scholarly journals Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires

2022 ◽  
Vol 14 (1) ◽  
pp. 194
Author(s):  
Andrey Sirin ◽  
Maria Medvedeva
Keyword(s):  
Remote Sensing ◽  
Forest Fires ◽  
Radiation Power ◽  
Fire Detection ◽  
Moisture Index ◽  
Peat Fire ◽  
Landsat 5 Tm ◽  
Burnt Areas ◽  
Peat Fires ◽  
Carbon Losses

Peat fires differ from other wildfires in their duration, carbon losses, emissions of greenhouse gases and highly hazardous products of combustion and other environmental impacts. Moreover, it is difficult to identify peat fires using ground-based methods and to distinguish peat fires from forest fires and other wildfires by remote sensing. Using the example of catastrophic fires in July–August 2010 in the Moscow region (the center of European Russia), in the present study, we consider the results of peat-fire detection using Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots, peat maps, and analysis of land cover pre- and post-fire according to Landsat-5 TM data. A comparison of specific (for detecting fires) and non-specific vegetation indices showed the difference index ΔNDMI (pre- and post-fire normalized difference moisture Index) to be the most effective for detecting burns in peatlands according to Landsat-5 TM data. In combination with classification (both unsupervised and supervised), this index offered 95% accuracy (by ground verification) in identifying burnt areas in peatlands. At the same time, most peatland fires were not detected by Terra/Aqua MODIS data. A comparison of peatland and other wildfires showed the clearest differences between them in terms of duration and the maximum value of the fire radiation power index. The present results may help in identifying peat (underground) fires and their burnt areas, as well as accounting for carbon losses and greenhouse gas emissions.

scholarly journals Assessing Wood and Soil Carbon Losses from a Forest-Peat Fire in the Boreo-Nemoral Zone

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 880
Author(s):  
Andrey Sirin ◽  
Alexander Maslov ◽  
Dmitry Makarov ◽  
Yakov Gulbe ◽  
Hans Joosten
Keyword(s):  
Soil Carbon ◽  
Stand Structure ◽  
Peat Soil ◽  
Burned Area ◽  
Tree Biomass ◽  
Carbon Loss ◽  
Forest Stand Structure ◽  
Peat Fire ◽  
Peat Fires ◽  
Carbon Losses

Forest-peat fires are notable for their difficulty in estimating carbon losses. Combined carbon losses from tree biomass and peat soil were estimated at an 8 ha forest-peat fire in the Moscow region after catastrophic fires in 2010. The loss of tree biomass carbon was assessed by reconstructing forest stand structure using the classification of pre-fire high-resolution satellite imagery and after-fire ground survey of the same forest classes in adjacent areas. Soil carbon loss was assessed by using the root collars of stumps to reconstruct the pre-fire soil surface and interpolating the peat characteristics of adjacent non-burned areas. The mean (median) depth of peat losses across the burned area was 15 ± 8 (14) cm, varying from 13 ± 5 (11) to 20 ± 9 (19). Loss of soil carbon was 9.22 ± 3.75–11.0 ± 4.96 (mean) and 8.0–11.0 kg m−2 (median); values exceeding 100 tC ha−1 have also been found in other studies. The estimated soil carbon loss for the entire burned area, 98 (mean) and 92 (median) tC ha−1, significantly exceeds the carbon loss from live (tree) biomass, which averaged 58.8 tC ha−1. The loss of carbon in the forest-peat fire thus equals the release of nearly 400 (soil) and, including the biomass, almost 650 tCO2 ha−1 into the atmosphere, which illustrates the underestimated impact of boreal forest-peat fires on atmospheric gas concentrations and climate.

scholarly journals Fires Monitoring by Means of Spectral Indices from Sentinel-2 Data: the Case Study of the Vesuvius and Lattari Mountains, Campania (Italy)

2020 ◽  
Author(s):  
Domenico Antonio Giuseppe Dell'Aglio ◽  
Carmine Gambardella ◽  
Massimiliano Gargiulo ◽  
Antonio Iodice ◽  
Rosaria Parente ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Fires ◽  
European Space Agency ◽  
Fire Risk ◽  
Fire Detection ◽  
Spectral Indices ◽  
Active Fire ◽  
Space Agency ◽  
Repeat Time ◽  
Sentinel 2

Forest fires are part of a set of natural disasters that have always affected regions of the world typically characterized by a tropical climate with long periods of drought. However, due to climate change in recent years, other regions of our planet have also been affected by this phenomenon, never seen before. One of them is certainly the Italian peninsula, and especially the regions of southern Italy. For this reason, the scientific community, as well as remote sensing one, is highly concerned in developing reliable techniques to provide useful support to the competent authorities. In particular, three specific tasks have been carried out in this work: (i) fire risk prevention, (ii) active fire detection, and (iii) post-fire area assessment. To accomplish these analyses, the capability of a set of spectral indices, derived from spaceborne remote sensing (RS) data, is assessed to monitor the forest fires. The spectral indices are obtained from Sentinel-2 multispectral images of the European Space Agency (ESA), which are free of charge and openly accessible. Moreover, the twin Sentinel-2 sensors allow to overcome some restrictions on time delivery and observation repeat time. The performance of the proposed analyses were assessed experimentally to monitor the forest fires occurred in two specific study areas during the summer of 2017: the volcano Vesuvius, near Naples, and the Lattari mountains, near Sorrento (both in Campania, Italy).

scholarly journals Wildfires: an Application of Remote Sensing and OBIA

2021 ◽  
Vol 17 ◽  
pp. 282-296
Author(s):  
Giuliana Bilotta ◽  
Salvatore Calcagno ◽  
Stefano Bonfa
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Information System ◽  
Forest Fires ◽  
Fire Risk ◽  
Soil Stability ◽  
Environmental Damage ◽  
Remotely Sensed Data ◽  
Burnt Areas ◽  
Processing Techniques

- To maintain soil stability and integrity, it is important to distinguish between soil covered by thick vegetation and that made arid and barren by fire, particularly when considering growing climate change. The safeguarding of these territories and the fight against its progressive environmental degradation requires great attention be paid to forest fires, particularly when considering the enormous environmental damage that fires have caused to important and widespread areas of the globe. The purpose of the contribution here is to compare processing techniques of high-resolution remotely sensed data from optical satellites to determine the best method of automatic discrimination of fire areas, thereby allowing the management of burnt areas in the context of subsequent fire risk. These integrated techniques were developed in a Geographic Information System (GIS) to get an accurate perimeter, and in general to analyze and manage data, geographic and otherwise, with spatial and geostatistical queries and analyzes. In a such a way that has an immediate reflection in the capability of immediately preparing acts, such as orders, decrees and other provisions, both for the protection of properties and territories and to lay a basis also for the prosecution and repression of crimes

scholarly journals A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6442 ◽  
Author(s):  
Panagiotis Barmpoutis ◽  
Periklis Papaioannou ◽  
Kosmas Dimitropoulos ◽  
Nikos Grammalidis
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Forest Fires ◽  
Large Scale ◽  
Fire Detection ◽  
Future Research ◽  
Optical Remote Sensing ◽  
Effective Prevention ◽  
Detection Systems ◽  
The World

The environmental challenges the world faces nowadays have never been greater or more complex. Global areas covered by forests and urban woodlands are threatened by natural disasters that have increased dramatically during the last decades, in terms of both frequency and magnitude. Large-scale forest fires are one of the most harmful natural hazards affecting climate change and life around the world. Thus, to minimize their impacts on people and nature, the adoption of well-planned and closely coordinated effective prevention, early warning, and response approaches are necessary. This paper presents an overview of the optical remote sensing technologies used in early fire warning systems and provides an extensive survey on both flame and smoke detection algorithms employed by each technology. Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments are studied. Finally, the strengths and weaknesses of fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire systems.

scholarly journals Research database of burnt areas over Russia based on high resolution remote sensing data

2021 ◽  
Author(s):  
A.V. Kashnitskii ◽  
I.V. Balashov ◽  
I.A. Saigin ◽  
F.V. Stytsenko ◽  
E.A. Loupian
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Forest Fires ◽  
Method Development ◽  
Remote Sensing Data ◽  
Mapping Method ◽  
Research Database ◽  
Sensing Data ◽  
Fully Automatic ◽  
Burnt Areas

The paper presents the sample database of vegetation cover damaged by wildfires, obtained from high spatial resolution remote sensing data (up to 10 meters per pixel). At the time of publication, more than 6 thousand fires with a total area of more than 12 million ha were mapped and confirmed with the focus on forest fires. The database covers the period from 2009 to 2020 and is constantly being updated. The presented database may be of interest for various scientific wildfire researches and can be used as training basis for a fully automatic high-resolution fire mapping method development.

scholarly journals INTEGRATED GIS SYSTEM FOR POST-FIRE HAZARD ASSESSMENTS WITH REMOTE SENSING

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 13-20
Author(s):  
V. Barrile ◽  
G. Bilotta ◽  
A. Fotia ◽  
E. Bernardo
Keyword(s):  
Remote Sensing ◽  
Forest Fires ◽  
Fossil Fuels ◽  
Fire Hazard ◽  
Remote Sensing Data ◽  
Fire Risk ◽  
Software Tool ◽  
Environmental Damage ◽  
Burnt Areas ◽  
Climate Crisis

Abstract. Fires continue to devour hundreds of thousands of hectares of forest even in 2020, generating gigantic damage to the ecosystem, if we think that we are in the midst of a climate crisis caused precisely by CO2 emissions into the atmosphere by man, due to burning of fossil fuels. The action to safeguard the territory and the fight against its progressive environmental degradation focus a great attention towards forest fires, also considering the enormous environmental damage that these have caused to important and very large areas of the globe. The aim of the contribution that we here propose is the design and implementation of a software tool that performs predictive functions of triggering possible forest fires, thanks to the integration and manipulation of data from different sources and processed by predictive mathematical models, to support decisions; the comparison of techniques for the processing of high-resolution remote sensing data from optical satellites for the best automatic discrimination of the areas covered by fire plays a fundamental role in the analysis. This allows managing the burnt areas also considering subsequent fire risks, and the integration of the techniques developed in a GIS in order to obtain an accurate perimeter and a fire risk map prevision.

scholarly journals Aerial Forest Fire Detection and Monitoring Using a Small UAV

2020 ◽  
Author(s):  
Joaquim Vasconcelos Reinolds de Sousa ◽  
Pedro Vieira Gamboa
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Detection Algorithm ◽  
The Body ◽  
Fire Fighting ◽  
Aerial Images ◽  
North East ◽  
Forest Fire Detection

In recent years, large patches of forest have been destroyed by fires, bringing tragic consequences for the environment and small settlements established around these regions. In this context, it is essential that fire fighting teams possess an increased situational awareness about the fire propagation, in order to promptly act in the extinguishing process. Recent advances in UAV technology allied with remote sensing and computer vision techniques show very promising UAVs applicability in forest fires detection and monitoring. Besides presenting lower operational costs, these vehicles are able to reach regions that are inaccessible or considered too dangerous for fire fighting crews operations. This paper describes the application of a real-time forest fire detection algorithm using aerial images captured by a video camera onboard    an Unmanned Aerial Vehicle (UAV). The forest fire detection algorithm consists of a rule-based colour model that uses both RGB and YCbCr colour spaces to identify fire pixels. An intuitive targeting system was also developed, allowing the detection of multiple fires at the same time. Additionally, a fire geolocation algorithm was developed in order to estimate the fire location in terms of latitude (φ),  longitude     (λ) and altitude (h). The geolocation algorithm consists of applying two coordinates systems transformations between the body-fixed frame, North-East-Down frame (NED) and Earth-Centered, Earth Fixed (ECEF) frame. Flight tests were performed during  a controlled burn in order to assess the fire detection algorithm performance. The algorithm was able to detect the fire with few false positive detections. Keywords: Aerial fire detection algorithm, Aerial fire monitoring, Forest fire, UAV, Remote sensing

scholarly journals Android mobile application for wildfire reporting and monitoring

2021 ◽  
Vol 10 (6) ◽  
pp. 3412-3421
Author(s):  
Rony Teguh ◽  
Fengky F. Adji ◽  
Benius Benius ◽  
Mohammad Nur Aulia
Keyword(s):  
Fire Detection ◽  
Satellite System ◽  
Decision Makers ◽  
Mobile Platform ◽  
Web Based ◽  
Detection Systems ◽  
Peat Fire ◽  
Close Range ◽  
Global Navigation Satellite ◽  
Peat Fires

Peat fires cause major environmental problems in Central Kalimantan Province, Indonesia and threaten human health and effect the social-economic sector. The lack of peat fire detection systems is one factor that causing these reoccurring fires. Therefore, in this study, we develop an Android mobile platform application and a web-based application to support the citizen-volunteers who want to contribute wildfires reports, and the decision-makers who wish to collect, visualize, and evaluate these wildfires reports. In this paper, the global navigation satellite system (GNSS) and a global position system (GPS) sensor from a smartphone’s camera, is a useful tool to show the potential fire and smoke’s close-range location. The exchangeable image (EXIF) file image and GPS metadata captured by a mobile phone can store and supply raw observation to our devices and sent it to the data center through global internet communication. This work’s results are the proposed application easy-to-use to monitoring potential peat fire by location and data activity. This paper focuses on developing an application for the mobile platform for peat fire reporting and a web-based application to collect peat fire location for decision-makers. Our main objective is to detect the potential and spread of fire in peatlands as early as possible by utilizing community reports using smartphones.

scholarly journals A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Learning Strategy ◽  
Early Stage ◽  
Fire Detection ◽  
Surveillance Systems ◽  
Region Detection ◽  
Decision Network ◽  
Fire Smoke ◽  
Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

scholarly journals An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing

2021 ◽  
Vol 13 (14) ◽  
pp. 2730
Author(s):  
Animesh Chandra Das ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Drought Stress ◽  
Land Surface ◽  
Climatic Factors ◽  
Standardized Precipitation Index ◽  
Landsat 8 ◽  
Drought Frequency ◽  
Moisture Index ◽  
Area Index ◽  
Drought Period

Drought is one of the detrimental climatic factors that affects the productivity and quality of tea by limiting the growth and development of the plants. The aim of this research was to determine drought stress in tea estates using a remote sensing technique with the standardized precipitation index (SPI). Landsat 8 OLI/TIRS images were processed to measure the land surface temperature (LST) and soil moisture index (SMI). Maps for the normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), and leaf area index (LAI), as well as yield maps, were developed from Sentinel-2 satellite images. The drought frequency was calculated from the classification of droughts utilizing the SPI. The results of this study show that the drought frequency for the Sylhet station was 38.46% for near-normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station demonstrated frequencies of 28.21%, 41.02%, and 30.77% for near-normal, normal, and moderately dry months, respectively. The correlation coefficients between the SMI and NDMI were 0.84, 0.77, and 0.79 for the drought periods of 2018–2019, 2019–2020 and 2020–2021, respectively, indicating a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought stress in tea estates demonstrate that 61%, 60%, and 60% of estates in the study area had lower yields than the actual yield during the drought period, which accounted for 7.72%, 11.92%, and 12.52% yield losses in 2018, 2019, and 2020, respectively. This research suggests that satellite remote sensing with the SPI could be a valuable tool for land use planners, policy makers, and scientists to measure drought stress in tea estates.

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