23,187 research outputs found
REMOTE SENSING OF FOLIAR NITROGEN IN CULTIVATED GRASSLANDS OF HUMAN DOMINATED LANDSCAPES
Foliar nitrogen (N) concentration of plant canopies plays a central role in a number of important ecosystem processes and continues to be an active subject in the field of remote sensing. Previous efforts to estimate foliar N at the landscape scale have primarily focused on intact forests and grasslands using aircraft imaging spectrometry and various techniques of statistical calibration and modeling. The present study was designed to extend this work by examining the potential to estimate the foliar N concentration of residential, agricultural and other cultivated grassland areas within a suburbanizing watershed. In conjunction with ground-based vegetation sampling, we developed Partial Least Squares (PLS) models for predicting mass-based foliar N across management types using input from airborne and field based imaging spectrometers. Results yielded strong predictive relationships for both ground- and aircraft-based sensors across sites that included turf grass, grazed pasture, hayfields and fallow fields. We also report on relationships between imaging spectrometer data and other important variables such as canopy height, biomass, and water content, results from which show strong promise for detection with high quality imaging spectrometry data and suggest that cultivated grassland offer opportunity for empirical study of canopy light dynamics. Finally, we discuss the potential for application of our results, and potential challenges, with data from the planned HyspIRI satellite, which will provide global coverage of data useful for vegetation N estimation
Exploration of a Polarized Surface Bidirectional Reflectance Model Using the Ground-Based Multiangle Spectropolarimetric Imager
Accurate characterization of surface reflection is essential for retrieval of aerosols using downward-looking remote sensors. In this paper, observations from the Ground-based Multiangle SpectroPolarimetric Imager (GroundMSPI) are used to evaluate a surface polarized bidirectional reflectance distribution function (PBRDF) model. GroundMSPI is an eight-band spectropolarimetric camera mounted on a rotating gimbal to acquire pushbroom imagery of outdoor landscapes. The camera uses a very accurate photoelastic-modulator-based polarimetric imaging technique to acquire Stokes vector measurements in three of the instrument's bands (470, 660, and 865 nm). A description of the instrument is presented, and observations of selected targets within a scene acquired on 6 January 2010 are analyzed. Data collected during the course of the day as the Sun moved across the sky provided a range of illumination geometries that facilitated evaluation of the surface model, which is comprised of a volumetric reflection term represented by the modified Rahman-Pinty-Verstraete function plus a specular reflection term generated by a randomly oriented array of Fresnel-reflecting microfacets. While the model is fairly successful in predicting the polarized reflection from two grass targets in the scene, it does a poorer job for two manmade targets (a parking lot and a truck roof), possibly due to their greater degree of geometric organization. Several empirical adjustments to the model are explored and lead to improved fits to the data. For all targets, the data support the notion of spectral invariance in the angular shape of the unpolarized and polarized surface reflection. As noted by others, this behavior provides valuable constraints on the aerosol retrieval problem, and highlights the importance of multiangle observations.NASAJPLCenter for Space Researc
Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping
The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUTNâ
â) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven- and eight-band combinations, the LUTNâ
â approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUTN50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications
Multivariate classification of prunus dulcis varieties using leaves of nursery plants and near infrared spectroscopy
The emergence of new almond tree (Prunus dulcis) varieties with agricultural interest is forcing the nursery plant industry to establish quality systems to keep varietal purity in the production stage. The aim of this study is to assess the capability of near-infrared spectroscopy (NIRS) to classify different Prunus dulcis varieties as an alternative to more expensive methods. Fresh and dried-powdered leaves of six different varieties of almond trees of commercial interest (Avijor, Guara, Isabelona, Marta, Pentacebas and Soleta) were used.Peer ReviewedPostprint (published version
Digital Color Imaging
This paper surveys current technology and research in the area of digital
color imaging. In order to establish the background and lay down terminology,
fundamental concepts of color perception and measurement are first presented
us-ing vector-space notation and terminology. Present-day color recording and
reproduction systems are reviewed along with the common mathematical models
used for representing these devices. Algorithms for processing color images for
display and communication are surveyed, and a forecast of research trends is
attempted. An extensive bibliography is provided
An intuitive control space for material appearance
Many different techniques for measuring material appearance have been
proposed in the last few years. These have produced large public datasets,
which have been used for accurate, data-driven appearance modeling. However,
although these datasets have allowed us to reach an unprecedented level of
realism in visual appearance, editing the captured data remains a challenge. In
this paper, we present an intuitive control space for predictable editing of
captured BRDF data, which allows for artistic creation of plausible novel
material appearances, bypassing the difficulty of acquiring novel samples. We
first synthesize novel materials, extending the existing MERL dataset up to 400
mathematically valid BRDFs. We then design a large-scale experiment, gathering
56,000 subjective ratings on the high-level perceptual attributes that best
describe our extended dataset of materials. Using these ratings, we build and
train networks of radial basis functions to act as functionals mapping the
perceptual attributes to an underlying PCA-based representation of BRDFs. We
show that our functionals are excellent predictors of the perceived attributes
of appearance. Our control space enables many applications, including intuitive
material editing of a wide range of visual properties, guidance for gamut
mapping, analysis of the correlation between perceptual attributes, or novel
appearance similarity metrics. Moreover, our methodology can be used to derive
functionals applicable to classic analytic BRDF representations. We release our
code and dataset publicly, in order to support and encourage further research
in this direction
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