An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types
Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an important sour...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/123 |
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| author | Antonella Belmonte Carmela Riefolo Gabriele Buttafuoco Annamaria Castrignanò |
| author_facet | Antonella Belmonte Carmela Riefolo Gabriele Buttafuoco Annamaria Castrignanò |
| author_sort | Antonella Belmonte |
| collection | DOAJ |
| description | Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an important source of spatial data at multiple scales. A crucial problem facing us is the fusion of multi-source spatial data of different natures and characteristics, among which there is the support size of measurement that unfortunately is little considered in RS. A data fusion approach of both sample (point) and grid (areal) data is proposed that explicitly takes into account spatial correlation and change of support in both increasing support (upscaling) and decreasing support (downscaling). The techniques of block cokriging and kriging downscaling were employed for the implementation of such an approach, respectively. The method is applied to soil sample data, jointly analysed with hyperspectral data measured in the laboratory, UAV, and satellite data (Planet and Sentinel 2) of an olive grove after filtering soil pixels. Each data type had its own support that was transformed to the same support as the soil sample data so that the data fusion approach could be applied. To demonstrate the statistical, as well as practical, effectiveness of such a method, it was compared by a cross-validation test with a univariate approach for predicting each soil property. The positive results obtained should stimulate advanced statistical techniques to be applied more and more widely to RS data. |
| format | Article |
| id | doaj-art-02639409d6b94f6599d14e0342d4623a |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-02639409d6b94f6599d14e0342d4623a2025-01-10T13:20:18ZengMDPI AGRemote Sensing2072-42922025-01-0117112310.3390/rs17010123An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different TypesAntonella Belmonte0Carmela Riefolo1Gabriele Buttafuoco2Annamaria Castrignanò3National Research Council of Italy, Institute for Electromagnetic Sensing of the Environment (CNR-IREA), Via Amendola 122/D, 70126 Bari, ItalyCREA-AA—Council for Agricultural Research and Economics, Via Celso Ulpiani, 5, 70125 Bari, ItalyNational Research Council of Italy, Institute for Agriculture and Forestry Systems in the Mediterranean, 87036 Rende, ItalyNational Research Council of Italy, Institute for Electromagnetic Sensing of the Environment (CNR-IREA), Via Amendola 122/D, 70126 Bari, ItalyRemote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an important source of spatial data at multiple scales. A crucial problem facing us is the fusion of multi-source spatial data of different natures and characteristics, among which there is the support size of measurement that unfortunately is little considered in RS. A data fusion approach of both sample (point) and grid (areal) data is proposed that explicitly takes into account spatial correlation and change of support in both increasing support (upscaling) and decreasing support (downscaling). The techniques of block cokriging and kriging downscaling were employed for the implementation of such an approach, respectively. The method is applied to soil sample data, jointly analysed with hyperspectral data measured in the laboratory, UAV, and satellite data (Planet and Sentinel 2) of an olive grove after filtering soil pixels. Each data type had its own support that was transformed to the same support as the soil sample data so that the data fusion approach could be applied. To demonstrate the statistical, as well as practical, effectiveness of such a method, it was compared by a cross-validation test with a univariate approach for predicting each soil property. The positive results obtained should stimulate advanced statistical techniques to be applied more and more widely to RS data.https://www.mdpi.com/2072-4292/17/1/123hyperspectral dataPLSRchange of supportdata fusionconvolutiondeconvolution |
| spellingShingle | Antonella Belmonte Carmela Riefolo Gabriele Buttafuoco Annamaria Castrignanò An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types Remote Sensing hyperspectral data PLSR change of support data fusion convolution deconvolution |
| title | An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types |
| title_full | An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types |
| title_fullStr | An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types |
| title_full_unstemmed | An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types |
| title_short | An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types |
| title_sort | approach for spatial statistical modelling remote sensing data of land cover by fusing data of different types |
| topic | hyperspectral data PLSR change of support data fusion convolution deconvolution |
| url | https://www.mdpi.com/2072-4292/17/1/123 |
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