Spatiotemporal Interpolation for Environmental Modelling
A variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting...
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doaj-88599b04e05c4728a133802a08148f392020-11-25T02:27:31ZengMDPI AGSensors1424-82202016-08-01168124510.3390/s16081245s16081245Spatiotemporal Interpolation for Environmental ModellingFerry Susanto0Paulo de Souza1Jing He2Data61, CSIRO, College Road, Sandy Bay TAS 7005, AustraliaData61, CSIRO, College Road, Sandy Bay TAS 7005, AustraliaCollege of Engineering and Science, Victoria University, Footscray VIC 3011, AustraliaA variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW) spatial interpolation method. In this study, we utilised one year of Tasmania’s South Esk Hydrology model developed by CSIRO. Root mean squared error statistical methods were performed for performance evaluations. Our results show that the proposed reduction approach is superior to the extension approach to STI. However, the proposed DDW provides little benefit compared to the conventional inverse distance weighting (IDW) method. We suggest that the improved IDW technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications.http://www.mdpi.com/1424-8220/16/8/1245spatiotemporal interpolationordinary kriginginverse distance weightingtriangular irregular networkdistribution-based distance weighting |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ferry Susanto Paulo de Souza Jing He |
spellingShingle |
Ferry Susanto Paulo de Souza Jing He Spatiotemporal Interpolation for Environmental Modelling Sensors spatiotemporal interpolation ordinary kriging inverse distance weighting triangular irregular network distribution-based distance weighting |
author_facet |
Ferry Susanto Paulo de Souza Jing He |
author_sort |
Ferry Susanto |
title |
Spatiotemporal Interpolation for Environmental Modelling |
title_short |
Spatiotemporal Interpolation for Environmental Modelling |
title_full |
Spatiotemporal Interpolation for Environmental Modelling |
title_fullStr |
Spatiotemporal Interpolation for Environmental Modelling |
title_full_unstemmed |
Spatiotemporal Interpolation for Environmental Modelling |
title_sort |
spatiotemporal interpolation for environmental modelling |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2016-08-01 |
description |
A variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW) spatial interpolation method. In this study, we utilised one year of Tasmania’s South Esk Hydrology model developed by CSIRO. Root mean squared error statistical methods were performed for performance evaluations. Our results show that the proposed reduction approach is superior to the extension approach to STI. However, the proposed DDW provides little benefit compared to the conventional inverse distance weighting (IDW) method. We suggest that the improved IDW technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications. |
topic |
spatiotemporal interpolation ordinary kriging inverse distance weighting triangular irregular network distribution-based distance weighting |
url |
http://www.mdpi.com/1424-8220/16/8/1245 |
work_keys_str_mv |
AT ferrysusanto spatiotemporalinterpolationforenvironmentalmodelling AT paulodesouza spatiotemporalinterpolationforenvironmentalmodelling AT jinghe spatiotemporalinterpolationforenvironmentalmodelling |
_version_ |
1724842588726362112 |