Multi-band remote sensing based retrieval model and 3D analysis of water depth in Hulun Lake, China

Abstract Hulun Lake, a large lake located on the cold and arid Hulunbeir grassland in the Inner Mongolia Autonomous Region, is the fifth largest in China and the largest in the north of the country. However, the information on the lake’s characteristics (e.g., water depth versus surface area) is scarce in literature. Based on the lake’s physiographic features, this study developed and used a model that merges the sunlight reflection band with the thermal infrared radiation band to simulate the lake’s characteristics. The model verification and error analysis indicated an optimal model structure of logarithm. Thus, this logarithmic model was selected to analyze the spectral data. The results indicated that the model did a good job in reproducing observed water depths and accurately predicted the depths on 24 September 2007. This showed that this model can be reliably applied to the cold and arid region. Subsequently, the results were used to generate a triangular irregular network (TIN) model, which in turn was used to compute the functional relations between water level, surface area, and volume. The correlation between water level and volume is superior to that between water level and area. The regression equation developed in this study can be used to estimate the volume when water elevation is known.

[1]  Kou Zhi-qing,et al.  The Climate Change and Its Effect on the Water Environment in the Hulun Lake Wetland , 2007 .

[2]  Jürgen Döllner,et al.  Abstract representations for interactive visualization of virtual 3D city models , 2009, Comput. Environ. Urban Syst..

[3]  P. C. Chu,et al.  Deep convection and deep water formation in the oceans : proceedings of the International Monterey Colloquium on Deep Convection and Deep Water Formation in the Oceans , 1991 .

[4]  David G. Barber,et al.  Remote Sensing of the Coastal Zone of Tropical Lakes Using Synthetic Aperture Radar and Optical Data , 2003 .

[5]  R. Holyer,et al.  Coastal bathymetry from hyperspectral observations of water radiance , 1998 .

[6]  DU Xin-dong,et al.  Study on Water Depth Extraction from Remote Sensing Imagery in Jiangsu Coastal Zone , 2007 .

[7]  William J. Emery,et al.  Relations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugarbeet plants , 2008 .

[8]  D. Civco,et al.  Satellite remote bathymetry : a new mechanism for modeling , 1992 .

[9]  LI Chang-you.  Reversing water depth in shallow lake of arid area using multi-spectral remote sensing information , 2009 .

[10]  W. Andrew Marcus,et al.  Remote sensing of stream depths with hydraulically assisted bathymetry (HAB) models , 2005 .

[11]  D. G. George.  Cover Bathymetric mapping using a Compact Airborne Spectrographic Imager (CASI) , 1997 .

[12]  Peter C. Chu,et al.  Geophysics of Deep Convection and Deep Water Formation in Oceans , 1991 .

[13]  Mohamed Al-Hussein,et al.  Integrating 3D visualization and simulation for tower crane operations on construction sites , 2006 .

[14]  Jennifer P. Cannizzaro,et al.  Estimating chlorophyll a concentrations from remote-sensing reflectance in optically shallow waters , 2006 .

[15]  Li Wen-ping,et al.  Application of 3D visualization concept layer model for coal-bed methane index system , 2009 .

[16]  F. J. Tanis,et al.  Optimization of multispectral sensors for bathymetry applications , 1986 .

[17]  李翀,et al.  呼伦湖水位、盐度变化(1961-2002年) , 2006 .

[18]  Nitin K. Tripathi,et al.  Bathymetric mapping in Kakinada Bay, India, using IRS-1D LISS-III data , 2002 .

[19]  Zhao Hui,et al.  Influences of climate change to ecological and environmental evolvement in the Hulun Lake wetland and its surrounding areas , 2008 .

[20]  Wang Yanjiao,et al.  Progress in Water Depth Mapping from Visible Remote Sensing Data , 2007 .