John L. Dwyer

The MODIS Reprojection Tool

The MODIS Reprojection Tool (MRT) is designed to help individuals work with MODIS Level-2G, Level-3, and Level-4 land data products. These products are referenced to a global tiling scheme in which each tile is approximately 10° latitude by 10° longitude and non-overlapping (Fig. 9.1). If desired, the user may reproject only selected portions of the product (spatial or parameter subsetting). The software may also be used to convert MODIS products to file formats (generic binary and GeoTIFF) that are more readily compatible with existing software packages. The MODIS land products distributed by the Land Processes Distributed Active Archive Center (LP DAAC) are in the Hierarchical Data Format - Earth Observing System (HDF-EOS), developed by the National Center for Supercomputing Applications at the University of Illinois at Urbana Champaign for the NASA EOS Program. Each HDF-EOS file is comprised of one or more science data sets (SDSs) corresponding to geophysical or biophysical parameters. Metadata are embedded in the HDF file as well as contained in a .met file that is associated with each HDF-EOS file. The MRT supports 8-bit, 16-bit, and 32-bit integer data (both signed and unsigned), as well as 32-bit float data. The data type of the output is the same as the data type of each corresponding input SDS.

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Abstract Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R -square values over 0.7 and field surveys with an accuracy of ≥ 95 % for cultivated croplands and ≥ 76 % for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

An overview of the Landsat Data Continuity Mission

The Landsat Data Continuity Mission (LDCM) is the follow-on mission to Landsat 7 and will be the eighth mission in the Landsat series. The mission is in development via an interagency partnership between the National Aeronautics and Space Administration (NASA) and the Department of Interior (DOI) / United States Geological Survey (USGS). The LDCM satellite will carry two earth-observing sensors, the Operational Land Imager (OLI) to collect image data for nine spectral bands in the reflective portion of the spectrum and the Thermal Infrared Sensor (TIRS) to collect coincident image data for two thermal spectral bands. The LDCM ground segment will control the satellite and will receive, process, archive, and distribute the science data collected by the OLI and TIRS instruments. The USGS Earth Resources Observation & Science Center (EROS) will distribute LDCM data products at no cost to requestors. The mission objective is to continues the Landsat programs collection, archive, and distribution of multispectral imagery affording global, synoptic, and repetitive coverage of the Earths land surfaces at a scale where natural and human-induced changes can be detected, differentiated, characterized, and monitored over time. The LDCM launch readiness date is currently December, 2012.

Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US

ABSTRACT Using the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NAFD project. Optimally, one high quality cloud-free image in each year or a total of 12,152 images would be used. However, to accommodate data gaps and cloud/shadow contamination 23,338 images were needed. In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.

Remotely Sensed Data Available from the US Geological Survey EROS Data Center

The Center for Earth Resources Observation Systems (EROS) is a field center of the geography discipline within the US geological survey (USGS) of the Department of the Interior. The EROS Data Center (EDC) was established in the early 1970s as the nation’s principal archive of remotely sensed data. Initially the EDC was responsible for the archive, reproduction, and distribution of black-and-white and color-infrared aerial photography acquired under numerous mapping programs conducted by various Federal agencies including the USGS, Department of Agriculture, Environmental Protection Agency, and NASA. The EDC was also designated the central archive for data acquired by the first satellite sensor designed for broad-scale earth observations in support of civilian agency needs for earth resource information. A four-band multispectral scanner (MSS) and a return-beam vidicon (RBV) camera were initially flown on the Earth Resources Technology Satellite-1, subsequently designated Landsat-1. The synoptic coverage, moderate spatial resolution, and multi-spectral view provided by these data stimulated scientists with an unprecedented perspective from which to study the Earth’s surface and to understand the relationships between human activity and natural systems.