Laboratory for Remote Sensing Hydrology and Spatial Modeling
Professor Ke-Sheng Cheng (Email: rslab@ntu.edu.tw)
RSLAB_BSE_NTU
No. 1, Section 4, Roosevelt Road
Bioenvironmental Syst. Eng., National Taiwan University
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: hydrological processes, environmental remote sensing and spatial modeling. Almost all hydrological and environmental processes exhibit heterogeneity and random variations in time 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.
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.
All-English Graduate Curriculum on Environmental Informatics and Risk Management (CEIRM)
Program overview
In light of its unique geographic and environmental characteristics, Taiwan is particularly vulnerable to natural disasters. Findings from many studies have demonstrated that climate changes can result in much greater impacts to island-countries like Taiwan than other continental countries. It is therefore of vital importance for both government agencies and engineering consultancy firms to rely on environmental data and information for sound engineering practices and decision making in disaster remediation, ecological restoration, soil and water conservation, adaptation to climate changes, etc. This comprehensive curriculum provides an integrated and multidisciplinary program that spans a very wide spectrum including environmental database building and analysis, environmental monitoring and instrumentation, landscape ecological measuring and planning, restoration ecology, spatiotemporal stochastic modeling and simulation, risk assessment and management, environmental system/process modeling, and climate change impact assessment.
The CEIRM emphasizes not only the fundamental theories and computer skills for environmental modeling and risk assessment, but also practical applications to solve real world problems. A few such applications include quantitative assessment of the climate change impact on urban inundation, drought indicators and drought forecasting, ecological indicators for river ecology restoration, evaluating the effect of urbanization on diurnal temperature variations, etc.
Featured Research
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.
An indicator kriging (IK) approach for remote sensing image classification is proposed. By introducing indicator variables for categorical data, the work of image classification is transformed into estimation of class-dependent probabilities in feature space using ordinary kriging. Individual pixels are then assigned to the class with maximum class probability. The approach is distribution free and yields perfect classification accuracies for training data provided that collocated data in feature space do not exist.
RSLAB - NTU
Prof. Ke-Sheng Cheng
RSLAB_BSE_NTU
No. 1, Section 4, Roosevelt Road
Bioenvironmental Syst. Eng., National Taiwan University