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A semi-analytical model for multitemporal prediction of chlorophyll-a in an Iowa lake using Hyperion data / R. Sugumaran in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 12 (December 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 12 (December 2012) . - pp 1253 - 1260 Titre : A semi-analytical model for multitemporal prediction of chlorophyll-a in an Iowa lake using Hyperion data Type de document : Article/Communication Auteurs : R. Sugumaran, Auteur ; Jacques Thomas, Auteur Année de publication : 2012 Article en page(s) : pp 1253 - 1260 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] image EO1-Hyperion [Termes IGN] modèle de transfert radiatif [Termes IGN] prédiction [Termes IGN] qualité des eaux [Termes IGN] surveillance hydrologique [Termes IGN] système d'information géographique [Termes IGN] télédétection spatiale Résumé : (Auteur) The aim of this study was to use an analytical approach to monitor water quality in an Iowa lake using multitemporal Hyperion satellite imagery. Cloud-free hyperspectral images were acquired from the Hyperion sensor on individual days in June, July, and August of 2006. Water samples with accurate locations using GPS were collected simultaneously with image acquisition. Water samples were analyzed for various water quality constituents. Chlorophyll-a (chl), was estimated for each sampling date using a bio-optical model with Specific Inherent Optical Properties (siops) of the lake and light field variables derived from a radiative transfer numerical model. The model was then applied to the Hyperion images to create spatially continuous CHL maps for the study area every month. These results were compared with traditional linear regression model outputs. Maps produced using the bio-optical model effectively demonstrated spatial and temporal variability of CHL for the lake. The CHL concentration from this model across the lake ranged from 28 to 121ug/L for the month of June, 18 to 111ug/L for July, and 31 to 125 ug/L for August. The validation and accuracy assessment for the bio-optical model with in-situ data showed R2 values of 0.90978, 0.96794 and 0.93057 for June, July, and August, respectively, and Nash-Sutcliffe coefficient values of 0.499478, 0.733072, and 0.878757 for June, July, and August, respectively. Numéro de notice : A2012-644 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.78.11.1253 En ligne : https://doi.org/10.14358/PERS.78.11.1253 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32090 [article]

Monitoring and modelling cropland loss in rapidly growing urban and depopulating rural counties using remotely sensed data and GIS / A.N. Petrov in Geocarto international, vol 20 n° 4 (December 2005 - February 2006)  

Public [article]  inGeocarto international > vol 20 n° 4 (December 2005 - February 2006) . - pp 45 - 52 Titre : Monitoring and modelling cropland loss in rapidly growing urban and depopulating rural counties using remotely sensed data and GIS Type de document : Article/Communication Auteurs : A.N. Petrov, Auteur ; R. Sugumaran, Auteur Année de publication : 2005 Article en page(s) : pp 45 - 52 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] automate cellulaire [Termes IGN] croissance urbaine [Termes IGN] développement durable [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-TM [Termes IGN] Iowa (Etats-Unis) [Termes IGN] modèle de Markov [Termes IGN] modélisation spatiale [Termes IGN] planification urbaine [Termes IGN] prédiction [Termes IGN] simulation dynamique [Termes IGN] surface cultivée [Termes IGN] surveillance agricole [Termes IGN] urbanisation [Termes IGN] utilisation du sol Résumé : (Auteur) Iowa is the leading agricultural state in the USA. Increasing suburban and rural development as well as industrial and commercial growth threaten Iowa's unique farmlands and cause their rapid conversion into other land uses. Thus, detecting spatial patterns of cropland loss and predicting future loss are important issues for better agricultural planning and management. This study provides a unique approach for monitoring and modeling cropland loss in Iowa, focusing on differences between rapidly growing urban counties and rural depopulating counties. The monitoring of farmland loss is based on classified 1984, 1992, and 2000 two-season Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) satellite imagery. The modeling approach used joint cellular automata - Markov simulation procedures to predict cropland loss for 2008 and 2016. Mapping of cropland dynamics, using remotely sensed data in urban and rural counties between 1984 and 2000, revealed that there was a significant decline of croplands in Iowa. The simulation of land cover changes for 2008 and 2016 showed continuing decline of croplands in both urban and rural counties. The results of the study can be used by local planners and managers for the development and application of sustainable agriculture practices in Iowa. Numéro de notice : A2005-551 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040508542363 Date de publication en ligne : 02/01/2008 En ligne : https://doi.org/10.1080/10106040508542363 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27687 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-05041	RAB	Revue	Centre de documentation	En réserve L003	Disponible