Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin

Abstract. Recently, many remote-sensing datasets providing features of individual fire events from gridded global burned area products have been released. Although very promising, these datasets still lack a quantitative estimate of their accuracy with respect to historical ground-based fire datasets. Here, we compared three state-of-the-art remote-sensing datasets (RSDs; Fire Atlas, FRY, and GlobFire) with a harmonized ground-based dataset (GBD) compiled by fire agencies monitoring systems across the southwestern Mediterranean Basin (2005–2015). We assessed the agreement between the RSDs and the GBD with respect to both burned area (BA) and number of fires (NF). RSDs and the GBD were aggregated at monthly and 0.25∘ resolutions, considering different individual fire size thresholds ranging from 1 to 500 ha. Our results show that all datasets were highly correlated in terms of monthly BA and NF, but RSDs severely underestimated both (by 38 % and 96 %, respectively) when considering all fires > 1 ha. The agreement between RSDs and the GBD was strongly dependent on individual fire size and strengthened when increasing the fire size threshold, with fires > 100 ha denoting a higher correlation and much lower error (BA 10 %; NF 35 %). The agreement was also higher during the warm season (May to October) in particular across the regions with greater fire activity such as the northern Iberian Peninsula. The Fire Atlas displayed a slightly better performance with a lower relative error, although uncertainty in the gridded BA product largely outpaced uncertainties across the RSDs. Overall, our findings suggest a reasonable agreement between RSDs and the GBD for fires larger than 100 ha, but care is needed when examining smaller fires at regional scales.

Download Full-text

Assessing the accuracy of remotely-sensed fire datasets across the Southwestern Mediterranean basin

10.5194/nhess-2020-230 ◽

2020 ◽

Author(s):

Luiz Felipe Galizia ◽

Thomas Curt ◽

Renaud Barbero ◽

Marcos Rodrigues

Keyword(s):

Mediterranean Basin ◽

Quantitative Estimate ◽

State Of The Art ◽

Warm Season ◽

Burned Area ◽

Fire Size ◽

Northern Iberian Peninsula ◽

Fire Atlas ◽

Highly Correlated ◽

Size Threshold

Abstract. Recently, many remote-sensing (RS) based datasets providing features of individual fire events from gridded global burned area products have been released. Although very promising, these datasets still lack a quantitative estimate of their accuracy with respect to historical ground-based fire databases. Here, we compared three state-of-the-art RS datasets (Fire Atlas, FRY and GlobFire) with high-quality ground databases compiled by regional fire agencies (AG) across the Southwestern Mediterranean basin (2005–2015). We assessed the spatial and temporal accuracy in estimated RS burned area (BA) and number of fires (NF) aggregated at monthly and 0.25° resolutions, considering different individual fire size thresholds ranging from 1 to 500 ha. Our results show that RS datasets were highly correlated with AG in terms of monthly BA and NF but severely underestimated both (by 38 % and 96 %, respectively) when considering all fires > 1 ha. Stronger agreement was found when increasing the fire size threshold, with fires > 100 ha denoting higher correlation and much lower error (BA 10 %; NF 35%). The agreement between RS and AG was also the highest during the warm season (May to October) in particular across the regions with greater fire activity such as the Northern Iberian Peninsula. The Fire Atlas displayed a slightly better performance, with a lower relative error, although uncertainty in gridded BA product largely outpaced uncertainties across the RS datasets. Overall, our findings suggest a reasonable agreement between RS and ground-based datasets for fires larger than 100 ha, but care is needed when examining smaller fires at regional scales.

Download Full-text

The Global Fire Atlas of individual fire size, duration, speed and direction

Earth System Science Data ◽

10.5194/essd-11-529-2019 ◽

2019 ◽

Vol 11 (2) ◽

pp. 529-552 ◽

Cited By ~ 49

Author(s):

Niels Andela ◽

Douglas C. Morton ◽

Louis Giglio ◽

Ronan Paugam ◽

Yang Chen ◽

...

Keyword(s):

Biomass Burning ◽

Arid Ecosystems ◽

Fire Season ◽

Burned Area ◽

Ecosystem Structure ◽

Earth System ◽

Fire Size ◽

Regional Patterns ◽

Fire Atlas ◽

Strong Control

Abstract. Natural and human-ignited fires affect all major biomes, altering ecosystem structure, biogeochemical cycles and atmospheric composition. Satellite observations provide global data on spatiotemporal patterns of biomass burning and evidence for the rapid changes in global fire activity in response to land management and climate. Satellite imagery also provides detailed information on the daily or sub-daily position of fires that can be used to understand the dynamics of individual fires. The Global Fire Atlas is a new global dataset that tracks the dynamics of individual fires to determine the timing and location of ignitions, fire size and duration, and daily expansion, fire line length, speed, and direction of spread. Here, we present the underlying methodology and Global Fire Atlas results for 2003–2016 derived from daily moderate-resolution (500 m) Collection 6 MCD64A1 burned-area data. The algorithm identified 13.3 million individual fires over the study period, and estimated fire perimeters were in good agreement with independent data for the continental United States. A small number of large fires dominated sparsely populated arid and boreal ecosystems, while burned area in agricultural and other human-dominated landscapes was driven by high ignition densities that resulted in numerous smaller fires. Long-duration fires in boreal regions and natural landscapes in the humid tropics suggest that fire season length exerts a strong control on fire size and total burned area in these areas. In arid ecosystems with low fuel densities, high fire spread rates resulted in large, short-duration fires that quickly consumed available fuels. Importantly, multiday fires contributed the majority of burned area in all biomass burning regions. A first analysis of the largest, longest and fastest fires that occurred around the world revealed coherent regional patterns of extreme fires driven by large-scale climate forcing. Global Fire Atlas data are publicly available through http://www.globalfiredata.org (last access: 9 August 2018) and https://doi.org/10.3334/ORNLDAAC/1642, and individual fire information and summary data products provide new information for benchmarking fire models within ecosystem and Earth system models, understanding vegetation–fire feedbacks, improving global emissions estimates, and characterizing the changing role of fire in the Earth system.

Download Full-text

The Global Fire Atlas of individual fire size, duration, speed, and direction

10.5194/essd-2018-89 ◽

2018 ◽

Cited By ~ 3

Author(s):

Niels Andela ◽

Douglas C. Morton ◽

Louis Giglio ◽

Ronan Paugam ◽

Yang Chen ◽

...

Keyword(s):

Biomass Burning ◽

Arid Ecosystems ◽

Fire Season ◽

Burned Area ◽

Ecosystem Structure ◽

Earth System ◽

Fire Size ◽

Regional Patterns ◽

Fire Atlas ◽

Strong Control

Abstract. Natural and human-ignited fires affect all major biomes, altering ecosystem structure, biogeochemical cycles, and atmospheric composition. Satellite observations provide global data on spatiotemporal patterns of biomass burning and evidence for rapid changes in global fire activity in response to land management and climate. Satellite imagery also provides detailed information on the daily or subdaily position of fires that can be used to understand the dynamics of individual fires. The Global Fire Atlas is a new global dataset that tracks the dynamics of individual fires to determine the timing and location of ignitions and fire size, duration, daily expansion, fire line length, speed, and direction of spread. Here we present the underlying methodology and Global Fire Atlas results for 2003–2016 derived from daily moderate resolution (500 m) Collection 6 MCD64A1 burned area data. The algorithm identified 13.3 million individual fires over the study period, and estimated fire perimeters were in good agreement with independent data for the continental United States. A small number of large fires dominated sparsely populated arid and boreal ecosystems, while burned area in agricultural and other human-dominated landscapes was driven by high ignition densities that resulted in numerous smaller fires. Long-duration fires in the boreal regions and natural landscapes in the humid tropics suggest that fire-season length exerts a strong control on fire size and total burned area in these areas. In arid ecosystems with low fuel densities, high fire spread rates resulted in large, short-duration fires that quickly consumed available fuels. Importantly, multi-day fires contributed the majority of burned area in all biomass burning regions. A first analysis of the largest, longest, and fastest fires that occurred around the world revealed coherent regional patterns of extreme fires driven by large-scale climate forcing. Global Fire Atlas data are publicly available through www.globalfiredata.org/ and https://doi.org/10.3334/ORNLDAAC/1642, and individual fire information and summary data products provide new information for benchmarking fire models within ecosystem and Earth system models, understanding vegetation-fire feedbacks, improving global emissions estimates, and characterizing the changing role of fire in the Earth system.

Download Full-text

PCAN—Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery

Remote Sensing ◽

10.3390/rs13071243 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1243

Author(s):

Wenxin Yin ◽

Wenhui Diao ◽

Peijin Wang ◽

Xin Gao ◽

Ya Li ◽

...

Keyword(s):

Remote Sensing ◽

Power Plants ◽

State Of The Art ◽

Thermal Power ◽

Image Interpretation ◽

Remote Sensing Image ◽

Thermal Power Plants ◽

Average Precision ◽

Deep Convolutional Neural Networks ◽

Multi Scale

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is a challenging task, because as facility objects TPPs are composed of various distinctive and irregular components. In this paper, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, based on the RetinaNet one-stage detector, a context attention multi-scale feature extraction network is proposed to fuse global spatial attention to strengthen the ability in representing irregular objects. In addition, we design a part-based attention module to adapt to TPPs containing distinctive components. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 68.15% mean average precision.

Download Full-text

Remote Sensing of Coastal Hydro-Environment with Portable Unmanned Aerial Vehicles (pUAVs) a State-of-the-Art Review

Journal of Hydro-environment Research ◽

10.1016/j.jher.2021.04.003 ◽

2021 ◽

Author(s):

Hieu Trung Kieu ◽

Adrian Wing-Keung Law

Keyword(s):

Remote Sensing ◽

Unmanned Aerial Vehicles ◽

State Of The Art ◽

Aerial Vehicles

Download Full-text

Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network

Remote Sensing ◽

10.3390/rs13050869 ◽

2021 ◽

Vol 13 (5) ◽

pp. 869

Author(s):

Zheng Zhuo ◽

Zhong Zhou

Keyword(s):

Remote Sensing ◽

Image Retrieval ◽

State Of The Art ◽

Remote Sensing Image ◽

Storage Space ◽

Remote Sensing Images ◽

Retrieval Method ◽

Organization Management ◽

Deep Feature ◽

Feature Transform

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures.

Download Full-text

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 ◽

10.3390/s21123982 ◽

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.

Download Full-text

Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2013.05.014 ◽

2014 ◽

Vol 26 ◽

pp. 64-79 ◽

Cited By ~ 125

Author(s):

Florent Mouillot ◽

Martin G. Schultz ◽

Chao Yue ◽

Patricia Cadule ◽

Kevin Tansey ◽

...

Keyword(s):

Remote Sensing ◽

Burned Area ◽

User Needs ◽

Future Developments ◽

Review Analysis

Download Full-text

A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

Remote Sensing ◽

10.3390/rs13101950 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1950

Author(s):

Cuiping Shi ◽

Xin Zhao ◽

Liguo Wang

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

Classification Accuracy ◽

Feature Fusion ◽

State Of The Art ◽

Rapid Development ◽

Remote Sensing Image ◽

Classification Performance ◽

Attention Mechanism ◽

Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

Download Full-text