scholarly journals Linkage between the Forest Fires and the Meteorological Parameters during the current climatic regime using Spatial Clustering, Regression, and Combination Matrix Analysis

2021 ◽  
Author(s):  
Manish Pandey ◽  
Aman Arora ◽  
Masood A Siddiqui ◽  
Satarupa Mitra ◽  
Naveen Pandey ◽  
...  
Keyword(s):  
Forest Fires ◽  
Meteorological Parameters ◽  
Spatial Clustering ◽  
Matrix Analysis ◽  
Climatic Regime

Nowcasting Lightning Occurrence Using Machine Learning Techniques: The Challenge of Identifying Outliers

2020 ◽  
Author(s):  
Amirhossein Mostajabi ◽  
Declan Finney ◽  
Marcos Rubinstein ◽  
Farhad Rachidi
Keyword(s):  
Machine Learning ◽  
Forest Fires ◽  
Meteorological Parameters ◽  
Warning System ◽  
Weather Forecast ◽  
Field Analysis ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Learning Techniques ◽  
Kennedy Space

<p>Lightning is formed in the atmosphere through the combination of complex dynamic and microphysical processes. Lightning can have a considerable influence on the environment and on the economy since it causes energy supply outages, forest fires, damages, injury and death of humans and livestock worldwide. Therefore, it is of great importance to be able to predict lightning incidence in order to protect people and installations. Despite numerous attempts to solve the important problem of lightning prediction (e.g., [1]–[3]), the complex processes and large number of parameters involved in the problem lend themselves to the potential application of a machine learning (ML) approach.</p><p>We recently proposed a ML-based lightning early-warning system with promising performance [4]. The proposed ML model is trained to nowcast lightning incidence during any one of  three consecutive 10-minute time intervals and within a circular area of 30 km radius around a meteorological station. The system uses the real-time measured values of four meteorological parameters that are relevant to the mechanisms of electric charge generation in thunderstorms, namely the air pressure at station level (QFE), the air temperature 2 m above ground, the relative humidity, and the wind speed. The proposed algorithm was implemented using the data from 12 meteorological stations in Switzerland between 2006-2017 with a granularity of ten minutes. The stations were selected to be well distributed among different ranges of altitude and terrain topographies.</p><p>The algorithm requires the filtering out of a portion of the data which are identified as outliers. However, the process of the automatic identification of outliers is a challenging task which could also affect the model’s performance. In this presentation, we discuss this problem and present approaches that can be used to optimize the process.</p><p> </p><p><strong>References</strong></p><p>[1]      D. Aranguren, J. Montanya, G. Solá, V. March, D. Romero, and H. Torres, “On the lightning hazard warning using electrostatic field: Analysis of summer thunderstorms in Spain,” J. Electrostat., vol. 67, no. 2–3, pp. 507–512, May 2009.</p><p>[2]      G. N. Seroka, R. E. Orville, and C. Schumacher, “Radar Nowcasting of Total Lightning over the Kennedy Space Center,” Weather Forecast., vol. 27, no. 1, pp. 189–204, Feb. 2012.</p><p>[3]      Q. Meng, W. Yao, and L. Xu, “Development of Lightning Nowcasting and Warning Technique and Its Application,” Adv. Meteorol., vol. 2019, pp. 1–9, Jan. 2019.</p><p>[4]      A. Mostajabi, D. L. Finney, M. Rubinstein, and F. Rachidi, “Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques,” npj Clim. Atmos. Sci., vol. 2, no. 1, p. 41, 2019.</p>

scholarly journals A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest

2019 ◽  
Vol 11 (21) ◽  
pp. 2579 ◽  
Author(s):  
Fernando Carvajal-Ramírez ◽  
João Manuel Pereira Ramalho Serrano ◽  
Francisco Agüera-Vega ◽  
Patricio Martínez-Carricondo
Keyword(s):  
Forest Fires ◽  
Point Cloud ◽  
Vegetation Index ◽  
Matrix Analysis ◽  
Mediterranean Forest ◽  
Estimation Methods ◽  
Surface Model ◽  
Error Matrix ◽  
Vegetation Height ◽  
Dense Point

Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way.

Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens

1996 ◽  
Vol 54 ◽  
pp. 282-283
Author(s):  
Badrinath Roysam ◽  
Hakan Ancin ◽  
Douglas E. Becker ◽  
Robert W. Mackin ◽  
Matthew M. Chestnut ◽  
...  
Keyword(s):  
Image Analysis ◽  
Structural Changes ◽  
Sampling Methods ◽  
Spatial Clustering ◽  
Three Dimensional ◽  
Field Of View ◽  
Cell Counting ◽  
Depth Of Field ◽  
Tissue Cell ◽  
Tissue Organization

This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.

Beetles sniff out forest fires

Nature ◽  
1999 ◽  
Author(s):  
Henry Gee
Keyword(s):  
Forest Fires
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