Spectral signature analysis of false positive burned area detection from agricultural harvests using Sentinel-2 data

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

Sustainability ◽

10.3390/su13010432 ◽

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.

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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.

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Spectral signature analysis to determine mangrove species delineation structured by anthropogenic effects

Ecological Indicators ◽

10.1016/j.ecolind.2021.108148 ◽

2021 ◽

Vol 130 ◽

pp. 108148

Author(s):

A.W. Zulfa ◽

K. Norizah ◽

O. Hamdan ◽

I. Faridah-Hanum ◽

P.P. Rhyma ◽

...

Keyword(s):

Anthropogenic Effects ◽

Spectral Signature ◽

Signature Analysis ◽

Species Delineation ◽

Mangrove Species

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Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)

Remote Sensing ◽

10.3390/rs12122013 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2013

Author(s):

Konstantinos Topouzelis ◽

Dimitris Papageorgiou ◽

Alexandros Karagaitanakis ◽

Apostolos Papakonstantinou ◽

Manuel Arias Ballesteros

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Field Experiments ◽

Spectral Unmixing ◽

Sea Surface ◽

Spectral Signature ◽

Unmanned Aerial Systems ◽

Real Environment ◽

Promising Tool ◽

Sentinel 2

Remote sensing is a promising tool for the detection of floating marine plastics offering extensive area coverage and frequent observations. While floating plastics are reported in high concentrations in many places around the globe, no referencing dataset exists either for understanding the spectral behavior of floating plastics in a real environment, or for calibrating remote sensing algorithms and validating their results. To tackle this problem, we initiated the Plastic Litter Projects (PLPs), where large artificial plastic targets were constructed and deployed on the sea surface. The first such experiment was realised in the summer of 2018 (PLP2018) with three large targets of 10 × 10 m. Hereafter, we present the second Plastic Litter Project (PLP2019), where smaller 5 × 5 m targets were constructed to better simulate near-real conditions and examine the limitations of the detection with Sentinel-2 images. The smaller targets and the multiple acquisition dates allowed for several observations, with the targets being connected in a modular way to create different configurations of various sizes, material composition and coverage. A spectral signature for the PET (polyethylene terephthalate) targets was produced through modifying the U.S. Geological Survey PET signature using an inverse spectral unmixing calculation, and the resulting signature was used to perform a matched filtering processing on the Sentinel-2 images. The results provide evidence that under suitable conditions, pixels with a PET abundance fraction of at least as low as 25% can be successfully detected, while pinpointing several factors that significantly impact the detection capabilities. To the best of our knowledge, the 2018 and 2019 Plastic Litter Projects are to date the only large-scale field experiments on the remote detection of floating marine litter in a near-real environment and can be used as a reference for more extensive validation/calibration campaigns.

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Fire Reference Perimeters Extracted from Sentinel-2 Data for Validation of Burned Area Products in Africa Biomes

10.1109/igarss47720.2021.9553180 ◽

2021 ◽

Author(s):

Matteo Sali ◽

Lorenzo Busetto ◽

Mirco Boschetti ◽

Magi Franquesa ◽

Emilio Chuvieco ◽

...

Keyword(s):

Burned Area ◽

Sentinel 2

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Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

PLoS ONE ◽

10.1371/journal.pone.0232962 ◽

2020 ◽

Vol 15 (5) ◽

pp. e0232962 ◽

Cited By ~ 4

Author(s):

Fiona Ngadze ◽

Kudzai Shaun Mpakairi ◽

Blessing Kavhu ◽

Henry Ndaimani ◽

Monalisa Shingirayi Maremba

Keyword(s):

Burned Area ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Burned Area Mapping ◽

Sentinel 2

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

Remote Sensing ◽

10.3390/rs12152422 ◽

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.

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