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Jeffrey T. Morisette, Louis Giglio, Ivan Csiszar, Alberto Setzer, Wilfrid Schroeder, Douglas Morton, and Christopher O. Justice

(CEOS) global validation activities. These international entities have helped define the role of regional partners in validating global fire products (see information online at http://gofc-fire.umd.edu ). Integration with GOFC/GOLD and CEOS maximizes the applicability of this research beyond Brazil to the international effort to better understand global fire product accuracy. The primary goal of this paper is to evaluate the characteristics of two fire detection algorithms, both of which are

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M. C. Hansen, R. S. DeFries, J. R. G. Townshend, M. Carroll, C. Dimiceli, and R. A. Sohlberg

tree cover map. The approach is an empirical, multiresolution calibration method that uses a regression tree algorithm to estimate the percent tree canopy cover ( Hansen et al., 2002 ) The regression tree is a nonlinear, flexible model appropriate for handling the variability present in global vegetation phenology. It also allows for the calibration of the model along the entire continuum of tree cover, avoiding the problems of using only endmembers for calibration. 2. Data This initial attempt

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T. F. Pinheiro, M. I. S. Escada, D. M. Valeriano, P. Hostert, F. Gollnow, and H. Müller

representative cells for each previous user-defined typology. In the classification step, this training set was used to run a decision tree classifier based on the C4.5 algorithm ( Quinlan 1993 ). We used 113 samples of forest degradation patterns to train the decision tree classifier [FOREST = 32; selective logging, high forest degradation (HFD1) = 7; conventional logging, high forest degradation (HFD2) = 13; selective logging, low forest degradation (LFD1) = 21; conventional logging, low forest degradation

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Xiaosong Li and Jin Zhang

keyhole markup language (KML) files and displayed in Google Earth, and the high-resolution images corresponding to each validation pixel were saved with a nominal spatial resolution of 2.5 m in Google Earth Pro. Also, the overpass time of the images was recorded. First, the land-cover type of each image was determined through analyzing the high spatial resolution image carefully. Then, the images were applied to a segmentation algorithm in ENVI 5 (from Exelis Visual Information Solutions, Inc.) to

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W. L. Ellenburg, R. T. McNider, J. F. Cruise, and John R. Christy

model [Decision Support System for Agro-technology Transfer (DSSAT); Jones et al. 2003 ; Hoogenboom et al. 2015 ] was also employed to estimate the latent energy fluxes from agricultural fields. DSSAT is a framework for biophysical modeling and includes a suite of more than 28 different crop models. It simulates crop growth and yield in response to management, climate, and soil conditions. The DSSAT evaporation algorithms have been validated and proven to be good predictors of evapotranspiration

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Bharat Rastogi, A. Park Williams, Douglas T. Fischer, Sam F. Iacobellis, Kathryn McEachern, Leila Carvalho, Charles Jones, Sara A. Baguskas, and Christopher J. Still

describe the downscaling methodology and how we calculated the fraction of time below the cloud top and above the cloud bottom. A schematic of the methodological flow is provided in Figure 3 . Figure 3. Algorithm for calculating monthly CCF and fog inundation. 2.2.3.1. Downscaled frequency of cloud cover The maps of CCF indicate the fraction of time when the land or ocean surface is below a cloud top. At the relatively coarse 1-km resolution of the GOES imagery, however, much topographic detail

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Pedro Sequera, Jorge E. González, Kyle McDonald, Steve LaDochy, and Daniel Comarazamy

-cover classification schemes assign a unique set of surface properties to the urban land class. The aim of this work is therefore to improve the land-cover classification scheme in Southern California using updated high-resolution airborne remote sensing data from the recent HyspIRI Mission Preparatory Flight campaigns and to assess the suitability of the updated regional atmospheric modeling system to represent T max and sea breeze. The new urban land classes are here derived through a classification algorithm

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Jason A. Hubbart and Chris Zell

analyses ( Barnes 1939 ), isotopic analyses ( Ellins et al. 1990 ; Genereux and Hooper 1998 ; Tetzlaff and Soulsby 2008 ), geochemical or in situ chemical signatures ( Newbury et al. 1969 ; Wels et al. 1991 ; Kish et al. 2010 ), various analytical methods ( Brutsaert and Nieber 1977 ; Birtles 1978 ), and automated algorithms applied to streamflow time series ( Nathan and McMahon 1990 ; Chapman 1999 ; Eckhardt 2008 ). Automated methods of baseflow computation were recently applied by Meyer

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Madhavi Jain, A. P. Dimri, and D. Niyogi

Abstract

Recent decades have witnessed rapid urbanization and urban population growth resulting in urban sprawl of cities. This paper analyzes the spatiotemporal dynamics of the urbanization process (using remote sensing and spatial metrics) that has occurred in Delhi, the capital city of India, which is divided into nine districts. The urban patterns and processes within the nine administrative districts of the city based on raw satellite data have been taken into consideration. Area, population, patch, edge, and shape metrics along with Pearson’s chi statistics and Shannon’s entropy have been calculated. Three types of urban patterns exist in the city: 1) highly sprawled districts, namely, West, North, North East, and East; 2) medium sprawled districts, namely, North West, South, and South West; and 3) least sprawled districts—Central and New Delhi. Relative entropy, which scales Shannon’s entropy values from 0 to 1, is calculated for the districts and time spans. Its values are 0.80, 0.92, and 0.50 from 1977 to 1993, 1993 to 2006, and 2006 to 2014, respectively, indicating a high degree of urban sprawl. Parametric and nonparametric correlation tests suggest the existence of associations between built-up density and population density, area-weighted mean patch fractal dimension (AWMPFD) and area-weighted mean shape index (AWMSI), compactness index and edge density, normalized compactness index and number of patches, and AWMPFD and built-up density.

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Robert Paul d'Entremont and Gary B. Gustafson

Meteorological Satellite Program (DMSP) satellites ( Hamill et al., 1992 ). In contrast, SERCAA cloud algorithms analyze DMSP data along with five-channel data from the National Oceanic and Atmospheric Administration's (NOAA) Television Infrared Observation Satellite (TIROS), five-channel data from Geostationary Operational Environmental Satellites (GOES), and three-channel data from the Japanese Geostationary Meteorological Satellite (GMS) and European Meteosat geostationary satellites ( Isaacs et al., 1994

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