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    Application of Geo-CropSim framework for rainfed sugarcane yield assessment in Thailand
    (2022-03-28)
    Bandaru, Varaprasad
    ;
    Chirumamilla, Pallavi
    ;
    Skakun, Sergii
    ;
    Lasko, Kristofer
    ;
    Yampracha, Sukunya
    The adoption of hybrid approaches that use remote sensing datasets in crop simulation models for yield assessment at spatial scale has been constrained by lack of crop type maps and high-resolution remote sensing datasets. To address that, Geo-CropSim, a geospatial crop simulation modeling framework, was developed to produce high quality remote sensing products and use them in Environmental Policy Integrated Climate (EPIC) model to estimate crop yields at various spatial scales. In this study, Geo-CropSim was implemented within the Chaiyaphum province, Thailand to estimate sugarcane yields under rainfed conditions for the 2018 at 500-m resolution. Results showed that the use of Harmonized Landsat Sentinel (HLS) data along with sentinel-1 SAR data allowed to produce a land cover map with an overall accuracy of 96%. Similarly, utilization of HLS data helped to estimate emergence dates and seasonal leaf area index capturing spatial differences driven by differences in climatic conditions. Geo-CropSim yield estimates ranged from 55-98 Mg/ha and showed a typical north-south gradient of decreased yields reflecting lower rainfall and soil quality in the south. Simulated yields showed good a correlation with district and field observed yields with RMSE values of 11.7 Mg/ha and 15.75 Mg/ha, respectively. Overall, simulated yields had uncertainty indicated by mean absolute percentage error (MAPE), less than 20% which is considered as acceptable accuracy for regional yield assessment. Even though it is required to test the framework's performance for long-term consistency over multiple regions, this study demonstrated Geo-CropSim's potential to produce sugarcane yields under rainfed conditions with reasonable accuracy.
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    Improvement on vehicle trajectory reconstruction using Geometric Road Network (GRN)
    (2018-11-17)
    Chomputawat, Anuthep
    ;
    Chatwiriya, Watchara
    ;
    Chomputawat, Sirikanya
    This paper presents the technique using geometric road network (GRN) to improve data fusion method for vehicle trajectory analysis and reconstruction. The experiment used two methods of data fusion which are Linear-interpolation-based one and Kalman-filter-based one and used the simulation of detection and localization of vehicle moving in straight, big zigzag, small zigzag and arbitrary trajectories, with localization errors and multi-level loss of data. By comparing the data fusion methods with GRN and the ones without GRN, the result shows that the data fusion methods with GRN can reduce localization errors better than the ones without GRN in all level of data loss and all type of trajectories. In conclusion, using GRN can improve the efficiency of vehicle trajectory analysis and reconstruction.
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    Evaporative estimation using data fusion
    (2008-12-01)
    Roengruen, P.
    ;
    Tipsuwannaporn, V.
    ;
    Numsomran, A.
    ;
    Harnnarong, Sh
    This paper present evaporative estimation of water using data fusion technique. There are many factors in evaporative locating process that provided for consideration together. So this paper will be study about factors with concerning in meteorology that influential to evaporation and explained about relation of these factors by statistical method. The used data obtained from Thai Meteorological Department which collects daily data for the environment with various sensors. The results of data fusion process will be shown value of daily evaporation that provided to comparing between the evaporative value of pan evaporation and calculated by hydrology formula. © 2008 SICE.
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    Fusion of principal component of multispectral bands with PAN band using HIS and wavelet transform
    (2002-01-01)
    Chitwong, S.
    ;
    Cheevasuvit, F.
    ;
    Homthong, J.
    Since RGB images derived from multispectral (TM) images will lose some information, in this paper we present the method to solve such problem by using principal component analysis (PCA) which transforms TM images into the principal component images (PCs), while the high resolution PAN data is decomposed by wavelet transform. Thus, RGB images are assigned by the first three principal component images which normally have approximately 95% of the information in the original images. The intensity image from RGB to HIS transformation is replaced by the lower frequency coefficient of wavelet transform of PAN data corresponding to multispectral images. HIS to RGB transformation is then applied. The fused RGB image using our method can obtain more details.