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.

scholarly journals A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS

2021 ◽  
Vol 13 (9) ◽  
pp. 1608
Author(s):  
Miguel M. Pinto ◽  
Ricardo M. Trigo ◽  
Isabel F. Trigo ◽  
Carlos C. DaCamara
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Computation Time ◽  
Practical Method ◽  
Google Earth ◽  
Burned Area ◽  
High Resolution Data ◽  
Burned Areas ◽  
Resolution Data ◽  
Sentinel 2

Mapping burned areas using satellite imagery has become a subject of extensive research over the past decades. The availability of high-resolution satellite data allows burned area maps to be produced with great detail. However, their increasing spatial resolution is usually not matched by a similar increase in the temporal domain. Moreover, high-resolution data can be a computational challenge. Existing methods usually require downloading and processing massive volumes of data in order to produce the resulting maps. In this work we propose a method to make this procedure fast and yet accurate by leveraging the use of a coarse resolution burned area product, the computation capabilities of Google Earth Engine to pre-process and download Sentinel-2 10-m resolution data, and a deep learning model trained to map the multispectral satellite data into the burned area maps. For a 1500 ha fire our method can generate a 10-m resolution map in about 5 min, using a computer with an 8-core processor and 8 GB of RAM. An analysis of six important case studies located in Portugal, southern France and Greece shows the detailed computation time for each process and how the resulting maps compare to the input satellite data as well as to independent reference maps produced by Copernicus Emergency Management System. We also analyze the feature importance of each input band to the final burned area map, giving further insight about the differences among these events.

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 Mapping Cropland Burned Area in Northeastern China by Integrating Landsat Time Series and Multi-Harmonic Model

2021 ◽  
Vol 13 (24) ◽  
pp. 5131
Author(s):  
Jinxiu Liu ◽  
Du Wang ◽  
Eduardo Eiji Maeda ◽  
Petri K. E. Pellikka ◽  
Janne Heiskanen
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Spatial Information ◽  
Majority Vote ◽  
Burned Area ◽  
Harmonic Model ◽  
Fire Dynamics ◽  
Area Index ◽  
Burned Areas ◽  
Straw Burning

Accurate cropland burned area estimation is crucial for air quality modeling and cropland management. However, current global burned area products have been primarily derived from coarse spatial resolution images which cannot fulfill the spatial requirement for fire monitoring at local levels. In addition, there is an overall lack of accurate cropland straw burning identification approaches at high temporal and spatial resolution. In this study, we propose a novel algorithm to capture burned area in croplands using dense Landsat time series image stacks. Cropland burning shows a short-term seasonal variation and a long-term dynamic trend, so a multi-harmonic model is applied to characterize fire dynamics in cropland areas. By assessing a time series of the Burned Area Index (BAI), our algorithm detects all potential burned areas in croplands. A land cover mask is used on the primary burned area map to remove false detections, and the spatial information with a moving window based on a majority vote is employed to further reduce salt-and-pepper noise and improve the mapping accuracy. Compared with the accuracy of 67.3% of MODIS products and that of 68.5% of Global Annual Burned Area Map (GABAM) products, a superior overall accuracy of 92.9% was obtained by our algorithm using Landsat time series and multi-harmonic model. Our approach represents a flexible and robust way of detecting straw burning in complex agriculture landscapes. In future studies, the effectiveness of combining different spectral indices and satellite images can be further investigated.

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.

Red Tide Detection Based on High Spatial Resolution Broad Band Satellite Data: A Case Study of GF-1

2019 ◽  
Vol 90 (sp1) ◽  
pp. 120
Author(s):  
Rong-Jie Liu ◽  
Jie Zhang ◽  
Bin-Ge Cui ◽  
Yi Ma ◽  
Ping-Jian Song ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
Red Tide ◽  
Broad Band

scholarly journals Automated mapping of burned areas in semi-arid ecosystems using modis time-series imagery

2015 ◽  
Vol XL-7/W3 ◽  
pp. 811-814 ◽  
Author(s):  
L. A. Hardtke ◽  
P. D. Blanco ◽  
H. F. del Valle ◽  
G. I. Metternicht ◽  
W. F. Sione
Keyword(s):  
Time Series ◽  
Reference Data ◽  
Regional Scale ◽  
Arid Ecosystems ◽  
Burned Area ◽  
Second Phase ◽  
Commission Error ◽  
Spatial And Temporal Patterns ◽  
Burned Areas ◽  
Semi Arid

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Standard satellite burned area and active fire products derived from the 500-m MODIS and SPOT are avail - able to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applica - tions. Consequently, we propose a novel algorithm for automated identification and mapping of burned areas at regional scale in semi-arid shrublands. The algorithm uses a set of the Normalized Burned Ratio Index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. The correlation between the size of burnt areas detected by the global fire products and independently-derived Landsat reference data ranged from R<sup>2</sup> = 0.01 - 0.28, while our algorithm performed showed a stronger correlation coefficient (R<sup>2</sup> = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

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 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.

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