scholarly journals Characterization of vegetation structural changes using multi-temporal LiDAR and its relationship with severity in Calcena wildfire

Ecosistemas ◽  
2021 ◽  
Vol 30 (2) ◽  
pp. 1-10
Author(s):  
Dario Domingo ◽  
Maria Teresa Lamelas ◽  
Maria Begoña García
Keyword(s):  
Structural Changes ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Differenced Normalized Burn Ratio ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Normalized Burn Ratio

La caracterización de los cambios estructurales y presencia de huecos tras el fuego puede proporcionar información muy relevante para comprender los efectos ecológicos de los incendios en ecosistemas mediterráneos. En el presente estudio se caracterizan estas variables tras el incendio de Calcena en masas forestales de pinar y encinar, y su relación con la severidad del mismo. Dicho incendio calcinó 4.573 hectáreas en 2012 afectando de forma parcial al Parque Natural de la Dehesa del Moncayo localizado en Aragón (España). Para ello se hace uso de información multi-temporal Light Detection and Ranging (LiDAR) de las coberturas de 2011 y 2016 del Plan Nacional de Ortofotografía Aérea (PNOA), así como imágenes Landsat 7 para estimar la severidad del incendio mediante el índice differenced Normalized Burn Ratio (dNBR). Se evalúan los cambios estructurales producidos utilizando métricas LiDAR pre y post-incendio, así como la distribución de los huecos en el dosel forestal, su tamaño, número y frecuencia, y se analizan sus correlaciones con la severidad del incendio. La severidad fue predominantemente baja (42.32 %) o mediabaja (30.38 %), y produjo una disminución de la altura, de la densidad del dosel forestal y de la diversidad estructural. El tamaño de los huecos se incrementó tras el incendio, reduciéndose el número de huecos pequeños e incrementándose aquellos de tamaño intermedio en torno a 0.2 ha. Los cambios en las métricas LiDAR relacionadas con la altura, variabilidad de la altura en el perfil vertical, y densidad del dosel forestal presentaron las mayores correlaciones, indicando que son las que sufren mayores modificaciones. Los resultados muestran el interés de utilizar los datos LiDAR para caracterizar cambios estructurales y apoyar decisiones en la gestión silvícola.

scholarly journals Characterization of environmental loads related concrete pavement deflection behavior using Light Detection and Ranging technology

2018 ◽  
Vol 11 (5) ◽  
pp. 470-480 ◽  
Author(s):  
Shuo Yang ◽  
Halil Ceylan ◽  
Kasthurirangan Gopalakrishnan ◽  
Sunghwan Kim ◽  
Peter C. Taylor ◽  
...  
Keyword(s):  
Concrete Pavement ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Environmental Loads

Thermal characterization of ABS-Graphene blended three dimensional printed functional prototypes for electro chemical energy storage

2018 ◽  
Vol 1 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Kamaljit Singh Boparai ◽  
Rupinder Singh
Keyword(s):  
Energy Storage ◽  
Structural Changes ◽  
Fused Deposition Modeling ◽  
Morphological Changes ◽  
Three Dimensional ◽  
Thermal Characterization ◽  
Chemical Energy ◽  
Flow Index ◽  
Fused Deposition

This study highlights the thermal characterization of ABS-Graphene blended three dimensional (3D) printed functional prototypes by fused deposition modeling (FDM) process. These functional prototypes have some applications as electro-chemical energy storage devices (EESD). Initially, the suitability of ABS-Graphene composite material for FDM applications has been examined by melt flow index (MFI) test. After establishing MFI, the feedstock filament for FDM has been prepared by an extrusion process. The fabricated filament has been used for printing 3D functional prototypes for printing of in-house EESD. The differential scanning calorimeter (DSC) analysis was conducted to understand the effect on glass transition temperature with the inclusion of Graphene (Gr) particles. It has been observed that the reinforced Gr particles act as a thermal reservoir (sink) and enhances its thermal/electrical conductivity. Also, FT-IR spectra realized the structural changes with the inclusion of Gr in ABS matrix. The results are supported by scanning electron microscopy (SEM) based micrographs for understanding the morphological changes.

scholarly journals Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Nicola Case ◽  
Alfonso Vitti
Keyword(s):  
Image Segmentation ◽  
Variational Model ◽  
Great Level ◽  
Radiometric Analysis ◽  
Variational Segmentation ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Original Procedure ◽  
Variational Image ◽  
Homogeneous Regions

Digital images, and in particular satellite images acquired by different sensors, may present defects due to many causes. Since 2013, the Landsat 7 mission has been affected by a well-known issue related to the malfunctioning of the Scan Line Corrector producing very characteristic strips of missing data in the imagery bands. Within the vast and interdisciplinary image reconstruction application field, many works have been presented in the last few decades to tackle the specific Landsat 7 gap-filling problem. This work proposes another contribution in this field presenting an original procedure based on a variational image segmentation model coupled with radiometric analysis to reconstruct damaged images acquired in a multi-temporal scenario, typical in satellite remote sensing. The key idea is to exploit some specific features of the Mumford–Shah variational model for image segmentation in order to ease the detection of homogeneous regions which will then be used to form a set of coherent data necessary for the radiometric reconstruction of damaged regions. Two reconstruction approaches are presented and applied to SLC-off Landsat 7 data. One approach is based on the well-known histogram matching transformation, the other approach is based on eigendecomposition of the bands covariance matrix and on the sampling from Gaussian distributions. The performance of the procedure is assessed by application to artificially damaged images for self-validation testing. Both of the proposed reconstruction approaches had led to remarkable results. An application to very high resolution WorldView-3 data shows how the procedure based on variational segmentation allows an effective reconstruction of images presenting a great level of geometric complexity.

scholarly journals Using Airborne Light Detection and Ranging (LIDAR) to Characterize Forest Stand Condition on the Kenai Peninsula of Alaska

2009 ◽  
Vol 24 (2) ◽  
pp. 95-102 ◽  
Author(s):  
Hans-Erik Andersen
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Grid Cell ◽  
Forest Stand ◽  
Light Detection And Ranging ◽  
Inventory Analysis ◽  
Light Detection ◽  
Individual Tree ◽  
Kenai Peninsula

Abstract Airborne laser scanning (also known as light detection and ranging or LIDAR) data were used to estimate three fundamental forest stand condition classes (forest stand size, land cover type, and canopy closure) at 32 Forest Inventory Analysis (FIA) plots distributed over the Kenai Peninsula of Alaska. Individual tree crown segment attributes (height, area, and species type) were derived from the three-dimensional LIDAR point cloud, LIDAR-based canopy height models, and LIDAR return intensity information. The LIDAR-based crown segment and canopy cover information was then used to estimate condition classes at each 10-m grid cell on a 300 × 300-m area surrounding each FIA plot. A quantitative comparison of the LIDAR- and field-based condition classifications at the subplot centers indicates that LIDAR has potential as a useful sampling tool in an operational forest inventory program.

Precision and shortcomings of yaw error estimation using spinner-based light detection and ranging

Wind Energy ◽  
2012 ◽  
Vol 16 (3) ◽  
pp. 353-366 ◽  
Author(s):  
Knud A. Kragh ◽  
Morten H. Hansen ◽  
Torben Mikkelsen
Keyword(s):  
Error Estimation ◽  
Light Detection And Ranging ◽  
Light Detection
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