Fast Algorithms for Estimating Aerosol Optical Depth and Correcting Thematic Mapper Imagery

Remotely sensed images collected by the satellites are usually contaminated by the effects of the atmospheric particles through absorption and scattering of the radiation from the earth surface. The objective of atmospheric correction is to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects, which is usually performed in two steps. First, the optical characteristics of the atmosphere are estimated and then the remotely sensed imagery is corrected by inversion procedures that derive the surface reflectance. In this paper we introduce an efficient algorithm to estimate the optical characteristics of the Thematic Mapper (TM) imagery and to remove the atmospheric effects from it. Our algorithm introduces a set of techniques to significantly improve the quality of the retrieved images. We pay a particular attention to the computational efficiency of the algorithm, thereby allowing us to correct large TM images quite fast. We also provide a parallel implementation of our algorithm and show its portability and its scalability on several parallel machines. (Also cross-referenced as UMIACS-TR-95-113

High Performance Computing Applications in Remote Sensing Studies for Land Cover Dynamics

Global and regional land cover studies require the ability to apply complex models on selected subsets of large amounts of multi-sensor and multi-temporal data sets that have been derived from raw instrument measurements using widely accepted pre-processing algorithms. The computational and storage requirements of most such studies far exceed what is possible on a single workstation environment. We have been pursuing a new approach that couples scalable and open distributed heterogeneous hardware with the development of high performance software for processing, indexing, and organizing remotely sensed data. Hierarchical data management tools are used to ingest raw data, create metadata, and organize the archived data so as to automatically achieve computational load balancing among the available nodes and minimize I/O overheads. We illustrate our approach with four specific examples. The first is the development of the first fast operational scheme for the atmospheric correction of Landsat TM scenes, while the second example focuses on image segmentation using a novel hierarchical connected components algorithm. Retrieval of global BRDF (Bidirectional Reflectance Distribution Function) in the red and near infrared wavelengths using four years (1983 to 1986) of Pathfinder AVHRR Land (PAL) data set is the focus of our third example. The fourth example is the development of a hierarchical data organization scheme that allows on-demand processing and retrieval of regional and global AVHRR data sets. Our results show that substantial improvements in computational times can be achieved by using the high performance computing technology

Earth Resources, A Continuing Bibliography with Indexes

This bibliography lists 460 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1984. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Atmospheric Correction of Landsat ETM+ Land Surface Imagery—Part I: Methods

To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, atmospheric correction is a necessary step. After reviewing historical development of atmospheric correction of Landsat thematic mapper (TM) imagery, we present a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of atmospheric and surface conditions. The companion paper will validate this method using ground measurements, and illustrate the improvements of several applications due to atmospheric correction.This work was supported in part by the U.S. National Aeronautics and Space Administration (NASA), Washington, DC, under Grant NAG5-6459

The suitability of using Landsat TM-5 Images for estimating chromophoric dissolved organic matter in subarctic Lakes

Recent trends of permafrost thawing in the subarctic are expected to cause increased release of dissolved organic carbon (DOC) to inland waters, which might have cascading effects on downstream aquatic ecosystems and release of CO2 to the atmosphere. This study therefore aimed at evaluating the applicability of an empirical band ratio algorithm for estimating chromophoric dissolved organic matter (CDOM; a proxy for DOC) from the easily accessible satellite images Landsat TM-5, to counter the inaccessibility of the region in general. The study targeted 14 smaller lakes in the Stordalen catchment in northern Sweden where values of CDOM absorbance had been obtained from the summer of 2009 that could be used to evaluate algorithm suitability. The satellite image type and algorithm have been successfully applied to predict CDOM in previous studies of lakes with relatively high absorbance, but in this study no significant correlations were found between the in situ measured and the remote sensing estimates for the studied lakes (in situ aCDOM (440) = 0.29 - 1.22 m-1; R2 ≤ 0.21); except for when lakes with certain characteristics were tested separately (shallow lakes R2 = 0.86). It was concluded that Landsat TM-5 images are not generally suitable for estimating CDOM in the Stordalen area. However higher quality satellite products probably would; since with a higher ground-, spectral- and radiometric resolution some disturbances could be reduced, more lakes could be included in the study and they would be more accurately recorded. Nonetheless more in situ collected data is needed for supporting the discussed deductions and for adaptive algorithm modifications

MODIS: Moderate-resolution imaging spectrometer. Earth observing system, volume 2B

The Moderate-Resolution Imaging Spectrometer (MODIS), as presently conceived, is a system of two imaging spectroradiometer components designed for the widest possible applicability to research tasks that require long-term (5 to 10 years), low-resolution (52 channels between 0.4 and 12.0 micrometers) data sets. The system described is preliminary and subject to scientific and technological review and modification, and it is anticipated that both will occur prior to selection of a final system configuration; however, the basic concept outlined is likely to remain unchanged

High Performance Computing Algorithms for Land mCover Dynamics Using Remote Sensing Data

Global and regional land cover studies require the ability to apply complex models on selected subsets of large amounts of multi-sensor and multi-temporal data sets that have been derived from raw instrument measurements using widely accepted pre-processing algorithms. The computational and storage requirements of most such studies far exceed what is possible on a single workstation environment. We have been pursuing a new approach that couples scalable and open distributed heterogeneous hardware with the development of high performance software for processing, indexing, and organizing remotely sensed data. Hierarchical data management tools are used to ingest raw data, create metadata, and organize the archived data so as to automatically achieve computational load balancing among the available nodes and minimize I/O overheads. We illustrate our approach with four specific examples. The first is the development of the first fast operational scheme for the atmospheric correction of Landsat TM scenes, while the second example focuses on image segmentation using a novel hierarchical connected components algorithm. Retrieval of global BRDF (Bidirectional Reflectance Distribution Function) in the red and near infrared wavelengths using four years (1983 to 1986) of Pathfinder AVHRR Land (PAL) data set is the focus of our third example. The fourth example is the development of a hierarchical data organization scheme that allows on-demand processing and retrieval of regional and global AVHRR data sets. Our results show that substantial improvements in computational times can be achieved by using the high performance computing technology. (Also cross-referenced as UMIACS-TR-98-18

Earth resources: A continuing bibliography with indexes (issue 51)

This bibliography lists 382 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Understanding changes in forest cover and carbon storage in early successionalforests of the Pacific Northwest using USDA Forest Service FIAand multi-temporal Landsat data

To effectively study dynamic processes like forest succession over long time periods one must effectively integrate data collected at many different times, locations and spatial scales. The purpose of this research is to integrate forest inventory data collected by the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program with multi-temporal satellite data to better understand early successional forest regrowth patterns and carbon storage in western Oregon forests. To detect and characterize continuous changes in early forest succession however, optical satellite images must first be transformed to a common radiometric scale to minimize sun, sensor, view-angle and atmospheric differences among images. We present a comparison of five atmospheric correction methods used to calibrate a nearly continuous, 20-year Landsat TM/ETM+ image data set (19-images) over western Oregon (path 46 row 29). We found that an automated ordination algorithm called multivariate alteration detection (MAD) (Canty et al., 2004), which statistically locates invariant pixels between a subject and a reference image yielded the most consistent common scale among images. Using the crossnormalized image-series we modeled percent tree cover measurements derived by ground survey and airphoto interpretation to the greater landscape. Developing a series of forest regrowth classes we identified a wide range of successional regrowth pathways 18 years after clearcut harvesting. We observed the propensity for faster regrowth on north facing aspects, shallow slopes and at low elevations. Finally, we utilized two sets of forest inventory data to evaluate a Landsat based curve-fitting model for predicting live forest carbon. At the pixel level, the model tended to over-predict carbon and performed better (i.e., higher correlation, lower RMSE) in the Coast Range ecoregion, likely the result of faster, less variable growth patterns. At the landscape scale, we found that the flux of forest carbon predicted by the curve-fit model was in absolute terms, well within the standard error of the inventory estimates. In the process of evaluating the curve-fit model, we discovered a new method for detecting subtle (i.e., forest to non-forest) land-use shifts with Landsat data. Identifying these types of land-use shifts is critically important to developing a more accurate comprehensive carbon budget from forests. We were also able to identify several potential improvements to estimating live forest carbon with the curve-fitting approach