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Accueil du site → Doctorat → Inde → 2017 → Management and Analysis of Multi source Remote Sensing Data for Modelling Multi level Crop Acreage Estimates

Nirma University (2017)

Management and Analysis of Multi source Remote Sensing Data for Modelling Multi level Crop Acreage Estimates

Dhani Ram Rajak

Titre : Management and Analysis of Multi source Remote Sensing Data for Modelling Multi level Crop Acreage Estimates

Auteur : Dhani Ram Rajak

Université de soutenance : Nirma University

Grade : Doctor of Philosophy (PhD) 2017

Résumé
The increasing global population is exerting pressure on natural resources to fulfil the increased human needs and demands. An efficient utilisation and management of the natural resources has become more important than ever to meet these enlarged demands. Crop products are one of the most important natural resources available to us. In-season assessment of crop area estimates helps planners and decision makers to efficiently manage the crop products. There has been tremendous improvement in in-season assessment of crop acreage newlineestimation, during last couple of decades. Remote Sensing (RS) is an important tool that newlineprovide near real time data which can be used for crop area estimation. RS data with different newlinespecifications (spatial, temporal, spectral resolutions, active/passive etc.) are typically used newlinefor crop area estimation at different scales. Non-availability of frequent high spatial resolution newlineoptical RS data is an issue that affects an early estimation of crop area estimation over large newlineareas. Typically, high spatial resolution (low temporal frequency) RS data is used for single newlinecrop area estimation over small regions. Similarly, frequently available (low spatial newlineresolution) optical RS data is used for multiple crop area estimation over large regions. While newlineestimating small area crop acreages, frequently available low spatial resolution data are not newlineused ; less frequently available high spatial resolution data are overlooked for large area crop newlineacreage estimation. Hence, an early estimation of small area crop acreage estimation is not newlinedone and crop acreage estimation over large area suffers due to crop field discrimination newlineambiguity. This situation may improve if we develop techniques of integrating the newlineinformation derived from high spatial resolution data as well as high temporal resolution data newlineat different scales. Currently there is no operational RS based system in India for an early newlineestimation of Rabi crop area which is an important information needed by agriculture newlinemanagement agencies at mu

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