Analyzing Remote Sensing Data in R: The landsat Package

Research and development on atmospheric and topographic correction methods for multispectral satellite data such as Landsat images has far outpaced the availability of those methods in geographic information systems software. As Landsat and other data become more widely available, demand for these improved correction methods will increase. Open source R statistical software can help bridge the gap between research and implementation. Sophisticated spatial data routines are already available, and the ease of program development in R makes it straightforward to implement new correction algorithms and to assess the results. Collecting radiometric, atmospheric, and topographic correction routines into the landsat package will make them readily available for evaluation for particular applications.

Analyzing Remote Sensing Data in R: The landsat Package

Research and development on atmospheric and topographic correction methods for multispectral satellite data such as Landsat images has far outpaced the availability of those methods in geographic information systems software. As Landsat and other data become more widely available, demand for these improved correction methods will increase. Open source R statistical software can help bridge the gap between research and implementation. Sophisticated spatial data routines are already available, and the ease of program development in R makes it straightforward to implement new correction algorithms and to assess the results. Collecting radiometric, atmospheric, and topographic correction routines into the landsat package will make them readily available for evaluation for particular applications

COMPARING ATMOSPHERIC CORRECTION METHODS FOR LANDSAT OLI DATA

Landsat data used for monitoring activities to land cover because it has spatial resolution and high temporal. To monitor land cover changes in an area, atmospheric correction is needed to be performed in order to obtain data with precise digital value picturing current condition. This study compared atmospheric correction methods namely Quick Atmospheric Correction (QUAC), Dark Object Subtraction (DOS) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH). The correction results then were compared to Surface Reflectance (SR) imagery data obtained from the United States Geological Survey (USGS) satelite. The three atmospheric correction methods were applied to Landsat OLI data path/row126/62 for 3 particular dates. Then, sample on vegetation, soil and bodies of water (waterbody) were retrieved from the image. Atmospheric correction results were visually observed and compared with SR sample on the absolute value, object spectral patterns, as well as location and time consistency. Visual observation indicates that there was a contrast change on images that had been corrected by using FLAASH method compared to SR, which mean that the atmospheric correction method was quite effective. Analysis on the object spectral pattern, soil, vegetation and waterbody of images corrected by using FLAASH method showed that it was not good enough eventhough the reflectant value differed greatly to SR image. This might be caused by certain variables of aerosol and atmospheric models used in Indonesia. QUAC and DOS made more appropriate spectral pattern of vegetation and water body than spectral library. In terms of average value and deviation difference, spectral patterns of soil corrected by using DOS was more compatible than QUAC

HAZE REMOVAL IN THE VISIBLE BANDS OF LANDSAT 8 OLI OVER SHALLOW WATER AREA

Haze is one of radiometric quality parameters in remote sensing imagery. With certain atmospheric correction, haze is possible to be removed. Nevertheless, an efficient method for haze removal is still a challenge. Many methods have been developed to remove or to minimize the haze disruption. While most of the developed methods deal with removing haze over land areas, this paper tried to focus to remove haze from shallow water areas. The method presented in this paper is a simple subtraction algorithm between a band that reflected by water and a band that absorbed by water. This paper used data from Landsat 8 with visible bands as a band that reflected by water while the band that absorbed by water represented by NIR, SWIR-1, and SWIR-2 bands. To validate the method, a reference data which relatively clear of cloud and haze contamination is selected. The pixel numbers from certain points are selected and collected from data scene, results scene and reference scene. Those pixel numbers, then being compared each other to get a correlation number between data scene to reference scene and between result scene and reference scene. The comparison shows that the method using NIR, SWIR-1, and SWIR-2 all significantly improved correlations numbers between result scene with reference scene to higher than 0.9. The comparison also indicates that haze removal result using NIR band had the highest correlation with reference data.

Image-based atmosphere correction using dark pixel subtraction technique in Penang Island, Malaysia / Wan Noni Afida Ab Manan, Arnis Asmat and Noordin Ahmad

Lack of study has found in emphasizing the appropriate chosen of atmospheric models with specific atmospheric condition. This study is attempting to answer whether selection of atmospheric models is based on regional atmospheric condition or geographic location. The proposed method is a combination of the radiative transfer equation and dark target subtraction technique. Two important atmospheric parameters in the radiative transfer equation which are visibility and aerosol loading are estimated from the image itself to be as an input in atmospheric modelling. ATCOR-2 was used to perform different atmospheric models (maritime and urban) which can represent regional climatic condition in Penang Island, Malaysia. By relating the determined aerosol optical thickness with visibility values, urban model at the visible (blue band) on image 2005 showed a high correlation coefficient which is r2 = 0.8896. It has been shown that determined the aerosol is highly correlated to the visibility range from 10 to 50 km. Study also found that optical satellite remotely sensed image data (Landsat TM blue band) can be used to determine the visibility value through the darkest pixel atmospheric correction algorithms

Validation of a Radiometric Normalization Procedure for Satellite-Derived Imagery Within a Change Detection Framework

Detecting changes in land cover through time using remotely sensed imagery is a powerful application that has seen increased use as imagery has become more widely available and inexpensive. Before a time series of remotely sensed imagery can be used for change detection, images must first be standardized for effects outside of real surface change. This thesis established a validation protocol to evaluate the effectiveness of an automated technique for normalizing temporally separate but spatially coincident imagery. Using the concept of pseudo-invariant features between master-slave image pairs, spatially coincident dark and bright points are identified from images and a regression equation is calculated to normalize slave images to a master. I used two sets of imagery to test the performance of the standardization process, a spatially coincident, but temporally variable time series, and spatially and temporally variable images. I tested the underlying statistical assumptions of this approach, and performed simple image subtraction to validate the reduction of master-slave differences using invariant locations. In addition I tested the possibility of reducing between-sensor differences by applying simple linear regression to comparable bands of MSS and TM sensors. Image subtraction showed decreases in master-slave differences as a result of the standardization process, and the process behaved appropriately when there should be no difference between master and slave images (adjacent, but temporally identical imagery). I also found that comparable bands between MSS and TM sensors are similar enough that linear regression may not significantly reduce between-sensor differences

Optimal land cover mapping and change analysis in northeastern oregon using landsat imagery.

Abstract The necessity for the development of repeatable, efficient, and accurate monitoring of land cover change is paramount to successful management of our planet’s natural resources. This study evaluated a number of remote sensing methods for classifying land cover and land cover change throughout a two-county area in northeastern Oregon (1986 to 2011). In the past three decades, this region has seen significant changes in forest management that have affected land use and land cover. This study employed an accuracy assessment-based empirical approach to test the optimality of a number of advanced digital image processing techniques that have recently emerged in the field of remote sensing. The accuracies are assessed using traditional error matrices, calculated using reference data obtained in the field. We found that, for single-time land cover classification, Bayes pixel-based classification using samples created with scale and shape segmentation parameters of 8 and 0.3, respectively, resulted in the highest overall accuracy. For land cover change detection, using Landsat-5 TM band 7 with a change threshold of 1.75 standard deviations resulted in the highest accuracy for forest harvesting and regeneration mapping

In-scene atmospheric correction for multispectral imagery

In-scene techniques for atmospheric correction of remotely sensed multispectral imagery have been collected and applied to a series of image sets. The methods used rely on image derived statistics and a minimal use of ancillary data. Five different schemes for estimating upwelled radiance are presented. Upwelled radiance predictions are compared to estimates derived from reflectance panel calibrations and the radiative transfer model LOWTRAN. Inversion to reflectance space is performed with an in-scene technique that draws upon knowledge of surface material reflectance spectra and the upwelled radiance estimates generated by accompanying techniques. This research attempts to answer the following questions: What features must be present for robust implementation of each routine? In which spectral bands does each routine provide good estimates of upwelled radiance? What are the limits in spectral and spatial resolution for each routine? Drawing on the conclusions of this research a modus operandi is suggested for in-scene atmospheric correction

Effectiveness of DOS (Dark-Object Subtraction) method and water index techniques to map wetlands in a rapidly urbanising megacity with Landsat 8 data

The objectives of this work were to examine the applicability of the Dark-Object Subtraction (DOS) atmospheric correction method and water-based index techniques to map wetlands in Dhaka megacity using Landsat 8 data. With the use of both raw data and DOS- corrected imagery, the analysis revealed that DOS- corrected images performed better in discriminating wetland areas. Furthermore, the Modified Normalised Water Index (MNDWI) was the most superior technique whilst the Normalised Difference Water Index (NDWI) was the least suitable in identifying the spatial locations of wetlands in a rapidly urbanising environment such as Dhaka