An assessment of large‐scale flood modelling based on LiDAR data

2021 ◽  
Vol 35 (8) ◽  
Author(s):  
Guénolé Choné ◽  
Pascale M. Biron ◽  
Thomas Buffin‐Bélanger ◽  
Iulia Mazgareanu ◽  
Jeff C. Neal ◽  
...  
Keyword(s):  
Large Scale ◽  
Lidar Data ◽  
Flood Modelling

Large-scale estimation of buildings’ thermal load using LiDAR data

2021 ◽  
Vol 231 ◽  
pp. 110626
Author(s):  
Marko Bizjak ◽  
Borut Žalik ◽  
Gorazd Štumberger ◽  
Niko Lukač
Keyword(s):  
Thermal Load ◽  
Large Scale ◽  
Lidar Data ◽  
Scale Estimation

scholarly journals Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2473
Author(s):  
Qinglie Yuan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Aidi Hizami Alias ◽  
Shaiful Jahari Hashim
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Spatial Information ◽  
Feature Fusion ◽  
Aerial Images ◽  
Semantic Gap ◽  
Superior Performance ◽  
Lidar Data ◽  
Building Extraction ◽  
Hierarchical Features

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.

Estimation of biophysical characteristics for highly variable mixed-conifer stands using small-footprint lidar

2006 ◽  
Vol 36 (5) ◽  
pp. 1129-1138 ◽  
Author(s):  
Jennifer L. Rooker Jensen ◽  
Karen S Humes ◽  
Tamara Conner ◽  
Christopher J Williams ◽  
John DeGroot
Keyword(s):  
Regression Model ◽  
Large Scale ◽  
Canopy Height ◽  
Training Data ◽  
Lidar Data ◽  
Mixed Conifer ◽  
Wood Volume ◽  
Small Footprint ◽  
Mean Square Errors ◽  
Single Regression

Although lidar data are widely available from commercial contractors, operational use in North America is still limited by both cost and the uncertainty of large-scale application and associated model accuracy issues. We analyzed whether small-footprint lidar data obtained from five noncontiguous geographic areas with varying species and structural composition, silvicultural practices, and topography could be used in a single regression model to produce accurate estimates of commonly obtained forest inventory attributes on the Nez Perce Reservation in northern Idaho, USA. Lidar-derived height metrics were used as predictor variables in a best-subset multiple linear regression procedure to determine whether a suite of stand inventory variables could be accurately estimated. Empirical relationships between lidar-derived height metrics and field-measured dependent variables were developed with training data and acceptable models validated with an independent subset. Models were then fit with all data, resulting in coefficients of determination and root mean square errors (respectively) for seven biophysical characteristics, including maximum canopy height (0.91, 3.03 m), mean canopy height (0.79, 2.64 m), quadratic mean DBH (0.61, 6.31 cm), total basal area (0.91, 2.99 m2/ha), ellipsoidal crown closure (0.80, 0.08%), total wood volume (0.93, 24.65 m3/ha), and large saw-wood volume (0.75, 28.76 m3/ha). Although these regression models cannot be generalized to other sites without additional testing, the results obtained in this study suggest that for these types of mixed-conifer forests, some biophysical characteristics can be adequately estimated using a single regression model over stands with highly variable structural characteristics and topography.

scholarly journals Modelling above-ground carbon dynamics using multi-temporal airborne lidar: insights from a Mediterranean woodland

2016 ◽  
Vol 13 (4) ◽  
pp. 961-973 ◽  
Author(s):  
W. Simonson ◽  
P. Ruiz-Benito ◽  
F. Valladares ◽  
D. Coomes
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Critical Role ◽  
Carbon Fluxes ◽  
Carbon Dynamics ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Low Carbon ◽  
Data Set ◽  
Multi Temporal

Abstract. Woodlands represent highly significant carbon sinks globally, though could lose this function under future climatic change. Effective large-scale monitoring of these woodlands has a critical role to play in mitigating for, and adapting to, climate change. Mediterranean woodlands have low carbon densities, but represent important global carbon stocks due to their extensiveness and are particularly vulnerable because the region is predicted to become much hotter and drier over the coming century. Airborne lidar is already recognized as an excellent approach for high-fidelity carbon mapping, but few studies have used multi-temporal lidar surveys to measure carbon fluxes in forests and none have worked with Mediterranean woodlands. We use a multi-temporal (5-year interval) airborne lidar data set for a region of central Spain to estimate above-ground biomass (AGB) and carbon dynamics in typical mixed broadleaved and/or coniferous Mediterranean woodlands. Field calibration of the lidar data enabled the generation of grid-based maps of AGB for 2006 and 2011, and the resulting AGB change was estimated. There was a close agreement between the lidar-based AGB growth estimate (1.22 Mg ha−1 yr−1) and those derived from two independent sources: the Spanish National Forest Inventory, and a tree-ring based analysis (1.19 and 1.13 Mg ha−1 yr−1, respectively). We parameterised a simple simulator of forest dynamics using the lidar carbon flux measurements, and used it to explore four scenarios of fire occurrence. Under undisturbed conditions (no fire) an accelerating accumulation of biomass and carbon is evident over the next 100 years with an average carbon sequestration rate of 1.95 Mg C ha−1 yr−1. This rate reduces by almost a third when fire probability is increased to 0.01 (fire return rate of 100 years), as has been predicted under climate change. Our work shows the power of multi-temporal lidar surveying to map woodland carbon fluxes and provide parameters for carbon dynamics models. Space deployment of lidar instruments in the near future could open the way for rolling out wide-scale forest carbon stock monitoring to inform management and governance responses to future environmental change.

scholarly journals Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image

2020 ◽  
Vol 12 (11) ◽  
pp. 1702 ◽  
Author(s):  
Thanh Huy Nguyen ◽  
Sylvie Daniel ◽  
Didier Guériot ◽  
Christophe Sintès ◽  
Jean-Marc Le Caillec
Keyword(s):  
Large Scale ◽  
Active Contour Model ◽  
Super Resolution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Force Model ◽  
Building Extraction ◽  
Snake Model ◽  
Object Based

Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%.

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