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  • Authors: L. Sokoletsky*; Z. Dubinsky; M. Shoshany§; N. STAMBLER;

    The bio-optical relationships between inherent and apparent optical properties, and between optical properties and phytoplankton pigment concentration (C) averaged in a layer (ΔZ), were derived from analysis of data collected during the period 1996–1998 in the Gulf of Aqaba (Eilat). Parametrization of these relationships was based on radiative transfer theory, Gershun's equation, minimization of model errors by least-square fitting, and on known optical models relating underwater remote sensed reflectance (R rsw) with the ratio of backscattering (b b) to vertical attenuation coefficient (K d) [or to absorption coefficient (a)]. These relationships explain a frequently used form of remote sensing algorithms for C estimation using ratio of water-leaving radiances measured at two or more wavelengths (λ). In this study, the possibility of using for this purpose a single wavelength in the blue range (λ=443 nm) within the framework of in situ and remote sensing algorithms for Case 1 waters was assessed.

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  • Authors: O. V. Toutoubalina; W. G. Rees;

    This study applies remote sensing techniques for monitoring non-ferrous metal smelting impacts in the extreme environment of northern Siberia. Ground and at-satellite reflectance and normalized difference vegetation index (NDVI) values for different vegetation types have been compared and a hybrid supervised-unsupervised classification of Landsat TM data performed, based on field and ancillary data. This has allowed us to distinguish several degrees of vegetation damage in tundra and forests. However, it was difficult to differentiate between some significant classes, such as damaged grass tundra and sparse dead larch forests with a grass understorey. We suggest possible refinement of our results, including the combination of images taken at different phenological stages and from different sensors. However, it should be noted that the north-Siberian environment presents unusually severe limitations of optical-infrared satellite observation possibilities and problems in imagery interpretation. Standard ind...

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  • Authors: Stephen D. Prince; P. J. Willson; D. M. Hunt; P. Halstead;

    Abstract The design requirements for a broad-band red and near infrared radiometer for monitoring vegetation from a light aircraft are discussed and an instrument which incorporates these characteristics, called an integrated camera and radiometer (ICAR), is described. It consists of two downward-looking, spectral radiometers and a solar radiometer coupled with a 35 mm camera and a data-logger in a convenient payload for mounting in a light aircraft. The distinctive features of the ICAR arc the synchronization of the camera and radiometers, the equivalence of their fields of view and the integral microcomputer which controls the instrument and acts as a programmable data-logger. The simultaneous aerial photographs are used to locate the data geographically, to select the fields of view which are of the desired target, to interpret the radiometer data, and to act as a record of the terrain conditions for visual interpretation. ICAR data are compared with data from an Exotech 100BX radiometer mounted in an ...

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  • Authors: Marc Simard; G. De Grandi; S.S. Saatchi; P. Mayaux;

    The objective of this paper is to investigate the complementarity of JERS-1 and ERS-1 data for mapping coastal tropical regions. We use a decision tree classifier to classify a coastal region of Ga...

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  • Authors: Ph. PUYOU-LASCASSIES; A. PODAIRE; M. GAY;

    Abstract The images from optical sensors with a broad path width (e.g. NOAA-AVHRR) are used for monitoring vegetation on a regional scale. The European agricultural land uses, which are generally heterogeneous, can be coarsely distinguished by these radiometers. Such sensors, however, do not allow the discrimination of seasonal radiometric changes of a given crop. Some future Earth observation platforms will carry two types of instruments on board. The first instrument will have moderate spatial resolution but a broad path width to allow almost daily observations of the emerged areas. The second will have high spatial resolution and a narrow path width to give the opportunity of making land use thematic maps from the few images recorded per year. The combination of these two types of data allows the medium resolution signal to be unmixed in order to restore the radiometric evolution of a particular crop or of a group of crops. From the application of a linear mixing model to coarse spatial resolution data...

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  • Authors: V. Haertel; Yosio Edemir Shimabukuro; R. Almeida-Filho;

    The concept of mixed pixels allows the interpretation of remote sensing digital image data at sub-pixel level. Fraction-image data, obtained using the notion of mixed pixels, offer a potentially powerful method to detect changes in land-cover over a given period of time. This study proposes a new approach to detect land-cover changes, using two sets of fraction-image data obtained from sets of multispectral image data acquired at two different dates, over the same area. Changes based on the selected pixel components are then used to generate the fraction-change image data, including both positive (increase) and negative (decrease) changes in each component. The proposed analysis is then performed in the fraction-change space in two different ways: (1) by implementing unsupervised classification methods and (2) by comparing the fraction-change images among themselves. The proposed methodology is tested on two sets of Landsat Thematic Mapper (TM) multispectral image data obtained at two different dates and ...

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  • Authors: E. Raymond Hunt;

    The reflectance ratio of the middle-infrared band (MIR) to the near-infrared band (NIR) is linearly related to the log(10) equivalent water thickness (EWT) for single leaves of different morphologies, whereas the MIR/NIR radiance ratio is correlated with the leaf area index (LAI). The hypothesis that the MIR/NIR ratio measures canopy EWT was tested by reanalyzing airborne Thematic Mapper Simulator and field data obtained across a large gradient of LAI in western Oregon, U.S.A. The measured airborne MIR/NIR reflectance ratios for canopies were not significantly different from the predicted ratios using leaf data for canopy EWT, except for two desert woodland sites. The interpretation of the MIR/NIR ratio is scale-dependent, because leaf EWT is determined primarily by variations in LAI.

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  • Authors: Tara Sharma; P. V. Satya Kiran; T. P. Singh; A. V. Trivedi; +1 Authors

    In the arid and semi-arid tropics, low annual rainfall together with high intensity rains has resulted in excess runoff, soil erosion and low moisture intake leading to poor crop yields. Therefore, adoption of soil and water conservation measures is necessary for the optimal utilization of natural resources and to increase the productivity of land on a sustainable basis. Remote sensing and GIS techniques can be used for generating development plans for the watershed area in consonance with the production potential and limitation of terrain resources, and can also be used for assessing the impact of these measures before actual implementation in the field. This paper describes a case study for the Jasdan taluka (district) of Rajkot in Gujarat, India. The aims are to prioritize watersheds on the basis of runoff generated, expressed as yield, due to existing land use conditions; to suggest soil and water conservation measures; and to evaluate the hydrologic response of these measures on runoff. The Soil Conservation Service Curve Number (CN) method was used for computing the runoff; subsequently runoff yield in percentage was calculated for prioritizing the watersheds. Satellite and other collateral data were used to identify the problems and potential in the watersheds and recommend measures for soil and water conservation. The impact of these measures was assessed by computing runoff under alternative land use and management practices. It was found that the runoff yield decreased by 42.88% of the pre-conservation value for the watershed.

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    Authors: Grignetti, A.; Salvatori, R.; Casacchia, R.; Manes, Fausto;

    Abstract A mediterranean vegetated coastal area has been classified on the basis of multi-temporal TM images and accurate field data. The latter was revealed to be particularly important due to the complexity of the area in terms of coverage degree, height structure of living vegetation and the occurrence of under-storey species. The recognition and classification of the different coenosis has taken into account that the spectral characteristics of vegetation depends on the season and on local climatic conditions. A major improvement in the spatial resolution of spectral data has been obtained by merging TM and SPOT-P by a RGB-IHS transformation that allowed an overall accuracy in the classification of 85 per cent to be achieved.

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    International Journal of Remote Sensing
    Article . 1997 . Peer-reviewed
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      International Journal of Remote Sensing
      Article . 1997 . Peer-reviewed
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  • Authors: K. Peleg;

    Quantification of the functional relation between remotely-sensed data and commensurable ground based observations is a basic prerequisite in many remote sensing studies. To this end, linear regression analysis is generally employed. Given two matrices of paired noise-infected measurements, classical linear regression is usually employed to find optimal parameters of a model calibration function which fits the observed readings best, in the minimal least squares sense. The squared coefficient of determination R = (variation due to the model)/(total variation) is a common quality measure of the chosen model, while the variance S r of the 'residuals' is a measure of the information that the chosen calibration function is unable to explain. The basic premise of regression analysis requires that the reference ground data must be precise and noiseless. Since in most remote sensing studies this condition is not met, classical regression is not an efficient tool for discovering the true functional relation between remotely-sensed data and ground observations. A new calibration method is proposed whereby the least-squares minimization is conducted on the amplitude matrices of the readings via the FFT. For a given model, R is always increased beyond the value obtained by conventional regression at the expense of a slight increase in S r . When one of the measurement sets may be considered noiseless, phase correction may be employed to reduce S r as well, below the value obtained by conventional regression. The new calibration method is a radical departure from classical statistics and has the potential of significantly improving statistical inference in remote sensing. The line taken is illustrated by numerical examples which compare the new calibration method to the classical regression technique. It is demonstrated, that the new method can discover better the true functional relation between satellite images or between ground based sensor arrays and satellite images, which may be occluded by noise.

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The following results are related to Rural Digital Europe. Are you interested to view more results? Visit OpenAIRE - Explore.
  • Authors: L. Sokoletsky*; Z. Dubinsky; M. Shoshany§; N. STAMBLER;

    The bio-optical relationships between inherent and apparent optical properties, and between optical properties and phytoplankton pigment concentration (C) averaged in a layer (ΔZ), were derived from analysis of data collected during the period 1996–1998 in the Gulf of Aqaba (Eilat). Parametrization of these relationships was based on radiative transfer theory, Gershun's equation, minimization of model errors by least-square fitting, and on known optical models relating underwater remote sensed reflectance (R rsw) with the ratio of backscattering (b b) to vertical attenuation coefficient (K d) [or to absorption coefficient (a)]. These relationships explain a frequently used form of remote sensing algorithms for C estimation using ratio of water-leaving radiances measured at two or more wavelengths (λ). In this study, the possibility of using for this purpose a single wavelength in the blue range (λ=443 nm) within the framework of in situ and remote sensing algorithms for Case 1 waters was assessed.

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  • Authors: O. V. Toutoubalina; W. G. Rees;

    This study applies remote sensing techniques for monitoring non-ferrous metal smelting impacts in the extreme environment of northern Siberia. Ground and at-satellite reflectance and normalized difference vegetation index (NDVI) values for different vegetation types have been compared and a hybrid supervised-unsupervised classification of Landsat TM data performed, based on field and ancillary data. This has allowed us to distinguish several degrees of vegetation damage in tundra and forests. However, it was difficult to differentiate between some significant classes, such as damaged grass tundra and sparse dead larch forests with a grass understorey. We suggest possible refinement of our results, including the combination of images taken at different phenological stages and from different sensors. However, it should be noted that the north-Siberian environment presents unusually severe limitations of optical-infrared satellite observation possibilities and problems in imagery interpretation. Standard ind...

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  • Authors: Stephen D. Prince; P. J. Willson; D. M. Hunt; P. Halstead;

    Abstract The design requirements for a broad-band red and near infrared radiometer for monitoring vegetation from a light aircraft are discussed and an instrument which incorporates these characteristics, called an integrated camera and radiometer (ICAR), is described. It consists of two downward-looking, spectral radiometers and a solar radiometer coupled with a 35 mm camera and a data-logger in a convenient payload for mounting in a light aircraft. The distinctive features of the ICAR arc the synchronization of the camera and radiometers, the equivalence of their fields of view and the integral microcomputer which controls the instrument and acts as a programmable data-logger. The simultaneous aerial photographs are used to locate the data geographically, to select the fields of view which are of the desired target, to interpret the radiometer data, and to act as a record of the terrain conditions for visual interpretation. ICAR data are compared with data from an Exotech 100BX radiometer mounted in an ...

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  • Authors: Marc Simard; G. De Grandi; S.S. Saatchi; P. Mayaux;

    The objective of this paper is to investigate the complementarity of JERS-1 and ERS-1 data for mapping coastal tropical regions. We use a decision tree classifier to classify a coastal region of Ga...

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  • Authors: Ph. PUYOU-LASCASSIES; A. PODAIRE; M. GAY;

    Abstract The images from optical sensors with a broad path width (e.g. NOAA-AVHRR) are used for monitoring vegetation on a regional scale. The European agricultural land uses, which are generally heterogeneous, can be coarsely distinguished by these radiometers. Such sensors, however, do not allow the discrimination of seasonal radiometric changes of a given crop. Some future Earth observation platforms will carry two types of instruments on board. The first instrument will have moderate spatial resolution but a broad path width to allow almost daily observations of the emerged areas. The second will have high spatial resolution and a narrow path width to give the opportunity of making land use thematic maps from the few images recorded per year. The combination of these two types of data allows the medium resolution signal to be unmixed in order to restore the radiometric evolution of a particular crop or of a group of crops. From the application of a linear mixing model to coarse spatial resolution data...

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  • Authors: V. Haertel; Yosio Edemir Shimabukuro; R. Almeida-Filho;

    The concept of mixed pixels allows the interpretation of remote sensing digital image data at sub-pixel level. Fraction-image data, obtained using the notion of mixed pixels, offer a potentially powerful method to detect changes in land-cover over a given period of time. This study proposes a new approach to detect land-cover changes, using two sets of fraction-image data obtained from sets of multispectral image data acquired at two different dates, over the same area. Changes based on the selected pixel components are then used to generate the fraction-change image data, including both positive (increase) and negative (decrease) changes in each component. The proposed analysis is then performed in the fraction-change space in two different ways: (1) by implementing unsupervised classification methods and (2) by comparing the fraction-change images among themselves. The proposed methodology is tested on two sets of Landsat Thematic Mapper (TM) multispectral image data obtained at two different dates and ...

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  • Authors: E. Raymond Hunt;

    The reflectance ratio of the middle-infrared band (MIR) to the near-infrared band (NIR) is linearly related to the log(10) equivalent water thickness (EWT) for single leaves of different morphologies, whereas the MIR/NIR radiance ratio is correlated with the leaf area index (LAI). The hypothesis that the MIR/NIR ratio measures canopy EWT was tested by reanalyzing airborne Thematic Mapper Simulator and field data obtained across a large gradient of LAI in western Oregon, U.S.A. The measured airborne MIR/NIR reflectance ratios for canopies were not significantly different from the predicted ratios using leaf data for canopy EWT, except for two desert woodland sites. The interpretation of the MIR/NIR ratio is scale-dependent, because leaf EWT is determined primarily by variations in LAI.

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  • Authors: Tara Sharma; P. V. Satya Kiran; T. P. Singh; A. V. Trivedi; +1 Authors

    In the arid and semi-arid tropics, low annual rainfall together with high intensity rains has resulted in excess runoff, soil erosion and low moisture intake leading to poor crop yields. Therefore, adoption of soil and water conservation measures is necessary for the optimal utilization of natural resources and to increase the productivity of land on a sustainable basis. Remote sensing and GIS techniques can be used for generating development plans for the watershed area in consonance with the production potential and limitation of terrain resources, and can also be used for assessing the impact of these measures before actual implementation in the field. This paper describes a case study for the Jasdan taluka (district) of Rajkot in Gujarat, India. The aims are to prioritize watersheds on the basis of runoff generated, expressed as yield, due to existing land use conditions; to suggest soil and water conservation measures; and to evaluate the hydrologic response of these measures on runoff. The Soil Conservation Service Curve Number (CN) method was used for computing the runoff; subsequently runoff yield in percentage was calculated for prioritizing the watersheds. Satellite and other collateral data were used to identify the problems and potential in the watersheds and recommend measures for soil and water conservation. The impact of these measures was assessed by computing runoff under alternative land use and management practices. It was found that the runoff yield decreased by 42.88% of the pre-conservation value for the watershed.

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    Authors: Grignetti, A.; Salvatori, R.; Casacchia, R.; Manes, Fausto;

    Abstract A mediterranean vegetated coastal area has been classified on the basis of multi-temporal TM images and accurate field data. The latter was revealed to be particularly important due to the complexity of the area in terms of coverage degree, height structure of living vegetation and the occurrence of under-storey species. The recognition and classification of the different coenosis has taken into account that the spectral characteristics of vegetation depends on the season and on local climatic conditions. A major improvement in the spatial resolution of spectral data has been obtained by merging TM and SPOT-P by a RGB-IHS transformation that allowed an overall accuracy in the classification of 85 per cent to be achieved.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Archivio della ricer...arrow_drop_down
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    International Journal of Remote Sensing
    Article . 1997 . Peer-reviewed
    Data sources: Crossref
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      International Journal of Remote Sensing
      Article . 1997 . Peer-reviewed
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  • Authors: K. Peleg;

    Quantification of the functional relation between remotely-sensed data and commensurable ground based observations is a basic prerequisite in many remote sensing studies. To this end, linear regression analysis is generally employed. Given two matrices of paired noise-infected measurements, classical linear regression is usually employed to find optimal parameters of a model calibration function which fits the observed readings best, in the minimal least squares sense. The squared coefficient of determination R = (variation due to the model)/(total variation) is a common quality measure of the chosen model, while the variance S r of the 'residuals' is a measure of the information that the chosen calibration function is unable to explain. The basic premise of regression analysis requires that the reference ground data must be precise and noiseless. Since in most remote sensing studies this condition is not met, classical regression is not an efficient tool for discovering the true functional relation between remotely-sensed data and ground observations. A new calibration method is proposed whereby the least-squares minimization is conducted on the amplitude matrices of the readings via the FFT. For a given model, R is always increased beyond the value obtained by conventional regression at the expense of a slight increase in S r . When one of the measurement sets may be considered noiseless, phase correction may be employed to reduce S r as well, below the value obtained by conventional regression. The new calibration method is a radical departure from classical statistics and has the potential of significantly improving statistical inference in remote sensing. The line taken is illustrated by numerical examples which compare the new calibration method to the classical regression technique. It is demonstrated, that the new method can discover better the true functional relation between satellite images or between ground based sensor arrays and satellite images, which may be occluded by noise.

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