• Journal of the Korean Geographical Society
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• v.45 no.4
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• pp.502-520
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• 2010

The main objective of the paper is to develop a GIS-based method for delineating spatial clusters. Major tasks are: (i) to devise a sustainable algorithm with reference to various methods developed in the fields of geographic boundary analysis and cluster detection; (ii) to develop a GIS-based program to implement the algorithm. The main results are as follows. First, it is recognized that the AMOEBA technique utilizing LISA is the best candidate. Second, a modified version of the AMOEBA technique is proposed and implemented in a GIS environment. Third, the validity and usefulness of the modified AMOEBA algorithm is assured by its applications to test and real data sets.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.57-81
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• 2019

Satellite images can be integrated into a crop model to strengthen the advantages of each technique for crop monitoring and to compensate for weaknesses of each other, which can be systematically applied for monitoring inaccessible croplands. The objective of this study was to outline the productivity of paddy rice based on simulation of the yield of all paddy fields in North Korea, using a grid crop model combined with optical satellite imagery. The grid GRAMI-rice model was used to simulate paddy rice yields for inaccessible North Korea based on the bidirectional reflectance distribution function-adjusted vegetation indices (VIs) and the solar insolation. VIs and solar insolation for the model simulation were obtained from the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) sensors of the Communication Ocean and Meteorological Satellite (COMS). Reanalysis data of air temperature were achieved from the Korea Local Analysis and Prediction System (KLAPS). Study results showed that the yields of paddy rice were reproduced with a statistically significant range of accuracy. The regional characteristics of crops for all of the sites in North Korea were successfully defined into four clusters through a spatial analysis using the K-means clustering approach. The current study has demonstrated the potential effectiveness of characterization of crop productivity based on incorporation of a crop model with satellite images, which is a proven consistent technique for monitoring of crop productivity in inaccessible regions.