Landsat Datasets Research Articles

Estimation of the Normalized Difference Vegetation Index (NDVI) Variation for Selected Regions in Iraq for two Years 1990 & 2001

The Normalized Difference Vegetation Index (NDVI) is commonly used as a measure of land surface greenness based on the assumption that NDVI value is positively proportional to the amount of green vegetation in an image pixel area. The Normalized Difference Vegetation Index data set of Landsat based on the remote sensing information is used to estimate the area of plant cover in region west of Baghdad during 1990-2001. The results show that in the period of 1990 and 2001 the plant area in region of Baghdad increased from (44760.25) hectare to (75410.67) hectare. The vegetation area increased during the period 1990-2001, and decreases the exposed area.

Developing Consistent Landsat Data Sets for Large Area Applications: The MRLC 2001 Protocol

One of the major efforts in large area land cover mapping over the last two decades was the completion of two U.S. National Land Cover Data sets (NLCD), developed with nominal 1992 and 2001 Landsat imagery under the auspices of the MultiResolution Land Characteristics (MRLC) Consortium. Following the successful generation of NLCD 1992, a second generation MRLC initiative was launched with two primary goals: (1) to develop a consistent Landsat imagery data set for the U.S. and (2) to develop a second generation National Land Cover Database (NLCD 2001). One of the key enhancements was the formulation of an image preprocessing protocol and implementation of a consistent image processing method. The core data set of the NLCD 2001 database consists of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images. This letter details the procedures for processing the original ETM+ images and more recent scenes added to the database. NLCD 2001 products include Anderson Level II land cover classes, percent tree canopy, and percent urban imperviousness at 30-m resolution derived from Landsat imagery. The products are freely available for download to the general public from the MRLC Consortium Web site at http://www.mrlc.gov.

Patterns in Forest Clearing Along the Appalachian Trail Corridor

Forest clearing in the vicinity of the Appalachian Trail National Park undermines the Trail's value as a wilderness retreat for millions of annual hikers. We estimate that 75,000 hectares of forest were lost to clearing during the decade of the 1990s inside a 16 km-wide corridor centered on the Trail. This loss represents 2.45 percent of forests within 8 km of the 3,500 km-long trail. Managed forest harvests in northern New England accounted for 76.8 percent of forest clearing. The factor most closely related to forest clearing is land ownership: only 0.29 percent of protected forests were cleared, while unprotected and managed forests were cleared at rates of 2.05 percent and 4.03 percent, respectively. A combination of boosted decision tree classifiers, multitemporal Kauth-Thomas transforms and the GeoCover Landsat dataset enabled a single, un-funded analyst to rapidly map land-cover change at 28.5-meter resolution within a 3.8 million hectare study area that spanned 16 Landsat scenes.

Scene selection and the use of NASA's global orthorectified Landsat dataset for land cover and land use change monitoring

This study examines the utility of NASA's circa 1990 and circa 2000 global orthorectified Landsat dataset for land cover and land use change mapping and monitoring across Africa. This is achieved by comparing the temporal and spatial variation of NDVI, measured independently by the NOAA‐AVHRR at the time of Landsat scene acquisition, against the seasonal mean for each Landsat scene extent. Decadal sequences of drift‐corrected NOAA‐AVHRR imagery were used to calculate NDVI means and standard deviations for the periods covered by the scenes composing the c.1990 and c.2000 Landsat datasets. The specific NOAA‐AVHRR NDVI values at the acquisition date of each individual Landsat scene were also calculated and the differences, both from the mean and scaled by standard deviation, were mapped for the Landsat scene footprints in the c.1990 and c.2000 datasets. The resulting maps show the temporal position of each Landsat scene within the seasonal NDVI cycle, and provide a valuable guide to assist in quantifying uncertainty and interpreting land cover and land use changes inferred from these Landsat data.

Internet Access to Remotely Sensed Data

Internet access to Landsat and other remotely sensed data sets, at little or no cost to the public, is an important emerging trend. This has many implications, including more widespread application of remotely sensed data and improved educational opportunities at all levels. The overall goal of improving public access is the “demystification” of remote sensing, which will result in a greater awareness about the benefits and limitations of the technology and its more frequent inclusion in public discourse. This paper outlines several programs that provide the public with access to free or inexpensive Landsat data sets via the Internet.

Application of a variance decomposition method to compare satellite and aerial inventory data: a tool for evaluating wildlife–habitat relationships

Summary Researchers frequently rely on forest inventories derived for other purposes when characterizing wildlife–habitat associations. When studied populations are remote and inventory coverage is incomplete for one or more data sets, researchers must often choose between the need for greater detail in habitat information and complete coverage of the land base. Forest inventories are often limited to canopy‐level information; however, different aspects of the overstorey and understorey may be important to wildlife. We compared the explanatory power of a forest resource inventory (FRI) and Landsat‐derived inventory to account for variation in vegetation communities available to woodland caribou Rangifer tarandus caribou. Forest canopy and understorey data were collected from stands in the Clay Belt region of Ontario, Canada, and cluster analysis and ordination were used to identify discrete community types. Canonical correspondence analysis revealed strong relationships between forest inventories and vegetation communities derived from field data. The integration of field‐based species and structural data with remote sensing land cover information provided sufficient detail among vegetation communities to identify features of known importance to woodland caribou. Variance decomposition revealed that FRI and Landsat variables had different capacities to explain variation in community composition, and overlap existed in the explanatory power. FRI species factors had greater explanatory power than Landsat habitat classes; however, Landsat structural variables accounted for more variation than FRI structural variables. Synthesis and applications. The results support the use of forest inventory attributes to infer vegetation characteristics in the overstorey and understorey, thus providing a tool for wildlife–habitat management. The need to account for woodland caribou habitat needs in forest management planning can be facilitated by using readily available vegetation descriptions within the FRI and Landsat data sets. This approach to community classification is of particular benefit for characterizing wildlife habitat in remote areas where collection of detailed vegetation information is impractical. When using habitat inventories derived from different data sources, variance decomposition can aid the researcher in identifying how explanatory power is structured within different data sets, quantifying the independent and confounded components of explained variation.

Linear spectral mixture models and support vector machines for remote sensing

Mixture modeling is becoming an increasingly important tool in the remote sensing community as researchers attempt to resolve subpixel, area information. This paper compares a well-established technique, linear spectral mixture models (LSMM), with a much newer idea based on data selection, support vector machines (SVM). It is shown that the constrained least squares LSMM is equivalent to the linear SVM, which relies on proving that the LSMM algorithm possesses the maximum margin property. This in turn shows that the LSMM algorithm can be derived from the same optimality conditions as the linear SVM, which provides important insights about the role of the bias term and rank deficiency in the matrix within the LSMM algorithm. It also highlights one of the main advantages for using the linear SVM algorithm in that it performs automatic pure pixel selection from a much larger database. In addition, extensions to the basic SVM algorithm allow the technique to be applied to data sets that exhibit spectral confusion (overlapping sets of pixels) and to data sets that have nonlinear mixture regions. Several illustrative examples, based on an area-labeled Landsat dataset, are used to demonstrate the potential of this approach.

North American landscape characterization project: The production of a continental scale three‐decade Landsat data set

The North American Landscape Characterization (NALC) project is a component of the National Aeronautics and Space Administration (NASA) Landsat Pathfinder program. Pathfinder projects are focused on the investigation of global change utilizing current remote sensing technologies. The NALC project is a cooperative effort between the U. S. Environmental Protection Agency (EPA), the U.S. Geological Survey (USGS), and NASA to make Landsat data available to the widest possible user community for scientific research and general public interest. The NALC project is principally funded by the EPA Office of Research and Development and the USGS's Earth Resources Observation Systems (EROS) Data Center (EDC). The objectives of the NALC project are to produce standardized remote sensing data sets, develop standardized analysis methods, and derive standardized land cover change products for a large portion of the North American continent (the conterminous United States and Mexico) (Lunetta and Sturdevant, 1993). The standard product is the NALC “triplicate”;, consisting of co‐registered Landsat multispectral scanner data for the years 1973, 1986, and 1991 (plus or minus one year), plus co‐registered 3 arcsecond digital terrain elevation data. Processing began with the 1986 scene, which was precision corrected (with full terrain correction) to a 60 meter Universal Transverse Mercator base. Automated cross‐correlation procedures were used to co‐register the 1970's and 1990's data to the 1980's base, and independent verifications of registration quality were performed on all triplicate components. The pertinent metadata were compiled in a relational database, which includes WRS2 path/rows, scene ID's, image dates, solar azimuth and elevation, verification RMSE's, and the number of verification control points. NALC triplicate data sets are being used for a number of applications, including the analysis of urbanization patterns, dynamics of climatic fluctuations, deforestation studies, and vegetation classification and mapping. These data are being distributed through the Earth Observing System Data and Information System (EOSDIS) Information Management System (IMS) at a cost of $15(U.S.) for each triplicate.

Passive microwave algorithms for sea ice concentration: A comparison of two techniques

The most comprehensive large-scale characterization of the global sea ice cover so far has been provided by satellite passive microwave data. Accurate retrieval of ice concentrations from these data is important because of the sensitivity of surface flux (e.g., heat, salt, and water) calculations to small changes in the amount of open water (leads and polynyas) within the polar ice packs. Two algorithms that have been used for deriving ice concentrations from multichannel data are compared. One is the NASA Team algorithm and the other is the Bootstrap algorithm, both of which were developed at NASA's Goddard Space Flight Center. The two algorithms use different channel combinations, reference brightness temperatures, weather filters, and techniques. Analyses are made to evaluate the sensitivity of algorithm results to variations of emissivity and temperature with space and time. To assess the difference in the performance of the two algorithms, analyses were performed with data from both hemispheres and for all seasons. The results show only small differences in the central Arctic in winter but larger disagreements in the seasonal regions and in summer. In some areas in the Antarctic, the Bootstrap technique shows ice concentrations higher than those of the Team algorithm by as much as 25%; whereas, in other areas, it shows ice concentrations lower by as much as 30%. The differences in the results are caused by temperature effects, emissivity effects, and tie point differences. The Team and the Bootstrap results were compared with available Landsat, advanced very high resolution radiometer (AVHRR) and synthetic aperture radar (SAR) data. AVHRR, Landsat, and SAR data sets all yield higher concentrations than the passive microwave algorithms. Inconsistencies among results suggest the need for further validation studies.

The application of Landsat image data to rangeland assessment and monitoring: An example from South Australia.

The potential of Landsat image data to assess and monitor an area of semi-arid rangelands in South Australia is demonstrated. The project is a cooperative ongoing one between CSIRO and a future operational user of Landsat, the South Australian Pastoral Board, and it has as its long-term aim the development of a Land Image-Based Resource Information System (LIBRIS) for rangeland management. Three separate stages of the project are described. The fist is the integration of tenure maps with Landsat image data to permit a useful analysis and interrogation of the combined Landsat data sets. The second is the derivation of vegetation indices from Landsat data that are applicable to the semi-arid rangelands. Indices of 'cover' and of 'greenness' have been derived, but this report concen- trates on the cover index only for it can be related to soil erosion hazard/landscape stability and, as such, it represents a simple, robust vegetation-related index derived directly from Landsat. Lastly this index is used to map both cover and trend (changes in cover). Cover changes over the period 1973-1980 are detected and reported for a test region of 8300 km2. With the integration of tenure boundaries the interrogation, mapping and reporting at the individual property level is demonstrable.

Estimation of Context for Statistical Classification of Multispectral Image Data

Recent investigations have demonstrated the effectiveness of a contextual classifier that combines spatial and spectral information employing a general statistical approach [1], [2]. This statistical classification algorithm exploits the tendency of certain ground-cover classes to occur more frequently in some spatial contexts than in others. Indeed, a key input to this algorithm is a statistical characterization of the context: the context function. Here we discuss an unbiased estimator of the context function which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context-function estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real Landsat data sets are presented and contrasted with results from noncontextual classifications and from contextual classifications utilizing other context-function estimation techniques.