Aerial strategies advance volcanic gas measurements at inaccessible, strongly degassing volcanoes

Volcanic emissions are a critical pathway in Earth’s carbon cycle. Here, we show that aerial measurements of volcanic gases using unoccupied aerial systems (UAS) transform our ability to measure and monitor plumes remotely and to constrain global volatile fluxes from volcanoes. Combining multi-scale measurements from ground-based remote sensing, long-range aerial sampling, and satellites, we present comprehensive gas fluxes—3760 ± [600, 310] tons day−1 CO2 and 5150 ± [730, 340] tons day−1 SO2—for a strong yet previously uncharacterized volcanic emitter: Manam, Papua New Guinea. The CO2/ST ratio of 1.07 ± 0.06 suggests a modest slab sediment contribution to the sub-arc mantle. We find that aerial strategies reduce uncertainties associated with ground-based remote sensing of SO2 flux and enable near–real-time measurements of plume chemistry and carbon isotope composition. Our data emphasize the need to account for time averaging of temporal variability in volcanic gas emissions in global flux estimates.

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Trends in volcanic degassing through eruption cycles: insights from satellite measurements

10.5194/egusphere-egu21-14092 ◽

2021 ◽

Author(s):

Simon Carn ◽

Vitali Fioletov ◽

Chris McLinden ◽

Nickolay Krotkov ◽

Can Li

Keyword(s):

Volcanic Emissions ◽

Ozone Monitoring Instrument ◽

Satellite Measurements ◽

Volcanic Gas ◽

Volcanic Degassing ◽

Superposed Epoch Analysis ◽

Decadal Scale ◽

Gas Measurements ◽

Epoch Analysis ◽

Lower Magnitude

Effective use of volcanic gas measurements for eruption forecasting and hazard mitigation at active volcanoes requires an understanding of long-term degassing behavior as context. Much recent progress has been made in quantifying global volcanic emissions of sulfur dioxide (SO2) and other gas species by expanding the coverage of ground-based sensor networks and through analysis of decadal-scale satellite datasets. Combined, these advances have provided valuable constraints on the magnitude and variability of SO2 emissions at over 120 actively degassing volcanoes worldwide. Being less constrained by the style or location of volcanic activity, satellite measurements can provide greater insight into trends in volcanic degassing during eruption cycles. Here, we present an analysis of ~15 years of volcanic SO2 measurements by the ultraviolet (UV) Ozone Monitoring Instrument (OMI) aboard NASA&#8217;s Aura satellite, focused on observed trends in SO2 emissions spanning eruptions of varying magnitude. The Aura/OMI measurements have been used to estimate annual mean SO2 emissions at ~100 volcanoes active between 2005 and 2020, around 80 of which erupted during the 15-year period. Superposed epoch analysis (SEA) of SO2 emission trends for the erupting volcanoes (with eruption magnitudes ranging from Volcanic Explosivity Index [VEI] 2 to 4) provides evidence that volcanoes exhibiting higher levels of SO2 emission in the years prior to eruption typically produce eruptions of lower magnitude, and vice versa. Post-eruptive SO2 degassing exceeds pre-eruptive emissions for several years after eruptions with VEI 3-4 and may scale with eruption size; perhaps consistent with larger eruptions being supplied by larger magma intrusions which continue to degas in subsequent years. The SEA is most robust for eruptions of intermediate magnitude (VEI 3) which are the most common events in the recent global eruption record covered by the OMI measurements. Limited observations of larger eruptions (VEI 5+) suggest significant differences in degassing trends during these larger events. Future work will extend the satellite-based estimates of volcanic SO2 emissions both forward and backward in time using other UV satellite instruments, generating longer records of SO2 degassing (extending back to 1978 for the strongest volcanic sources of SO2) that will be used to further explore and constrain these relationships. &#160;

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Multi-Scale Remote Sensing for Fall Armyworm Monitoring and Early Warning Systems

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323181 ◽

2020 ◽

Author(s):

Ma. Luisa Buchaillot ◽

Jill Cairns ◽

Esnath Hamadziripi ◽

Kenneth Wilson ◽

David Hughes ◽

...

Keyword(s):

Remote Sensing ◽

Early Warning ◽

Fall Armyworm ◽

Early Warning Systems ◽

Warning Systems ◽

Multi Scale ◽

Monitoring And Early Warning

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Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10070488 ◽

2021 ◽

Vol 10 (7) ◽

pp. 488

Author(s):

Peng Li ◽

Dezheng Zhang ◽

Aziguli Wulamu ◽

Xin Liu ◽

Peng Chen

Keyword(s):

Remote Sensing ◽

Scene Understanding ◽

Deep Understanding ◽

Remote Sensing Images ◽

Convolutional Network ◽

Scene Graph ◽

Multi Scale ◽

Relationship Extraction ◽

High Level ◽

Graph Generation

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

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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.

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A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

Remote Sensing ◽

10.3390/rs13122333 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2333

Author(s):

Lilu Zhu ◽

Xiaolu Su ◽

Yanfeng Hu ◽

Xianqing Tai ◽

Kun Fu

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Temporal Correlation ◽

Local Association ◽

Sensing Data ◽

Multi Scale ◽

Correlation Networks ◽

Efficient Integration ◽

Spatio Temporal ◽

Query Algorithm

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

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Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

Remote Sensing ◽

10.3390/rs13061131 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1131

Author(s):

Tao Yu ◽

Pengju Liu ◽

Qiang Zhang ◽

Yi Ren ◽

Jingning Yao

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Net Primary Production ◽

Remote Sensing Data ◽

Forest Degradation ◽

Observation Data ◽

Area Index ◽

Sensing Data ◽

Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

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