scholarly journals Remote Sensing of Local Warming Trend in Alberta, Canada during 2001–2020, and Its Relationship with Large-Scale Atmospheric Circulations

2021 ◽  
Vol 13 (17) ◽  
pp. 3441
Author(s):  
Quazi K. Hassan ◽  
Ifeanyi R. Ejiagha ◽  
M. Razu Ahmed ◽  
Anil Gupta ◽  
Elena Rangelova ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Correlation Analysis ◽  
Large Scale ◽  
Kendall Test ◽  
Warming Trend ◽  
Small Magnitude ◽  
Teleconnection Patterns ◽  
Local Warming ◽  
Slope Estimator ◽  
Time Series Images

Here, the objective was to study the local warming trend and its driving factors in the natural subregions of Alberta using a remote-sensing approach. We applied the Mann–Kendall test and Sen’s slope estimator on the day and nighttime MODIS LST time-series images to map and quantify the extent and magnitude of monthly and annual warming trends in the 21 natural subregions of Alberta. We also performed a correlation analysis of LST anomalies (both day and nighttime) of the subregions with the anomalies of the teleconnection patterns, i.e., Pacific North American (PNA), Pacific decadal oscillation (PDO), Arctic oscillation (AO), and sea surface temperature (SST, Niño 3.4 region) indices, to identify the relationship. May was the month that showed the most significant warming trends for both day and night during 2001–2020 in most of the subregions in the Rocky Mountains and Boreal Forest. Subregions of Grassland and Parkland in southern and southeastern parts of Alberta showed trends of cooling during daytime in July and August and a small magnitude of warming in June and August at night. We also found a significant cooling trend in November for both day and night. We identified from the correlation analysis that the PNA pattern had the most influence in the subregions during February to April and October to December for 2001–2020; however, none of the atmospheric oscillations showed any significant relationship with the significant warming/cooling months.

scholarly journals Correction: Quantification of Local Warming Trend: A Remote Sensing-Based Approach

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0196882 ◽  
Author(s):  
Khan Rubayet Rahaman ◽  
Quazi K. Hassan ◽  
Ehsan H. Chowdhury
Keyword(s):  
Remote Sensing ◽  
Warming Trend ◽  
Local Warming

scholarly journals Quantification of Local Warming Trend: A Remote Sensing-Based Approach

PLoS ONE ◽  
2017 ◽  
Vol 12 (1) ◽  
pp. e0169423 ◽  
Author(s):  
Khan Rubayet Rahaman ◽  
Quazi K. Hassan
Keyword(s):  
Remote Sensing ◽  
Warming Trend ◽  
Local Warming

scholarly journals Atmospheric Teleconnection-Based Extreme Drought Prediction in the Core Drought Region in China

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 232 ◽  
Author(s):  
Qinggang Gao ◽  
Jong-Suk Kim ◽  
Jie Chen ◽  
Hua Chen ◽  
Joo-Heon Lee
Keyword(s):  
Large Scale ◽  
Principal Component ◽  
Predictive Performance ◽  
Kendall Test ◽  
Mitigation Measures ◽  
Coefficient Of Determination ◽  
Atmospheric Teleconnection ◽  
Teleconnection Patterns ◽  
Drought Prediction ◽  
Quartile Group

This paper aims to improve the predictability of extreme droughts in China by identifying their relationship with atmospheric teleconnection patterns (ATPs). Firstly, a core drought region (CDR) is defined based on historical drought analysis to investigate possible prediction methods. Through the investigation of the spatial-temporal characteristics of spring drought using a modified Mann–Kendall test, the CDR is found to be under a decadal drying trend. Using principal component analysis, four principal components (PCs), which explain 97% of the total variance, are chosen out of eight teleconnection indices. The tree-based model reveals that PC1 and PC2 can be divided into three groups, in which extreme spring drought (ESD) frequency differs significantly. The results of Poisson regression on ESD and PCs showed good predictive performance with R-squared value larger than 0.8. Furthermore, the results of applying the neural networks for PCs showed a significant improvement in the issue of under-estimation of the upper quartile group in ESD, with a high coefficient of determination of 0.91. This study identified PCs of large-scale ATPs that are candidate parameters for ESD prediction in the CDR. We expect that our findings can be helpful in undertaking mitigation measures for ESD in China.

scholarly journals Contrasting surface warming of a marginal basin due to large-scale climatic patterns and local forcing

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Naomi Krauzig ◽  
Pierpaolo Falco ◽  
Enrico Zambianchi
Keyword(s):  
Large Scale ◽  
Warming Trend ◽  
Tyrrhenian Sea ◽  
Climate Conditions ◽  
Surface Warming ◽  
Teleconnection Patterns ◽  
Sst Variability ◽  
Local Forcing ◽  
The Mediterranean ◽  
Forcing Mechanisms

Abstract The Mediterranean Sea is one of the first regions where sea surface temperature (SST) increase was linked to greenhouse effects and global warming. Due to its sensitivity to climate variability and its high impact on local and remote climate conditions, much effort has been made to assess the SST variability in the Mediterranean as a whole. However, the Mediterranean is composed of several basins, each of which plays a different role in its conveyor belt’s function. This study focuses on the basin of the Tyrrhenian Sea which represents one of the crucial areas for deep mixing of the Mediterranean main water masses. Thirty-seven years (1982–2018) of satellite-derived data were used to investigate the SST variability in relation to large-scale and local forcing mechanisms. A significant warming trend of 0.034 ± 0.004 °C/year was found, which led to an average warming of 1.288 ± 0.129 °C over the considered period. The observed warming presents time-dependent spatial patterns as well as changes in the seasonal cycle. Our results highlight that the Tyrrhenian’s individual long-term surface variability has different characteristics than the Mediterranean as a whole and provide insight into the relative influence of large-scale teleconnection patterns and local air-sea interaction on this variability.

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Remote Sensing Methods for the Biophysical Characterization of Protected Areas Globally: Challenges and Opportunities

2021 ◽  
Vol 10 (6) ◽  
pp. 384
Author(s):  
Javier Martínez-López ◽  
Bastian Bertzky ◽  
Simon Willcock ◽  
Marine Robuchon ◽  
María Almagro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Large Scale ◽  
Anthropogenic Activities ◽  
Global Scale ◽  
Area Network ◽  
Biophysical Characterization ◽  
Challenges And Opportunities ◽  
Scale Characterization

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale.

scholarly journals Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

2021 ◽  
Vol 13 (11) ◽  
pp. 2220
Author(s):  
Yanbing Bai ◽  
Wenqi Wu ◽  
Zhengxin Yang ◽  
Jinze Yu ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
Flood Events ◽  
Test Set ◽  
Source Data ◽  
Flood Disasters ◽  
Temporary Water ◽  
Sentinel 2

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.

scholarly journals Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 130
Author(s):  
Sebastian Rößler ◽  
Marius S. Witt ◽  
Jaakko Ikonen ◽  
Ian A. Brown ◽  
Andreas J. Dietz
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Air Temperature ◽  
Kendall Test ◽  
The Arctic ◽  
Boreal Winter ◽  
Snowmelt Runoff ◽  
River Catchment ◽  
Catchment Areas ◽  
Runoff Model

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

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