Engineering topics

Prentiss, Dylan

Publications and source records attributed to Prentiss, Dylan.

3 records

NASA NTRS2001

AVIRIS Reflectance Retrievals: UCSB Users Manual

The following write-up is designed to help students and researchers take Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) radiance data and retrieve surface reflectance. In the event that the software is not available, but a user has access to a reflectance product, this document is designed to provide a better understanding of how AVIRIS reflectance was retrieved. This guide assumes that the reader has both a basic understanding of the UNIX computing environment, and that of spectroscopy. Knowledge of the Interactive Data Language (IDL) and the Environment for Visualizing Images (ENVI) is helpful. This is a working document, and many of the fine details described in the following pages have been previously undocumented. After having read this document the reader should be able to process AVIRIS to reflectance, provided access to all of the code is possible. The AVIRIS radiance data itself is pre-processed at the Jet Propulsion Laboratory (JPL) in Pasadena, California. The first section of this paper describes how to read data from tape and byte-swap the data. Section 2 describes the procedure in preparing support files before running the 'h2o' suite of programs. Section 3 describes the four programs used in the process, h2olut9.f, h2ospl9.f, vlsfit9.f and rfl9.f.

Roberts, Dar A.↗

NASA NTRS2001

AVIRIS Land-Surface Mapping in Support of the Boreal Ecosystem-Atmosphere Study (BOREAS)

A key scientific objective of the original Boreal Ecosystem-Atmospheric Study (BOREAS) field campaign (1993-1996) was to obtain the baseline data required for modeling and predicting fluxes of energy, mass, and trace gases in the boreal forest biome. These data sets are necessary to determine the sensitivity of the boreal forest biome to potential climatic changes and potential biophysical feedbacks on climate. A considerable volume of remotely-sensed and supporting field data were acquired by numerous researchers to meet this objective. By design, remote sensing and modeling were considered critical components for scaling efforts, extending point measurements from flux towers and field sites over larger spatial and longer temporal scales. A major focus of the BOREAS follow-on program is concerned with integrating the diverse remotely sensed and ground-based data sets to address specific questions such as carbon dynamics at local to regional scales. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has the potential of contributing to BOREAS through: (1) accurate retrieved apparent surface reflectance; (2) improved landcover classification; and (3) direct assessment of biochemical/biophysical information such as canopy liquid water and chlorophyll concentration through pigment fits. In this paper, we present initial products for major flux tower sites including: (1) surface reflectance of dominant cover types; (2) a land-cover classification developed using spectral mixture analysis (SMA) and Multiple Endmember Spectral Mixture Analysis (MESMA); and (3) liquid water maps. Our goal is to compare these land-cover maps to existing maps and to incorporate AVIRIS image products into models of photosynthetic flux.

Roberts, Dar A.↗

NASA NTRS2001

Multi-Sensor Characterization of the Boreal Forest: Initial Findings

Results are presented in an initial apriori knowledge approach toward using complementary multi-sensor multi-temporal imagery in characterizing vegetated landscapes over a site in the Boreal Ecosystem-Atmosphere Study (BOREAS). Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data were segmented using multiple endmember spectral mixture analysis and binary decision tree approaches. Individual date/sensor land cover maps had overall accuracies between 55.0% - 69.8%. The best eight land cover layers from all dates and sensors correctly characterized 79.3% of the cover types. An overlay approach was used to create a final land cover map. An overall accuracy of 71.3% was achieved in this multi-sensor approach, a 1.5% improvement over our most accurate single scene technique, but 8% less than the original input. Black spruce was evaluated to be particularly undermapped in the final map possibly because it was also contained within jack pine and muskeg land coverages.

Reith, Ernest↗