Laboratory for Remote Sensing Hydrology and Spatial Modeling

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.

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.

Featured Research

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



Research Topics

Remote sensing

• Locating landslides using multi-temporal satellite images
• Landuse/landcover (LULC) classification and change detection (Hypothesis-test-based landcover change detection using multi-temporal satellite images) 
• Reservoir trophic state monitoring
• Real-time rainfall forecasting
• Coastal water quality monitoring
• Crop water stress monitoring
• Estimation of path radiance using radiometric control areas
• Estimation of carbon concentration using GOSAT remote sensing images
• Paddy cooling effect on ambient air temperatures
• Effects of urbanization on landscape patterns and diurnal temperature difference (Comparing landcover patterns in Tokyo, Kyoto, and Taipei using an urbanization index)

Geostatistics and spatial modeling

• Spatial interpolation with kriging
• Geometric correction for remote sensing images using geostatistics
• Remote sensing image classification using indicator kriging
• Mapping heavy metal contamination
• Gamma random field simulation by a covariance matrix transformation method
• Spatialtemporal random field simulation

Hydrologic processes modeling

• Bivariate gamma simulation - A frequency-factor based approach
• L-moment-ratio based goodness-of-fit tests
• Evaluating uncertainties in parameter estimation and hydrological model performance
• Assessing the impact of climate change on annual typhoon rainfall in Shihmen Reservoir watershed in northern Taiwan
• Hydrological frequency analysis with presence of outliers
• Design storm hyetograph
• Raingauge network design (Raingauge network evaluation and augmentation using geostatistics)
• Stochastic modeling of rainfall processes
• Drought-lead-time forecasting for reservoir operation
• Effect of climate change on rainfall characteristics
• Rainfall-runoff modeling
• Scaling properties in hydrological processes
• Hydrological frequency analysis of multisite extreme rainfalls