Jeffrey G. Masek

Landsat-7 ETM+ as an observatory for land cover: Initial radiometric and geometric comparisons with Landsat-5 Thematic Mapper

Abstract The Landsat-7 ETM+ sensor offers several enhancements over the Landsat-4,5 Thematic Mapper (TM) sensor, including increased spectral information content, improved geodetic accuracy, reduced noise, reliable calibration, the addition of a panchromatic band, and improved spatial resolution of the thermal band. In this paper, we present some initial comparisons between Landsat-5 TM and Landsat-7 ETM+ imagery in order to quantify these improvements. We find that the ETM+ continues the record of TM observations, and, in many respects, substantially improves upon the earlier sensor. Specific improvements include lower spatial noise levels, improved information content, and geodetic accuracy of systematically corrected products to 50–100 m. These improvements are likely to have significant benefits for land-cover mapping and change detection applications.

Geo-registration of Landsat data by robust matching of wavelet features

The goal of our project is to build an operational system, which will provide a sub-pixel accuracy registration of Landsat-5 and Landsat-7 data. Integrated within an automated mass processing/analysis system for Landsat data (REALM), the input to our registration method consists of scenes that have been geometrically and radiometrically corrected, as well as pre-processed for the detection of clouds and cloud shadows. Such pre-processed scenes are then geo-registered relative to a database of Landsat chips. This paper describes our registration process, including the use of a database of landmark chips, a feature extraction performed by an overcomplete wavelet representation, and feature matching using statistically robust techniques. Knowing the approximate longitudes and latitudes of the four corners of the scene, a subset of chips which represent landmarks included in the scene are extracted from the database. For each of these selected landmark chips, a corresponding window is extracted from the scene, and each chip-window pair is registered using our robust wavelet feature matching. First results and future directions are presented in the paper.

Robust matching of wavelet features for sub-pixel registration of Landsat data

For many Earth and space science applications, automatic geo-registration at sub-pixel accuracy has become a necessity. In this work, we are focusing on building an operational system, which will provide a sub-pixel accuracy registration of Landsat-5 and Landsat-7 data. The input to our registration method consists of scenes that have been geometrically and radiometrically corrected. Such preprocessed scenes are then geo-registered relative to a database of Landsat chips. The method assumes a transformation composed of a rotation and a translation, and utilizes rotation- and translation-invariant wavelets to extract image features that are matched using statistically robust feature matching and a partial Hausdorff distance metric. The registration process is described and results on four Landsat input scenes of the Washington, D.C., area are presented.

Research Environment for Advanced Landsat Monitoring (REALM): A computational approach for Landsat-7 global science

Abstract Landsat-7 science mission requires large-area analysis of land to cover to quantify anthropogenic and natural changes in Earths terrestrial environment. Such a goal involves processing (and reprocessing) thousands of Enhanced Thematic Mapper Plus (ETM+) scenes, but current analysis methodologies that rely on “handcrafting” individual scenes cannot scale to this data flow. As an alternative, we have constructed a prototype computer system, REALM (Research Environment for Advanced Landsat Monitoring), to automate preprocessing and analysis of large volume of Landsat imagery. Users can submit arbitrary algorithms to the database using a query language to generate science results “on the fly.” The current prototype, running on a cluster of Linux-based PCs, has created a preliminary forest-cover map for the northeastern US from 9 GB of ETM+ data in just 25 min, for an aggregate throughput of 6 MB/s. The exercise demonstrates the processing Landsat data over very large areas is now feasible.

How many bits

Since 1982, Landsat has acquired multispectral data using 8-bit data. With many new and highly advanced systems and sensors being developed, we wish to evaluate the benefits of higher radiometric precision for forestry applications. Two industrial forestry sites in central Virginia are chosen to carry out this analysis because of the large variability in standing biomass associated with recovery from past harvest. IKONOS imagery and Earth Observing Systems Advanced Land Imager (ALI) imagery are used in the study. These sensors acquire data using 11 and 12 bits, respectively. By subsampling to Landsat spatial resolution, and truncating the dynamic range and radiometric resolution, we can create Landsat-like image products at a variety of radiometric resolutions. Results show that although a difference can be seen when comparing datasets of higher and lower radiometric resolution, those differences appear to be small when trying to discriminate the boundaries of disturbed stands.

The first four years of the Landsat 7 mission: a review

The Landsat 7 mission is making major advances in our understanding of the Earths land and coastal oceans and the role of human activities as a force for global change. It has achieved unprecedented successes in satellite and sensor performance, mission management and operations, data acquisition and distribution, and science. In summary, the Landsat 7 satellite mission is realizing a long-held dream for the entire Landsat program: to provide continuing seasonal, global, high-resolution data for a myriad of important science and applications uses.

Systematic Earth observation: significant progress as we enter the 21/sup st/ century with Landsat 7

The Landsat Earth observation approach introduced in 1972 created a new way of monitoring land cover and land use globally. The Landsat 7 mission, successfully launched on April 15, 1999, continues those observations and demonstrates significant progress in precise numerical radiometry, spectral differentiation and seasonally repetitive monitoring as we enter the 21/sup st/ century. A long-term data acquisition plan was designed to ensure that substantially cloud-free, seasonal coverage would be recorded and archived in the U.S. for all land areas of the globe. Substantial improvements in calibration procedures have also been made to ensure long-term stability in the acquired spectral radiometry. A Landsat Science Team consisting of representatives from U.S. universities and government agencies has been addressing the technical and analytical means to process and analyze the core of this observation record. The expected outcome of these efforts is a rapid improvement in understanding the Earth system, as well as conceptual knowledge that will underpin significant advancements in the application of this technology for commercial, operational, educational and research purposes. The lessons learned from the Landsat 7 mission are expected to have a significant, positive influence on future Landsat-like missions.

REALM: an image database for Landsat 7 data

In order to fulfill the science mission of Landsat-7, users must explore advanced methodologies for analyzing large volumes of Landsat data. REALM (Research Environment for Advanced Landsat Monitoring) is a parallel database system implemented at University of Maryland to create on-demand Landsat analyses using 100s-1000s of scenes. REALM includes automated preprocessing modules, automated navigation and space/time indexing, and a query language that permits users to submit custom algorithms to the image database. A simple example, calculating forest-cover for the Eastern United States, illustrates the utility of this approach. Aggregate throughput for this query amounts to 6-8 MB/sec, sufficient to create a continental-scale forest-cover map in less than 12 hours.