scholarly journals Drone-Image-Based Method of Estimating Forest-Fire Fuel Loads

2021 ◽  
Vol 21 (5) ◽  
pp. 123-130
Author(s):  
Sunjoo Lee ◽  
Choongshik Woo ◽  
Sungyong Kim ◽  
Youngjin Lee ◽  
Chungeun Kwon ◽  
...  
Keyword(s):  
Forest Fire ◽  
Goodness Of Fit ◽  
Field Survey ◽  
Blue Band ◽  
Crown Area ◽  
Fuel Loads ◽  
Object Based ◽  
Forest Fire Prevention ◽  
The Difference ◽  
Individual Trees

A method of estimating forest-fire fuel loads was developed using drones to collect information about the height and diameter-at-breast-height (DBH) of individual trees. It was conducted for forest fire prevention monitoring (Control, 20% thinned, and 40% thinned area) located in Goseong-gun, Gangwon-do. Object-based images and 3D-model red/green/blue band characteristics were superimposed to select and extract individual trees. A digital crown height model was developed based on the difference between the heights of digital surface and terrain models. In addition, the DBH was estimated based on the crown area. The 40%-thinned area exhibited the highest accuracy (95%) for extracting individual trees, and the difference between the field-survey and drone-image heights was in the range of 0.64-2.02 m. The goodness-of-fit of the DBH-crown area model was 0.61. The difference between the imageand field-survey-based forest-fire fuel loads ranged from -1.20 to 0.40 ton/ha.

Applying the Anderson-Darling Test to Suicide Clusters

Crisis ◽  
2013 ◽  
Vol 34 (6) ◽  
pp. 434-437 ◽  
Author(s):  
Donald W. MacKenzie
Keyword(s):  
Poisson Process ◽  
Goodness Of Fit ◽  
Small Samples ◽  
Massachusetts Institute Of Technology ◽  
Homogeneous Poisson Process ◽  
Cornell University ◽  
The Difference ◽  
Suicide Clusters ◽  
Institute Of Technology ◽  
Anderson Darling

Background: Suicide clusters at Cornell University and the Massachusetts Institute of Technology (MIT) prompted popular and expert speculation of suicide contagion. However, some clustering is to be expected in any random process. Aim: This work tested whether suicide clusters at these two universities differed significantly from those expected under a homogeneous Poisson process, in which suicides occur randomly and independently of one another. Method: Suicide dates were collected for MIT and Cornell for 1990–2012. The Anderson-Darling statistic was used to test the goodness-of-fit of the intervals between suicides to distribution expected under the Poisson process. Results: Suicides at MIT were consistent with the homogeneous Poisson process, while those at Cornell showed clustering inconsistent with such a process (p = .05). Conclusions: The Anderson-Darling test provides a statistically powerful means to identify suicide clustering in small samples. Practitioners can use this method to test for clustering in relevant communities. The difference in clustering behavior between the two institutions suggests that more institutions should be studied to determine the prevalence of suicide clustering in universities and its causes.

La protezione dagli incendi boschivi nelle Alpi centro-occidentali | Forest Fire-Prevention in the Central Eastern Alps

2000 ◽  
Vol 151 (9) ◽  
pp. 325-335 ◽  
Author(s):  
Giovanni Bovio
Keyword(s):  
Forest Fire ◽  
Eastern Alps ◽  
Fire Prevention ◽  
Forest Fire Prevention ◽  
Central Eastern

Important forest fire prevention developments of the Lombardy, Piedmont and Aosta Valley regions are highlighted in this study and a certain number of activities considered able to improve the situation are proposed.

scholarly journals Critical Land Detection using Remote Sensing Device and Geographic Information (Case Study of Critical Land Basic of Catchments Area, Alang, Wonogiri)

2017 ◽  
Vol 19 (1) ◽  
pp. 1
Author(s):  
Beny Harjadi
Keyword(s):  
Remote Sensing ◽  
Field Survey ◽  
Critical Level ◽  
Satellite Image ◽  
Geographic Information ◽  
Level Data ◽  
The Difference

Work criteria and indicator of Catchments Area need to be determined because the success and the failure of cultivating Catchments Area can be monitored and evaluated through the determined criteria. Criteria Indicators in utilizing land, one of them is determined based on the erosion index and the ability of utilizing land, for analyzing the land critical level. However, the determination of identification and classification of land critical level has not been determined; as a result the measurement of how wide the real critical land is always changed all the year. In this study, it will be tried a formula to determine the land critical/eve/ with various criteria such as: Class KPL (Ability of Utilizing Land) and the difference of the erosion tolerance value with the great of the erosion compared with land critical level analysis using remote sensing devices. The aim of studying land critical level detection using remote sensing tool and Geographic Information System (SIG) are:1. The backwards and the advantages of critical and analysis method2. Remote Sensing Method for critical and classification3. Critical/and surveyed method in the field (SIG) Collecting and analyzing data can be found from the field survey and interpretation of satellite image visually and using computer. The collected data are analyzed as:a. Comparing the efficiency level and affectivity of collecting biophysical data through field survey, sky photo interpretation, and satellite image analysis.b. Comparing the efficiency level and affectivity of land critical level data that are found from the result of KPL with the result of the measurement of the erosion difference and erosion tolerance.

scholarly journals Forest Fire Prevention Using WSN Assisted IOT

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1317 ◽  
Author(s):  
Vrince Vimal ◽  
Madhav Ji Nigam
Keyword(s):  
Internet Of Things ◽  
Sensor Network ◽  
Forest Fire ◽  
Clustering Algorithm ◽  
Base Station ◽  
Sensor Nodes ◽  
Fire Prevention ◽  
New Era ◽  
Data Flows ◽  
Forest Fire Prevention

Internet of Things is the mainstay of the new era since its application becomes the future of day-to-day life. This work targets the IoT network assisted by WSN to prevent forest fire. We propose two-layer architecture of sensor network assisted by IoT enabled UAVs. The data flows in the proposed architecture in bottom-up fashion i.e., data is sensed by the nodes, which are deployed in the forest area (and sense temperature continuously). This data is transmitted to upper layer consisting of UAVs, which take appropriate action (to sprinkle water to bring temperature down to prevent fire). All the UAVs are interconnected to each other as well as to base station. The sensor nodes are clustered using two-step clustering algorithm, which takes care of the isolated nodes. The scheme has been equated to another WSN assisted IoT clustering technique. The proposed scheme outperforms the existing in terms of congestion at the UAV stations, number of alive nodes and remaining energy of the network.  

scholarly journals Delineation of Tree Patches in a Mangrove-Marsh Transition Zone by Watershed Segmentation of Aerial Photographs

2020 ◽  
Vol 12 (13) ◽  
pp. 2086
Author(s):  
Himadri Biswas ◽  
Keqi Zhang ◽  
Michael S. Ross ◽  
Daniel Gann
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Aerial Photographs ◽  
Watershed Segmentation ◽  
Marsh Vegetation ◽  
Tree Detection ◽  
Object Based ◽  
Segmented Images ◽  
Individual Trees ◽  
True Color

Mangrove migration, or transgression in response to global climatic changes or sea-level rise, is a slow process; to capture it, understanding both the present distribution of mangroves at individual patch (single- or clumped trees) scale, and their rates of change are essential. In this study, a new method was developed to delineate individual patches and to estimate mangrove cover from very high-resolution (0.08 m spatial resolution) true color (Red (R), Green (G), and Blue (B) spectral channels) aerial photography. The method utilizes marker-based watershed segmentation, where markers are detected using a vegetation index and Otsu’s automatic thresholding. Fourteen commonly used vegetation indices were tested, and shadows were removed from the segmented images to determine their effect on the accuracy of tree detection, cover estimation, and patch delineation. According to point-based accuracy analysis, we obtained adjusted overall accuracies >90% in tree detection using seven vegetation indices. Likewise, using an object-based approach, the highest overlap accuracy between predicted and reference data was 95%. The vegetation index Excess Green (ExG) without shadow removal produced the most accurate mangrove maps by separating tree patches from shadows and background marsh vegetation and detecting more individual trees. The method provides high precision delineation of mangrove trees and patches, and the opportunity to analyze mangrove migration patterns at the scale of isolated individuals and patches.

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