Seasonal variation in spectral signatures of five genera of rainforest trees

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) 01/2012;
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Available from: Monica Papes, Mar 06, 2015
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    ABSTRACT: The Earth Observing One (EO-1) satellite was launched in November 2000 as a technology demonstration mission with an estimated 1-year lifespan. It has now successfully completed 12 years of high spatial resolution imaging operations from low Earth orbit. EO-1’s two main instruments, Hyperion and the Advanced Land Imager (ALI), have both served as prototypes for new generation satellite missions. ALI, an innovative multispectral instrument, is the forerunner of the Operational Land Imager (OLI) onboard the Landsat Data Continuity Mission’s (LDCM) Landsat-8 satellite, recently launched in Feb. 2013. Hyperion, a hyperspectral instrument, serves as the heritage orbital spectrometer for future global platforms, including the proposed NASA Hyperspectral Infrared Imager (HyspIRI) and the forthcoming (in 2017) German satellite, EnMAP. This JSTARS Special Issue is dedicated to EO-1. This paper serves as an introduction to the Hyperion and ALI instruments, their capabilities, and the important contributions this mission has made to the science and technology communities. This paper also provides an overview of the EO-1 mission, including the several operational phases which have characterized its lifetime. It also briefly describes calibration and validation activities, and gives an overview of the spin-off technologies, including disaster monitoring and new Web-based tools which can be adapted for use in future missions.
    Full-text · Article · Apr 2013 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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    ABSTRACT: Plant species identification and mapping based on remotely-sensed spectral signatures is a challenging task with the potential to contribute enormously to ecological studies. Success in this task rests upon the appropriate collection and use of costly field-based training data, and researchers are in need of ways to improve collection efficiency based on quantitative evidence. Using imaging spectrometer data collected by the Carnegie Airborne Observatory for hundreds of field-identified tree crowns in Kruger National Park, South Africa, we developed woody plant species classification models and evaluated how classification accuracy increases with increasing numbers of training crowns. First, we show that classification accuracy must be estimated while respecting the crown as the basic unit of data; otherwise, accuracy will be overestimated and the amount of training data needed to perform successful classification will be underestimated. We found that classification accuracy and the number of training crowns needed to perform successful classification varied depending on the number and spectral separability of species in the model. We also used a modified Michaelis-Menten function to describe the empirical relationship between training crowns and model accuracy, and show how this function may be useful for predicting accuracy. This framework can assist researchers in designing field campaigns to maximize the efficiency of field data collection, and thus the amount of biodiversity information gained from remote species identification models.
    Full-text · Article · Feb 2014 · Remote Sensing

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