Extraction of spectral information from Landsat TM data and merger with SPOT panchromatic imagery — a contribution to the study of geological structures

Yésou, H.; Besnus, Yves; Rolet, J.

doi:10.1016/0924-2716(93)90069-y

Cited by 101 publications

(51 citation statements)

References 11 publications

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“…Its revised version, RUSLE (Renard et al 1997), became mostly used to provide estimates of soil loss (Demirci and Karaburun 2012;Kumar et al 2014;Ganasri and Ramesh 2015;Napoli et al 2016;Rawat et al 2016). Other studies (Baumgardner et al 1986;Yesou et al 1993;Escadafal et al 1994;Hill et al 1994;Haboudane et al 2002) have shown the interest of spectral indices based on soil reflectance, such as form index (FI), coloration index (CI), brightness index (BI), and normalized difference vegetation index (NDVI), for characterizing soil surface state, especially in arid and semi-arid lands.…”

Section: Introductionmentioning

confidence: 99%

Soil erosion modeled with USLE, GIS, and remote sensing: a case study of Ikkour watershed in Middle Atlas (Morocco)

et al. 2017

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The Ikkour watershed located in the Middle Atlas Mountain (Morocco) has been a subject of serious soil erosion problems. This study aimed to assess the soil erosion susceptibility in this mountainous watershed using Universal Soil Loss Equation (USLE) and spectral indices integrated with Geographic Information System (GIS) environment. The USLE model required the integration of thematic factors' maps which are rainfall aggressiveness, length and steepness of the slope, vegetation cover, soil erodibility, and erosion control practices. These factors were calculated using remote sensing data and GIS. The USLE-based assessment showed that the estimated total annual potential soil loss was about 70.66 ton ha. This soil loss is favored by the steep slopes and degraded vegetation cover. The spectral index method, offering a qualitative evaluation of water erosion, showed different degrees of soil degradation in the study watershed according to FI, BI, CI, and NDVI. The results of this study displayed an agreement between the USLE model and spectral index approach, and indicated that the predicted soil erosion rate can be due to the most rugged land topography and an increase in agricultural areas. Indeed, these results can further assist the decision makers in implementation of suitable conservation program to reduce soil erosion.

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Section: Introductionmentioning

confidence: 99%

Soil erosion modeled with USLE, GIS, and remote sensing: a case study of Ikkour watershed in Middle Atlas (Morocco)

et al. 2017

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“…Combination of optical and radar data, therefore, may improve the feature detection and mapping. Sensor merging of radar and optical data was performed using principal component analysis (PCA) [33][34][35] . This approach integrates the disparate information content of multisensor data in one image by combining image channels of different sensors in one image and then calculates PCA.…”

Section: Sensor Merging and Digital Image Enhancementmentioning

confidence: 99%

Potential of RISAT-1 SAR Data in Detecting Palaeochannels in Parts of the Thar Desert, India

Kumar¹,

Rajawat²

2017

Current Science

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In the present study, we have demonstrated the potential of RISAT-1 Synthetic Aperture Radar (SAR) data to detect palaeochannels in parts of Thar Desert, India, which may be utilized as one of the guides of geoarchaeological exploration, besides forming groundwater prospective zones. Palaeochannels have been detected using RISAT-1 SAR MRS datasets in the southern parts of Jaisalmer and northeastern parts of Barmer districts, Rajasthan. These palaeochannels of length varying between 14 and 36 km and width varying between 20 and 65 m are present within parabolic sand dune complexes. Palaeochannels have been detected as distinct dark tone on RISAT-1 SAR data compared to feeble expression on corresponding LANDSAT-OLI FCC datasets. This is due to sand-filled valleys, acting as radar smooth surface and absorbing the radar energy with negligible backscatter and enhanced topography due to side-looking capability of RISAT-1 SAR. High-resolution Cartosat DEM has been utilized to prepare topographical profiles, supporting the geomorphological interpretation. Merging of RISAT-1 SAR and LANDSAT ETM datasets using PCA techniques led to enhancements of palaeochannels on merged FCC data products. Like polarization of RISAT-1, SAR data could further enhance and aid in detecting palaeochannels. The entire region was flooded in August 2006 and water had flown through these palaeochannels, which subsequently dried up and facilitated their easy detection; they are otherwise difficult to interpret using pre-flood images. Analysis of sequential post-flood images has been taken up for detailed study of the area, as there is scope to detect additional hitherto unknown palaeochannels.

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“…These include approaches to assess pixel composition such as linear unmixing (also called sub-pixel analysis approaches) [38][39][40], supervised classification approaches such as neural networks [41][42][43] and machine learning [44][45][46][47], unsupervised classification approaches that include principal component analysis (PCA) -and independent component analysis (ICA) -based approaches [4,48,49], and others [31,[50][51][52][53]. In addition, algorithms for pre-processing of spectral image data have also been developed to increase the effectiveness of target detection and composition assessment [54][55][56].…”

Section: Introductionmentioning

confidence: 99%

“…Spectral imaging was developed by NASA and the DoD for remote sensing and aerial surveillance [1][2][3][4][5][6][7] and applications in the remote sensing field continue to comprise a large portion of the spectral imaging market. However, within the last 2 decades, many new applications of spectral imaging have been described, especially in the biomedical imaging field [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A theoretical‐experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods

Leavesley

Sweat

Abbott

et al. 2017

Journal of Biophotonics

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Spectral imaging technologies have been used for many years by the remote sensing community. More recently, these approaches have been applied to biomedical problems, where they have shown great promise. However, biomedical spectral imaging has been complicated by the high variance of biological data and the reduced ability to construct test scenarios with fixed ground truths. Hence, it has been difficult to objectively assess and compare biomedical spectral imaging assays and technologies. Here, we present a standardized methodology that allows assessment of the performance of biomedical spectral imaging equipment, assays, and analysis algorithms. This methodology incorporates real experimental data and a theoretical sensitivity analysis, preserving the variability present in biomedical image data. We demonstrate that this approach can be applied in several ways: to compare the effectiveness of spectral analysis algorithms, to compare the response of different imaging platforms, and to assess the level of target signature required to achieve a desired performance. Results indicate that it is possible to compare even very different hardware platforms using this methodology. Future applications could include a range of optimization tasks, such as maximizing detection sensitivity or acquisition speed, providing high utility for investigators ranging from design engineers to biomedical scientists.

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Extraction of spectral information from Landsat TM data and merger with SPOT panchromatic imagery — a contribution to the study of geological structures

Cited by 101 publications

References 11 publications

Soil erosion modeled with USLE, GIS, and remote sensing: a case study of Ikkour watershed in Middle Atlas (Morocco)

Soil erosion modeled with USLE, GIS, and remote sensing: a case study of Ikkour watershed in Middle Atlas (Morocco)

Potential of RISAT-1 SAR Data in Detecting Palaeochannels in Parts of the Thar Desert, India

A theoretical‐experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods

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