scholarly journals A Fire Hazard Assessment Using Sentinel Imagery; A Case Study Over Gippsland Australia

2020 ◽  
Vol 5 (7) ◽  
pp. 377-381
Author(s):  
Sanjeev Kumar Raut ◽  
David Nhemaphuki ◽  
Rebanta Aryal ◽  
Prakash Lakandri
Keyword(s):  
Fire Hazard ◽  
Fire Regimes ◽  
Burned Area ◽  
United States Geological Survey ◽  
Area Index ◽  
Burned Areas ◽  
Before And After ◽  
History Of ◽  
Sentinel 2

Accurate and the efficient rapid mapping of the fire-damaged areas are the most fundamental things for any places to retain from environmental loss. To support the fire management, make definite strategy and planning, and restore the vegetation, it is important to detect the area before and after the fire damages. Under climate change conditions, heat and drought may trigger tough fire regimes in terms of number and dimension of fires. To deliver the rapid information of the area damaged by the fires, Burned Area Index (BAI), Normalized Burned Ratio (NBR) and their versions are applied to map burned areas from high-resolution optical satellite data. The new MSI sensor aboard Sentinel-2 satellites records the more spectral information in the red edge spectral region making it more convenient to the development of new indices for the burned area mapping. Recently, Australia had confronted a devastating bushfire recorded in the history of the nation. In this project, NBR deployed to detect burned areas at around 10m-20m spatial resolution based on pre and post-fire Sentinel-2 images. A dNBR (differentiated Normalized Burned Ratio) was calculated while burn severity was mapped as purposed by United States Geological Survey (USGS). It observed that more than half of the East Gippsland region i.e. about 53% of the area affected by the wildfire while 38% remained unburned and 8.4% showed the regrowth.

scholarly journals Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

2019 ◽  
Vol 11 (6) ◽  
pp. 622 ◽  
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
National Level ◽  
Burned Area ◽  
Commission Error ◽  
Burned Areas ◽  
Sentinel 2

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

Fire regimes and post-fire evolution of burned areas in selected plant biomes of the planet studying the phenology of the landscape with time series of satellite images

2021 ◽  
Author(s):  
Nikos Koutsias ◽  
Anastasia Karamitsou ◽  
Foula Nioti ◽  
Frank Coutelieris
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Fire History ◽  
Fire Regime ◽  
Fire Regimes ◽  
Burned Areas ◽  
History Of ◽  
Sentinel 2

<p>Plant biomes and climatic zones are characterized by a specific type of fire regime which can be determined from the history of fires in the area and it is a synergy mainly of the climatic conditions and the functional characteristics of the types of vegetation. They correspond also to specific phenology types, a feature that can be useful for various applications related to vegetation monitoring, especially when remote sensing methods are used. Both the assessment of fire regime from the reconstruction of fire history and the monitoring of post-fire evolution of the burned areas can be studied with satellite remote sensing based on satellite time series images. The free availability of (i) Landsat satellite imagery by US Geological Survey (USGS, (ii) Sentinel-2 satellite imagery by ESA and (iii) MODIS satellite imagery by NASA / USGS allow low-cost data acquisition and processing (eg 1984-present) which otherwise would require very high costs. The purpose of this work is to determine the fire regime as well as the patterns of post-fire evolution of burned areas in selected vegetation/climate zones for the entire planet by studying the phenology of the landscape with time series of satellite images. More specifically, the three research questions we are negotiating are: (i) the reconstruction of the history of fires in the period 1984-2017 and the determination of fire regimes with Landsat and Sentinel-2 satellite data , (ii) the assessment of pre-fire phenological pattern of vegetation and (iii) the monitoring and comparative evaluation of post-fire evolution patterns of the burned areas.</p><p><strong>Acknowledgements</strong></p><p>This research has been co-financed by the Operational Program "Human Resources Development, Education and Lifelong Learning" and is co-financed by the European Union (European Social Fund) and Greek national funds.</p><p> </p>

scholarly journals Forest Burned Area Mapping Using Bi-Temporal Sentinel-2 Imagery Based on a Convolutional Neural Network: Case Study in Golestan Forest

2021 ◽  
Vol 10 (1) ◽  
pp. 6
Author(s):  
Fattah Hatami Maskouni ◽  
Seyd Teymoor Seydi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Burned Area ◽  
Kappa Score ◽  
Burned Areas ◽  
The North ◽  
North Of Iran ◽  
Decision Making Processes ◽  
Sentinel 2

Forest areas are profoundly important to the planet, since they offer considerable advantages. The mapping and estimation of burned areas covered with trees are critical during decision making processes. In such cases, remote sensing can be of great help. This paper presents a method to estimate burned areas based on the Sentinel-2 imagery using a convolutional neural network (CNN) algorithm. The framework touches change detection using pre- and post-fire datasets. The proposed framework utilizes a multi-scale convolution block to extract deep features. We investigate the performance of the proposed method via visual and numerical analyses. The case study for this research is Golestan Forest, which is located in the north of Iran. The results of the burned area detection process show that the proposed method produces a performance accuracy rate of more than 97% in terms of overall accuracy, with a Kappa score greater than 0.933.

scholarly journals DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5138
Author(s):  
Seyd Teymoor Seydi ◽  
Mahdi Hasanlou ◽  
Jocelyn Chanussot
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Burned Area ◽  
High Resolution Data ◽  
Burned Areas ◽  
Heterogeneous Datasets ◽  
Burned Area Mapping ◽  
Net Framework ◽  
Sentinel 2

Wildfires are one of the most destructive natural disasters that can affect our environment, with significant effects also on wildlife. Recently, climate change and human activities have resulted in higher frequencies of wildfires throughout the world. Timely and accurate detection of the burned areas can help to make decisions for their management. Remote sensing satellite imagery can have a key role in mapping burned areas due to its wide coverage, high-resolution data collection, and low capture times. However, although many studies have reported on burned area mapping based on remote sensing imagery in recent decades, accurate burned area mapping remains a major challenge due to the complexity of the background and the diversity of the burned areas. This paper presents a novel framework for burned area mapping based on Deep Siamese Morphological Neural Network (DSMNN-Net) and heterogeneous datasets. The DSMNN-Net framework is based on change detection through proposing a pre/post-fire method that is compatible with heterogeneous remote sensing datasets. The proposed network combines multiscale convolution layers and morphological layers (erosion and dilation) to generate deep features. To evaluate the performance of the method proposed here, two case study areas in Australian forests were selected. The framework used can better detect burned areas compared to other state-of-the-art burned area mapping procedures, with a performance of >98% for overall accuracy index, and a kappa coefficient of >0.9, using multispectral Sentinel-2 and hyperspectral PRISMA image datasets. The analyses of the two datasets illustrate that the DSMNN-Net is sufficiently valid and robust for burned area mapping, and especially for complex areas.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

2021 ◽  
Vol 13 (1) ◽  
pp. 432
Author(s):  
Aru Han ◽  
Song Qing ◽  
Yongbin Bao ◽  
Li Na ◽  
Yuhai Bao ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Change ◽  
Fire Severity ◽  
Dynamic Change ◽  
Burned Area ◽  
Fire Event ◽  
Vegetation Dynamic ◽  
Area Index ◽  
Sentinel 2

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

scholarly journals Efektivitas Terapi Seni Mewarnai Terhadap Depresi Pada Remaja Putri

2020 ◽  
Vol 2 (2) ◽  
pp. 109-120
Author(s):  
Sayuri Asnani
Keyword(s):  
Art Therapy ◽  
Beck Depression Inventory ◽  
Severe Depression ◽  
Study Data ◽  
Research Subject ◽  
Before And After ◽  
History Of ◽  
Depression Level ◽  
Case Study Data

This study aims to find the effectiveness of coloring art therapy against depression in a teenage girl in Yogyakarta. The hypothesis in this study is that there is a difference in the level of depression of the research subject before and after being given coloring art therapy. The level of depression after coloring art therapy was lower than before coloring art therapy. The sample in this study was a 13-year-old girl in Yogyakarta who had a history of being away from her parents and experienced moderate-severe depression. Coloring art therapy is given individually, twice, using drawing art media and markers. The subject’s depression level was measured using the Beck Depression Inventory (BDI) II scale from initial to final measurements. This study used one research subject with a single one-shot case study pre-experimental design. The data were analyzed by using visual inspection and qualitative analysis. The results of this study indicate that coloring art therapy is effective in reducing depression in young women.Abstrak. Penelitian ini bertujuan untuk mengetahui efektivitas terapi seni mewarnai terhadap depresi pada remaja putri di Yogyakarta. Hipotesis dalam penelitian ini adalah ada perbedaan tingkat depresi subjek antara sebelum diberikan terapi seni mewarnai dengan setelah terapi seni mewarnai. Tingkat depresi setelah terapi seni mewarnai lebih rendah dibanding sebelum terapi seni mewarnai.  Sampel dalam penelitian ini adalah remaja putri di Yogyakarta yang berusia 13 tahun, memiliki riwayat pernah berjauhan dari orangtua dan mengalami depresi sedang-berat. Terapi seni mewarnai ini diberikan secara individual, dua kali, menggunakan media seni gambar dan spidol. Depresi subjek diukur dengan menggunakan skala Beck Depression Inventory (BDI) II dari pengukuran awal sampai akhir. Penelitian ini menggunakan 1 subjek penelitian dengan desain pre-eksperimen single one shot case study. Data analisis dengan inspeksi visual dan analisa kualitatif. Hasil penelitian ini menunjukkan bahwa terapi seni mewarnai efektif dalam menurunkan depresi pada remaja putri.

scholarly journals A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2422
Author(s):  
Lisa Knopp ◽  
Marc Wieland ◽  
Michaela Rättich ◽  
Sandro Martinis
Keyword(s):  
Deep Learning ◽  
Network Performance ◽  
Near Infrared ◽  
Remote Sensing Data ◽  
Economic Consequences ◽  
Validation Dataset ◽  
Burned Area ◽  
Burned Areas ◽  
Spectral Bands ◽  
Sentinel 2

Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been developed to segment burned areas with satellite imagery. However, these methods mostly require extensive preprocessing, while deep learning techniques—which have successfully been applied to other segmentation tasks—have yet to be fully explored. In this work, we combine sensor-specific and methodological developments from the past few years and suggest an automatic processing chain, based on deep learning, for burned area segmentation using mono-temporal Sentinel-2 imagery. In particular, we created a new training and validation dataset, which is used to train a convolutional neural network based on a U-Net architecture. We performed several tests on the input data and reached optimal network performance using the spectral bands of the visual, near infrared and shortwave infrared domains. The final segmentation model achieved an overall accuracy of 0.98 and a kappa coefficient of 0.94.

scholarly journals Observing Post-Fire Vegetation Regeneration Dynamics Exploiting High-Resolution Sentinel-2 Data

Proceedings ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 10 ◽  
Author(s):  
Federico Filipponi ◽  
Giacinto Manfron
Keyword(s):  
Research Study ◽  
Vegetation Restoration ◽  
Vegetation Recovery ◽  
Area Index ◽  
Burned Areas ◽  
Disturbed Areas ◽  
Phenological Metrics ◽  
The Impact ◽  
Sentinel 2 ◽  
Dynamics Of Vegetation

Information related to the impact of wildfire disturbances on ecosystems is of paramount interest to account for environmental loss, to plan strategies for facilitating ecosystem restoration, and to monitor the dynamics of vegetation restoration. Phenological metrics can represent a good candidate to monitor and quantify vegetation recovery after natural hazards like wildfire disturbances. Satellite observations have been demonstrated to be a suitable tool for wildfire disturbed areas monitoring, allowing both the identification of burned areas and the monitoring of vegetation recovery. This research study aims to identify post-fire vegetation restoration dynamics for the area surrounding Naples (Italy), affected by severe wildfires events in 2017. Sentinel-2 satellite data were used to extract phenological metrics from the estimated Leaf Area Index (LAI) and to relate such metrics to environmental variables in order to evaluate the vegetation restoration and landslide susceptibility for different land use classes.

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