Study on land cover remote sensing monitoring and LUCC analysis in frontier small river basin

2007 ◽  
Author(s):  
Dandan Wang ◽  
Xiping Yuan ◽  
Shu Gan
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
River Basin ◽  
Small River ◽  
Remote Sensing Monitoring ◽  
Small River Basin

scholarly journals Aerodynamic roughness length estimation from very high-resolution imaging LIDAR observations over the Heihe basin in China

2010 ◽  
Vol 14 (12) ◽  
pp. 2661-2669 ◽  
Author(s):  
J. Colin ◽  
R. Faivre
Keyword(s):  
Remote Sensing ◽  
Surface Roughness ◽  
Land Cover ◽  
River Basin ◽  
Land Surface ◽  
Surface Density ◽  
Roughness Length ◽  
Aerodynamic Roughness Length ◽  
Passive Remote Sensing ◽  
Aerodynamic Roughness

Abstract. Roughness length of land surfaces is an essential variable for the parameterisation of momentum and heat exchanges. The growing interest in the estimation of the surface turbulent flux parameterisation from passive remote sensing leads to an increasing development of models, and the common use of simple semi-empirical formulations to estimate surface roughness. Over complex surface land cover, these approaches would benefit from the combined use of passive remote sensing and land surface structure measurements from Light Detection And Ranging (LIDAR) techniques. Following early studies based on LIDAR profile data, this paper explores the use of imaging LIDAR measurements for the estimation of the aerodynamic roughness length over a heterogeneous landscape of the Heihe river basin, a typical inland river basin in the northwest of China. The point cloud obtained from multiple flight passes over an irrigated farmland area were used to separate the land surface topography and the vegetation canopy into a Digital Elevation Model (DEM) and a Digital Surface Model (DSM) respectively. These two models were then incorporated in two approaches: (i) a strictly geometrical approach based on the calculation of the plan surface density and the frontal surface density to derive a geometrical surface roughness; (ii) a more aerodynamic approach where both the DEM and DSM are introduced in a Computational Fluid Dynamics model (CFD). The inversion of the resulting 3-D wind field leads to a fine representation of the aerodynamic surface roughness. Examples of the use of these three approaches are presented for various wind directions together with a cross-comparison of results on heterogeneous land cover and complex roughness element structures.

Remote Sensing Monitoring and Analysis on Land Utilization Status on Aksu River Basin of Xinjiang Uygur Autonomous Region

2011 ◽  
Vol 282-283 ◽  
pp. 177-180
Author(s):  
Bai Lian Lai
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Great Influence ◽  
Land Development ◽  
Tarim River ◽  
Tarim River Basin ◽  
Land Utilization ◽  
Factors Affecting ◽  
Remote Sensing Monitoring ◽  
The Tarim River Basin

Aksu river is one of the biggest branches in the Tarim river basin, where the land utilization and its changes have great influence on the water supply in the lower reaches, and they are also the main factors affecting its ecological restoration. This paper, with the help of three-level intermediate resolution and remote sensor, carries out the remote sensing monitoring and analysis on its land utilization, analyses the changes of farmland and construction land, and provides basis for the planning of land development and the comprehensive assessment for the ecological environment.

scholarly journals Land Use/Land Cover Change Detection Study Using Remote Sensing and GIS Technique in Puthimari River Basin-A Transboundary Basin Between Bhutan and India

2021 ◽  
pp. 26-34
Author(s):  
Swapnali Barman ◽  
Jaivir Tyagi ◽  
Waikhom Rahul Singh
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
River Basin ◽  
Remote Sensing And Gis ◽  
Rural Settlement ◽  
Land Use Land Cover ◽  
Dense Vegetation ◽  
Gis Technique

Using remote sensing and GIS technique, we analyse the change detection of different land use/land cover (LULC) types that has taken place in Puthimari river basin during a two-decade period from 1999 to 2019. Supervised classification method with maximum likelihood algorithm have been applied to prepare the LULC maps. The LULC change detection has been performed employing a post-classification detection method. Puthimari is a north bank sub-catchment of River Brahmaputra, the northern part of which falls in Bhutan and the rest falls in the Assam state of India. The primary LULC types of the basin are, dense vegetation which is predominant in the upper catchment, crop land and rural settlement. Thus, five different classes have been considered for the analysis, viz., dense vegetation, water bodies, silted water, cropland and rural settlement. The results showed that the rural settlement and water bodies in the basin increased by 42.70% and 30.31% from 1999 to 2019. However, dense vegetation, silted water and cropland decreased by 9.24%, 27.47% and 28.10% during these two decades.

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