Professor Ke-Sheng Cheng
Dept of Bioenvironmental Systems Engineering &
Master Program in Statistics
Email: rslab@ntu.edu.tw
RSLAB_BSE_NTU
No. 1, Section 4, Roosevelt Road
Bioenvironmental Syst. Eng., National Taiwan University
Recent Publications
Cheng, K.-S.; Ho, C.-Y.;Teng, J.-H. Wind and Sea BreezeCharacteristics for the OffshoreWindFarms in the Central Coastal Area ofTaiwan. Energies 2022, 15, 992. https://doi.org/10.3390/en15030992
Quantifying Uncertainty in Land use/Land cover Classification Accuracy - a Stochastic Simulation Approach, 2021. [K.S. Cheng et al., Frontiers in Environmental Science - Environmental Informatics and Remote Sensing, 2021]
Wind Characteristics in the Taiwan Strait: A Case Study of the First Offshore Wind Farm in Taiwan. K.S. Cheng, C.Y. Ho, J.H. Teng, Energies, 2020.
A multidecadal change analysis for irrigation ponds in Taoyuan,Taiwan, using multisource data. Paddy and Water Environment, 2019.
Does urbanization increase diurnal land surface temperature variation? Evidence and implications. Landscape and Urban Planning, 2017.
On the criteria of model performance evaluation for real-time flood forecasting Stochastic Environmental Research and Risk Assessment, 2017.
A Feature Space Indicator Kriging Approach for Remote Sensing Image Classification. IEEE Transactions on Geoscience and Remote Sensing. Chiang, J.L., Liou, J.J., Wei, C., Cheng, K.S., 2014.
Spatial and Temporal Rainfall Patterns in Central Dry Zone, Myanmar – A Hydrological Cross-Scale Analysis. Mya Thandar Toe, Mamoru Kanzaki, Tsung-Hsun Lien, Ke-Sheng Cheng, Terrestrial, Atmospheric, Oceanic Sciences. 28(3): 425 - 436, 2017.
Multisite Spatiotemporal Streamflow Simulation - With an Application to Irrigation Water Shortage Risk Assessment. Hsieh, H.I., Su, M.D., Cheng, K.S., 2014. Terrestrial, Atmospheric, Oceanic Sciences, 25(2): 255-266.
Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling Remote Sensing, 2016. L.H. Hsiao and K.S. Cheng.
Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study,we propose a bootstrap resampling and reclassification approach that can be applied for assessing not only the uncertainty in classification results of the bootstrap-training data sets, but also the classification uncertainty of individual pixels in the study area. Two measures of pixel-specific classification uncertainty, namely the maximum class probability and Shannon entropy, were derived from the class probability vector of individual pixels and used for the identification of unclassifiedpixels. Unclassified pixels that are identified using the traditional chi-square threshold technique represent outliers of individual LULC classes, but they are not necessarily associated with higher classification uncertainty. By contrast, unclassified pixels identified using the equal-likelihood technique are associated with higher classification uncertainty and they mostly occur on or near the borders of different land-cover.
Flowchart of the bootstrap-based LULC reclassification approach
Remote sensing
Geostatistics and spatial modeling
Hydrologic processes modeling
RSLAB - NTU
Prof. Ke-Sheng Cheng
RSLAB_BSE_NTU
No. 1, Section 4, Roosevelt Road
Bioenvironmental Syst. Eng., National Taiwan University