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A Method for Urban Vegetation Classification Using Airborne LiDAR Data and High Resolution Remote Sensing Images
一种应用机载LiDAR数据和高分辨率遥感影像提取城市绿地信息的方法

YU Bailang,LIU Hongxing,WU Jianping,
余柏蒗
,刘红星,吴健平

中国图象图形学报 , 2010,
Abstract: The urban vegetation is a principal biological component of the urban landscape. Identifying and mapping the urban vegetation are important to urban management and planning. This paper presents a new object-based two-stage method to classify urban vegetation using airborne LiDAR data and high resolution aerial photographs through a case study of downtown Houston, USA. By exploiting the spectral information plus 2D geometric attributes from high resolution aerial photographs and 3D morphological information from airborne LiDAR data, a detailed and accurate classification of urban vegetation has been achieved. In the first stage, the aerial photographs are segmented into image objects. Based on the spectral and 2D geometric attributes, these objects are divided into six categories: non-shaded vegetation, shaded vegetation, water, building, open space, and shade. Vegetation objects, including non-shaded and shaded vegetation, are derived separately. In the second stage, the normalized Digital Surface Model derived from airborne LiDAR data is introduced to characterize the 3D geometric properties (height and roughness) of each vegetation object. Based on these properties, the vegetation objects are further classified into trees, shrubs/hedges, and grass-covered lawns. The overall classification accuracy of vegetation is analyzed and reported as high as 93.46%. The sources of errors are ascribed to the shade in aerial photo and the miscalculation of Digital Terrain Model from LiDAR data. This research suggests that the combination of morphological information of LiDAR and the spectral information from image data renders a powerful tool for a detailed investigation of urban vegetation.
The Use of LiDAR Terrain Data in Characterizing Surface Roughness and Microtopography  [PDF]
Kristen M. Brubaker,Wayne L. Myers,Patrick J. Drohan,Douglas A. Miller,Elizabeth W. Boyer
Applied and Environmental Soil Science , 2013, DOI: 10.1155/2013/891534
Abstract: The availability of light detection and ranging data (LiDAR) has resulted in a new era of landscape analysis. For example, improvements in LiDAR data resolution may make it possible to accurately model microtopography over a large geographic area; however, data resolution and processing costs versus resulting accuracy may be too costly. We examined two LiDAR datasets of differing resolutions, a low point density (0.714?points/m2 spacing) 1?m DEM available statewide in Pennsylvania and a high point density (10.28?points/m2 spacing) 1?m DEM research-grade DEM, and compared the calculated roughness between both resulting DEMs using standard deviation of slope, standard deviation of curvature, a pit fill index, and the difference between a smoothed splined surface and the original DEM. These results were then compared to field-surveyed plots and transects of microterrain. Using both datasets, patterns of roughness were identified, which were associated with different landforms derived from hydrogeomorphic features such as stream channels, gullies, and depressions. Lowland areas tended to have the highest roughness values for all methods, with other areas showing distinctive patterns of roughness values across metrics. However, our results suggest that the high-resolution research-grade LiDAR did not improve roughness modeling in comparison to the coarser statewide LiDAR. We conclude that resolution and initial point density may not be as important as the algorithm and methodology used to generate a LiDAR-derived DEM for roughness modeling purposes. 1. Introduction Over the past several decades, geomorphologists, soil scientists, ecologists, foresters, and hydrologists have increasingly utilized terrain data for landscape classification [1–4], predicting forest communities [5], predicting soil properties [6–9], and understanding riparian zones and their stream networks [10]. Due to improvements in data acquisition, computing power, and storage capacity, terrain data has become increasingly available at finer and finer resolutions and at broader scales, from National Elevation Dataset (NED) and Shuttle Radar Topography Mission (SRTM) to LiDAR. Although LiDAR-derived DEMs have been shown to be extremely accurate when compared to non-LiDAR generated DEMs [11], the accuracy of LiDAR-derived DEMs for measuring landscape microtopography is debated [12]. This can be due to data interpretation difficulties arising from abiotic (such as slope complexity) and biotic terrain factors (such as evergreen vegetation and coarse woody debris) [13, 14]. LiDAR processing
Detecting tropical forest biomass dynamics from repeated airborne Lidar measurements  [PDF]
V. Meyer,S. S. Saatchi,J. Chave,J. Dalling
Biogeosciences Discussions , 2013, DOI: 10.5194/bgd-10-1957-2013
Abstract: Reducing uncertainty of terrestrial carbon cycle depends strongly on the accurate estimation of changes of global forest carbon stock. However, this is a challenging problem from either ground surveys or remote sensing techniques in tropical forests. Here, we examine the feasibility of estimating changes of tropical forest biomass from two airborne Lidar measurements acquired about 10 yr apart over Barro Colorado Island (BCI), Panama from high and medium resolution airborne sensors. The estimation is calibrated with the forest inventory data over 50 ha that was surveyed every 5 yr during the study period. We estimated the aboveground forest biomass and its uncertainty for each time period at different spatial scales (0.04, 0.25, 1.0 ha) and developed a linear regression model between four Lidar height metrics and the aboveground biomass. The uncertainty associated with estimating biomass changes from both ground and Lidar data was quantified by propagating measurement and prediction errors across spatial scales. Errors associated with both the mean biomass stock and mean biomass change declined with increasing spatial scales. Biomass changes derived from Lidar and ground estimates were largely (36 out 50 plots) in the same direction at the spatial scale of 1 ha. Lidar estimation of biomass was accurate at the 1 ha scale (R2 = 0.7 and RMSEmean = 28.6 Mg ha 1). However, to predict biomass changes, errors became comparable to ground estimates only at about 10-ha or more. Our results indicate that the 50-ha BCI plot lost a~significant amount of biomass ( 0.8 ± 2.2 Mg ha 1 yr 1) over the past decade (2000–2010). Over the entire island and during the same period, mean AGB change is 0.4 ± 3.7 Mg ha 1 yr 1. Old growth forests lost biomass ( 0.7 ± 3.5 Mg ha 1 yr 1), whereas the secondary forests gained biomass (+0.4 ± 3.4 Mg ha 1 yr 1). Our analysis demonstrates that repeated Lidar surveys, even with two different sensors, is able to estimate biomass changes in old-growth tropical forests at landscape scales (>10 ha).
AMALi – the Airborne Mobile Aerosol Lidar for Arctic research
I. S. Stachlewska, R. Neuber, A. Lampert, C. Ritter,G. Wehrle
Atmospheric Chemistry and Physics (ACP) & Discussions (ACPD) , 2010,
Abstract: The Airborne Mobile Aerosol Lidar (AMALi) is an instrument developed at the Alfred Wegener Institute for Polar and Marine Research for reliable operation under the challenging weather conditions at the Earth's polar regions. Since 2003 the AMALi has been successfully deployed for measurements in ground-based installation and zenith- or nadir-pointing airborne configurations during several scientific campaigns in the Arctic. The lidar provides backscatter profiles at two wavelengths (355/532 nm or 1064/532 nm) together with the linear depolarization at 532 nm, from which aerosol and cloud properties can be derived. This paper presents the characteristics and capabilities of the AMALi system and gives examples of its usage for airborne and ground-based operations in the Arctic. As this backscatter lidar normally does not operate in aerosol-free layers special evaluation schemes are discussed, the nadir-pointing iterative inversion for the case of an unknown boundary condition and the two-stream approach for the extinction profile calculation if a second lidar system probes the same air mass. Also an intercomparison of the AMALi system with an established ground-based Koldewey Aerosol Raman Lidar (KARL) is given.
Aerodynamic roughness length estimation from very high-resolution imaging LIDAR observations over the Heihe basin in China
J. Colin,R. Faivre,M. Menenti
Hydrology and Earth System Sciences Discussions , 2010, DOI: 10.5194/hessd-7-3397-2010
Abstract: Roughness length of land surfaces is an essential variable for the parameterisation of momentum and heat exchanges. The growing interest about the estimation of the surface turbulent flux parameterisation from passive remote sensing lead 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. LIDAR points were used to extract a Digital Surface Model (DSM) and a Digital Elevation Model (DEM) from a single flight pass over an irrigated area covered by field crops, small trees arrays and tree hedges, with a ground resolution of 1 m and a total surface of 7.2 km2. As a first step, the DSM is used to estimate the plan surface density and frontal surface density of obstacles to wind flow and compute a displacement height and roughness length following strictly geometrical approaches. In a second step, both the DSM and DEM are introduced in a Computational Fluid Dynamics model (CFD) to calculate wind fields from the surface to the top of the Planetary Boundary Layer (PBL), and invert wind profiles for each calculation grid and compute a roughness length. Examples of the use of these three approaches are presented for various wind direction together with a cross-comparison of results on heterogeneous land cover and complex roughness element structures.
The RAMNI airborne lidar for cloud and aerosol research
F. Cairo, G. Di Donfrancesco, L. Di Liberto,M. Viterbini
Atmospheric Measurement Techniques (AMT) & Discussions (AMTD) , 2012,
Abstract: We describe an airborne lidar for the characterization of atmospheric aerosol. The system has been set up in response to the need to monitor extended regions where the air traffic may be posed at risk by the presence of potentially harmful volcanic ash, and to study the characteristics of volcanic emissions both near the source region and when transported over large distances. The lidar provides backscatter and linear depolarization profiles at 532 nm, from which aerosol and cloud properties can be derived. The paper presents the characteristics and capabilities of the lidar system and gives examples of its airborne deployment. Observations from three flights, aimed at assessing the system capabilities in unperturbed atmospheric conditions, and at characterizing the emissions near a volcanic ash source (Mt. Etna) and transported far away from the source, are presented and discussed.
The RAMNI airborne lidar for cloud and aerosol research  [PDF]
F. Cairo,G. Di Donfrancesco,L. Di Liberto,M. Viterbini
Atmospheric Measurement Techniques Discussions , 2012, DOI: 10.5194/amtd-5-1253-2012
Abstract: We describe an airborne lidar for the characterization of atmospheric aerosol. The system has been set up in response to the need to monitor extended regions where the air traffic may be posed at risk by the presence of potentially harmful volcanic ash, and to study the characteristics of volcanic emissions both near the source region and when transported over large distances. The lidar provides backscatter and linear depolarization profiles at 532 nm, from which aerosol and cloud properties can be derived. The paper presents the characteristics and capabilities of the lidar system and gives examples of its airborne deployment. Observations from three flights, aimed at assessing the system capabilities in unperturbed atmospheric conditions, and at characterizing the emissions near a volcanic ash source region, the Mt. Etna, and transported far away from the source, are presented and discussed.
AMALi – the Airborne Mobile Aerosol Lidar for Arctic research  [PDF]
I. S. Stachlewska,R. Neuber,A. Lampert,C. Ritter
Atmospheric Chemistry and Physics Discussions , 2009,
Abstract: The Airborne Mobile Aerosol Lidar (AMALi) is an instrument developed at the Alfred Wegener Institute for Polar and Marine Research for a trouble-free operation under the challenging weather conditions at the Earth's polar regions. Since 2003 the AMALi has been successfully deployed for measurements in the ground-based installation and the zenith- or nadir-aiming airborne configurations during several scientific campaigns in the Arctic. The lidar provides profiles of the total backscatter at two wavelengths, from which aerosol and cloud properties are derived. It measures also the linear depolarization of the backscattered return, allowing for the discrimination of thermodynamic cloud phase and the identification of the presence of non-spherical aerosol particles. This paper presents the capability characteristics and performance of the past and present state of the AMALi system, as well as discusses the ground-based and airborne evaluation schemes applied to invert the data.
A method for parameterising roughness and topographic sub-grid scale effects in hydraulic modelling from LiDAR data
A. Casas, S. N. Lane, D. Yu,G. Benito
Hydrology and Earth System Sciences (HESS) & Discussions (HESSD) , 2010,
Abstract: High resolution airborne laser data provide new ways to explore the role of topographic complexity in hydraulic modelling parameterisation, taking into account the scale-dependency between roughness and topography. In this paper, a complex topography from LiDAR is processed using a spatially and temporally distributed method at a fine resolution. The surface topographic parameterisation considers the sub-grid LiDAR data points above and below a reference DEM, hereafter named as topographic content. A method for roughness parameterisation is developed based on the topographic content included in the topographic DEM. Five subscale parameterisation schemes are generated (topographic contents at 0, ±5, ±10, ±25 and ±50 cm) and roughness values are calculated using an equation based on the mixing layer theory (Katul et al., 2002), resulting in a co-varied relationship between roughness height and topographic content. Variations in simulated flow across spatial subscales show that the sub grid-scale behaviour of the 2-D model is not well-reflected in the topographic content of the DEM and that subscale parameterisation must be modelled through a spatially distributed roughness parameterisation. Variations in flow predictions are related to variations in the roughness parameter. Flow depth-derived results do not change systematically with variation in roughness height or topographic content but they respond to their interaction. Finally, subscale parameterisation modifies primarily the spatial structure (level of organisation) of simulated 2-D flow linearly with the additional complexity of subscale parameterisation.
A method for parameterising roughness and topographic sub-grid scale effects in hydraulic modelling from LiDAR data
A. Casas,S. N. Lane,D. Yu,G. Benito
Hydrology and Earth System Sciences Discussions , 2010, DOI: 10.5194/hessd-7-2261-2010
Abstract: High resolution airborne laser data provide new ways to explore the role of topographic complexity in hydraulic modelling parameterisation, taking into account the scale-dependency between roughness and topography. In this paper, a complex topography from LiDAR is processed using a spatially and temporally distributed method at a fine resolution. The surface topographic parameterisation considers the sub-grid LiDAR data points above and below a reference DEM, hereafter named as topographic content. A method for roughness parameterisation is developed based on the topographic content included in the topographic DEM. Five subscale parameterisation schemes are generated (topographic contents at 0, ±5, ±10, ±25 and ±50 cm) and roughness values are calculated using an equation based on the mixing layer theory (Katul et al., 2002), resulting in a co-varied relationship between roughness height and topographic content. Variations in simulated flow across spatial subscales show that the sub grid-scale behaviour of the 2-D model is not well-reflected in the topographic content of the DEM and that subscale parameterisation must be modelled through a spatially distributed roughness parameterisation. Variations in flow predictions are related to variations in the roughness parameter. Flow depth-derived results do not change systematically with variation in roughness height or topographic content but they respond to their interaction. Finally, subscale parameterisation modifies primarily the spatial structure (level of organisation) of simulated 2-D flow linearly with the additional complexity of subscale parameterisation.
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