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  4. 2003
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  2. Journals
  3. Remote Sensing of Environment
  4. 2003
Showing papers in "Remote Sensing of Environment in 2003"
Journal Article•10.1016/S0034-4257(03)00079-8•
Thermal remote sensing of urban climates

[...]

James A. Voogt1, Timothy R. Oke2•
University of Western Ontario1, University of British Columbia2
15 Aug 2003-Remote Sensing of Environment
TL;DR: In this article, the authors review the use of thermal remote sensing in the study of urban climates, focusing primarily on the urban heat island effect and progress made towards answering the methodological questions posed by Roth et al.

2,666 citations

Journal Article•10.1016/S0034-4257(02)00135-9•
Monitoring vegetation phenology using MODIS

[...]

Xiaoyang Zhang1, Mark A. Friedl1, Crystal B. Schaaf1, Alan H. Strahler1, John C.F. Hodges1, Feng Gao1, Bradley C. Reed, Alfredo Huete2 •
Boston University1, University of Arizona2
01 Mar 2003-Remote Sensing of Environment
TL;DR: In this article, a new methodology to monitor global vegetation phenology from time series of satellite data is presented, which uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics.

2,540 citations

Journal Article•10.1016/S0034-4257(03)00184-6•
An Enhanced Contextual Fire Detection Algorithm for MODIS

[...]

Louis Giglio1, J. Descloitres, Christopher O. Justice2, Yoram J. Kaufman1•
Goddard Space Flight Center1, University of Maryland, College Park2
15 Oct 2003-Remote Sensing of Environment
TL;DR: An improved replacement detection algorithm is presented that offers increased sensitivity to smaller, cooler fires as well as a significantly lower false alarm rate.

1,876 citations

Journal Article•10.1016/S0034-4257(03)00132-9•
An assessment of the effectiveness of decision tree methods for land cover classification

[...]

Mahesh Pal1, Paul M. Mather1•
University of Nottingham1
30 Aug 2003-Remote Sensing of Environment
TL;DR: The results indicate that the performance of the univariate DT is acceptably good in comparison with that of other classifiers, except with high-dimensional data, and the use of attribute selection methods does not appear to be justified in terms of accuracy increases.

1,221 citations

Journal Article•10.1016/S0034-4257(03)00131-7•
Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression

[...]

P.M. Hansen, Jan K. Schjoerring
30 Aug 2003-Remote Sensing of Environment
TL;DR: In this paper, a linear regression analysis of hyperspectral reflectance (438 to 884 nm) data was performed on five different growth stages of winter wheat in a field experiment, including two cultivars, three plant densities, and four levels of N application.

1,143 citations

Journal Article•10.1016/S0034-4257(02)00188-8•
Remote sensing of soil salinity: potentials and constraints

[...]

Graciela Metternicht1, J.A. Zinck2•
Curtin University1, International Institute of Minnesota2
25 Apr 2003-Remote Sensing of Environment
TL;DR: In this article, the authors reviewed various sensors (e.g., aerial photographs, satellite and airborne multispectral sensors, microwave sensors, video imagery, airborne geophysics, hyperspectral sensor, and electromagnetic induction meters) and approaches used for remote identification and mapping of salt-affected areas.

1,097 citations

Journal Article•10.1016/S0034-4257(03)00075-0•
The spatiotemporal form of urban growth: measurement, analysis and modeling

[...]

Martin Herold1, Noah Goldstein1, Keith C. Clarke1•
University of California, Santa Barbara1
15 Aug 2003-Remote Sensing of Environment
TL;DR: The combined approach using remote sensing, spatial metrics and urban modeling is powerful, and may prove a productive new direction for the improved understanding, representation and modeling of the spatiotemporal forms due to the process of urbanization.

1,033 citations

Journal Article•10.1016/S0034-4257(02)00136-0•
Estimating impervious surface distribution by spectral mixture analysis

[...]

Changshan Wu1, Alan Murray1•
Ohio State University1
10 Apr 2003-Remote Sensing of Environment
TL;DR: In this article, the authors estimate the distribution of impervious surface, a major component of the vegetation-impervious surface-soil (V-I-S) model, through a fully constrained linear spectral mixture model using Landsat Enhanced Thematic Mapper Plus (ETM+) data within the metropolitan area of Columbus, OH.

889 citations

Journal Article•10.1016/S0034-4257(03)00174-3•
Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices

[...]

Lei Ji1, Albert J. Peters1•
University of Nebraska–Lincoln1
15 Sep 2003-Remote Sensing of Environment
TL;DR: The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data as mentioned in this paper, however, an analysis was conducted on time series of monthly NDVI (1989-2000) during the growing season in the north and central U.S. Great Plains.

878 citations

Journal Article•10.1016/S0034-4257(02)00127-X•
Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data

[...]

Lawrence C. Rowan1, John C. Mars1•
United States Geological Survey1
01 Mar 2003-Remote Sensing of Environment
TL;DR: In this article, an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image of the Mountain Pass, California area indicates that several important lithologic groups can be mapped in areas with good exposure by using spectral matching techniques.

814 citations

Journal Article•10.1016/S0034-4257(02)00035-4•
Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem

[...]

B. Combal1, Frédéric Baret1, Marie Weiss1, A Trubuil2, D Macé2, Agnès Pragnère1, Ranga B. Myneni3, Yuri Knyazikhin3, L.B. Wang •
Institut national de la recherche agronomique1, Matra2, Boston University3
01 Jan 2003-Remote Sensing of Environment
TL;DR: In this paper, the use of prior information to reduce the uncertainties associated to the estimation of canopy biophysical variables in the radiative transfer model inversion process was investigated, and the results showed that the prior information significantly improves the accuracy of the estimation.
Journal Article•10.1016/S0034-4257(02)00196-7•
Spectral discrimination of vegetation types in a coastal wetland

[...]

K.S. Schmidt, Andrew K. Skidmore
25 Apr 2003-Remote Sensing of Environment
TL;DR: In this paper, the authors investigated whether vegetation associations can be differentiated using hyperspectral reflectance in the visible to shortwave infrared spectral range, and how well species can be separated based on their spectra.
Journal Article•10.1016/S0034-4257(03)00039-7•
Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions

[...]

Giles M. Foody1, Doreen S. Boyd2, Mark E. J. Cutler3•
University of Southampton1, Kingston University2, University of Newcastle3
15 Jun 2003-Remote Sensing of Environment
TL;DR: In this paper, the authors investigated the transferability of predictive relations for the estimation of tropical forest biomass from Landsat TM data between sites in Brazil, Malaysia and Thailand using three types of predictive relation, based on vegetation indices, multiple regression and feed forward neural networks, were developed for biomass estimation at each site.
Journal Article•10.1016/J.RSE.2003.04.001•
Dual-season mapping of wetland inundation and vegetation for the central Amazon basin

[...]

Ll Hess1•
University of California, Santa Barbara1
15 Nov 2003-Remote Sensing of Environment
TL;DR: In this article, the authors used L-band synthetic aperture radar (SAR) imagery acquired by the Japanese Earth Resources Satellite-1 to map the central Amazon region and produce the first high-resolution wetlands map for the region.
Journal Article•10.1016/S0034-4257(02)00151-7•
Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features

[...]

Daniel A. Sims1, John A. Gamon1•
California State University1
10 Apr 2003-Remote Sensing of Environment
TL;DR: In this article, the authors measured a wide range of species to determine the influence of variable tissue morphologies and canopy structures on the relationship between water spectral reflectance and vegetation properties.
Journal Article•10.1016/S0034-4257(02)00197-9•
Water content estimation in vegetation with MODIS reflectance data and model inversion methods

[...]

Pablo J. Zarco-Tejada1, Carlos Rueda1, Susan L. Ustin1•
University of California, Davis1
25 Apr 2003-Remote Sensing of Environment
TL;DR: In this paper, the applicability of radiative transfer model inversion methods to MODIS-equivalent synthetic spectra with random input variables to test different inversion assumptions is investigated, investigating its spectral capability for water content estimation.
Journal Article•10.1016/S0034-4257(03)00007-5•
Satellite-measured growth of the urban heat island of Houston, Texas

[...]

David R. Streutker1•
Rice University1
30 May 2003-Remote Sensing of Environment
TL;DR: Growth of the surface temperature urban heat island (UHI) of Houston, TX is determined by comparing two sets of heat island measurements taken 12 years apart as discussed by the authors, which reveals a mean growth in magnitude of 0.8 K, or 35%.
Journal Article•10.1016/S0034-4257(03)00036-1•
Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship

[...]

William P. Kustas1, John M. Norman2, Martha C. Anderson2, Andrew N. French3•
Agricultural Research Service1, University of Wisconsin-Madison2, Goddard Space Flight Center3
15 Jun 2003-Remote Sensing of Environment
TL;DR: In this article, the relationship between vegetation indices and radiometric surface temperature for estimating model parameters used in computing spatially distributed fluxes and available moisture is exploited in a disaggregation procedure for estimating subpixel variation in surface temperature with aircraft imagery collected over the US Southern Great Plains.
Journal Article•10.1016/S0034-4257(03)00070-1•
Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products.

[...]

Martin J. Wooster1, Boris Zhukov, Dieter Oertel•
King's College London1
30 Jun 2003-Remote Sensing of Environment
TL;DR: In this article, an alternative method for the remote determination of the fire radiative energy (FRE) was proposed based on analysis of fire pixel radiances in the middle infrared spectral region.
Journal Article•10.1016/S0034-4257(03)00135-4•
Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE

[...]

Philip E. Dennison1, Dar A. Roberts1•
University of California, Santa Barbara1
15 Oct 2003-Remote Sensing of Environment
TL;DR: In this paper, a method of selecting endmembers from a spectral library for use in multiple endmember spectral mixture analysis (MESMA) was presented, which was used to map land cover in the Santa Ynez Mountains above Santa Barbara, CA, USA.
Journal Article•10.1016/S0034-4257(02)00173-6•
An improved strategy for regression of biophysical variables and Landsat ETM+ data.

[...]

Warren B. Cohen1, Thomas K. Maiersperger2, Stith T. Gower3, David P. Turner2•
United States Forest Service1, Oregon State University2, University of Wisconsin-Madison3
10 Apr 2003-Remote Sensing of Environment
TL;DR: In this article, the authors compared three different regression models to predict the leaf area index (LAI) for an agro-ecosystem and live tree canopy cover for a needleleaf evergreen boreal forest.
Journal Article•10.1016/S0034-4257(03)00008-7•
Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America

[...]

Tomas Brandtberg1•
West Virginia University1
30 May 2003-Remote Sensing of Environment
TL;DR: In this paper, leaf-off individual trees in a deciduous forest in the eastern USA were detected and analyzed in small footprint, high sampling density lidar data, using a SAAB TopEye laser profiling system, with a sampling density of approximately 12 returns per square meter.
Journal Article•10.1016/S0034-4257(03)00096-8•
Mapping nonnative plants using hyperspectral imagery

[...]

Emma C. Underwood1, Susan L. Ustin1, Deanne DiPietro1•
University of California, Davis1
30 Jul 2003-Remote Sensing of Environment
TL;DR: In this article, the authors evaluated three different methods of processing hyperspectral imagery: minimum noise fraction (MNF), continuum removal, and band ratio indices for mapping iceplant (Carpobrotus edulis) and jubata grass (Cortaderia jubaata) in California's coastal habitat.
Journal Article•10.1016/J.RSE.2003.07.002•
Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment

[...]

Rasmus Fensholt1, Inge Sandholt1•
University of Copenhagen1
15 Sep 2003-Remote Sensing of Environment
TL;DR: In this paper, two different configurations of a shortwave infrared water stress index (SIWSI) are derived from the MODIS near and short-wave infrared data, which are compared to in situ top layer soil moisture measurements from the semiarid Senegal 2001 and 2002, serving as an indicator of canopy water content.
Journal Article•10.1016/J.RSE.2003.04.006•
Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery

[...]

Kali E. Sawaya1, Leif G. Olmanson1, Nathan J. Heinert1, Patrick L. Brezonik1, Marvin E. Bauer1 •
University of Minnesota1
30 Nov 2003-Remote Sensing of Environment
TL;DR: In this article, the potential of high-resolution IKONOS and QuickBird satellite imagery for mapping and analysis of land and water resources at local scales in Minnesota is assessed in a series of three applications.
Journal Article•10.1016/J.RSE.2003.04.007•
Remote sensing applications for precision agriculture: A learning community approach

[...]

Santhosh K. Seelan1, Soizik Laguette1, Grant M. Casady1, G. A. Seielstad1•
University of North Dakota1
30 Nov 2003-Remote Sensing of Environment
TL;DR: Farmers and ranchers in rural areas were connected via wide-bandwidth satellite link to a central distribution center at the University of North Dakota and participated actively in evaluating the usefulness of inputs derived from remotely sensed data, sometimes even by conducting experiments on fertilizer and fungicide applications and assessing the economic benefits.
Journal Article•10.1016/S0034-4257(03)00094-4•
Retrieval of leaf area index in different vegetation types using high resolution satellite data

[...]

Roberto Colombo, Dario Bellingeri1, Dante Fasolini, Carlo M. Marino1•
University of Milan1
30 Jun 2003-Remote Sensing of Environment
TL;DR: In this article, the spectral vegetation indices (SVIs) were combined with image textural information and geostatistical parameters derived from high-resolution satellite data for mapping the leaf area index (LAI) of different vegetation types.
Journal Article•10.1016/J.RSE.2003.08.010•
Intercalibration of vegetation indices from different sensor systems

[...]

Michael D. Steven1, Tim J. Malthus2, Frédéric Baret3, Hui Xu4, Mark Chopping5 •
University of Nottingham1, University of Edinburgh2, Institut national de la recherche agronomique3, National Oceanic and Atmospheric Administration4, Agricultural Research Service5
30 Dec 2003-Remote Sensing of Environment
TL;DR: In this article, the reflected spectral radiances were convoluted with the spectral response functions of a range of satellite instruments to simulate their responses, allowing vegetation indices from one instrument to be intercalibrated against another.
Journal Article•10.1016/S0034-4257(03)00074-9•
Development of a geospatial model to quantify, describe and map urban growth

[...]

Emily Hoffhine Wilson1, James D. Hurd1, Daniel L. Civco1, Michael P. Prisloe1, Chester Arnold1 •
University of Maryland College of Agriculture and Natural Resources1
15 Aug 2003-Remote Sensing of Environment
TL;DR: In this paper, the authors developed an urban growth model, which is based on land cover derived from remotely sensed satellite imagery, determines the geographic extent, patterns, and classes of urban growth over time.
Journal Article•10.1016/J.RSE.2003.04.005•
Multi-site evaluation of IKONOS data for classification of tropical coral reef environments

[...]

Serge Andréfouët1, Philip A. Kramer2, Damaris Torres-Pulliza3, Karen E. Joyce4, Eric J. Hochberg5, Rodrigo Garza-Perez6, Peter J. Mumby7, Bernhard Riegl8, Hiroya Yamano9, William H. White10, Mayalen Zubia11, John C. Brock3, Stuart R. Phinn4, Abdulla Naseer12, Bruce G. Hatcher12, Frank E. Muller-Karger1 •
University of South Florida1, University of Miami2, United States Geological Survey3, University of Queensland4, University of Hawaii5, CINVESTAV6, University of Exeter7, Nova Southeastern University8, National Institute for Environmental Studies9, University of Newcastle10, University of French Polynesia11, Dalhousie University12
30 Nov 2003-Remote Sensing of Environment
TL;DR: In this article, IKONOS images of different coral reef sites distributed around the world were processed to assess the potential of 4-m resolution multispectral data for coral reef habitat mapping.
...

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