Application of Remote Sensing in Forest Fire

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Drones drones	4.8	6.1	2017	17.9 Days	CHF 2600
Fire fire	3.2	3.5	2018	15 Days	CHF 2400
Forests forests	2.9	4.5	2010	16.9 Days	CHF 2600
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400

30 pages, 22298 KiB

Open AccessArticle

Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria

by Nadia Zikiou, Holly Rushmeier, Manuel I. Capel, Tarek Kandakji, Nelson Rios and Mourad Lahdir

Remote Sens. 2024, 16(9), 1517; https://doi.org/10.3390/rs16091517 - 25 Apr 2024

Abstract

Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is [...] Read more.

Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0. Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84. An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately mapping fire severity, we can better assess the impacts of climate change and land use changes, facilitating proactive measures to mitigate future fire incidents. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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16 pages, 1176 KiB

Open AccessArticle

A Control-Theoretic Spatio-Temporal Model for Wildfire Smoke Propagation Using UAV-Based Air Pollutant Measurements

by Prabhash Ragbir, Ajith Kaduwela, Xiaodong Lan, Adam Watts and Zhaodan Kong

Drones 2024, 8(5), 169; https://doi.org/10.3390/drones8050169 - 24 Apr 2024

Abstract

Wildfires have the potential to cause severe damage to vegetation, property and most importantly, human life. In order to minimize these negative impacts, it is crucial that wildfires are detected at the earliest possible stages. A potential solution for early wildfire detection is [...] Read more.

Wildfires have the potential to cause severe damage to vegetation, property and most importantly, human life. In order to minimize these negative impacts, it is crucial that wildfires are detected at the earliest possible stages. A potential solution for early wildfire detection is to utilize unmanned aerial vehicles (UAVs) that are capable of tracking the chemical concentration gradient of smoke emitted by wildfires. A spatiotemporal model of wildfire smoke plume dynamics can allow for efficient tracking of the chemicals by utilizing both real-time information from sensors as well as future information from the model predictions. This study investigates a spatiotemporal modeling approach based on subspace identification (SID) to develop a data-driven smoke plume dynamics model for the purposes of early wildfire detection. The model was learned using CO₂ concentration data which were collected using an air quality sensor package onboard a UAV during two prescribed burn experiments. Our model was evaluated by comparing the predicted values to the measured values at random locations and showed mean errors of 6.782 ppm and 30.01 ppm from the two experiments. Additionally, our model was shown to outperform the commonly used Gaussian puff model (GPM) which showed mean errors of 25.799 ppm and 104.492 ppm, respectively. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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17 pages, 6108 KiB

Open AccessArticle

Postfire Forest Regrowth Algorithm Using Tasseled-Cap-Retrieved Indices

by Nataliya Stankova and Daniela Avetisyan

Remote Sens. 2024, 16(3), 597; https://doi.org/10.3390/rs16030597 - 05 Feb 2024

Viewed by 741

Abstract

Wildfires are a common disturbance factor worldwide, especially over the last decade due to global climate change. Monitoring postfire forest regrowth provides fundamental information needed to enhance the management and support of ecosystem recovery after fires. The purpose of this study is to [...] Read more.

Wildfires are a common disturbance factor worldwide, especially over the last decade due to global climate change. Monitoring postfire forest regrowth provides fundamental information needed to enhance the management and support of ecosystem recovery after fires. The purpose of this study is to propose an algorithm for postfire forest regrowth monitoring using tasseled-cap-derived indices. A complex approach is used for its implementation, for which a model is developed based on three components—Disturbance Index (DI), Vector of Instantaneous Condition (VIC), and Direction Angle (DA). The final product—postfire regrowth (PFIR)—allows for a quantitative assessment of the intensity of regrowth. The proposed methodology is based on the linear orthogonal transformation of multispectral satellite images—tasseled cap transformation (TCT)—that increases the degree of identification of the three main components that change during a fire—soil, vegetation, and water/moisture—and implies a higher accuracy of the assessments. The results provide a thematic raster representing the intensity of the regrowth classes, which are defined after the PFIR threshold values are determined (HRI—high regrowth intensity; MRI—moderate regrowth intensity; and LRI—low regrowth intensity). The accuracy assessment procedure is conducted using very-high-resolution (VHR) aerial and satellite data from World View (WV) sensors, as well as multispectral Sentinel 2A images. Three different forest test sites affected by fire in Bulgaria are examined. The results show that the classified thematic raster maps are distinguished by a good performance in monitoring the regrowth dynamics, with an average overall accuracy of 62.1% for all three test sites, ranging from 73.9% to 48.4% for the individual forests. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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20 pages, 8637 KiB

Open AccessArticle

Forecast Zoning of Forest Fire Occurrence: A Case Study in Southern China

by Xiaodong Jing, Xusheng Li, Donghui Zhang, Wangjia Liu, Wanchang Zhang and Zhijie Zhang

Forests 2024, 15(2), 265; https://doi.org/10.3390/f15020265 - 30 Jan 2024

Viewed by 680

Abstract

Forest fires in the southern region of China pose significant threats to ecological balance, human safety, and socio-economic stability. Forecast zoning the occurrence of these fires is crucial for timely and effective response measures. This study employs the random forest algorithm and geospatial [...] Read more.

Forest fires in the southern region of China pose significant threats to ecological balance, human safety, and socio-economic stability. Forecast zoning the occurrence of these fires is crucial for timely and effective response measures. This study employs the random forest algorithm and geospatial analysis, including kernel density and standard deviation ellipse methods, to predict forest fire occurrences. Historical fire data analysis reveals noteworthy findings: (i) Decreasing Trend in Forest Fires: The annual forest fire count in the southern region exhibits a decreasing trend from 2001 to 2019, indicating a gradual reduction in fire incidence. Spatial autocorrelation in fire point distribution is notably observed. (ii) Excellent Performance of Prediction Model: The constructed forest fire prediction model demonstrates outstanding performance metrics, achieving high accuracy, precision, recall, F1-scores, and AUC on the testing dataset. (iii) Seasonal Variations in High-Risk Areas: The probability of high-risk areas for forest fires in the southern region shows seasonal variations across different months. Notably, March to May sees increased risk in Guangxi, Guangdong, Hunan, and Fujian. June to August concentrates risk in Hunan and Jiangxi. September to November and December to February have distinct risk zones. These findings offer detailed insights into the seasonal variations of fire risk, providing a scientific basis for the prevention and control of forest fires in the southern region of China. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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18 pages, 4479 KiB

Open AccessArticle

Forest Canopy Fuel Loads Mapping Using Unmanned Aerial Vehicle High-Resolution Red, Green, Blue and Multispectral Imagery

by Álvaro Agustín Chávez-Durán, Mariano García, Miguel Olvera-Vargas, Inmaculada Aguado, Blanca Lorena Figueroa-Rangel, Ramón Trucíos-Caciano and Ernesto Alonso Rubio-Camacho

Forests 2024, 15(2), 225; https://doi.org/10.3390/f15020225 - 24 Jan 2024

Viewed by 856

Abstract

Canopy fuels determine the characteristics of the entire complex of forest fuels due to their constant changes triggered by the environment; therefore, the development of appropriate strategies for fire management and fire risk reduction requires an accurate description of canopy forest fuels. This [...] Read more.

Canopy fuels determine the characteristics of the entire complex of forest fuels due to their constant changes triggered by the environment; therefore, the development of appropriate strategies for fire management and fire risk reduction requires an accurate description of canopy forest fuels. This paper presents a method for mapping the spatial distribution of canopy fuel loads (CFLs) in alignment with their natural variability and three-dimensional spatial distribution. The approach leverages an object-based machine learning framework with UAV multispectral data and photogrammetric point clouds. The proposed method was developed in the mixed forest of the natural protected area of “Sierra de Quila”, Jalisco, Mexico. Structural variables derived from photogrammetric point clouds, along with spectral information, were used in an object-based Random Forest model to accurately estimate CFLs, yielding R² = 0.75, RMSE = 1.78 Mg, and an average

{B i a s}_{r e l}

= 18.62%. Canopy volume was the most significant explanatory variable, achieving a mean decrease in impurity values greater than 80%, while the combination of texture and vegetation indices presented importance values close to 20%. Our modelling approach enables the accurate estimation of CFLs, accounting for the ecological context that governs their dynamics and spatial variability. The high precision achieved, at a relatively low cost, encourages constant updating of forest fuels maps to enable researchers and forest managers to streamline decision making on fuel and forest fire management. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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23 pages, 6090 KiB

Open AccessArticle

Downwind Fire and Smoke Detection during a Controlled Burn—Analyzing the Feasibility and Robustness of Several Downwind Wildfire Sensing Modalities through Real World Applications

by Patrick Chwalek, Hall Chen, Prabal Dutta, Joshua Dimon, Sukh Singh, Constance Chiang and Thomas Azwell

Fire 2023, 6(9), 356; https://doi.org/10.3390/fire6090356 - 12 Sep 2023

Cited by 1 | Viewed by 1607

Abstract

Wildfires have played an increasing role in wreaking havoc on communities, livelihoods, and ecosystems globally, often starting in remote regions and rapidly spreading into inhabited areas where they become difficult to suppress due to their size and unpredictability. In sparsely populated remote regions [...] Read more.

Wildfires have played an increasing role in wreaking havoc on communities, livelihoods, and ecosystems globally, often starting in remote regions and rapidly spreading into inhabited areas where they become difficult to suppress due to their size and unpredictability. In sparsely populated remote regions where freshly ignited fires can propagate unimpeded, the need for distributed fire detection capabilities has become increasingly urgent. In this work, we evaluate the potential of a multitude of different sensing modalities for integration into a distributed downwind fire detection system, something which does not exist today. We deployed custom sensor-rich data logging units over a multi-day-controlled burn event hosted by the Marin County Fire Department in Marin County, CA. Under the experimental conditions, nearly all sensing modalities exhibited signature behaviors of a nearby active fire, but with varying degrees of sensitivity. We present promising preliminary findings from these field tests but also note that future work is needed to assess more prosaic concerns. Larger scale trials will be needed to determine the practicality of specific sensing modalities in outdoor settings, and additional environmental data and testing will be needed to determine the sensor system lifetime, data delivery performance, and other technical considerations. Crucially, this work provides the preliminary justification underscoring that future work is potentially valuable and worth pursuit. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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23 pages, 9805 KiB

Open AccessArticle

Study of the Application of FY-3D/MERSI-II Far-Infrared Data in Wildfire Monitoring

by Wei Zheng, Jie Chen, Cheng Liu, Tianchan Shan and Hua Yan

Remote Sens. 2023, 15(17), 4228; https://doi.org/10.3390/rs15174228 - 28 Aug 2023

Viewed by 760

Abstract

In general, the far-infrared channel in the wavelength range of 10.5–12.0 µm plays an auxiliary role in wildfire detection as its sensitivity to high-temperature targets is far lower than the mid-infrared channel in the wavelength range of 3.5–4.0 µm at the same spatial [...] Read more.

In general, the far-infrared channel in the wavelength range of 10.5–12.0 µm plays an auxiliary role in wildfire detection as its sensitivity to high-temperature targets is far lower than the mid-infrared channel in the wavelength range of 3.5–4.0 µm at the same spatial resolution (1 km, which is the spatial resolution of infrared channels in most satellites used for wildfire monitoring in daily operational mode). The Medium-Resolution Spectral Imager II onboard the Fengyun-3D polar orbiting meteorological satellite (FY-3D/MERSI-II) contains far-infrared channels with a spatial resolution of 250 m at the wavelengths of 10.8 μm and 12.0 μm, which promotes the application of far-infrared channels in wildfire monitoring. In this study, the features of FY-3D/MERSI-II far-infrared channels in fire monitoring are discussed. The sensitivity of 10.8 μm (250 m) to fire spots and the influence of solar radiation reflection on the infrared channels are quantitatively analyzed. The method of using 10.8 μm (250 m) as a major data source to detect fire spots is proposed, and several typical wildfire cases are used to verify the proposed method. The results show that the 10.8 μm (250 m) far-infrared channel has the same advantages as the existing method in wildfire monitoring in terms of a more precise positioning of the detected fire pixel, avoiding interference by solar radiation reflections, and reflecting stronger fire regions in large fire fields. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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27 pages, 8593 KiB

Open AccessArticle

Machine Learning for Predicting Forest Fire Occurrence in Changsha: An Innovative Investigation into the Introduction of a Forest Fuel Factor

by Xin Wu, Gui Zhang, Zhigao Yang, Sanqing Tan, Yongke Yang and Ziheng Pang

Remote Sens. 2023, 15(17), 4208; https://doi.org/10.3390/rs15174208 - 27 Aug 2023

Cited by 4 | Viewed by 1925

Abstract

Affected by global warming and increased extreme weather, Hunan Province saw a phased and concentrated outbreak of forest fires in 2022, causing significant damage and impact. Predicting the occurrence of forest fires can enhance the ability to make early predictions and strengthen early [...] Read more.

Affected by global warming and increased extreme weather, Hunan Province saw a phased and concentrated outbreak of forest fires in 2022, causing significant damage and impact. Predicting the occurrence of forest fires can enhance the ability to make early predictions and strengthen early warning and responses. Currently, fire prevention and extinguishing in China’s forests and grasslands face severe challenges due to the overlapping of natural and social factors. Existing forest fire occurrence prediction models mostly take into account vegetation, topographic, meteorological and human activity factors; however, the occurrence of forest fires is closely related to the forest fuel moisture content. In this study, the traditional driving factors of forest fire such as satellite hotspots, vegetation, meteorology, topography and human activities from 2004 to 2021 were introduced along with forest fuel factors (vegetation canopy water content and evapotranspiration from the top of the vegetation canopy), and a database of factors for predicting forest fire occurrence was constructed. And a forest fire occurrence prediction model was built using machine learning methods such as the Random Forest model (RF), the Gradient Boosting Decision Tree model (GBDT) and the Adaptive Augmentation Model (AdaBoost). The accuracy of the models was verified using Area Under Curve (AUC) and four other metrics. The RF model with an AUC value of 0.981 was more accurate than all other models in predicting the probability of forest fire occurrence, followed by the GBDT (AUC = 0.978) and AdaBoost (AUC = 0.891) models. The RF model, which has the best accuracy, was selected to predict the monthly forest fire probability in Changsha in 2022 and combined with the Inverse Distance Weight Interpolation method to plot the monthly forest fire probability in Changsha. We found that the monthly spatial and temporal distribution of forest fire probability in Changsha varied significantly, with March, April, May, September, October, November and December being the months with higher forest fire probability. The highest probability of forest fires occurred in the central and northern regions. In this study, the core drivers affecting the occurrence of forest fires in Changsha City were found to be vegetation canopy evapotranspiration and vegetation canopy water content. The RF model was identified as a more suitable forest fire occurrence probability prediction model for Changsha City. Meanwhile, this study found that vegetation characteristics and combustible factors have more influence on forest fire occurrence in Changsha City than meteorological factors, and surface temperature has less influence on forest fire occurrence in Changsha City. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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14 pages, 2715 KiB

Open AccessArticle

Deforestation as the Prominent Driver of the Intensifying Wildfire in Cambodia, Revealed through Geospatial Analysis

by Min-Sung Sim, Shi-Jun Wee, Enner Alcantara and Edward Park

Remote Sens. 2023, 15(13), 3388; https://doi.org/10.3390/rs15133388 - 03 Jul 2023

Viewed by 1301

Abstract

Cambodia has the most fires per area in Southeast Asia, with fire activity have significantly increased since the early 2000s. Wildfire occurrences are multi-factorial in nature, and isolating the relative contribution of each driver remains a challenge. In this study, we quantify the [...] Read more.

Cambodia has the most fires per area in Southeast Asia, with fire activity have significantly increased since the early 2000s. Wildfire occurrences are multi-factorial in nature, and isolating the relative contribution of each driver remains a challenge. In this study, we quantify the relative importance of each driver of fire by analyzing annual spatial regression models of fire occurrence across Cambodia from 2003 to 2020. Our models demonstrated satisfactory performance, explaining 69 to 81% of the variance in fire occurrence. We found that deforestation was consistently the dominant driver of fire across 48 to 70% of the country throughout the study period. Although the influence of low precipitation on fires has increased in 2019 and 2020, the period is not long enough to establish any significant trends. During the study period, wind speed, elevation, and soil moisture had a slight influence of 6–20% without any clear trend, indicating that deforestation continues to be the main driver of fire. Our study improves the current understanding of the drivers of biomass fires across Cambodia, and the methodological framework developed here (quantitative decoupling of the drivers) has strong potential to be applied to other fire-prone areas around the world. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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23 pages, 6199 KiB

Open AccessArticle

Geovisualization and Analysis of Landscape-Level Wildfire Behavior Using Repeat Pass Airborne Thermal Infrared Imagery

by Keaton Shennan, Douglas A. Stow, Atsushi Nara, Gavin M. Schag and Philip Riggan

Fire 2023, 6(6), 240; https://doi.org/10.3390/fire6060240 - 16 Jun 2023

Viewed by 1326

Abstract

Geovisualization tools can supplement the statistical analyses of landscape-level wildfire behavior by enabling the discovery of nuanced information regarding the relationships between fire spread, topography, fuels, and weather. The objectives of this study were to develop and evaluate the effectiveness of geovisualization tools [...] Read more.

Geovisualization tools can supplement the statistical analyses of landscape-level wildfire behavior by enabling the discovery of nuanced information regarding the relationships between fire spread, topography, fuels, and weather. The objectives of this study were to develop and evaluate the effectiveness of geovisualization tools for analyzing wildfire behavior and specifically to apply those tools to study portions of the Thomas and Detwiler wildfire events that occurred in California in 2017. Fire features such as active fire fronts and rate of spread (ROS) vectors derived from repetitive airborne thermal infrared (ATIR) imagery sequences were incorporated into geovisualization tools hosted in a web geographic information systems application. This geovisualization application included ATIR imagery, fire features derived from ATIR imagery (rate of spread vectors and fire front delineations), growth form maps derived from NAIP imagery, and enhanced topographic rasters for visualizing changes in local topography. These tools aided in visualizing and analyzing landscape-level wildfire behavior for study portions of the Thomas and Detwiler fires. The primary components or processes of fire behavior analyzed in this study were ROS, spotting, fire spread impedance, and fire spread over multidirectional slopes. Professionals and researchers specializing in wildfire-related topics provided feedback on the effectiveness and utility of the geovisualization tools. The geovisualization tools were generally effective for visualizing and analyzing (1) fire spread over multidirectional slopes; (2) differences in spread magnitudes within and between sequences over time; and (3) the relative contributions of fuels, slope, and weather at any given point within the sequences. Survey respondents found the tools to be moderately effective, with an average effectiveness score of 6.6 (n = 5) for the visualization tools on a scale of 1 (ineffective) to 10 (effective) for postfire spread analysis and visualizing fire spread over multidirectional slopes. The results of the descriptive analysis indicate that medium- and fine-scale topographic features, roads, and riparian fuels coincided with cases of fire spread impedance and exerted control over fire behavior. Major topographic features such as ridges and valleys slowed, or halted, fire spread consistently between study areas. The relationships between spotting, fuels, and topography were inconclusive. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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15 pages, 17184 KiB

Open AccessArticle

Exploring the Influence of Forest Tenure and Protection Status on Post-Fire Recovery in Southeast Australia

by Sven Huettermann, Simon Jones, Mariela Soto-Berelov and Samuel Hislop

Forests 2023, 14(6), 1098; https://doi.org/10.3390/f14061098 - 25 May 2023

Viewed by 1017

Abstract

Research Highlights: We used Landsat time series data to investigate the role forest tenure and protection status play in the recovery of a forest after a fire. Background and Objectives: Changing fire regimes put forests in southeast Australia under increasing pressure. [...] Read more.

Research Highlights: We used Landsat time series data to investigate the role forest tenure and protection status play in the recovery of a forest after a fire. Background and Objectives: Changing fire regimes put forests in southeast Australia under increasing pressure. Our investigation aimed to explore the impact of different forest management structures on a forest’s resilience to fire by looking at the post-fire recovery duration. Materials and Methods: The analysis included a total of 60.6 Mha of land containing 25.4 Mha of forest in southeast Australia. Multispectral time series data from Landsat satellites and a local reference dataset were used to model attributes of disturbance and recovery over a period of 33 years. Results: Protected public forest spectrally recovered 0.4 years faster than protected private forest. No other significant effects in relation to different tenure and protection status were found. Climatic and topographic variables were found to have much greater influence on post-fire spectral recovery. Conclusions: Protected area status in public forests resulted in slightly faster recovery, compared with the private protected forest estate. However, factors outside the control of land managers and policy makers, i.e., climatic and topographic variables, appear to have a much greater impact on post-fire recovery. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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13 pages, 2537 KiB

Open AccessArticle

An Efficient Wildfire Detection System for AI-Embedded Applications Using Satellite Imagery

by George L. James, Ryeim B. Ansaf, Sanaa S. Al Samahi, Rebecca D. Parker, Joshua M. Cutler, Rhode V. Gachette and Bahaa I. Ansaf

Fire 2023, 6(4), 169; https://doi.org/10.3390/fire6040169 - 20 Apr 2023

Cited by 4 | Viewed by 3865

Abstract

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine [...] Read more.

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (MobileNet) was trained to identify key features of various satellite images that contained fire or without fire. Then, the trained system is used to classify new satellite imagery and sort them into fire or no fire classes. A cloud-based development studio from Edge Impulse Inc. is used to create a NN model based on the transferred learning algorithm. The effects of four hyperparameters are assessed: input image resolution, depth multiplier, number of neurons in the dense layer, and dropout rate. The computational cost is evaluated based on the simulation of deploying the neural network model on an Arduino Nano 33 BLE device, including Flash usage, peak random access memory (RAM) usage, and network inference time. Results supported that the dropout rate only affects network prediction performance; however, the number of neurons in the dense layer had limited effects on performance and computational cost. Additionally, hyperparameters such as image size and network depth significantly impact the network model performance and the computational cost. According to the developed benchmark network analysis, the network model MobileNetV2, with 160 × 160 pixels image size and 50% depth reduction, shows a good classification accuracy and is about 70% computationally lighter than a full-depth network. Therefore, the proposed methodology can effectively design an ML application that instantly and efficiently analyses imagery from a spacecraft/weather balloon for the detection of wildfires without the need of an earth control centre. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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14 pages, 9540 KiB

Open AccessArticle

Forest Fire Patterns and Lightning-Caused Forest Fire Detection in Heilongjiang Province of China Using Satellite Data

by Qiangying Jiao, Meng Fan, Jinhua Tao, Weiye Wang, Di Liu and Ping Wang

Fire 2023, 6(4), 166; https://doi.org/10.3390/fire6040166 - 19 Apr 2023

Cited by 8 | Viewed by 3173

Abstract

Large forest fires can cause significant damage to forest ecosystems and threaten human life and property. Heilongjiang Province is a major forested area in China with the highest number and concentration of lightning-caused forest fires in the country. This study examined the spatial [...] Read more.

Large forest fires can cause significant damage to forest ecosystems and threaten human life and property. Heilongjiang Province is a major forested area in China with the highest number and concentration of lightning-caused forest fires in the country. This study examined the spatial and temporal distribution patterns of forest fires in Heilongjiang Province, as well as the ability of satellite remote sensing to detect these fires using VIIRS 375 m fire point data, ground history forest fire point data, and land cover dataset. The study also investigated the occurrence patterns of lightning-caused forest fires and the factors affecting satellite identification of these fires through case studies. Results show that April has the highest annual number of forest fires, with 77.6% of forest fires being caused by lightning. However, less than 30% of forest fires can be effectively detected by satellites, and lightning-caused forest fires account for less than 15% of all fires. There is a significant negative correlation between the two. Lightning-caused forest fires are concentrated in the Daxing’an Mountains between May and July, and are difficult to monitor by satellites due to cloud cover and lack of satellite transit. Overall, the trend observed in the number of forest fire pixels that are monitored by satellite remote sensing systems is generally indicative of the trends in the actual number of forest fires. However, lightning-caused forest fires are the primary cause of forest fires in Heilongjiang Province, and satellite remote sensing is relatively weak in monitoring these fires due to weather conditions and the timing of satellite transit. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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15 pages, 4892 KiB

Open AccessArticle

Protected Areas Conserved Forests from Fire and Deforestation in Vietnam’s Central Highlands from 2001 to 2020

by Samuel J. Ebright, Amanda B. Stan, Hoàng Văn Sâm and Peter Z. Fulé

Fire 2023, 6(4), 164; https://doi.org/10.3390/fire6040164 - 18 Apr 2023

Cited by 3 | Viewed by 1530

Abstract

As a tropical nation with ~40% forested land area and 290 protected areas in the Indo-Burma Biodiversity Hotspot, Vietnam holds an important part of global forests. Despite a complex history of multiple colonial rules, war, rapid economic development and societal growth, Vietnam was [...] Read more.

As a tropical nation with ~40% forested land area and 290 protected areas in the Indo-Burma Biodiversity Hotspot, Vietnam holds an important part of global forests. Despite a complex history of multiple colonial rules, war, rapid economic development and societal growth, Vietnam was one of a few Southeast Asian countries to reverse deforestation trends and sustain net forest cover gain since the 1990s. However, a considerable amount of Vietnam’s forest gain has been from plantation forestry, as Vietnam’s policies have promoted economic development. In the Central Highlands region of Vietnam, widespread forest degradation and deforestation has occurred recently in some areas due to plantation forestry and other factors, including fire-linked deforestation, but protected areas here have been largely effective in their conservation goals. We studied deforestation, wildfires, and the contribution of fire-linked deforestation from 2001 to 2020 in an area near the Da Lat Plateau of the Central Highlands of Vietnam. We stratified our study area to distinguish legally protected areas and those in the surrounding landscape matrix without formal protection. Using satellite-derived data, we investigated four questions: (1) Have regional deforestation trends continued in parts of the Central Highlands from 2001 to 2020? (2) Based on remotely sensed fire detections, how has fire affected the Central Highlands and what proportion of deforestation is spatiotemporally linked to fire? (3) Were annual deforestation and burned area lower in protected areas relative to the surrounding land matrix? (4) Was the proportion of fire-linked deforestation lower in protected areas than in the matrix? To answer these questions, we integrated the Global Forest Change and FIRED VIETNAM datasets. We found that 3794 fires burned 8.7% of the total study area and 13.6% of the area became deforested between 2001 and 2020. While nearly half of fires were linked to deforestation, fire-linked deforestation accounted for only a small part of forest loss. Across the entire study area, 54% of fire-linked deforestation occurred in natural forests and 46% was in plantation forests. Fire ignitions in the study area were strongly linked to the regional dry season, November to March, and instrumental climate data from 1971 to 2020 showed statistically significant increasing trends in minimum, mean, and maximum temperatures. However, the total area burned did not have a significant increasing trend. Regional trends in deforestation continued in Vietnam’s Central Highlands from 2001 to 2020, and nearly half of all detected fires can be spatially and temporally linked to forest loss. However, protected areas in the region effectively conserved forests relative to the surrounding landscape. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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17 pages, 3432 KiB

Open AccessFeature PaperArticle

Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China

by Chaoxue Tan and Zhongke Feng

Sustainability 2023, 15(7), 6292; https://doi.org/10.3390/su15076292 - 06 Apr 2023

Cited by 9 | Viewed by 2421

Abstract

Forest fire is a primary disaster that destroys forest resources and the ecological environment, and has a serious negative impact on the safety of human life and property. Predicting the probability of forest fires and drawing forest fire risk maps can provide a [...] Read more.

Forest fire is a primary disaster that destroys forest resources and the ecological environment, and has a serious negative impact on the safety of human life and property. Predicting the probability of forest fires and drawing forest fire risk maps can provide a reference basis for forest fire control management in Hunan Province. This study selected 19 forest fire impact factors based on satellite monitoring hotspot data, meteorological data, topographic data, vegetation data, and social and human data from 2010–2018. It used random forest, support vector machine, and gradient boosting decision tree models to predict the probability of forest fires in Hunan Province and selected the RF algorithm to create a forest fire risk map of Hunan Province to quantify the potential forest fire risk. The results show that the RF algorithm performs best compared to the SVM and GBDT algorithms with 91.68% accuracy, 91.96% precision, 92.78% recall, 92.37% F1, and 97.2% AUC. The most important drivers of forest fires in Hunan Province are meteorology and vegetation. There are obvious differences in the spatial distribution of seasonal forest fire risks in Hunan Province, and winter and spring are the seasons with high forest fire risks. The medium- and high-risk areas are mostly concentrated in the south of Hunan. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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23 pages, 7472 KiB

Open AccessArticle

Real-Time Wildfire Detection Algorithm Based on VIIRS Fire Product and Himawari-8 Data

by Da Zhang, Chunlin Huang, Juan Gu, Jinliang Hou, Ying Zhang, Weixiao Han, Peng Dou and Yaya Feng

Remote Sens. 2023, 15(6), 1541; https://doi.org/10.3390/rs15061541 - 11 Mar 2023

Cited by 4 | Viewed by 2742

Abstract

Wildfires have a significant impact on the atmosphere, terrestrial ecosystems, and society. Real-time monitoring of wildfire locations is crucial in fighting wildfires and reducing human casualties and property damage. Geostationary satellites offer the advantage of high temporal resolution and are gradually being used [...] Read more.

Wildfires have a significant impact on the atmosphere, terrestrial ecosystems, and society. Real-time monitoring of wildfire locations is crucial in fighting wildfires and reducing human casualties and property damage. Geostationary satellites offer the advantage of high temporal resolution and are gradually being used for real-time fire detection. In this study, we constructed a fire label dataset using the stable VNP14IMG fire product and used the random forest (RF) model for fire detection based on Himawari-8 multiband data. The band calculation features related brightness temperature, spatial features, and auxiliary data as input used in this framework for model training. We also used a recursive feature elimination method to evaluate the impact of these features on model accuracy and to exclude redundant features. The daytime and nighttime RF models (RF-D/RF-N) are separately constructed to analyze their applicability. Finally, we extensively evaluated the model performance by comparing them with the Japan Aerospace Exploration Agency (JAXA) wildfire product. The RF models exhibited higher accuracy, with recall and precision rates of 95.62% and 59%, respectively, and the recall rate for small fires was 19.44% higher than that of the JAXA wildfire product. Adding band calculation features and spatial features, as well as feature selection, effectively reduced the overfitting and improved the model’s generalization ability. The RF-D model had higher fire detection accuracy than the RF-N model. Omission errors and commission errors were mainly concentrated in the adjacent pixels of the fire clusters. In conclusion, our VIIRS fire product and Himawari-8 data-based fire detection model can monitor the fire location in real time and has excellent detection capability for small fires, making it highly significant for fire detection. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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22 pages, 3654 KiB

Open AccessArticle

Quantifying Forest Litter Fuel Moisture Content with Terrestrial Laser Scanning

by Jonathan L. Batchelor, Eric Rowell, Susan Prichard, Deborah Nemens, James Cronan, Maureen C. Kennedy and L. Monika Moskal

Remote Sens. 2023, 15(6), 1482; https://doi.org/10.3390/rs15061482 - 07 Mar 2023

Cited by 3 | Viewed by 2001

Abstract

Electromagnetic radiation at 1550 nm is highly absorbed by water and offers a novel way to collect fuel moisture data, along with 3D structures of wildland fuels/vegetation, using lidar. Two terrestrial laser scanning (TLS) units (FARO s350 (phase shift, PS) and RIEGL vz-2000 [...] Read more.

Electromagnetic radiation at 1550 nm is highly absorbed by water and offers a novel way to collect fuel moisture data, along with 3D structures of wildland fuels/vegetation, using lidar. Two terrestrial laser scanning (TLS) units (FARO s350 (phase shift, PS) and RIEGL vz-2000 (time of flight, TOF)) were assessed in a series of laboratory experiments to determine if lidar can be used to estimate the moisture content of dead forest litter. Samples consisted of two control materials, the angle and position of which could be manipulated (pine boards and cheesecloth), and four single-species forest litter types (Douglas-fir needles, ponderosa pine needles, longleaf pine needles, and southern red oak leaves). Sixteen sample trays of each material were soaked overnight, then allowed to air dry with scanning taking place at 1 h, 2 h, 4 h, 8 h, 12 h, and then in 12 h increments until the samples reached equilibrium moisture content with the ambient relative humidity. The samples were then oven-dried for a final scanning and weighing. The spectral reflectance values of each material were also recorded over the same drying intervals using a field spectrometer. There was a strong correlation between the intensity and standard deviation of intensity per sample tray and the moisture content of the dead leaf litter. A multiple linear regression model with a break at 100% gravimetric moisture content produced the best model with R2 values as high as 0.97. This strong relationship was observed with both the TOF and PS lidar units. At fuel moisture contents greater than 100% gravimetric water content, the correlation between the pulse intensity values recorded by both scanners and the fuel moisture content was the strongest. The relationship deteriorated with distance, with the TOF scanner maintaining a stronger relationship at distance than the PS scanner. Our results demonstrate that lidar can be used to detect and quantify fuel moisture across a range of forest litter types. Based on our findings, lidar may be used to quantify fuel moisture levels in near real-time and could be used to create spatial maps of wildland fuel moisture content. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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