Sustainable Development: Evaluating Optimal Technique for Spatial Data Forecast

Proceedings

Gupta, Rajiv¹  Kumar, Gaurav² 

[1] Author to whom correspondence should be addressed.;Civil Engineering Department, BITS, Pilani 333031, India

关键词: time series extrapolation;    GIS;    raster forecasting;    NDVI;    change detection;   

DOI  :  10.3390/proceedings2221371

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

This paper is an approach to forecast the spatial data in time series domain. Normally in GIS (Geographical Information System), we need raster forecasting. Moving average, exponential smoothing, and linear regression methods of forecasting are used over one-dimensional data. Present work concentrates on using these methods on satellite images applying them from pixel to pixel of historical temporal satellite data. An example set of satellite images from years 2011 to 2015 has been used to forecast the image in the year 2016. GIS tools have been developed in ArcGIS 10.1 using python to implement the methods of forecasting. Forecasted and actual images of the year 2016 have been compared by calculating the Normalized Difference Vegetation Indices (NDVI) and change detection to identify the best method.

【 授权许可】

CC BY