Professor Ke-Sheng Cheng (Email: rslab@ntu.edu.tw)
RSLAB_BSE_NTU
No. 1, Section 4, Roosevelt Road
Bioenvironmental Syst. Eng., National Taiwan University
2020 Hydrologic Year Drought Monitoring and Early Warning in Taiwan
2020水文年乾旱監測與預警
以上圖資是依據水文年分析,例如1993水文年代表1993年5月上旬起至1994年4月下旬止。
橫軸標記:M-2代表第M月第二旬。縱軸代表變動尺度標準化降雨指標(Variable-scale SPI),該指標低於 -0.5時,代表發生不同程度之乾旱。
圖中紅色折線代表今年度(2020水文年)五月上旬至九月中旬臺灣北部與南部之區域乾旱嚴重度時間變化情形。與歷史乾旱事件比較,今年度之乾旱嚴重情況介於1993水文年(黑色折線)與2003水文年(青色折線)的乾旱嚴重度之間。預估到2021年4月下旬止,臺灣北部與南部可能均仍處於嚴重乾旱狀況。
1.
Introduction
The Laboratory for Remote Sensing Hydrology and Spatial Modeling at the Department of Bioenvironmental Systems Engineering, National Taiwan University (RSLAB) focuses its research works on three inter-related fields: hydrologic processes, environmental remote sensing, and spatial modeling. Almost all hydrologic and environmental processes exhibit heterogeneity and random variations in temporal and spatial domains. These processes often involve multi-scale phenomena, and thus stochastic modeling and simulation, in addition to fundamental physical principles, plays an essential role in modeling these complex processes. At RSLAB, we dedicate our research efforts to stochastic modeling of hydrological and environmental processes using field measurements and remote sensing images. Stochastic modeling and simulation enables us not only to monitor and/or forecast hydrological and environmental changes but also assess the risks and impacts of such changes.
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.
Spatiotemporal correlation matrix of multisite 10-day-period flows
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