Fuel Modelling Characterisation Using Low-Density LiDAR in the Mediterranean: An Application to a Natural Protected Area

Fuel structure and characteristics are important to better understand and predict wildfire behaviour. The aim of the present study was to develop a methodology for characterising fuel models using low-density and free LiDAR data that facilitate the work of managers of protected territories. Field inventories were carried out in order to understand the characteristics of the stand and the variables that fuel models must include. This information, together with the use of the intensity and structure provided by LiDAR, was used to perform statistical analyses. The linear regressions obtained to characterise the stand of the mixed Quercus spp.–Pinus ssp.-dominated stand had an R2 value ranging from 0.4393 to 0.66. While working with low-density LiDAR data (which has more difficulties crossing the canopy), in addition to the obtained results, we performed the statistical analysis of the dominant stand to obtain models with R2 values ranging from 0.8201 to 0.8677. The results of this research show that low-density LiDAR data are significant; however, in mixed stands, it is necessary to only use the dominant stratum because other components generate noise, which reduces the predictive capacity of the models. Additionally, by using the decision tree developed in combination, it is possible to update the mapping of fuel models in inaccessible areas, thereby significantly reducing costs.

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Semi-Automatic Classification of Building From Low-Density Lidar Data and Worldview-2 Images Through OBIA Technique

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323916 ◽

2020 ◽

Author(s):

C. Zarro ◽

S.L. Ullo ◽

G. Meoli ◽

M. Focareta

Keyword(s):

Automatic Classification ◽

Lidar Data ◽

Low Density

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Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8518441 ◽

2018 ◽

Author(s):

Daniele Marinelli ◽

Claudia Paris ◽

Lorenzo Bruzzone

Keyword(s):

Parameter Estimation ◽

Point Clouds ◽

Lidar Data ◽

Low Density ◽

Density Point ◽

Tree Crown ◽

Individual Tree

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Soluble low-density lipoprotein receptor-related protein 1 as a biomarker of coronary risk: Predictive capacity and association with clinical events

Atherosclerosis ◽

10.1016/j.atherosclerosis.2019.06.904 ◽

2019 ◽

Vol 287 ◽

pp. 93-99 ◽

Cited By ~ 3

Author(s):

David de Gonzalo-Calvo ◽

Roberto Elosua ◽

Angela Vea ◽

Isaac Subirana ◽

Sergi Sayols-Baixeras ◽

...

Keyword(s):

Low Density Lipoprotein ◽

Density Lipoprotein ◽

Low Density ◽

Lipoprotein Receptor ◽

Coronary Risk ◽

Related Protein ◽

Predictive Capacity ◽

Clinical Events ◽

Density Lipoprotein Receptor ◽

Lipoprotein Receptor Related Protein

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An Automatic Lane Marking Detection Method With Low-Density Roadside LiDAR Data

IEEE Sensors Journal ◽

10.1109/jsen.2021.3057999 ◽

2021 ◽

Vol 21 (8) ◽

pp. 10029-10038

Author(s):

Ciyun Lin ◽

Yingzhi Guo ◽

Wenjun Li ◽

Hui Liu ◽

Dayong Wu

Keyword(s):

Detection Method ◽

Lidar Data ◽

Low Density ◽

Lane Marking

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Evaluation of the use of low-density LiDAR data to estimate structural attributes and biomass yield in a short-rotation willow coppice: an example in a field trial

Annals of Forest Science ◽

10.1007/s13595-017-0665-7 ◽

2017 ◽

Vol 74 (4) ◽

Cited By ~ 8

Author(s):

María Castaño-Díaz ◽

Pedro Álvarez-Álvarez ◽

Brian Tobin ◽

Maarten Nieuwenhuis ◽

Elías Afif-Khouri ◽

...

Keyword(s):

Field Trial ◽

Biomass Yield ◽

Lidar Data ◽

Low Density ◽

Short Rotation

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JUS PEPAYA (CARICA PAPAYA L) DAPAT MENURUNKAN KADAR LDL DAN KOLESTEROL TOTAL PADA USIA 40-70 TAHUN

Jurnal Kesehatan Kusuma Husada ◽

10.34035/jk.v9i2.274 ◽

2018 ◽

pp. 153-161

Author(s):

Junendri Ardian ◽

Ari Natalia Probandari ◽

K Kusnandar

Keyword(s):

High Density Lipoprotein ◽

Carica Papaya ◽

Low Density Lipoprotein ◽

Intervention Group ◽

Density Lipoprotein ◽

High Density ◽

Control Group ◽

Low Density ◽

Post Test ◽

Research Show

ABSTRAK Hiperkolesterolemia merupakan suatu kondisi dimana kolesterol dalam darah meningkat melebihi ambang normal yang ditandai dengan meningkatnya kadar kolesterol total terutama Low Density Lipoprotein (LDL) dan diikuti dengan penurunan kadar High Density Lipoprotein (HDL) darah, Tujuan penelitian adalah menganalisis pengaruh pemberian asupan jus pepaya terhadap kadar LDL dan kolesterol total pada ibu rumah tangga usia 40-70 tahun yang menderita hiperkolesterolemia. Metode: Jenis penelitian ini merupakan penelitian true eksperimental dengan desain penelitian pre and post test with control group. Total subjek sebanyak 20 orang yang dibagi kedalam dua kelompok, yaitu Intervensi Jus pepaya dan kelompok kontrol. Hasil penelitian menunjukkan bahwa kadar kolesterol total pada kelompok jus pepaya mengalami penurunan yang bermakna (p<0,05). Sedangkan pada kelompok kontrol tidak bermakna (p>0,05). Kadar LDL pada kelompok jus pepaya mengalami penurunan yang bermakna (p<0,05). Sedangkan pada kelompok kontrol tidak bermakna (p>0,05). Kesimpulan jus pepaya (carica papaya l) dapat menurunkan kadar LDL (Low Density Lipoprotein) dan kolesterol total. Kata kunci: jus pepaya, kolesterol total, LDL. ABSTRACT Hypercholesterolemia is a conditions where cholesterol in the bloods increased beyond the normal standard, its showed with cholesterol total level’s was increased especially Low Density Lipoprotein (LDL) and followed by the decreasing of High Density Lipoprotein (HDL) in blood, The purpose of this research is analyzing the influence’s of papaya juice intake for LDL’s levels and cholestrol’s total at the age of 40 – 70 years old for housewives with hypercholesterolemia. The method this research is true experimental researchs with pre and post test control group designs. The subject in this research are 20 people and separated into two groups, the first group is papaya juice’s intervention group, and the second is control group. The result of this research show that papaya juice’s has significant result for decreasing cholesterol’s total with p value’s < 0,05 , while the control group has no significant result because its p value’s >0,05. The result for LDL’s levels show that papaya juice’s has significant result too for decreasing LDL’s levels with p value’s < 0,05, while the control group has no significant result because its p value’s >0,05. The conclusion in this research is papaya juice (carica papaya l) can decreased ldl’s levels and cholesterol’s total. Keywords: cholesterol’s total, LDL, papaya juice.

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Using ForeStereo and LIDAR data to assess fire and canopy structure-related risks in relict Abies pinsapo Boiss. forests

PeerJ ◽

10.7717/peerj.10158 ◽

2020 ◽

Vol 8 ◽

pp. e10158

Author(s):

Álvaro Cortés-Molino ◽

Isabel Aulló-Maestro ◽

Ismael Fernandez-Luque ◽

Antonio Flores-Moya ◽

José A. Carreira ◽

...

Keyword(s):

Stand Structure ◽

Secondary Growth ◽

Canopy Structure ◽

Airborne Lidar ◽

Distribution Area ◽

Lidar Data ◽

Fire Simulation ◽

Entire Area ◽

Fuel Models ◽

Abies Pinsapo

In this study we combine information from aerial LIDAR and hemispherical images taken in the field with ForeStereo—a forest inventory device—to assess the vulnerability and to design conservation strategies for endangered Mediterranean fir forests based on the mapping of fire risk and canopy structure spatial variability. We focused on the largest continuous remnant population of the endangered tree species Abies pinsapo Boiss. spanning 252 ha in Sierra de las Nieves National Park (South Andalusia, Spain). We established 49 sampling plots over the study area. Stand structure variables were derived from ForeStereo device, a proximal sensing technology for tree diameter, height and crown dimensions and stand crown cover and basal area retrieval from stereoscopic hemispherical images photogrammetry. With this information, we developed regression models with airborne LIDAR data (spatial resolution of 0.5 points∙m−2). Thereafter, six fuel models were fitted to the plots according to the UCO40 classification criteria, and then the entire area was classified using the Nearest Neighbor algorithm on Sentinel imagery (overall accuracy of 0.56 and a KIA-Kappa Coefficient of 0.46). FlamMap software was used for fire simulation scenarios based on fuel models, stand structure, and terrain data. Besides the fire simulation, we analyzed canopy structure to assess the status and vulnerability of this fir population. The assessment shows a secondary growth forest that has an increasing presence of fuel models with the potential for high fire spread rate fire and burn probability. Our methodological approach has the potential to be integrated as a support tool for the adaptive management and conservation of A. pinsapo across its whole distribution area (<4,000 ha), as well as for other endangered circum-Mediterranean fir forests, as A. numidica de Lannoy and A. pinsapo marocana Trab. in North Africa.

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