Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data

2022 ◽  
Vol 268 ◽  
pp. 112764
Author(s):  
Rodrigo Vieira Leite ◽  
Carlos Alberto Silva ◽  
Eben North Broadbent ◽  
Cibele Hummel do Amaral ◽  
Veraldo Liesenberg ◽  
...  
Keyword(s):  
Large Scale ◽  
Fuel Load ◽  
Lidar Data ◽  
Tropical Savanna

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 Pyrolysis Characteristics of Undervalued Wood Varieties in the Portuguese Charcoal Sector

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2537
Author(s):  
Felix Charvet ◽  
Felipe Silva ◽  
Luís Ruivo ◽  
Luís Tarelho ◽  
Arlindo Matos ◽  
...  
Keyword(s):  
Large Scale ◽  
Fixed Bed ◽  
Management Control ◽  
Fixed Bed Reactor ◽  
Fuel Load ◽  
Charcoal Production ◽  
Pyrolysis Characteristics ◽  
Major Market ◽  
Burnt Wood ◽  
Fuel Particles

Charcoal production in Portugal is mostly based on the valorization of woody residues from cork oak and holm oak, the latter being considered a reference feedstock in the market. Nevertheless, since wildfire prevention became a priority in Portugal, after the recent dramatic wildfires, urgent actions are being conducted to reduce the fuel load in the forests, which is increasing the amount of biomass that is available for valorization. Additionally, biomass residues from agriculture, forest management, control of invasive species, partially burnt wood from post-fire recovery actions, and waste wood from storm devastated forests need also to be considered within the national biomass valorization policies. This has motivated the present work on whether the carbonization process can be used to valorize alternative woody biomasses not currently used on a large scale. For this purpose, slow pyrolysis experiments were carried out with ten types of wood, using a fixed bed reactor allowing the controlled heating of large fuel particles at 0.1 to 5 °C/min and final temperatures within 300–450 °C. Apart from an evaluation of the mass balance of the process, emphasis was given to the properties of the resulting charcoals considering its major market in Portugal—barbecue charcoal for both recreational and professional purposes.

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.

Simulation and thermal imaging of the 2006 Esperanza Wildfire in southern California: application of a coupled weather–wildland fire model

2014 ◽  
Vol 23 (6) ◽  
pp. 755 ◽  
Author(s):  
Janice L. Coen ◽  
Philip J. Riggan
Keyword(s):  
Thermal Imaging ◽  
Large Scale ◽  
Transient Flow ◽  
Wildland Fire ◽  
Fire Spread ◽  
Early Morning ◽  
Fuel Load ◽  
Dynamic Interactions ◽  
Riverside County ◽  
Layer Flow

The 2006 Esperanza Fire in Riverside County, California, was simulated with the Coupled Atmosphere–Wildland Fire Environment (CAWFE) model to examine how dynamic interactions of the atmosphere with large-scale fire spread and energy release may affect observed patterns of fire behaviour as mapped using the FireMapper thermal-imaging radiometer. CAWFE simulated the meteorological flow in and near the fire, the fire’s growth as influenced by gusty Santa Ana winds and interactions between the fire and weather through fire-induced winds during the first day of burning. The airflow was characterised by thermally stratified, two-layer flow channelled between the San Bernardino and San Jacinto mountain ranges with transient flow accelerations driving the fire in Cabazon Peak’s lee. The simulation reproduced distinguishing features of the fire including its overall direction and width, rapid spread west-south-westward across canyons, spread up canyons crossing its southern flank, splitting into two heading regions and feathering of the fire line. The simulation correctly depicted the fire’s location at the time of an early-morning incident involving firefighter fatalities. It also depicted periods of deep plume growth, but anomalously described downhill spread of the head of the fire under weak winds that was less rapid than observed. Although capturing the meteorological flow was essential to reproducing the fire’s evolution, fuel factors including fuel load appeared to play a secondary role.

Estimation of forest surface fuel load using airborne lidar data

2016 ◽  
Author(s):  
Yang Chen ◽  
Xuan Zhu ◽  
Marta Yebra ◽  
Sarah Harris ◽  
Nigel Tapper
Keyword(s):  
Airborne Lidar ◽  
Fuel Load ◽  
Lidar Data ◽  
Airborne Lidar Data
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