Dataset Information

A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication.

ABSTRACT:

Background

Wildland fire (wildfire; bushfire) pollution contributes to poor air quality, a risk factor for premature death. The frequency and intensity of wildfires are expected to increase; improved tools for estimating exposure to fire smoke are vital. New-generation satellite-based sensors produce high-resolution spectral images, providing real-time information of surface features during wildfire episodes. Because of the vast size of such data, new automated methods for processing information are required.

Objective

We present a deep fully convolutional neural network (FCN) for predicting fire smoke in satellite imagery in near-real time (NRT).

Methods

The FCN identifies fire smoke using output from operational smoke identification methods as training data, leveraging validated smoke products in a framework that can be operationalized in NRT. We demonstrate this for a fire episode in Australia; the algorithm is applicable to any geographic region.

Results

The algorithm has high classification accuracy (99.5% of pixels correctly classified on average) and precision (average intersection over union?=?57.6%).

Significance

The FCN algorithm has high potential as an exposure-assessment tool, capable of providing critical information to fire managers, health and environmental agencies, and the general public to prevent the health risks associated with exposure to hazardous smoke from wildland fires in NRT.

SUBMITTER: Larsen A

PROVIDER: S-EPMC7796988 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication.

Larsen Alexandra A Hanigan Ivan I Reich Brian J BJ Qin Yi Y Cope Martin M Morgan Geoffrey G Rappold Ana G AG

Journal of exposure science & environmental epidemiology 20200727 1

<h4>Background</h4>Wildland fire (wildfire; bushfire) pollution contributes to poor air quality, a risk factor for premature death. The frequency and intensity of wildfires are expected to increase; improved tools for estimating exposure to fire smoke are vital. New-generation satellite-based sensors produce high-resolution spectral images, providing real-time information of surface features during wildfire episodes. Because of the vast size of such data, new automated methods for processing inf ...[more]

PMID: 32719441

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Project description:This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS). Prediction results are interpolated using Inverse Distance Weight (IDW). This study was conducted in stages: (1) Image preprocessing; (2) calculating numerical data extracted from the LANDSAT band imagery using vegetation indices; (3) analyzing correlation coefficients between VI; (4) prediction using RF and CART; (5) comparing performances between RF and CART using ANN, SVM, k-nn, and MARS; (6) testing the accuracy of prediction using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE); (7) interpolating with IDW. Correlation coefficient of VI data shows a positive correlation, the lowest r (0.07) and the highest r (0.98). The experiments show that the RF and CART algorithms have efficiency and effectivity in determining the aridity areas better than the ANN, SVM, k-nn, and MARS algorithm. RF has a difference between the predicted results and 1.04% survey data MAPE and the smallest value close to zero is 0.05 MSE. CART has a difference between the predicted results and 1.05% survey data MAPE and the smallest value approaching to zero which is 0.05 MSE. The prediction results of VI show that in 2020 most of the study areas were low vegetation areas with the Normalized Difference Vegetation Index (NDVI) < 0.21, had an indication of drought with the Vegetation Health Index (VHI) < 31.10, had a Vegetation Condition Index (VCI) in some areas between 35%-50% (moderate drought) and < 35% (high drought). The Burn Area Index (dBAI) values are between -3, 971 and -2,376 that show the areas have a low fire risk, and index values are between -0, 208 and -0,412 that show the areas are starting vegetation growth. The result of this study shows that the machine learning algorithms is an accurate and stable algorithm in predicting the risks of drought and land fire based on the VI data extracted from the LANDSAT 8 OLL imagery. The VI data contain the record of vegetation condition and its environment, including humidity, temperatures, and the environmental vegetation health.

Dataset Information

A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication.

Background

Objective

Methods

Results

Significance

Publications

A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication.

Similar Datasets

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