamment” soil classes, respectively. The arrows indicate possible outliers.
Figure 13. Distribution of samples in the reduced bi-dimensional factor space given by the first and second principal components (PC1 and PC2, respectively). The labels “F” and “P” refer to “fluvent” and “psamment” soil classes, respectively. The arrows indicate possible outliers.
Related topics:
GeographyComputer ScienceRemote SensingSoil ClassificationSoil taxonomy
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