Sustainable Development: Evaluating Optimal Technique for Spatial Data Forecast

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 concentrat...
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Autor*in:	Gaurav Kumar [verfasserIn] Rajiv Gupta [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	time series extrapolation GIS raster forecasting NDVI change detection

Übergeordnetes Werk:	In: Proceedings - MDPI AG, 2018, 2(2018), 22, p 1371
Übergeordnetes Werk:	volume:2 ; year:2018 ; number:22, p 1371

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DOI / URN:	10.3390/proceedings2221371

Katalog-ID:	DOAJ005987903

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spelling	10.3390/proceedings2221371 doi (DE-627)DOAJ005987903 (DE-599)DOAJ203ca679e70c4b44ac56e8247a09c173 DE-627 ger DE-627 rakwb eng Gaurav Kumar verfasserin aut Sustainable Development: Evaluating Optimal Technique for Spatial Data Forecast 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. time series extrapolation GIS raster forecasting NDVI change detection General Works A Rajiv Gupta verfasserin aut In Proceedings MDPI AG, 2018 2(2018), 22, p 1371 (DE-627)896671828 (DE-600)2904077-2 25043900 nnns volume:2 year:2018 number:22, p 1371 https://doi.org/10.3390/proceedings2221371 kostenfrei https://doaj.org/article/203ca679e70c4b44ac56e8247a09c173 kostenfrei https://www.mdpi.com/2504-3900/2/22/1371 kostenfrei https://doaj.org/toc/2504-3900 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2018 22, p 1371
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author	Gaurav Kumar
spellingShingle	Gaurav Kumar misc time series extrapolation misc GIS misc raster forecasting misc NDVI misc change detection misc General Works misc A Sustainable Development: Evaluating Optimal Technique for Spatial Data Forecast
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title	Sustainable Development: Evaluating Optimal Technique for Spatial Data Forecast
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title_auth	Sustainable Development: Evaluating Optimal Technique for Spatial Data Forecast
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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