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Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprs-archives-xli-b3-441-2016.
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LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.
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Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprsarchives-xli-b3-441-2016.
Full textAbstract:
LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.
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Yastikli, N., and Z. Cetin. "AUTOMATIC 3D BUILDING MODEL GENERATIONS WITH AIRBORNE LiDAR DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 411–14. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-411-2017.
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LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D building models can be generated successfully using raw LiDAR point cloud data.
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El-Ashmawy, N., and A. Shaker. "Raster Vs. Point Cloud LiDAR Data Classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 79–83. http://dx.doi.org/10.5194/isprsarchives-xl-7-79-2014.
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Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surface. These effects may lead to uneven distribution of point cloud or some gaps that may affect the classification process. Researchers have investigated the conversion of LiDAR range point data to raster image for terrain modelling. Interpolation techniques have been used to achieve the best representation of surfaces, and to fill the gaps between the LiDAR footprints. Interpolation methods are also investigated to generate LiDAR range and intensity image data for land cover classification applications. In this paper, different approach has been followed to classifying the LiDAR data (range and intensity) for land cover mapping. The methodology relies on the classification of the point cloud data based on their range and intensity and then converted the classified points into raster image. The gaps in the data are filled based on the classes of the nearest neighbour. Land cover maps are produced using two approaches using: (a) the conventional raster image data based on point interpolation; and (b) the proposed point data classification. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to compare the results of the two approaches. Five different land cover classes can be distinguished in that area: buildings, roads and parking areas, trees, low vegetation (grass), and bare soil. The results show that an improvement of around 10 % in the classification results can be achieved by using the proposed approach.
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Xu, Hong Gen, Ting Li, and Fang Wu. "Knowledge-Based Classification Method for Urban Area Objects Feature Extraction Based on LIDAR Points." Applied Mechanics and Materials 128-129 (October 2011): 1157–62. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1157.
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Laser scanning technology can quickly capture a large area of high-precise 3D spatial data, and get the information of buildings, roads, vegetation and other urban objects from raw data. Based on this information general frame of these objects can be modelling. In this paper, an object-based classification method is proposed for urban objects based on LIDAR points: determine the contents of the objects contained in the scene; extract inherent features of different objects; establish objects feature knowledge database; combine and compare objects’ features and distribution of LIDAR points; derive a set of rule to express the point cloud classification which can be received by computer through fuzzy judgement. The method has been applied to LIDAR points by LYNX. The experiment results show that the proposed classification method is promising and usable.
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Muckenhuber, Stefan, Hannes Holzer, and Zrinka Bockaj. "Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities." Sensors 20, no. 11 (June 10, 2020): 3309. http://dx.doi.org/10.3390/s20113309.
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Development and validation of reliable environment perception systems for automated driving functions requires the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, a perception sensor is replaced by a sensor model. A major challenge for state-of-the-art sensor models is to represent the large variety of material properties of the surrounding objects in a realistic manner. Since lidar sensors are considered to play an essential role for upcoming automated vehicles, this paper presents a new lidar modelling approach that takes material properties and corresponding lidar capabilities into account. The considered material property is the incidence angle dependent reflectance of the illuminated material in the infrared spectrum and the considered lidar property its capability to detect a material with a certain reflectance up to a certain range. A new material classification for lidar modelling in the automotive context is suggested, distinguishing between 7 material classes and 23 subclasses. To measure angle dependent reflectance in the infrared spectrum, a new measurement device based on a time of flight camera is introduced and calibrated using Lambertian targets with defined reflectance values at
10
%
,
50
%
, and
95
%
. Reflectance measurements of 9 material subclasses are presented and 488 spectra from the NASA ECOSTRESS library are considered to evaluate the new measurement device. The parametrisation of the lidar capabilities is illustrated by presenting a lidar measurement campaign with a new Infineon lidar prototype and relevant data from 12 common lidar types.
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El-Ashmawy, N., and A. Shaker. "COMBINED MULTIPLE CLASSIFIED DATASETS CLASSIFICATION APPROACH FOR POINT CLOUD LIDAR DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 349–56. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-349-2019.
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<p><strong>Abstract.</strong> Airborne Laser scanners using the Light Detection And Ranging (LiDAR) technology is a powerful tool for 3D data acquisition that records the backscattered energy as well. LiDAR has been successfully used in various applications including 3D modelling, feature extraction, and land cover information extraction. Airborne LiDAR data are usually acquired from different flight trajectories producing data in different strips with significant overlapped areas. Combining these data is required to get benefit of the multiple strips’ data that acquired from different trajectories. This paper introduces an approach called CMCD “Combined Multiple Classified Datasets” to maximize the benefits of the multiple LiDAR strips’ data in land cover information extraction. This approach relies on classifying each strip data then combining the results based on the <i>a posteriori</i> probability of each class of the classified data and the position of the classified points.</p><p>Two datasets from different overlapped areas are selected to test the proposed CMCD approach; both are captured from different flight trajectories. A comparison has been conducted between the CMCD results and the results of the common merging data approaches. The results indicated that the classification accuracy of the proposed CMCD approach has improved the classification accuracy of the merged data-layers by 6% and 10% for the two datasets.</p>
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Homainejad, N., S. Zlatanova, and N. Pfeifer. "A VOXEL-BASED METHOD FOR THE THREE-DIMENSIONAL MODELLING OF HEATHLAND FROM LIDAR POINT CLOUDS: FIRST RESULTS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 697–704. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-697-2022.
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Abstract. Bushfires are an intrinsic part of the New South Wales’ (NSW) environment in Australia, especially in the Blue Mountains region (11400km2), that is dominated by fire prone vegetation that includes heathland. Many of the Australian native plants in this region are fire-prone and combustible, and many species even require fire to regenerate. The classification of the lateral and vertical distribution of living vegetation is necessary to manage the complexity of bushfires. Currently, interpretation of aerial and satellite images is the prevalent method for the classification of vegetation in NSW. The result does not represent important vegetation structural attributes, such as vegetation height, subcanopy height, and destiny. This paper presents an automated method for the three-dimensional modelling of heathland and important heathland parameters, such as heath shrub height and continuity, and sparse tree and mallee height and density in support of bushfire behaviour modelling. For this study airborne lidar point clouds with a density of 120 points per square meter are used. For the processing and modelling the study is divided into a point cloud processing phase and a voxel-based modelling phase. The point cloud processing phase consists of the normalisation of the height and extraction of the above ground vegetation, while the voxel phase consists of seeded region growing for segmentation, and K-means clustering for the classification of the vegetation into three different canopy layers: a) heath shrubs, b) sparse trees and mallee, c) tall trees.
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Bellakaout, A., M. Cherkaoui, M. Ettarid, and A. Touzani. "Automatic 3D Extraction of Buildings, Vegetation and Roads from LIDAR Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 173–80. http://dx.doi.org/10.5194/isprs-archives-xli-b3-173-2016.
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Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain; it is becoming one of the important tools in the geosciences for studying objects and earth surface. Classification of Lidar data for extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, extraction of different derived data for geographical information systems (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, an automatic process is greatly required to handle the large amount of data collected because the manual process is time consuming and very expensive. <br><br> This paper is presenting an approach for automatic classification of aerial Lidar data into five groups of items: buildings, trees, roads, linear object and soil using single return Lidar and processing the point cloud without generating DEM. Topological relationship and height variation analysis is adopted to segment, preliminary, the entire point cloud preliminarily into upper and lower contours, uniform and non-uniform surface, non-uniform surfaces, linear objects, and others. <br><br> This primary classification is used on the one hand to know the upper and lower part of each building in an urban scene, needed to model buildings façades; and on the other hand to extract point cloud of uniform surfaces which contain roofs, roads and ground used in the second phase of classification. A second algorithm is developed to segment the uniform surface into buildings roofs, roads and ground, the second phase of classification based on the topological relationship and height variation analysis, The proposed approach has been tested using two areas : the first is a housing complex and the second is a primary school. The proposed approach led to successful classification results of buildings, vegetation and road classes.
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Vicari, Matheus B., Mathias Disney, Phil Wilkes, Andrew Burt, Kim Calders, and William Woodgate. "Leaf and wood classification framework for terrestrial LiDAR point clouds." Methods in Ecology and Evolution 10, no. 5 (January 30, 2019): 680–94. http://dx.doi.org/10.1111/2041-210x.13144.
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Liu, Shunan, Zhengwei Cao, Jiajia Liu, Chunan Lv, and Guoqiang Zhong. "Fusion of Multiple Basic Element Features for Airborne LiDAR in-house Surveys." Journal of Combinatorial Mathematics and Combinatorial Computing 120, no. 1 (June 30, 2024): 03–16. http://dx.doi.org/10.61091/jcmcc120-01.
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When using airborne LiDAR point clouds for city modelling and road extraction, point cloud classification is a crucial step. There are numerous ways for classifying point clouds, but there are still issues like redundant multi-dimensional feature vector data and poor point cloud classification in intricate situations. A point cloud classification method built on the fusing of multikernel feature vectors is suggested as a solution to these issues. The technique employs random forest to classify point cloud data by merging colour information, and it extracts feature vectors based on point primitives and object primitives, respectively. In this study, a densely populated area was chosen as the study area. Light airborne LIDAR mounted on a delta wing was used to collect point cloud data at a low altitude (170 m) over a dense cross-course. The point cloud data were then combined, corrected, and enhanced with texture data, and the houses were vectorized on the point cloud. The accuracy of the results was then assessed. With a median inaccuracy of 4.8 cm and a point cloud data collection rate of 83.3%, using airborne LIDAR to measure house corners can significantly lighten the labour associated with external house corner measurements.This test extracts the texture information of point cloud data through the efficient processing of high-density point cloud data, providing a reference for the application of texture information of airborne LIDAR data and a clear understanding of its accuracy.
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Spadavecchia, C., M. B. Campos, M. Piras, E. Puttonen, and A. Shcherbacheva. "WOOD-LEAF UNSUPERVISED CLASSIFICATION OF SILVER BIRCH TREES FOR BIOMASS ASSESSMENT USING OBLIQUE POINT CLOUDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 14, 2023): 1795–802. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1795-2023.
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Abstract. Forests play a fundamental role in carbon stocking since about a third of the carbon dioxide produced by activities of human origin is absorbed by forests. Forest biomass is an essential indicator of carbon dioxide absorption, enabling an understanding the interaction between forest dynamics and climate change effects. However, biomass and wood material changes are challenging to quantify in forest stands. Nowadays, recent 3D remote sensing technologies, such as laser scanning systems, have allowed accurate measures of single trees. This study evaluates three approaches to classify wood and non-wood materials and quantify biomass based on LiDAR data, aiming at biomass change detection. Specifically, we propose an automated methodology for estimating the single tree-level biomass of a portion of forest monitored through a LiDAR oblique acquisition. The classification of wood and foliage points was performed with machine learning algorithms, while the tree modelling was conducted rigorously through a Quantitative Structure Model (QSM). The purpose of this study is to evaluate (1) two different unsupervised and one semi-supervised classification approaches for wood and foliage separation and (2) the accuracy of the biomass assessment performed on a QSM-based approach on innovative LiDAR acquisitions. The results are promising; the wood-leaf classification performs effectively in all 20 silver birches considered; as regards the biomass, when the noise is limited, it is estimated in a manner consistent with the reference values calculated using an appropriate allometric equation. Higher values are found mainly in dense undergrowth, which negatively affects the modelling of the tree. The research is ongoing, and further tests will be performed to generalize the methodology on different tree species, deepen the multitemporal variability, and improve the accuracy of the assessment.
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Bellakaout, A., M. Cherkaoui, M. Ettarid, and A. Touzani. "Automatic 3D Extraction of Buildings, Vegetation and Roads from LIDAR Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 173–80. http://dx.doi.org/10.5194/isprsarchives-xli-b3-173-2016.
Full textAbstract:
Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain; it is becoming one of the important tools in the geosciences for studying objects and earth surface. Classification of Lidar data for extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, extraction of different derived data for geographical information systems (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, an automatic process is greatly required to handle the large amount of data collected because the manual process is time consuming and very expensive. <br><br> This paper is presenting an approach for automatic classification of aerial Lidar data into five groups of items: buildings, trees, roads, linear object and soil using single return Lidar and processing the point cloud without generating DEM. Topological relationship and height variation analysis is adopted to segment, preliminary, the entire point cloud preliminarily into upper and lower contours, uniform and non-uniform surface, non-uniform surfaces, linear objects, and others. <br><br> This primary classification is used on the one hand to know the upper and lower part of each building in an urban scene, needed to model buildings façades; and on the other hand to extract point cloud of uniform surfaces which contain roofs, roads and ground used in the second phase of classification. A second algorithm is developed to segment the uniform surface into buildings roofs, roads and ground, the second phase of classification based on the topological relationship and height variation analysis, The proposed approach has been tested using two areas : the first is a housing complex and the second is a primary school. The proposed approach led to successful classification results of buildings, vegetation and road classes.
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Botequim, Brigite, Paulo M. Fernandes, José G. Borges, Eduardo González-Ferreiro, and Juan Guerra-Hernández. "Improving silvicultural practices for Mediterranean forests through fire behaviour modelling using LiDAR-derived canopy fuel characteristics." International Journal of Wildland Fire 28, no. 11 (2019): 823. http://dx.doi.org/10.1071/wf19001.
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Wildfires cause substantial environmental and socioeconomic impacts and threaten many Spanish forested landscapes. We describe how LiDAR-derived canopy fuel characteristics and spatial fire simulation can be integrated with stand metrics to derive models describing fire behaviour. We assessed the potential use of very-low-density airborne LiDAR (light detection and ranging) data to estimate canopy fuel characteristics in south-western Spain Mediterranean forests. Forest type-specific equations were used to estimate canopy fuel attributes, namely stand height, canopy base height, fuel load, bulk density and cover. Regressions explained 61–85, 70–85, 38–96 and 75–95% of the variability in field estimated stand height, canopy fuel load, crown bulk density and canopy base height, respectively. The weakest relationships were found for mixed forests, where fuel loading variability was highest. Potential fire behaviour for typical wildfire conditions was predicted with FlamMap using LiDAR-derived canopy fuel characteristics and custom fuel models. Classification tree analysis was used to identify stand structures in relation to crown fire likelihood and fire suppression difficulty levels. The results of the research are useful for integrating multi-objective fire management decisions and effective fire prevention strategies within forest ecosystem management planning.
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Andersen, Mikkel Skovgaard, Áron Gergely, Zyad Al-Hamdani, Frank Steinbacher, Laurids Rolighed Larsen, and Verner Brandbyge Ernstsen. "Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment." Hydrology and Earth System Sciences 21, no. 1 (January 3, 2017): 43–63. http://dx.doi.org/10.5194/hess-21-43-2017.
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Abstract. The transition zone between land and water is difficult to map with conventional geophysical systems due to shallow water depth and often challenging environmental conditions. The emerging technology of airborne topobathymetric light detection and ranging (lidar) is capable of providing both topographic and bathymetric elevation information, using only a single green laser, resulting in a seamless coverage of the land–water transition zone. However, there is no transparent and reproducible method for processing green topobathymetric lidar data into a digital elevation model (DEM). The general processing steps involve data filtering, water surface detection and refraction correction. Specifically, the procedure of water surface detection and modelling, solely using green laser lidar data, has not previously been described in detail for tidal environments. The aim of this study was to fill this gap of knowledge by developing a step-by-step procedure for making a digital water surface model (DWSM) using the green laser lidar data. The detailed description of the processing procedure augments its reliability, makes it user-friendly and repeatable. A DEM was obtained from the processed topobathymetric lidar data collected in spring 2014 from the Knudedyb tidal inlet system in the Danish Wadden Sea. The vertical accuracy of the lidar data is determined to ±8 cm at a 95 % confidence level, and the horizontal accuracy is determined as the mean error to ±10 cm. The lidar technique is found capable of detecting features with a size of less than 1 m2. The derived high-resolution DEM was applied for detection and classification of geomorphometric and morphological features within the natural environment of the study area. Initially, the bathymetric position index (BPI) and the slope of the DEM were used to make a continuous classification of the geomorphometry. Subsequently, stage (or elevation in relation to tidal range) and a combination of statistical neighbourhood analyses (moving average and standard deviation) with varying window sizes, combined with the DEM slope, were used to classify the study area into six specific types of morphological features (i.e. subtidal channel, intertidal flat, intertidal creek, linear bar, swash bar and beach dune). The developed classification method is adapted and applied to a specific case, but it can also be implemented in other cases and environments.
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Vivek Nanda, Vishnu Mahesh, Perver Baran, Laura Tateosian, Stacy A. C. Nelson, and Jianxin Hu. "Classification of tree forms in aerial LiDAR point clouds using CNN for 3D tree modelling." International Journal of Remote Sensing 44, no. 22 (November 17, 2023): 7156–86. http://dx.doi.org/10.1080/01431161.2023.2282405.
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Nelson, Kailyn, Laura Chasmer, and Chris Hopkinson. "Quantifying Lidar Elevation Accuracy: Parameterization and Wavelength Selection for Optimal Ground Classifications Based on Time since Fire/Disturbance." Remote Sensing 14, no. 20 (October 11, 2022): 5080. http://dx.doi.org/10.3390/rs14205080.
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Pre- and post-fire airborne lidar data provide an opportunity to determine peat combustion/loss across broad spatial extents. However, lidar measurements of ground surface elevation are prone to uncertainties. Errors may be introduced in several ways, particularly associated with the timing of data collection and the classification of ground points. Ground elevation data must be accurate and precise when estimating relatively small elevation changes due to combustion and subsequent carbon losses. This study identifies the impact of post-fire vegetation regeneration on ground classification parameterizations for optimal accuracy using TerraScan and LAStools with airborne lidar data collected in three wavelengths: 532 nm, 1064 nm, and 1550 nm in low relief boreal peatland environments. While the focus of the study is on elevation accuracy and losses from fire, the research is also highly pertinent to hydrological modelling, forestry, geomorphological change, etc. The study area includes burned and unburned boreal peatlands south of Fort McMurray, Alberta. Lidar and field validation data were collected in July 2018, following the 2016 Horse River Wildfire. An iterative ground classification analysis was conducted whereby validation points were compared with lidar ground-classified data in five environments: road, unburned, burned with shorter vegetative regeneration (SR), burned with taller vegetative regeneration (TR), and cumulative burned (both SR and TR areas) in each of the three laser emission wavelengths individually, as well as combinations of 1550 nm and 1064 nm and 1550 nm, 1064 nm, and 532 nm. We find an optimal average elevational offset of ~0.00 m in SR areas with a range (RMSE) of ~0.09 m using 532 nm data. Average accuracy remains the same in cumulative burned and TR areas, but RMSE increased to ~0.13 m and ~0.16 m, respectively, using 1550 nm and 1064 nm combined data. Finally, data averages ~0.01 m above the field-measured ground surface in unburned boreal peatland and transition areas (RMSE of ~0.19 m) using all wavelengths combined. We conclude that the ‘best’ offset for depth of burn within boreal peatlands is expected to be ~0.01 m, with single point measurement uncertainties upwards of ~0.25 m (RMSE) in areas of tall, dense vegetation regeneration. The importance of classification parameterization identified in this study also highlights the need for more intelligent adaptative classification routines, which can be used in other environments.
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Wei, X., and X. Yao. "A Hybrid GWR-Based Height Estimation Method for Building Detection in Urban Environments." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2 (November 11, 2014): 23–29. http://dx.doi.org/10.5194/isprsannals-ii-2-23-2014.
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LiDAR has become important data sources in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated methods. The aerial photos, on the other hand, provide continuous spectral information of buildings. But the segmentation of the aerial photos cannot distinguish between the road surfaces and the building roof. This paper develops a geographically weighted regression (GWR)-based method to identify buildings. The method integrates characteristics derived from the sparse LiDAR data and from aerial photos. In the GWR model, LiDAR data provide the height information of spatial objects which is the dependent variable, while the brightness values from multiple bands of the aerial photo serve as the independent variables. The proposed method can thus estimate the height at each pixel from values of its surrounding pixels with consideration of the distances between the pixels and similarities between their brightness values. Clusters of contiguous pixels with higher estimated height values distinguish themselves from surrounding roads or other surfaces. A case study is conducted to evaluate the performance of the proposed method. It is found that the accuracy of the proposed hybrid method is better than those by image classification of aerial photos along or by height extraction of LiDAR data alone. We argue that this simple and effective method can be very useful for automatic detection of buildings in urban areas.
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Kippers, R. G., L. Moth, and S. J. Oude Elberink. "AUTOMATIC MODELLING OF 3D TREES USING AERIAL LIDAR POINT CLOUD DATA AND DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 179–84. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-179-2021.
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Abstract. 3D tree objects can be used in various applications, like estimation of physiological equivalent temperature (PET). During this project, a method is designed to extract 3D tree objects from a country-wide point cloud. To apply this method on large scale, the algorithm needs to be efficient. Extraction of trees is done in two steps: point-wise classification using the PointNet deep learning network, and Watershed segmentation to split points into individual trees. After that, 3D tree models are made. The method is evaluated on 3 areas, a park, city center and housing block in the city of Deventer, the Netherlands. This resulted into an average accuracy of 92% and a F1-score of 0.96.
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Zaki, N. A. M., Z. A. Latif, M. N. Suratman, and M. Z. Zainal. "MODELLING THE CARBON STOCKS ESTIMATION OF THE TROPICAL LOWLAND DIPTEROCARP FOREST USING LIDAR AND REMOTELY SENSED DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 187–94. http://dx.doi.org/10.5194/isprsannals-iii-7-187-2016.
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Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear regression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland <i>Dipterocarp</i> forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respectively, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p < 0.01) with correlation coefficient (r) is 0.909 which shown that there a good relationship between carbon and DBH predictors to improve the inventory estimates of carbon using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland <i>Dipterocarp</i> forest.
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Zaki, N. A. M., Z. A. Latif, M. N. Suratman, and M. Z. Zainal. "MODELLING THE CARBON STOCKS ESTIMATION OF THE TROPICAL LOWLAND DIPTEROCARP FOREST USING LIDAR AND REMOTELY SENSED DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 187–94. http://dx.doi.org/10.5194/isprs-annals-iii-7-187-2016.
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Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear regression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland <i>Dipterocarp</i> forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respectively, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p < 0.01) with correlation coefficient (r) is 0.909 which shown that there a good relationship between carbon and DBH predictors to improve the inventory estimates of carbon using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland <i>Dipterocarp</i> forest.
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Varela-González, M., B. Riveiro, P. Arias-Sánchez, H. González-Jorge, and J. Martínez-Sánchez. "A CityGML extension for traffic-sign objects that guides the automatic processing of data collected using Mobile Mapping technology." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 415–20. http://dx.doi.org/10.5194/isprsarchives-xl-1-415-2014.
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The rapid evolution of integral schemes, accounting for geometric and semantic data, has been importantly motivated by the advances in the last decade in mobile laser scanning technology; automation in data processing has also recently influenced the expansion of the new model concepts. This paper reviews some important issues involved in the new paradigms of city 3D modelling: an interoperable schema for city 3D modelling (cityGML) and mobile mapping technology to provide the features that composing the city model. This paper focuses in traffic signs, discussing their characterization using cityGML in order to ease the implementation of LiDAR technology in road management software, as well as analysing some limitations of the current technology in the labour of automatic detection and classification.
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Peykova, Ana, Dessislava Petrova-Antonova, and Kaloyan Karamitov. "Data Processing and Enrichment of LiDAR-Derived Traffic Data." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W5-2024 (June 27, 2024): 263–70. http://dx.doi.org/10.5194/isprs-annals-x-4-w5-2024-263-2024.
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Abstract. With the escalating demand for efficient traffic management and the increasing complexity of traffic control, diverse sensor technologies have been implemented to measure traffic in real-time. The road-side LiDAR emerges as a novel technology addressing the data gap in multimodal traffic analyses. LiDAR sensing return time to precisely capture distance and reflectivity, generating point cloud data encompassing all traffic trajectory information. It overcomes challenges posed by illumination conditions like light, dust and fog, which often affect camera sensor performance. In addition, LiDAR sensing minimises the effect of changing object position and angles, simplifying object detection and recognition.This paper tackles the challenges of analysing LiDAR-derived traffic data by proposing a method for traffic trajectory data enrichment. The methodology followed includes creating a semantic map, bridging the physical space and raw data, transforming from a local to a standard Coordinate Reference System (CRS) and enriching data trajectory representation. Three use cases are presented based on the dataset obtained after enrichment: object classification, permissible directions violation detection, and traffic flow density. The proposed method is validated using traffic data from a LiDAR system of 6 sensors located in one of the busiest intersections in Sofia, Bulgaria. The raw sensor data is processed by a fusion box called the Augmented LiDAR Box, delivering time series frames with labelled moving objects in .osef format. The results prove that the proposed data enrichment method successfully transforms the trajectories into semantic sequences, opening up new avenues for trajectory analysis and intersection traffic micro-modelling.
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Singh, Minerva, Damian Evans, Jean-Baptiste Chevance, Boun Suy Tan, Nicholas Wiggins, Leaksmy Kong, and Sakada Sakhoeun. "Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia." PeerJ 7 (October 22, 2019): e7841. http://dx.doi.org/10.7717/peerj.7841.
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This study develops a modelling framework by utilizing multi-sensor imagery for classifying different forest and land use types in the Phnom Kulen National Park (PKNP) in Cambodia. Three remote sensing datasets (Landsat optical data, ALOS L-band data and LiDAR derived Canopy Height Model (CHM)) were used in conjunction with three different machine learning (ML) regression techniques (Support Vector Machines (SVM), Random Forests (RF) and Artificial Neural Networks (ANN)). These ML methods were implemented on (a) Landsat spectral data, (b) Landsat spectral band & ALOS backscatter data, and (c) Landsat spectral band, ALOS backscatter data, & LiDAR CHM data. The Landsat-ALOS combination produced more accurate classification results (95% overall accuracy with SVM) compared to Landsat-only bands for all ML models. Inclusion of LiDAR CHM (which is a proxy for vertical canopy heights) improved the overall accuracy to 98%. The research establishes that majority of PKNP is dominated by cashew plantations and the nearly intact forests are concentrated in the more inaccessible parts of the park. The findings demonstrate how different RS datasets can be used in conjunction with different ML models to map forests that had undergone varying levels of degradation and plantations.
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Magosi, Zoltan Ferenc, Hexuan Li, Philipp Rosenberger, Li Wan, and Arno Eichberger. "A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving." Sensors 22, no. 15 (July 29, 2022): 5693. http://dx.doi.org/10.3390/s22155693.
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Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated.
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Mihu-Pintilie and Nicu. "GIS-based Landform Classification of Eneoli thic Archaeological Sites in the Plateau-plain Transition Zone (NE Romania): Habitation Practices vs. Flood Hazard Perception." Remote Sensing 11, no. 8 (April 15, 2019): 915. http://dx.doi.org/10.3390/rs11080915.
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The landforms of the Earth’s surface ranging from large-scale features to local topography are factors that influence human behavior in terms of habitation practices. The ability to extract geomorphological settings using geoinformatic techniques is an important aspect of any environmental analysis and archaeological landscape approach. Morphological data derived from DEMs with high accuracies (e.g., LiDAR data), can provide valuable information related to landscape modelling and landform classification processes. This study applies the first landform classification and flood hazard vulnerability of 730 Eneolithic (ca. 5000–3500 BCE) settlement locations within the plateau-plain transition zone of NE Romania. The classification was done using the SD (standard deviation) of TPI (Topographic Position Index) for the mean elevation (DEV) around each archaeological site, and HEC-RAS flood hazard pattern generated for 0.1% (1000 year) discharge insurance. The results indicate that prehistoric communities preferred to place their settlements for defensive purposes on hilltops, or in the close proximity of a steep slope. Based on flood hazard pattern, 8.2% out of the total sites had been placed in highly vulnerable areas. The results indicate an eco-cultural niche connected with habitation practices and flood hazard perception during the Eneolithic period in the plateau-plain transition zone of NE Romania and contribute to archaeological predictive modelling.
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Revilla, Sergio, María Lamelas, Darío Domingo, Juan de la Riva, Raquel Montorio, Antonio Montealegre, and Alberto García-Martín. "Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping." Remote Sensing 13, no. 3 (January 20, 2021): 342. http://dx.doi.org/10.3390/rs13030342.
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Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction.
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Yoshida, Keisuke, Shijun Pan, Junichi Taniguchi, Satoshi Nishiyama, Takashi Kojima, and Md Touhidul Islam. "Airborne LiDAR-assisted deep learning methodology for riparian land cover classification using aerial photographs and its application for flood modelling." Journal of Hydroinformatics 24, no. 1 (January 1, 2022): 179–201. http://dx.doi.org/10.2166/hydro.2022.134.
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Abstract In response to challenges in land cover classification (LCC), many researchers have experimented recently with classification methods based on artificial intelligence techniques. For LCC mapping of the vegetated Asahi River in Japan, the current study uses deep learning (DL)-based DeepLabV3+ module for image segmentation of aerial photographs. We modified the existing model by concatenating data on its resultant output port to access the airborne laser bathymetry (ALB) dataset, including voxel-based laser points and vegetation height (i.e. digital surface model data minus digital terrain model data). Findings revealed that the modified approach improved the accuracy of LCC greatly compared to our earlier unsupervised ALB-based method, with 25 and 35% improvement, respectively, in overall accuracy and the macro F1-score for November 2017 dataset (no–leaf condition). Finally, by estimating flow-resistance parameters in flood modelling using LCC mapping-derived data, we conclude that the upgraded DL methodology produces better fit between numerically analyzed and observed peak water levels.
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Wei, Xuebin, and Xiaobai Yao. "3D Model Construction in an Urban Environment from Sparse LiDAR Points and Aerial Photos—a Statistical Approach." GEOMATICA 69, no. 3 (September 2015): 271–84. http://dx.doi.org/10.5623/cig2015-302.
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Light Detection and Ranging (LiDAR) has become an important data source in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated algorithms. The aerial photos, on the other hand, provide continuous spectral information on buildings. However, the accuracy of classified building boundaries from aerial photos is constrained when building roofs and their surroundings share analogous spectral characteristics. This paper develops a statistical approach that can integrate characteristic variables derived from sparse LiDAR points and air photos to detect buildings by estimating object heights and identifying clusters of similar heights. Within this study, the approach chooses a local regression method, namely geographically-weighted regression (GWR), to account for local variations of building surface height. In the GWR model, LiDAR data provide the height information of spatial objects, which is the dependent variable, while the brightness values from visible bands of the aerial photo serve as the independent variables. The established GWR model estimates the height at each pixel based on height values of its surrounding pixels with consideration of the distances between the pixels as well as similarities between their brightness values in visible bands. Clusters of contiguous pixels with higher estimated height val ues distinguish themselves from surrounding roads or other surfaces. A case study is conducted to evaluate the performance of the proposed method. It is found that the accuracy of the proposed statistical method is better than those by image classification of aerial photos alone or by building extraction of LiDAR data alone. The results demonstrate that this simple and effective method can be very useful for automatic detection of buildings in urban areas. The approach can be most helpful for studies of urban areas where more suitable but expensive high resolution data are not available.
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Wang, Di, Stéphane Momo Takoudjou, and Eric Casella. "LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR." Methods in Ecology and Evolution 11, no. 3 (January 23, 2020): 376–89. http://dx.doi.org/10.1111/2041-210x.13342.
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Chebrolu, Nived, Philipp Lottes, Alexander Schaefer, Wera Winterhalter, Wolfram Burgard, and Cyrill Stachniss. "Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields." International Journal of Robotics Research 36, no. 10 (July 23, 2017): 1045–52. http://dx.doi.org/10.1177/0278364917720510.
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There is an increasing interest in agricultural robotics and precision farming. In such domains, relevant datasets are often hard to obtain, as dedicated fields need to be maintained and the timing of the data collection is critical. In this paper, we present a large-scale agricultural robot dataset for plant classification as well as localization and mapping that covers the relevant growth stages of plants for robotic intervention and weed control. We used a readily available agricultural field robot to record the dataset on a sugar beet farm near Bonn in Germany over a period of three months in the spring of 2016. On average, we recorded data three times per week, starting at the emergence of the plants and stopping at the state when the field was no longer accessible to the machinery without damaging the crops. The robot carried a four-channel multi-spectral camera and an RGB-D sensor to capture detailed information about the plantation. Multiple lidar and global positioning system sensors as well as wheel encoders provided measurements relevant to localization, navigation, and mapping. All sensors had been calibrated before the data acquisition campaign. In addition to the data recorded by the robot, we provide lidar data of the field recorded using a terrestrial laser scanner. We believe this dataset will help researchers to develop autonomous systems operating in agricultural field environments. The dataset can be downloaded from http://www.ipb.uni-bonn.de/data/sugarbeets2016/ .
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Trouvé, Raphael, Ruizhu Jiang, Melissa Fedrigo, Matt D. White, Sabine Kasel, Patrick J. Baker, and Craig R. Nitschke. "Combining Environmental, Multispectral, and LiDAR Data Improves Forest Type Classification: A Case Study on Mapping Cool Temperate Rainforests and Mixed Forests." Remote Sensing 15, no. 1 (December 22, 2022): 60. http://dx.doi.org/10.3390/rs15010060.
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Predictive vegetation mapping is an essential tool for managing and conserving high conservation-value forests. Cool temperate rainforests (Rainforest) and cool temperate mixed forests (Mixed Forest, i.e., rainforest spp. overtopped by large remnant Eucalyptus trees) are threatened forest types in the Central Highlands of Victoria. Logging of these forest types is prohibited; however, the surrounding native Eucalyptus forests can be logged in some areas of the landscape. This requires accurate mapping and delineation of these vegetation types. In this study, we combine niche modelling, multispectral imagery, and LiDAR data to improve predictive vegetation mapping of these two threatened ecosystems in southeast Australia. We used a dataset of 1586 plots partitioned into four distinct forest types that occur in close proximity in the Central Highlands: Eucalyptus, Tree fern, Mixed Forest, and Rainforest. We calibrated our model on a training dataset and validated it on a spatially distinct testing dataset. To avoid overfitting, we used Bayesian regularized multinomial regression to relate predictors to our four forest types. We found that multispectral predictors were able to distinguish Rainforest from Eucalyptus forests due to differences in their spectral signatures. LiDAR-derived predictors were effective at discriminating Mixed Forest from Rainforest based on forest structure, particularly LiDAR predictors based on existing domain knowledge of the system. For example, the best predictor of Mixed Forest was the presence of Rainforest-type understorey overtopped by large Eucalyptus crowns, which is effectively aligned with the regulatory definition of Mixed Forest. Environmental predictors improved model performance marginally, but helped discriminate riparian forests from Rainforest. However, the best model for classifying forest types was the model that included all three classes of predictors (i.e., spectral, structural, and environmental). Using multiple data sources with differing strengths improved classification accuracy and successfully predicted the identity of 88% of the plots. Our study demonstrated that multi-source methods are important for capturing different properties of the data that discriminate ecosystems. In addition, the multi-source approach facilitated adding custom metrics based on domain knowledge which in turn improved the mapping of high conservation-value forest.
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Mittelmeier, Niko, Julian Allin, Tomas Blodau, Davide Trabucchi, Gerald Steinfeld, Andreas Rott, and Martin Kühn. "An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects." Wind Energy Science 2, no. 2 (October 18, 2017): 477–90. http://dx.doi.org/10.5194/wes-2-477-2017.
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Abstract. For offshore wind farms, wake effects are among the largest sources of losses in energy production. At the same time, wake modelling is still associated with very high uncertainties. Therefore current research focusses on improving wake model predictions. It is known that atmospheric conditions, especially atmospheric stability, crucially influence the magnitude of those wake effects. The classification of atmospheric stability is usually based on measurements from met masts, buoys or lidar (light detection and ranging). In offshore conditions these measurements are expensive and scarce. However, every wind farm permanently produces SCADA (supervisory control and data acquisition) measurements. The objective of this study is to establish a classification for the magnitude of wake effects based on SCADA data. This delivers a basis to fit engineering wake models better to the ambient conditions in an offshore wind farm. The method is established with data from two offshore wind farms which each have a met mast nearby. A correlation is established between the stability classification from the met mast and signals within the SCADA data from the wind farm. The significance of these new signals on power production is demonstrated with data from two wind farms with met mast and long-range lidar measurements. Additionally, the method is validated with data from another wind farm without a met mast. The proposed signal consists of a good correlation between the standard deviation of active power divided by the average power of wind turbines in free flow with the ambient turbulence intensity (TI) when the wind turbines were operating in partial load. It allows us to distinguish between conditions with different magnitudes of wake effects. The proposed signal is very sensitive to increased turbulence induced by neighbouring turbines and wind farms, even at a distance of more than 38 rotor diameters.
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Pirotti, F., A. Guarnieri, A. Masiero, A. Vettore, and E. Lingua. "Processing lidar waveform data for 3D visual assessment of forest environments." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5 (June 6, 2014): 493–99. http://dx.doi.org/10.5194/isprsarchives-xl-5-493-2014.
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The objective of this report is to present and discuss a work-flow for extracting, from full-waveform (FW) lidar data, formats which are compatible with common information systems (GIS) and statistical software packages. Full-waveform, specifically for forestry, got attention from the scientific community because a more in-depth analysis can add valuable information for classification and modelling of related variables (e.g. biomass). In order to assess if this is feasible and if the results are useful, the end-user has to deal with raw datasets from lidar sensors. In this study case we propose and test a work-flow which is implemented through a selfdeveloped software integrating ad-hoc C++ libraries and a graphical user interface for an easier approach by end-users. This software allows the user to add raw FW data and produce several products which can successively be easily imported in GIS or statistical software. To achieve this we used some state-of-the-art methods which have been extensively reported in literature and we discuss results and future developments. Results show that this software package can effectively work as a tool for linking raw FW data with forest-related spatial processing by providing punctual information directly derived from the FW data or area-based aggregated information for a more generalized description of the earth surface.
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Sicard, M., G. D'Amico, A. Comerón, L. Mona, L. Alados-Arboledas, A. Amodeo, H. Baars, et al. "EARLINET: potential operationality of a research network." Atmospheric Measurement Techniques Discussions 8, no. 7 (July 1, 2015): 6599–659. http://dx.doi.org/10.5194/amtd-8-6599-2015.
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Abstract. In the framework of ACTRIS summer 2012 measurement campaign (8 June–17 July 2012), EARLINET organized and performed a controlled exercise of feasibility to demonstrate its potential to perform operational, coordinated measurements and deliver products in near-real time. Eleven lidar stations participated to the exercise which started on 9 July 2012 at 06:00 UT and ended 72 h later on 12 July at 06:00 UT. For the first time the Single-Calculus Chain (SCC), the common calculus chain developed within EARLINET for the automatic evaluation of lidar data from raw signals up to the final products, was used. All stations sent in real time measurements of 1 h of duration to the SCC server in a predefined netcdf file format. The pre-processing of the data was performed in real time by the SCC while the optical processing was performed in near-real time after the exercise ended. 98 and 84 % of the files sent to SCC were successfully pre-processed and processed, respectively. Those percentages are quite large taking into account that no cloud screening was performed on lidar data. The paper shows time series of continuous and homogeneously obtained products retrieved at different levels of the SCC: range-square corrected signals (pre-processing) and daytime backscatter and nighttime extinction coefficient profiles (optical processing), as well as combined plots of all direct and derived optical products. The derived products include backscatter- and extinction-related Ångström exponents, lidar ratios and color ratios. The combined plots reveal extremely valuable for aerosol classification. The efforts made to define the measurements protocol and to configure properly the SCC pave the way for applying this protocol for specific applications such as the monitoring of special events, atmospheric modelling, climate research and calibration/validation activities of spaceborne observations.
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Behley, Jens, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Jürgen Gall, and Cyrill Stachniss. "Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset." International Journal of Robotics Research 40, no. 8-9 (April 20, 2021): 959–67. http://dx.doi.org/10.1177/02783649211006735.
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A holistic semantic scene understanding exploiting all available sensor modalities is a core capability to master self-driving in complex everyday traffic. To this end, we present the SemanticKITTI dataset that provides point-wise semantic annotations of Velodyne HDL-64E point clouds of the KITTI Odometry Benchmark. Together with the data, we also published three benchmark tasks for semantic scene understanding covering different aspects of semantic scene understanding: (1) semantic segmentation for point-wise classification using single or multiple point clouds as input; (2) semantic scene completion for predictive reasoning on the semantics and occluded regions; and (3) panoptic segmentation combining point-wise classification and assigning individual instance identities to separate objects of the same class. In this article, we provide details on our dataset showing an unprecedented number of fully annotated point cloud sequences, more information on our labeling process to efficiently annotate such a vast amount of point clouds, and lessons learned in this process. The dataset and resources are available at http://www.semantic-kitti.org .
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Park, Yujin, and Jean-Michel Guldmann. "Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach." Computers, Environment and Urban Systems 75 (May 2019): 76–89. http://dx.doi.org/10.1016/j.compenvurbsys.2019.01.004.
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Ortiz-Amezcua, Pablo, Alodía Martínez-Herrera, Antti J. Manninen, Pyry P. Pentikäinen, Ewan J. O’Connor, Juan Luis Guerrero-Rascado, and Lucas Alados-Arboledas. "Wind and Turbulence Statistics in the Urban Boundary Layer over a Mountain–Valley System in Granada, Spain." Remote Sensing 14, no. 10 (May 11, 2022): 2321. http://dx.doi.org/10.3390/rs14102321.
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Urban boundary layer characterization is currently a challenging and relevant issue, because of its role in weather and air quality modelling and forecast. In many cities, the effect of complex topography at local scale makes this modelling even more complicated. This is the case of mid-latitude urban areas located in typical basin topographies, which usually present low winds and high turbulence within the atmospheric boundary layer (ABL). This study focuses on the analysis of the first ever measurements of wind with high temporal and vertical resolution throughout the ABL over a medium-sized city surrounded by mountains in southern Spain. These measurements have been gathered with a scanning Doppler lidar system and analyzed using the Halo lidar toolbox processing chain developed at the Finnish Meteorological Institute. We have used the horizontal wind product and the ABL turbulence classification product to carry out a statistical study using a two-year database. The data availability in terms of maximum analyzed altitudes for statistically significant results was limited to around 1000–1500 m above ground level (a.g.l.) due to the decreasing signal intensity with height that also depends on aerosol load. We have analyzed the differences and similarities in the diurnal evolution of the horizontal wind profiles for different seasons and their modelling with Weibull and von Mises probability distributions, finding a general trend of mean daytime wind from the NW with mean speeds around 3–4 m/s at low altitudes and 6–10 m/s at higher altitudes, and weaker mean nocturnal wind from the SE with similar height dependence. The highest speeds were observed during spring, and the lowest during winter. Finally, we studied the turbulent sources at the ABL with temporal (for each hour of the day) and height resolution. The results show a clear convective activity during daytime at altitudes increasing with time, and a significant wind-shear-driven turbulence during night-time.
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Pöchtrager, Markus, Gudrun Styhler-Aydın, Marina Döring-Williams, and Norbert Pfeifer. "Digital reconstruction of historic roof structures: developing a workflow for a highly automated analysis." Virtual Archaeology Review 9, no. 19 (July 20, 2018): 21. http://dx.doi.org/10.4995/var.2018.8855.
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<p>Planning on adaptive reuse, maintenance and restoration of historic timber structuresrequiresextensive architectural and structural analysis of the actual condition. Current methods for a modellingof roof constructions consist of several manual steps including the time-consuming dimensional modelling. The continuous development of terrestrial laser scanners increases the accuracy, comfort and speed of the surveying work inroof constructions. Resultingpoint clouds enabledetailed visualisation of theconstructionsrepresented by single points or polygonal meshes, but in fact donot containinformation about the structural system and the beam elements. The developed workflow containsseveral processing steps on the point cloud dataset. The most important among them arethenormal vector computation, the segmentation of points to extract planarfaces, a classification of planarsegmentsto detect the beam side facesand finally theparametric modelling of the beams on the basis of classified segments. Thisenablesa highly automated transitionfrom raw point cloud data to a geometric model containing beams of the structural system. The geometric model,as well as additional information about the structural properties of involved wooden beams and their joints,is necessaryinput for a furtherstructural modellingof timber constructions. The results of the workflow confirm that the proposed methods work well for beams with a rectangularcross-section and minor deformations. Scan shadows and occlusionof beamsby additional installationsor interlockingbeamsdecreases the modelling performance, but in generala high level ofaccuracy and completeness isachieved ata high degree of automation</p><div data-canvas-width="62.83200000000001"><strong>Highlights:</strong></div><div data-canvas-width="62.83200000000001"> </div><div><ul><li><p>This article presents a novel approach to automated reconstruction of beam structures by modelling geometry from segmented point clouds.</p></li><li><p>Wooden beams are modelled as cuboids, thus a rectangular cross-section with minor deformation is required.</p></li><li><p>An accuracy of less than 1 cm can be reached for modelled beams, compared to the reference LiDAR point cloud.</p></li></ul></div>
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Gruen, A., S. Schubiger, R. Qin, G. Schrotter, B. Xiong, J. Li, X. Ling, C. Xiao, S. Yao, and F. Nuesch. "SEMANTICALLY ENRICHED HIGH RESOLUTION LOD 3 BUILDING MODEL GENERATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W15 (September 23, 2019): 11–18. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w15-11-2019.
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<p><strong>Abstract.</strong> This paper reports about an effort to generate LoD3 models of buildings semi-automatically, with the highest possible level of automation. It is work in progress. We use multi-sensor data like aerial images from a 5-head camera with a GSD of 10&thinsp;cm, UAV images, and aerial and mobile LiDAR point clouds. We distinguish two cases: LoD2 models are available and they are not. We apply Multi-Photo Geometrically Constrained Least Squares Matching for different kind of point measurements. The regularity of many building fa&ccedil;ades in Singapore leads us to the idea to generalize the measurement procedure towards using measurement macros (geometrical primitives, i.e. windows, doors, etc.) and combine reality-based with procedural modelling. In parallel we try to model these fa&ccedil;ade elements from LiDAR point cloud data. In another research line we do building detection by a novel approach to land-cover classification, incorporating features of the fa&ccedil;ades to improve the classification accuracy. To generate the semantic labels of the fa&ccedil;ades, we developed a spatially unrelated mean-shift clustering method to yield structurally confined segments. It is the characteristic of automated and even semi-automated procedures that the results need some amount of editing. We therefore work on interactive post-editing approaches on CityGML building models containing semantic information of each surface. Maintaining the semantic information throughout the editing process is essential but often lack the support from current tools. Accordingly, we implement a method to synchronize CityGML models. Overall this project consists of a great number of different algorithmic components, which can only be coarsely explained in this paper.</p>
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Žabota, Barbara, Matjaž Mikoš, and Milan Kobal. "Rockfall Modelling in Forested Areas: The Role of Digital Terrain Model Grid Cell Size." Applied Sciences 11, no. 4 (February 5, 2021): 1461. http://dx.doi.org/10.3390/app11041461.
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This article examines how digital terrain model (DTM) grid cell size influences rockfall modelling using a probabilistic process-based model, Rockyfor3D, while taking into account the effect of forest on rockfall propagation and runout area. Two rockfall sites in the Trenta valley, NW Slovenia, were chosen as a case study. The analysis included DTM square grid cell sizes of 1, 2, 5, and 10 m, which were extracted from LiDAR data. In the paper, we compared results of rockfall propagation and runout areas, maximum kinetic energy, and maximum passing height between different grid cell sizes and forest/no forest scenario, namely by using goodness-of-fit indices (average index, success index, distance to the perfect classification, true skill statistics). The results show that the accuracy of the modelled shape of rockfall propagation and runout area decreases with larger DTM grid cell sizes. The forest has the important effect of reducing the rockfall propagation only at DTM1 and DTM2 and only if the distance between the source area and forest is large enough. Higher deviations of the maximum kinetic energy are present at DTMs with larger grid cell size, while differences are smaller at more DTMs with smaller grid cell sizes. Maximum passing height varies the most at DTM1 in the forest scenario, while at other DTMs, it does not experience larger deviations in the two scenarios.
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Wang, Yanjun, Yunhao Lin, Huiqing Huang, Shuhan Wang, Shicheng Wen, and Hengfan Cai. "A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework." Remote Sensing 15, no. 18 (September 8, 2023): 4432. http://dx.doi.org/10.3390/rs15184432.
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Deep learning has gained widespread interest in the task of building semantic segmentation modelling using remote sensing images; however, neural network models require a large number of training samples to achieve better classification performance, and the models are more sensitive to error patches in the training samples. The training samples obtained in semi-supervised classification methods need less reliable weakly labelled samples, but current semi-supervised classification research puts the generated weak samples directly into the model for applications, with less consideration of the impact of the accuracy and quality improvement of the weak samples on the subsequent model classification. Therefore, to address the problem of generating and optimising the quality of weak samples from training data in deep learning, this paper proposes a semi-supervised building classification framework. Firstly, based on the test results of the remote sensing image segmentation model and the unsupervised classification results of LiDAR point cloud data, this paper quickly generates weak image samples of buildings. Secondly, in order to improve the quality of the spots of the weak samples, an iterative optimisation strategy of the weak samples is proposed to compare and analyse the weak samples with the real samples and extract the accurate samples from the weak samples. Finally, the real samples, the weak samples, and the optimised weak samples are input into the semantic segmentation model of buildings for accuracy evaluation and analysis. The effectiveness of this paper’s approach was experimentally verified on two different building datasets, and the optimised weak samples improved by 1.9% and 0.6%, respectively, in the test accuracy mIoU compared to the initial weak samples. The results demonstrate that the semi-supervised classification framework proposed in this paper can be used to alleviate the model’s demand for a large number of real-labelled samples while improving the ability to utilise weak samples, and it can be used as an alternative to fully supervised classification methods in deep learning model applications that require a large number of training samples.
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Mihu-Pintilie, Alin, Casandra Brașoveanu, and Cristian Constantin Stoleriu. "Using UAV Survey, High-Density LiDAR Data and Automated Relief Analysis for Habitation Practices Characterization during the Late Bronze Age in NE Romania." Remote Sensing 14, no. 10 (May 20, 2022): 2466. http://dx.doi.org/10.3390/rs14102466.
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The characterization of prehistoric human behavior in terms of habitation practices using GIS cartography methods is an important aspect of any modern geoarchaeological approach. Furthermore, using unmanned aerial vehicle (UAV) surveys to identify archaeological sites with temporal resolution during the spring agro-technical works and automated mapping of the geomorphological features based on LiDAR-derived DEM can provide valuable information about the human–landscape relationships and lead to accurate archaeological and cartographic products. In this study, we applied a GIS-based landform classification method to relief characterization of 362 Late Bronze Age (LBA) settlements belonging to Noua Culture (NC) (cal. 1500/1450-1100 BCE) located in the Jijia catchment (NE Romania). For this purpose, we used an adapted version of Topographic Position Index (TPI) methodology, abbreviated DEV, which consists of: (1) application of standard deviation of TPI for the mean elevation (DEV) around each analyzed LBA site (1000 m buffer zone); (2) classification of the archaeological site’s location using six slope position classes (first method), or ten morphological classes by combining the parameters from two small-DEV and large-DEV neighborhood sizes (second method). The results indicate that the populations belonging to Noua Culture preferred to place their settlements on hilltops but close to the steep slope and on the small hills/local ridges in large valleys. From a geoarchaeological perspective, the outcomes indicate a close connection between occupied landform patterns and habitation practices during the Late Bronze Age and contribute to archaeological predictive modelling in the Jijia catchment (NE Romania).
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Bernales, A. M., J. A. Antolihao, C. Samonte, F. Campomanes, R. J. Rojas, A. M. dela Serna, and J. Silapan. "MODELLING THE RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND LANDSCAPE PATTERNS OF LAND USE LAND COVER CLASSIFICATION USING MULTI LINEAR REGRESSION MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 851–56. http://dx.doi.org/10.5194/isprs-archives-xli-b8-851-2016.
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The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric “Effective mesh size” was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.
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Bernales, A. M., J. A. Antolihao, C. Samonte, F. Campomanes, R. J. Rojas, A. M. dela Serna, and J. Silapan. "MODELLING THE RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND LANDSCAPE PATTERNS OF LAND USE LAND COVER CLASSIFICATION USING MULTI LINEAR REGRESSION MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 851–56. http://dx.doi.org/10.5194/isprsarchives-xli-b8-851-2016.
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The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric “Effective mesh size” was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.
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Büyüksalih, I., S. Bayburt, M. Schardt, and G. Büyüksalih. "FOREST STEM VOLUME CALCULATION USING AIRBORNE LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 265–70. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-265-2017.
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Airborne LiDAR data have been collected for the city of Istanbul using Riegl laser scanner Q680i with 400&thinsp;kHz and an average flight height of 600&thinsp;m. The flight campaign was performed by a helicopter and covers an area of 5400&thinsp;km<sup>2</sup>. According to a flight speed of 80 knot a point density of more than 16 points/m<sup>2</sup> and a laser footprint size of 30&thinsp;cm could be achieved. As a result of bundle adjustment, in total, approximately 17,000 LAS files with the file size of 500&thinsp;m by 700&thinsp;m have been generated for the whole city. The main object classes Ground, Building, Vegetation (medium, high) were derived from these LAS files using the macros in Terrasolid software. The forest area under investigation is located northwest of the city of Istanbul, main tree species occurring in the test site are pine (pinus pinaster), oak (quercus) and beech (fagus). In total, 120 LAS tiles covering the investigation area have been analysed using the software IMPACT of Joanneum Research Forschungsgesellschaft, Graz, Austria. First of all, the digital terrain model (DTM) and the digital surface models (DSM) were imported and converted into a raster file from the original laser point clouds with a spatial resolution of 50&thinsp;cm. Then, a normalized digital surface model (nDSM) was derived as the difference between DSM and the DTM. Tree top detection was performed by multi – resolution filter operations and tree crowns were segmented by a region growing algorithms develop specifically for this purpose. Breast Height Diameter (BHD) was calculated on the base of tree height and crown areas derived from image segmentation applying allometric functions found in literature. The assessment of stem volume was then calculated as a function of tree height and BHD. A comparison of timber volume estimated from the LiDAR data and field plots measured by the Forest Department of Istanbul showed R2 of 0.46. The low correlation might arise either from the low quality of the field plots or from the inadequacy of the allometric functions used for BHD and stem volume modelling. Further investigations therefore will concentrate both on improving the quality of field measurements and the adoption of the allometric functions to the specific site condition of the forests under investigation. Finally stand boundaries of the forest area made available by the forest department of Istanbul were superimposed to the LiDAR data and the single tree measurements aggregated to the stand level. <br><br> Aside from the LiDAR data, a Pleiades multispectral image characterized by four spectral bands (blue, green, red and near infrared) and a GSD of 2.8&thinsp;m has been used for the classification of different tree species. For this purpose the near infrared band covering the spectral range of 0.75&thinsp;μm to 0.90&thinsp;μm has been utilized and the IMPACT software used.
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Zhang, J., S. Huang, E. H. Hogg, V. Lieffers, Y. Qin, and F. He. "Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data." Biogeosciences Discussions 10, no. 12 (December 4, 2013): 19005–44. http://dx.doi.org/10.5194/bgd-10-19005-2013.
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Abstract. Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present biomass carbon storage in Alberta, Canada, by taking advantage of a spatially explicit dataset derived from a combination of forest inventory data from 1968 plots and spaceborne light detection and ranging (LiDAR) canopy height data. Ten climatic variables together with elevation, were used for model development and assessment. Four approaches, including spatial interpolation, non-spatial and spatial regression models, and decision-tree based modelling with random forests algorithm (a machine-learning technique), were compared to find the "best" estimates. We found that the random forests approach provided the best accuracy for biomass estimates. Non-spatial and spatial regression models gave estimates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total biomass stock in Alberta forests was estimated to be 3.11 × 109 Mg, with the average biomass density of 77.59 Mg ha−1. At the species level, three major tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.91 × 109 Mg biomass, accounting for 61% of total estimated biomass. Spatial distribution of biomass varied with natural regions, land cover types, and species. And the relative importance of predictor variables on determining biomass distribution varied with species. This study showed that the combination of ground-based inventory data, spaceborne LiDAR data, land cover classification, climatic and environmental variables was an efficient way to estimate the quantity, distribution and variation of forest biomass carbon stocks across large regions.
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Trouvé, Raphaël, Ruizhu Jiang, Patrick J. Baker, Sabine Kasel, and Craig R. Nitschke. "Identifying Old-Growth Forests in Complex Landscapes: A New LiDAR-Based Estimation Framework and Conservation Implications." Remote Sensing 16, no. 1 (December 29, 2023): 147. http://dx.doi.org/10.3390/rs16010147.
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Old-growth forests provide many ecosystem services and benefits. However, they are becoming increasingly rare and thus are an urgent priority for conservation. Accurately mapping old-growth forests is a critical step in this process. Here, we used LiDAR, an improved individual tree crown delineation algorithm for broadleaved forests, Gaussian mixture modelling, and a rule-based classification key to map the extent and location of old-growth forests across a topographically and ecologically complex landscape of 337,548 ha in southeastern Australia. We found that variation in old growth extent was largely driven by the old growth definition, which is a human construct, rather than by uncertainty in the technical aspect of the work. Current regulations define a stand as old growth if it was recruited prior to 1900 (i.e., >120 years old) and is undisturbed (i.e., <10% regrowth canopy cover and no visible disturbance traces). Only 2.7% (95% confidence intervals ranging from 1.4 to 4.9%) of the forests in the study landscape met these criteria. However, this definition is overly restrictive as it leaves many multi-aged stands with ecologically mature elements (e.g., one or more legacy trees amid regrowth) unprotected. Removing the regrowth filter, an indicator of past disturbances, increased the proportion of old-growth forests from 2.7% to 15% of the landscape. Our analyses also revealed that 60% of giant trees (>250 cm in diameter at breast height) were located within 50 m of cool temperate rainforests and cool temperate mixed forests (i.e., streamlines). We discuss the implication of our findings for the conservation and management of high-conservation-value forests in the region.
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Fang, X., J. W. Pomeroy, C. J. Westbrook, X. Guo, A. G. Minke, and T. Brown. "Prediction of snowmelt derived streamflow in a wetland dominated prairie basin." Hydrology and Earth System Sciences Discussions 7, no. 1 (February 10, 2010): 1103–41. http://dx.doi.org/10.5194/hessd-7-1103-2010.
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Abstract. The eastern Canadian Prairies are dominated by cropland, pasture, woodland and wetland areas. The region is characterized by many poor and internal drainage systems and large amounts of surface water storage. Consequently, basins here have proven challenging to hydrological model predictions which assume good drainage to stream channels. The Cold Regions Hydrological Modelling platform (CRHM) is an assembly system that can be used to set up physically based, flexible, object oriented models. CRHM was used to create a prairie hydrological model for the externally drained Smith Creek Research Basin (~400 km2), east-central Saskatchewan. Physically based modules were sequentially linked in CRHM to simulate snow processes, frozen soils, variable contributing area and wetland storage and runoff generation. Five "representative basins" (RBs) were used and each was divided into seven hydrological response units (HRUs): fallow, stubble, grassland, river channel, open water, woodland, and wetland as derived from a supervised classification of SPOT 5 imagery. Two types of modelling approaches calibrated and uncalibrated, were set up for 2007/08 and 2008/09 simulation periods. For the calibrated modelling, only the surface depression capacity of upland area was calibrated in the 2007/08 simulation period by comparing simulated and observed hydrographs; while other model parameters and all parameters in the uncalibrated modelling were estimated from field observations of soils and vegetation cover, SPOT 5 imagery, and analysis of drainage network and wetland GIS datasets as well as topographic map based and LiDAR DEMs. All the parameters except for the initial soil properties and antecedent wetland storage were kept the same in the 2008/09 simulation period. The model performance in predicting snowpack, soil moisture and streamflow was evaluated and comparisons were made between the calibrated and uncalibrated modelling for both simulation periods. Calibrated and uncalibrated predictions of snow accumulation were very similar and compared fairly well with the distributed field observations for the 2007/08 period with slightly poorer results for the 2008/09 period. Soil moisture content at a point during the early spring was adequately simulated and very comparable between calibrated and uncalibrated results for both simulation periods. The calibrated modelling had somewhat better performance in simulating spring streamflow in both simulation periods, whereas the uncalibrated modelling was still able to capture the streamflow hydrographs with good accuracy. This suggests that prediction of prairie basins without calibration is possible if sufficient data on meteorology, basin landcover and physiography are available.
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Diara, F., and F. Rinaudo. "FROM REALITY TO PARAMETRIC MODELS OF CULTURAL HERITAGE ASSETS FOR HBIM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W15 (August 22, 2019): 413–19. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w15-413-2019.
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<p><strong>Abstract.</strong> The ability of managing big amounts of metric information coming from a LiDAR survey and the ability to reproduce high quality 3D models from them are still vivid problems to solve. Is it possible to create detailed models, geometrically and metrically correct, without using a large amount (often redundant) of metric data, such as massive point clouds? Obviously yes, but there are several ways to create a fitting 3D model for a specific research. A good solution is given by NURBS based algorithms that ensure high details of modelling. However, NURBS models can't be used directly on BIM platforms, because they need to be parametrized. In this sense, a parametric model is based on real measurements but each object could be interpreted and approximated based on objective and subjective (critic) view and also based on LODs (levels of detail or development) concerning a particular analysis. This kind of modelling of Cultural Heritage assets, fundamental for HBIM creation, need to be correctly planned especially for classification and definition of its historical features connected to an informative system, because nowadays information and then the semantic dimension are a necessary key points towards documentation analysis.</p> <p>Established this brief introduction, this schematic work will focus on the analysis of FreeCAD open BIM software and Rhinoceros as NURBS 3D modeller for Cultural Heritage is concerned, and whether and how they could integrate their tools for the purpose of managing dynamic high detailed data for the creation of an HBIM platform.</p>