Name: Remote Sensing and GIS in Climatological Modelling (en Inglés)
SKU: 57122848
Price: 41280.00 CLP
Availability: InStock
Author: Samanta, Sailesh
ISBN: 9781500258504

portada Remote Sensing and GIS in Climatological Modelling (en Inglés)

Remote Sensing and GIS in Climatological Modelling (en Inglés)

Formato

Libro Físico

Autor

Sailesh Samanta

Editorial

Createspace

Idioma

Inglés

N° páginas

160

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x1.1 cm

Peso

0.22 kg.

ISBN13

9781500258504

Categorías

Sistemas de información geográfica (sig) y percepción remota

Remote Sensing and GIS in Climatological Modelling (en Inglés)

Sailesh Samanta (Autor) · Createspace · Tapa Blanda

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Reseña del libro "Remote Sensing and GIS in Climatological Modelling (en Inglés)"

This book presents an introductory course of study in Remote Sensing and Geographic information systems (GIS) and its application for climatological modelling. Remote Sensing, GIS and spatial modelling are becoming powerful tools in agricultural research and natural resource management. Spatial information about climate variables is very important for a sound agricultural planning. Many disciplines use these climate variables (temperature, precipitation, atmospheric pressure, cloudiness and humidity) as a basis to understand the processes they study. Evidently the information is limited to the available meteorological stations and therefore to discrete points of the space. To understand the weather and climatic condition over a continuous space (where weather or climate station is not present) one needs to resort to the modern techniques like spatial interpolation by GIS. So far the work on climatological interpolation dovetailed to themes like 'commercial agriculture' has been scanty. This book is focused on statistical model to forecast the temperature and the precipitation using remote sensing and geographic information system (GIS) techniques. It also examines statistical approaches for interpolating climatic data over large regions, providing different interpolation techniques for climate variables for use in agricultural research.