Volume II-4/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 177-184, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-177-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 177-184, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-177-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  14 Jul 2015

14 Jul 2015

SPATIO-TEMPORAL CHANGE MODELING OF LULC: A SEMANTIC KRIGING APPROACH

S. Bhattacharjee and S. K. Ghosh S. Bhattacharjee and S. K. Ghosh
  • School of Information Technology, Indian Institute of Technology Kharagpur, West Bengal-721302, India

Keywords: Remote sensing, Prediction, Kriging, LULC, Data semantic, Ontology

Abstract. Spatio-temporal land-use/ land-cover (LULC) change modeling is important to forecast the future LULC distribution, which may facilitate natural resource management, urban planning, etc. The spatio-temporal change in LULC trend often exhibits non-linear behavior, due to various dynamic factors, such as, human intervention (e.g., urbanization), environmental factors, etc. Hence, proper forecasting of LULC distribution should involve the study and trend modeling of historical data. Existing literatures have reported that the meteorological attributes (e.g., NDVI, LST, MSI), are semantically related to the terrain. Being influenced by the terrestrial dynamics, the temporal changes of these attributes depend on the LULC properties. Hence, incorporating meteorological knowledge into the temporal prediction process may help in developing an accurate forecasting model. This work attempts to study the change in inter-annual LULC pattern and the distribution of different meteorological attributes of a region in Kolkata (a metropolitan city in India) during the years 2000-2010 and forecast the future spread of LULC using semantic kriging (SemK) approach. A new variant of time-series SemK is proposed, namely Rev-SemKts to capture the multivariate semantic associations between different attributes. From empirical analysis, it may be observed that the augmentation of semantic knowledge in spatio-temporal modeling of meteorological attributes facilitate more precise forecasting of LULC pattern.