The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
D A May - One of the best experts on this subject based on the ideXlab platform.
-
the development and Operational Application of nonlinear algorithms for the measurement of sea surface temperatures with the noaa polar orbiting environmental satellites
Journal of Geophysical Research, 1998Co-Authors: C C Walton, William G Pichel, John Sapper, D A MayAbstract:Since 1990, the NOAA National Environmental Satellite Data and Information Service (NESDIS) has provided satellite-derived sea surface temperature (SST) measurements based on nonlinear SST algorithms, using advanced very high resolution radiometer (AVHRR) multiple-infrared window channel data. This paper develops linear and nonlinear SST algorithms from the radiative transfer equation. It is shown that the nonlinear algorithms are more accurate than linear algorithms but that the functional dependence of the nonlinearity is data dependent. This theoretical discussion (sections 2–4) is followed with a discussion in section 5 of the accuracy over a 9-year period of the satellite-derived SST measurements provided by NOAA NESDIS when compared with coincident drifting buoys. Between 1989 and 1998 the global scatter of the daytime satellite SST against drifting buoy measurements has decreased from ∼0.8° to 0.5°C, while the nighttime scatter has remained fairly constant at 0.5°C. An exception to these accuracy measurements occurred after the eruption of Mount Pinatubo in June 1991.
Danie Du Plessis - One of the best experts on this subject based on the ideXlab platform.
-
A critical evaluation of the Operational Application of various settlement typologies in South Africa : research article
Stads- en Streeksbeplanning, 2013Co-Authors: Danie Du PlessisAbstract:This article critically evaluates the definition and Operational Application of various settlement typologies across selected government departments for the purposes of the planning, implementation and monitoring of development programmes. Both quantitative and qualitative research methods are applied and informant and group interviews are conducted with 21 different government departments or entities. Nine different typologies are identified and compared on the basis of the requirements highlighted during the group interviews and international best practice. Discussions with the various interest groups highlight the need for a functional typology that consists of a number of categories or classes that can be combined as needed and not be restricted to a simple urban-rural dichotomy. A more dynamic and accessible linkage between the spatial units of analysis of the various typologies is also required. It is found that the South African City Network/Council for Scientific and Industrial Research (CSIR) settlement typology meets most of the criteria set by the literature as well as the groups interviewed. In view of the widespread use of the Statistics South Africa (Stats SA) data and typologies, it is recommended that the South African City Network/CSIR typologies should dovetail as much as possible with the 2011-census data and classification system. 'n Kritiese evaluering van die operasionele toepassing van verskillende nedersettingstipologiee in Suid-Afrika Hierdie artikel evalueer die definisie en toepassings van verskeie nedersettingstipologiee wat deur verskillende regeringsdepartemente en ander openbare instellings gebruik word vir die doeleindes van beplanning, implementering, en monitering van ontwikkelingsprogramme. Beide kwantitatiewe en kwalitatiewe navorsingsmetodes word gebruik en groeps-onderhoude word onderneem met 21 verskillende regeringsdepartemente en entiteite. Nege verskillende klassifikasiesisteme word vergelyk, gebaseer op die vereistes soos geidentifiseer tydens die groepsonderhoude en die oorsig van internasionale beste praktyk. Die behoefte aan 'n funksionele tipologie wat voorsiening maak vir 'n aantal klasse wat saamgevoeg kan word soos nodig en nie net beperk is tot 'n landelik-stedelike onderskeid nie word beklemtoon gedurende die groepsonderhoude. Die resultate van die navorsing toon ook 'n duidelike behoefte aan 'n meer dinamiese en toeganklike skakel tussen die ruimtelike eenhede van analise in die verskillende klassifikasiesisteme. Die navorsing bevind dat die tipologie soos ontwikkel deur die Suid-Afrikaanse Stadnetwerk en die Wetenskaplike en Nywerheids Navorsingsraad (WNNR) aan meeste van die kriteria wat uit die literatuur oorsig en die groepsonderhoude geidentifiseer is, voldoen. In lig van die wye gebruik van die Statistiek Suid-Afrika-data en -klassifikasie is dit noodsaaklik dat die WNNR-klassifikasie en die 2011-Sensusdata en tipologie optimaal geintegreer word. Chebisiso e tebileng ea malulo a fapanaeng ka hara naha ea Afrika Borwa Serapa sena se shebisisa polelo le tshebediso ea malulo a fapaneng hara mafapha a fapaneng a mmuso ka mabaka a ho rera, ho etsa le ho shebella hoa manane a tswelopele. Mekhoa e mebedi ea ho phethahatsa patlisiso e leng oa ho batla taba tsa dinomoro (quantitative) le ho batla taba tsa maikutlo (qualitative) e sebedisotsoe serapeng sena. Tlhatlhobo ea sehlopha sa difapha tse 21 tsa mmuso di ile tsa phethahala. Mekhoa e robong e ile ea fumanoa ea ba ea bapisoa le ditlhoko tse ileng tsa buoa mahareng a tlhatlhobo tsa dihlopha. Dipuo le dihlopha tse amahanang le ditaba tsena di bontshitse ho hlokahala e se ke ea ba le ho felloa ke sebaka sa hore di chenchoe. Kamahano ea dibaka le mefuta ea bolulo e hloka ho ba teng le eona. Ho fumanehile hore South African City Network kapa Council for Scientific and Industrial Research e leng (CSIR), ke eona e shebehallang e le eona e khonang ho etsa tsena tsohle tse sehlopha se hlahlobiloeng se buileng ka tsona. Ka baka la sena, ho bonahala hore palo ea sechaba ea 2011 e kenyelletsoe e be e sebelletsane le CSIR ho phethahatsa mosebetsi oa ho akaralletsa dibaka.
-
A critical evaluation of the Operational Application of various settlement typologies in South Africa
2013Co-Authors: Isabel Schmidt, Danie Du PlessisAbstract:This article critically evaluates the definition and Operational Application of various settlement typologies across selected government departments for the purposes of the planning, implementation and monitoring of development programmes. Both quantitative and qualitative research methods are applied and informant and group interviews are conducted with 21 different government departments or entities. Nine different typologies are identified and compared on the basis of the requirements highlighted during the group interviews and international best practice. Discussions with the various interest groups highlight the need for a functional typology that consists of a number of categories or classes that can be combined as needed and not be restricted to a simple urban-rural dichotomy. A more dynamic and accessible linkage between the spatial units of analysis of the various typologies is also required. It is found that the South African City Network/ Council for Scientific and Industrial Research (CSIR) settlement typology meets most of the criteria set by the literature as well as the groups interviewed. In view of the widespread use of the Statistics South Africa (Stats SA) data and typologies, it is recommended that the South African City Network/CSIR typologies should dovetail as much as possible with the 2011-census data and classification system.
Danny Marks - One of the best experts on this subject based on the ideXlab platform.
-
the airborne snow observatory fusion of scanning lidar imaging spectrometer and physically based modeling for mapping snow water equivalent and snow albedo
Remote Sensing of Environment, 2016Co-Authors: Thomas H Painter, Daniel F Berisford, J Boardman, Kathryn J Bormann, Jeffrey S Deems, Frank Gehrke, Andrew Hedrick, Michael J Joyce, Ross Laidlaw, Danny MarksAbstract:Abstract Snow cover and its melt dominate regional climate and water resources in many of the world's mountainous regions. Snowmelt timing and magnitude in mountains are controlled predominantly by absorption of solar radiation and the distribution of snow water equivalent (SWE), and yet both of these are very poorly known even in the best-instrumented mountain regions of the globe. Here we describe and present results from the Airborne Snow Observatory (ASO), a coupled imaging spectrometer and scanning lidar, combined with distributed snow modeling, developed for the measurement of snow spectral albedo/broadband albedo and snow depth/SWE. Snow density is simulated over the domain to convert snow depth to SWE. The result presented in this paper is the first Operational Application of remotely sensed snow albedo and depth/SWE to quantify the volume of water stored in the seasonal snow cover. The weekly values of SWE volume provided by the ASO program represent a critical increase in the information available to hydrologic scientists and resource managers in mountain regions.
C. Reynolds - One of the best experts on this subject based on the ideXlab platform.
-
Evaluating the Operational Application of SMAP for Global Agricultural Drought Monitoring
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019Co-Authors: Iliana E. Mladenova, John D. Bolten, Wade T. Crow, Nazmus Sazib, Michael H. Cosh, Compton J. Tucker, C. ReynoldsAbstract:Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regional agricultural drought monitoring is one of the most logical Application areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimeters of the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entire root zone. The assimilation of remotely sensed soil moisture products into continuous soil water balance models provides a way of addressing this shortcoming. Here, we describe the assimilation of NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impact of SMAP on USDA FAS drought monitoring capabilities. The assimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for random errors inherent in its rainfall forcing data. The performance of this SMAP-based assimilation system is evaluated using two approaches. At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moisture and the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ- based soil moisture estimates is carried out at seven of the SMAP core Cal/Val sites located in the USA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance Operational USDA FAS root-zone soil moisture information.
Giles M Foody - One of the best experts on this subject based on the ideXlab platform.
-
crop classification by support vector machine with intelligently selected training data for an Operational Application
Journal of remote sensing, 2008Co-Authors: Ajay Mathur, Giles M FoodyAbstract:The accuracy of a supervised classification is dependent to a large extent on the training data used. The aim in training is often to capture a large training set to fully describe the classes spectrally, commonly with the requirements of a conventional statistical classifier in mind. However, it is not always necessary to provide a complete description of the classes, especially if using a support vector machine (SVM) as the classifier. An SVM seeks to fit an optimal hyperplane between the classes and uses only some of the training samples that lie at the edge of the class distributions in feature space (support vectors). This should allow the definition of the most informative training samples prior to the analysis. An approach to identify informative training samples was demonstrated for the classification of agricultural classes in south-western part of Punjab state, India. A small, intelligently selected, training dataset was acquired in the field with the aid of ancillary information. This dataset contained the data from training sites that were predicted before the classification to be amongst the most informative for an SVM classification. The intelligent training collection scheme yielded a classification of comparable accuracy, ∼91%, to one derived using a larger training set acquired by a conventional approach. Moreover, from inspection of the training sets it was apparent that the intelligently defined training set contained a greater proportion of support vectors (0.70), useful training sites, than that acquired by the conventional approach (0.41). By focusing on the most informative training samples, the intelligent scheme required less investment in training than the conventional approach and its adoption would have reduced the total financial outlay in classification production and evaluation by ∼26%. Additionally, the analysis highlighted the possibility to further reduce the training set size without any significant negative impact on classification accuracy.
-
Crop classification by support vector machine with intelligently selected training data for an Operational Application
International Journal of Remote Sensing, 2008Co-Authors: Ajay Mathur, Giles M FoodyAbstract:The accuracy of supervised classification is dependent to a large extent on the training data used. The aim is often to capture a large training set to fully describe the classes spectrally, commonly with the requirements of a conventional statistical classifier in-mind. However, it is not always necessary to provide a complete description of the classes, especially if using a support vector machine (SVM) as the classifier. A SVM seeks to fit an optimal hyperplane between the classes and uses only some of the training samples that lie at the edge of the class distributions in feature space (support vectors). This should allow the definition of the most informative training samples prior to the analysis. An approach to identify informative training samples was demonstrated for the classification of agricultural classes in south-western part of Punjab state, India. A small, intelligently selected, training data set was acquired in the field with the aid of ancillary information. This data set contained the data from training sites that were predicted before the classification to be amongst the most informative for a SVM classification. The intelligent training collection scheme yielded a classification of comparable accuracy, ~91%, to one derived using a larger training set acquired by a conventional approach. Moreover, from inspection of the training sets it was apparent that the intelligently defined training set contained a greater proportion of support vectors (0.70), useful training sites, than that acquired by the conventional approach (0.41). By focusing on the most informative training samples, the intelligent scheme required less investment in training than the conventional approach and its adoption would have reduced total financial outlay in classification production and evaluation by ~26%. Additionally, the analysis highlighted the possibility to further reduce the training set size without any significant negative impact on classification accuracy
