Analyzing Remote Sensing Data in R: The landsat Package

An effective removal of atmospheric and topographic effects on remote-sensing imagery is an essential preprocessing step for mapping land cover accurately in mountain areas. Various techniques that remove these effects have been proposed and consist of specific combinations of an atmospheric and a topographic correction (TC) method. However, it is possible to generate a wide range of new combined correction methods by applying alternative combinations of atmospheric and TC methods. At present, a systematic overview of the statistical performance and data input requirement of preprocessing techniques is missing. In order to assess the individual and combined impacts of atmospheric and TC methods, 15 permutations of two atmospheric and/or four TC methods were evaluated statistically and compared to the uncorrected imagery. Furthermore, results of the integrated ATCOR3 method were included. This evaluation was performed in a study area in the Romanian Carpathian mountains. Results showed that the combination of a transmittance-based atmospheric correction (AC), which corrects the effects of Rayleigh scattering and water-vapour absorption, and a pixel-based C or Minnaert TC, which account for diffuse sky irradiance, reduced the image distortions most efficiently. Overall results indicated that TC had a larger impact than AC and there was a trade-off between the statistical performance of preprocessing techniques and their data requirement. However, the normalized difference vegetation index analysis indicated that atmospheric methods resulted in a larger impact on the spectral information in bands 3 and 4.

Communications in Soil Science and Plant Analysis, 2008

Natural Hazards and Earth System Science, 2010

Solar radiation reflected by the Earth's surface to satellite sensors is modified by its interaction with the atmosphere. The objective of applying an atmospheric correction is to determine true surface reflectance values and to retrieve physical parameters of the Earth's surface, including surface reflectance, by removing atmospheric effects from satellite images. Atmospheric correction is arguably the most important part of the pre-processing of satellite remotely sensed data. Such a correction is especially important in cases where multi-temporal images are to be compared and analyzed. For agricultural applications, in which several vegetation indices are applied for monitoring purposes, multi-temporal images are used. The integration of vegetation indices from remotely sensed images with other hydrometeorological data is widely used for monitoring natural hazards such as droughts. Indeed, the most important task is to retrieve the true values of the vegetation status from the satellite-remotely sensed data. Any omission of considering the effects of the atmosphere when vegetation indices from satellite images are used, may lead to major discrepancies in the final outcomes. This paper highlights the importance of considering atmospheric effects when vegetation indices, such as DVI, NDVI, SAVI, MSAVI and SARVI, are used (or considered) and presents the results obtained by applying the darkest-pixel atmospheric correction method on ten Landsat TM/ETM+ images of Cyprus acquired from July to December 2008. Finally, in this analysis, an attempt is

Various changes in the atmosphere of the earth and different illuminations resulting from rough terrain change the spectral reflection values of satellite images. Studies making use of real reflection values belonging to the object will provide more accurate data. The atmospheric correction process to be applied in this study is used to prevent the negative effects resulting from atmosphere and different illuminations in order to represent the reflections from the ground on the image in the best way possible. Using atmospheric correction, differentiations in reflection values sensed by different sensors or platforms resulting from atmosphere and some technical problems will be prevented. In this study, the aim is to determine the changes in the spectral reflection values concerning land use following the atmospheric correction to be applied on Landsat image data. For this reason, atmospheric correction was applied on Landsat image data. The relations of each band with each other before and after the correction were determined. The changes between spectral reflection values of all bands before and after correction regarding three different land uses as forest, agricultural area and residential area were examined visually and statistically.

2008

Solar radiation reflected by the Earth's surface to satellite sensors is modified by its interaction with the atmosphere. The objective of applying an atmospheric correction is to determine true surface reflectance values and to retrieve physical parameters of the Earth's surface, including surface reflectance, by removing atmospheric effects from satellite images. Atmospheric correction is arguably the most important part of the pre-processing of satellite remotely sensed data. Such a correction is especially important in cases where multi-temporal images are to be compared and analyzed. For agricultural applications, in which several vegetation indices are applied for monitoring purposes, multi-temporal images are used. The integration of vegetation indices from remotely sensed images with other hydrometeorological data is widely used for monitoring natural hazards such as droughts. Indeed, the most important task is to retrieve the true values of the vegetation status from the satellite-remotely sensed data. Any omission of considering the effects of the atmosphere when vegetation indices from satellite images are used, may lead to major discrepancies in the final outcomes. This paper highlights the importance of considering atmospheric effects when vegetation indices, such as DVI, NDVI, SAVI, MSAVI and SARVI, are used (or considered) and presents the results obtained by applying the darkest-pixel atmospheric correction method on ten Landsat TM/ETM+ images of Cyprus acquired from July to December 2008. Finally, in this analysis, an attempt is

2014

Radiometric correction is a prerequisite for generating high-quality scientific data, making it possible to discriminate between product artefacts and real changes in Earth processes as well as accurately produce land cover maps and detect changes. This work contributes to the automatic generation of surface reflectance products for Landsat satellite series. Surface reflectances are generated by a new approach developed from a previous simplified radiometric (atmospheric + topographic) correction model. The proposed model keeps the core of the old model (incidence angles and cast-shadows through a digital elevation model [DEM], Earth-Sun distance, etc.) and adds new characteristics to enhance and automatize ground reflectance retrieval. The new model includes the following new features: (1) A fitting model based on reference values from pseudoinvariant areas that have been automatically extracted from existing reflectance products (Terra MODIS MOD09GA) that were selected also automatically by applying quality criteria that include a geostatistical pattern model. This guarantees the consistency of the internal and external series, making it unnecessary to provide extra atmospheric data for the acquisition date and time, dark objects or dense vegetation. (2) A spatial model for atmospheric optical depth that uses detailed DEM and MODTRAN simulations. (3) It is designed so that large time-series of images can be processed automatically to produce consistent Landsat surface reflectance time-series. (4) The approach can handle most images, acquired now or in the past, regardless of the processing system, with the exception of those with extremely high cloud coverage. The new methodology has been successfully applied to a series of near 300 images of the same area including MSS, TM and ETM+ imagery as well as to different formats and processing systems (LPGS and NLAPS from the USGS; CEOS from ESA) for different degrees of cloud coverage (up to 60%) and SLC-off. Reflectance products have been validated with some example applications: time series robustness (for a pixel in a pseudoinvariant area, deviations are only 1.04% on average along the series), spectral signatures generation (visually coherent with the MODIS ones, but more similar between dates), and classification (up to 4 percent points better than those obtained with the original manual method or the CDR products). In conclusion, this new approach, that could also be applied to other sensors with similar band configurations, offers a fully automatic and reasonably good procedure for the new era of long time-series of spatially detailed global remote sensing data.

International Journal of Image Processing (IJIP …, 2011

Remotely sensed data is an effective source of information for monitoring changes in land use and land cover. However remotely sensed images are often degraded due to atmospheric effects or physical limitations. Atmospheric correction minimizes or removes the atmospheric influences that are added to the pure signal of target and to extract more accurate information. The atmospheric correction is often considered critical pre-processing step to achieve full spectral information from every pixel especially with hyperspectral and multispectral data. In this paper, multispectral atmospheric correction approaches that require no ancillary data are presented in spatial domain and transform domain. We propose atmospheric correction using linear regression model based on the wavelet transform and Fourier transform. They are tested on Landsat image consisting of 7 multispectral bands and their performance is evaluated using visual and statistical measures. The application of the atmospheric correction methods for vegetation analyses using Normalized Difference Vegetation Index is also presented in this paper.

International Journal of Web-Based Learning and Teaching Technologies, 2021

The findings of a number of recent empirical studies on blended learning support this pedagogy claiming many advantages such as the facilitation of independent and collaborative learning experiences. This study compares the attitudes towards blended learning of undergraduate students in the UAE before and after a full course exposure to blended learning, comparing results to the attitudes of students in a traditional course. An experimental research design was chosen for this research study, specifically a two-group pretest-posttest research design. Results show that exposure to blended learning serves as a trigger for changing students' attitudes towards blended learning in a positive manner and that lack of exposure does not change student attitudes.

sailing vessel of Maritime Southeast Asia A Borobudur ship is an 8th to 9th-century wooden double outrigger sailing vessel of Maritime Southeast Asia, depicted in some bas-reliefs of the Borobudur Buddhist monument in Central Java, Indonesia.[1] It is a ship of the Javanese people, and derivative vessels of similar size continued to be used in East Java coastal trade at least until the 1940s. https://en.wikipedia.org/wiki/Borobudur_ship

Esta actividad nos permite realizar un trabajo colaborativo, teniendo en cuenta los espacios y contenidos del modulo, permitiendo y fomentando nuevos valores en el desarrollo profesional del estudiante.