The magnitude of river morphological changes are better analyzed through the use of quantitative approaches, wherein resolution accuracy and uncertainty assessment are treated as crucial key-factors. In this sense, th...The magnitude of river morphological changes are better analyzed through the use of quantitative approaches, wherein resolution accuracy and uncertainty assessment are treated as crucial key-factors. In this sense, the creation of precise DEMs （Digital Elevation Models） of rivers represents an affordable tool to analyze geomorphic variations and budgets, except for wetted areas, where reliable channel digitalization can normally be obtained only using expensive bathymetric surveys. The proposed work aims at improving channel surface models without having available bathymetric sensors, by deriving dry areas elevations from LiDAR data and water depth of wetted areas from aerial photos through a predictive depth-colour relationship. The methodology was applied to two different sub-reaches of the Piave River, a gravel-bed river which suffered severe flood events in 2010. Erosion and deposition patterns were identified through DEM differencing, showing a predominance of scour processes which can lead to channel instability situations. The bathymetric output was compared to other previously-derived models confirming the accuracy of the in-channel elevation estimates. Finally, a discussion on the role played by longitudinal protections during the studied flood events is proposed, focusing the attention on the incidence of two major bank erosions that removed significant volumes of stable areas.展开更多
This paper presents algorithmic components and corresponding software routines for extracting shoreline features from remote sensing imagery and LiDAR data. Conceptually, shoreline features are treated as boundary lin...This paper presents algorithmic components and corresponding software routines for extracting shoreline features from remote sensing imagery and LiDAR data. Conceptually, shoreline features are treated as boundary lines between land objects and water objects. Numerical algorithms have been identified and de-vised to segment and classify remote sensing imagery and LiDAR data into land and water pixels, to form and enhance land and water objects, and to trace and vectorize the boundaries between land and water ob-jects as shoreline features. A contouring routine is developed as an alternative method for extracting shore-line features from LiDAR data. While most of numerical algorithms are implemented using C++ program-ming language, some algorithms use available functions of ArcObjects in ArcGIS. Based on VB .NET and ArcObjects programming, a graphical user’s interface has been developed to integrate and organize shoreline extraction routines into a software package. This product represents the first comprehensive software tool dedicated for extracting shorelines from remotely sensed data. Radarsat SAR image, QuickBird multispectral image, and airborne LiDAR data have been used to demonstrate how these software routines can be utilized and combined to extract shoreline features from different types of input data sources: panchromatic or single band imagery, color or multi-spectral image, and LiDAR elevation data. Our software package is freely available for the public through the internet.展开更多
Automatic road detection, in dense urban areas, is a challenging application in the remote sensing community. This is mainly because of physical and geometrical variations of road pixels, their spectral similarity to ...Automatic road detection, in dense urban areas, is a challenging application in the remote sensing community. This is mainly because of physical and geometrical variations of road pixels, their spectral similarity to other features such as buildings, parking lots and sidewalks, and the obstruction by vehicles and trees. These problems are real obstacles in precise detection and identification of urban roads from high-resolution satellite imagery. One of the promising strategies to deal with this problem is using multi-sensors data to reduce the uncertainties of detection. In this paper, an integrated object-based analysis framework was developed for detecting and extracting various types of urban roads from high-resolution optical images and Lidar data. The proposed method is designed and implemented using a rule-oriented approach based on a masking strategy. The overall accuracy (OA) of the final road map was 89.2%, and the kappa coefficient of agreement was 0.83, which show the efficiency and performance of the method in different conditions and interclass noises. The results also demonstrate the high capability of this object-based method in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite images and Lidar data.展开更多
The automatic classification of power lines from airborne light detection and ranging(LiDAR)data is a crucial task for power supply management.The methods for power line classification can be either supervised or unsu...The automatic classification of power lines from airborne light detection and ranging(LiDAR)data is a crucial task for power supply management.The methods for power line classification can be either supervised or unsupervised.Supervised methods might achieve high accuracy for small areas,but it is time consuming to collect training data over areas of different conditions and complexity.Therefore,unsupervised methods that can automatically work over different areas without sophisticated parameter tuning are in great demand.In this paper,we presented a hierarchical unsupervised LiDAR-based power line classification method that first screened the power line candidate points(including the power line corridor direction detection based on a layered Hough transform,connectivity analysis,and Douglas–Peucker simplification algorithm),followed by the extraction of contextual linear and angular features for each candidate laser points,and finally by setting the feature threshold values to identify the power line points.We tested the method over both forest and urban areas and found that the precision,recall and quality rates were up to 96.7%,88.8%and 78.3%,respectively,for the test datasets and were higher than the ones from a previously developed supervised classification method.Overall,our approach has the advantages of achieving relatively high accuracy and being relatively fast.展开更多
We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominate...We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.展开更多
植被分布信息是开展自然资源保护、生态系统健康评价的重要数据基础。山区地形起伏大、植被类型结构复杂,传统植被遥感分类基于二维影像进行制图,无法反映植被垂直结构和三维空间分布特征。为探索实景三维模型在植被精细分类与制图上的...植被分布信息是开展自然资源保护、生态系统健康评价的重要数据基础。山区地形起伏大、植被类型结构复杂,传统植被遥感分类基于二维影像进行制图,无法反映植被垂直结构和三维空间分布特征。为探索实景三维模型在植被精细分类与制图上的应用潜力,该文提出了一种融合光学影像与激光雷达(light detecting and ranging, LiDAR)数据的实景三维模型山地植被制图方法。选取广东内伶仃岛为研究区,利用无人机航测获取的实景三维模型、多光谱与LiDAR点云构建多源数据集,开展多源数据配准和特征提取;进一步采用LightGBM算法实现植被精细分类并评估多源数据特征的分类性能;最后,通过二维制图向三维模型的映射获得植被语义三维模型。结果表明,实景三维模型可以有效区分植被类型,其与多光谱、LiDAR数据的特征融合能更全面地描述山区地形与植被结构特性,二维分类总精度比仅使用单一数据提升4.28%~11.29%。基于实景三维模型的植被三维制图总精度达到92.06%,Kappa系数为0.89,能够真实直观地反映山地植被的立体层次分布规律,提高植被精细信息提取的准确性。研究验证了实景三维模型与多源数据的融合在自然资源监测中的巨大潜力,为区域植被精细化和立体化信息提取提供新的思路和方法。展开更多
文摘The magnitude of river morphological changes are better analyzed through the use of quantitative approaches, wherein resolution accuracy and uncertainty assessment are treated as crucial key-factors. In this sense, the creation of precise DEMs （Digital Elevation Models） of rivers represents an affordable tool to analyze geomorphic variations and budgets, except for wetted areas, where reliable channel digitalization can normally be obtained only using expensive bathymetric surveys. The proposed work aims at improving channel surface models without having available bathymetric sensors, by deriving dry areas elevations from LiDAR data and water depth of wetted areas from aerial photos through a predictive depth-colour relationship. The methodology was applied to two different sub-reaches of the Piave River, a gravel-bed river which suffered severe flood events in 2010. Erosion and deposition patterns were identified through DEM differencing, showing a predominance of scour processes which can lead to channel instability situations. The bathymetric output was compared to other previously-derived models confirming the accuracy of the in-channel elevation estimates. Finally, a discussion on the role played by longitudinal protections during the studied flood events is proposed, focusing the attention on the incidence of two major bank erosions that removed significant volumes of stable areas.
文摘This paper presents algorithmic components and corresponding software routines for extracting shoreline features from remote sensing imagery and LiDAR data. Conceptually, shoreline features are treated as boundary lines between land objects and water objects. Numerical algorithms have been identified and de-vised to segment and classify remote sensing imagery and LiDAR data into land and water pixels, to form and enhance land and water objects, and to trace and vectorize the boundaries between land and water ob-jects as shoreline features. A contouring routine is developed as an alternative method for extracting shore-line features from LiDAR data. While most of numerical algorithms are implemented using C++ program-ming language, some algorithms use available functions of ArcObjects in ArcGIS. Based on VB .NET and ArcObjects programming, a graphical user’s interface has been developed to integrate and organize shoreline extraction routines into a software package. This product represents the first comprehensive software tool dedicated for extracting shorelines from remotely sensed data. Radarsat SAR image, QuickBird multispectral image, and airborne LiDAR data have been used to demonstrate how these software routines can be utilized and combined to extract shoreline features from different types of input data sources: panchromatic or single band imagery, color or multi-spectral image, and LiDAR elevation data. Our software package is freely available for the public through the internet.
文摘Automatic road detection, in dense urban areas, is a challenging application in the remote sensing community. This is mainly because of physical and geometrical variations of road pixels, their spectral similarity to other features such as buildings, parking lots and sidewalks, and the obstruction by vehicles and trees. These problems are real obstacles in precise detection and identification of urban roads from high-resolution satellite imagery. One of the promising strategies to deal with this problem is using multi-sensors data to reduce the uncertainties of detection. In this paper, an integrated object-based analysis framework was developed for detecting and extracting various types of urban roads from high-resolution optical images and Lidar data. The proposed method is designed and implemented using a rule-oriented approach based on a masking strategy. The overall accuracy (OA) of the final road map was 89.2%, and the kappa coefficient of agreement was 0.83, which show the efficiency and performance of the method in different conditions and interclass noises. The results also demonstrate the high capability of this object-based method in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite images and Lidar data.
基金the National Natural Science Foundation of China(grant numbers 41601426 and 41771462)the Hunan Provincial Natural Science Foundation(grant number 2018JJ3155)+1 种基金the Open Foundation of Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying,Map-ping and Geoinformation,Wuhan University(grant number GCWD201806)the China Scholarship Council(grant number 201708430040).
文摘The automatic classification of power lines from airborne light detection and ranging(LiDAR)data is a crucial task for power supply management.The methods for power line classification can be either supervised or unsupervised.Supervised methods might achieve high accuracy for small areas,but it is time consuming to collect training data over areas of different conditions and complexity.Therefore,unsupervised methods that can automatically work over different areas without sophisticated parameter tuning are in great demand.In this paper,we presented a hierarchical unsupervised LiDAR-based power line classification method that first screened the power line candidate points(including the power line corridor direction detection based on a layered Hough transform,connectivity analysis,and Douglas–Peucker simplification algorithm),followed by the extraction of contextual linear and angular features for each candidate laser points,and finally by setting the feature threshold values to identify the power line points.We tested the method over both forest and urban areas and found that the precision,recall and quality rates were up to 96.7%,88.8%and 78.3%,respectively,for the test datasets and were higher than the ones from a previously developed supervised classification method.Overall,our approach has the advantages of achieving relatively high accuracy and being relatively fast.
基金made possible by a scholarship from the Australian Government(International Postgraduate Research Scholarship-awarded in 2009)a Southern Cross University Postgraduate Research Scholarship(SCUPRS in 2009)
文摘We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.
文摘植被分布信息是开展自然资源保护、生态系统健康评价的重要数据基础。山区地形起伏大、植被类型结构复杂,传统植被遥感分类基于二维影像进行制图,无法反映植被垂直结构和三维空间分布特征。为探索实景三维模型在植被精细分类与制图上的应用潜力,该文提出了一种融合光学影像与激光雷达(light detecting and ranging, LiDAR)数据的实景三维模型山地植被制图方法。选取广东内伶仃岛为研究区,利用无人机航测获取的实景三维模型、多光谱与LiDAR点云构建多源数据集,开展多源数据配准和特征提取;进一步采用LightGBM算法实现植被精细分类并评估多源数据特征的分类性能;最后,通过二维制图向三维模型的映射获得植被语义三维模型。结果表明,实景三维模型可以有效区分植被类型,其与多光谱、LiDAR数据的特征融合能更全面地描述山区地形与植被结构特性,二维分类总精度比仅使用单一数据提升4.28%~11.29%。基于实景三维模型的植被三维制图总精度达到92.06%,Kappa系数为0.89,能够真实直观地反映山地植被的立体层次分布规律,提高植被精细信息提取的准确性。研究验证了实景三维模型与多源数据的融合在自然资源监测中的巨大潜力,为区域植被精细化和立体化信息提取提供新的思路和方法。
