Shrutilipi Bhattacharjee
ABSTRACT
The climatological dynamics and weather patterns have been studied extensively in the field of remote sensing (RS) and geographic information system (GIS). The meteorological parameters, closely related to the earth surface, play important roles in climatological study. Prediction of these parameters is motivating especially when datasets contain missing and erroneous values. Geostatistical analysis is mandatory for prediction as it facilitates improved modeling of spatial proximities, hence reducing estimation error. However, the interdependencies between the atmospheric and terrestrial contexts play a critical role for proximity estimation. It is challenging to model cross-correlation between these two factors, considering the anisotropy in the terrain. This hybrid approach may facilitate better prediction accuracy for the meteorological parameters. This research work focuses on contextual land-atmospheric interaction modeling of influencing meteorological parameters in the terrain for spatial interpolation. The newly proposed interpolation method is named as semantic kriging (SemK). Theoretical analyses and empirical evidences prove the method to produce better results than most of the existing techniques in literature.
- Bhattacharjee, S., and Ghosh, S. K. Performance evaluation of semantic kriging: A euclidean vector analysis approach. Geoscience and Remote Sensing Letters, IEEE 12, 6 (2015), 1185--1189.Google Scholar
Cross Ref
- Bhattacharjee, S., and Ghosh, S. K. Spatio-temporal change modeling of lulc: A semantic kriging approach. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (2015), 177--184. DOI:10.5194/isprsannals-II-4-W2-177-2015.Google Scholar
- Bhattacharjee, S., and Ghosh, S. K. Time-series augmentation of semantic kriging for the prediction of meteorological parameters. In 28th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2015) (2015), IEEE. (Accepted).Google ScholarCross Ref
- Bhattacharjee, S., Mitra, P., and Ghosh, S. K. Spatial interpolation to predict missing attributes in GIS using semantic kriging. IEEE Transactions on Geoscience and Remote Sensing 52, 8 (2014), 4771--4780.Google ScholarCross Ref
- Hengl, T., Heuvelink, G. B., Tadić, M. P., and Pebesma, E. J. Spatio-temporal prediction of daily temperatures using time-series of modis lst images. Theoretical and applied climatology 107, 1-2 (2012), 265--277.Google Scholar
- Li, J., and Heap, A. D. A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics 6, 3 (2011), 228--241.Google ScholarCross Ref
- Mahmood, R., Quintanar, A. I., Conner, G., Leeper, R., Dobler, S., Pielke Sr, R. A., Beltran-Przekurat, A., Hubbard, K. G., Niyogi, D., Bonan, G., et al. Impacts of land use/land cover change on climate and future research priorities. Bulletin of the American Meteorological Society 91, 1 (2010), 37--46.Google ScholarCross Ref
- Sertel, E., Ormeci, C., and Robock, A. Modelling land cover change impact on the summer climate of the marmara region, turkey. International Journal of Global Warming 3, 1 (2011), 194--202.Google ScholarCross Ref
Recommendations
Prediction of meteorological parameters: an a-posteriori probabilistic semantic kriging approach
SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information SystemsMeteorological parameters are often considered as crucial factors for climatological pattern analysis. Predictions of these parameters have been studied extensively in the field of remote sensing and GIS. It is one of the most critical steps involved in ...
Use of vegetation index and meteorological parameters for the prediction of crop yield in India
Monsoon rainfall distribution over the Indian sub-continent is inconsistent every year. Due to uncertainty and dependence on the monsoon onset and weather conditions, estimation of crop yield in India is difficult. In this paper, analyses of the crop ...
Land-Cover Hierarchical Classification Method Study of TM Image in Loess Hilly Ravine Area
CSSS '12: Proceedings of the 2012 International Conference on Computer Science and Service SystemThis article puts forward a land-cover hierarchical classification method of TM image in loess hilly ravine area. This method selects different typological samples of ground object, analysises the statistics feature of the samples, confirms the TM image'...
Comments