Remote sensing-based study on the relationship between land brightness temperature and vegetation abundance in Wuhan city

2008 ◽  
Author(s):  
Chunling Zhang ◽  
Hua Yu ◽  
Peng Gong ◽  
Weimin Ju ◽  
Huan Pei
Keyword(s):  
Remote Sensing ◽  
Brightness Temperature ◽  
Wuhan City ◽  
The Relationship

Research on the Method of Working-Out near Ground Air Temperature Based on Low Altitude Remote Sensing Aerial Data

2012 ◽  
Vol 598 ◽  
pp. 215-219
Author(s):  
Xu Yuan ◽  
Qing Lin Meng
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Brightness Temperature ◽  
Air Temperature ◽  
Thermal Environment ◽  
Object Surface ◽  
Low Altitude ◽  
Urban Thermal Environment ◽  
The Relationship ◽  
Working Out

In the thermal environment which influences people's life, air temperature 1.5m high is the most important and direct. Through remote sensing we can quickly get the object surface temperature. But the air temperature can’t be got through it directly. [1]If we can excogitate the method of working-out the air temperature 1.5m high from the altitude remote sensing aerial data, the relate research on the urban thermal environment will be convenient and efficient. This paper is written to research this method and analyze the feasibility by means of analysing the relationship between the radiation brightness temperature, the underlay surface temperature and the air temperature 1.5m high.

scholarly journals Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks

2021 ◽  
Vol 13 (4) ◽  
pp. 742
Author(s):  
Jian Peng ◽  
Xiaoming Mei ◽  
Wenbo Li ◽  
Liang Hong ◽  
Bingyu Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Fundamental Problem ◽  
Semantic Representation ◽  
Feature Learning ◽  
Essential Elements ◽  
Complex Scene ◽  
Feature Representations ◽  
The Right ◽  
The Relationship

Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features. Various convolutional neural network architectures have been proposed for automatically learning features from images. However, is the current way of configuring the same architecture to learn all the data while ignoring the differences between images the right one? It seems to be contrary to our intuition: it is clear that some images are easier to recognize, and some are harder to recognize. This problem is the gap between the characteristics of the images and the learning features corresponding to specific network structures. Unfortunately, the literature so far lacks an analysis of the two. In this paper, we explore this problem from three aspects: we first build a visual-based evaluation pipeline of scene complexity to characterize the intrinsic differences between images; then, we analyze the relationship between semantic concepts and feature representations, i.e., the scalability and hierarchy of features which the essential elements in CNNs of different architectures, for remote sensing scenes of different complexity; thirdly, we introduce CAM, a visualization method that explains feature learning within neural networks, to analyze the relationship between scenes with different complexity and semantic feature representations. The experimental results show that a complex scene would need deeper and multi-scale features, whereas a simpler scene would need lower and single-scale features. Besides, the complex scene concept is more dependent on the joint semantic representation of multiple objects. Furthermore, we propose the framework of scene complexity prediction for an image and utilize it to design a depth and scale-adaptive model. It achieves higher performance but with fewer parameters than the original model, demonstrating the potential significance of scene complexity.

Đánh giá nồng độ lactate và procalcitonin ở bệnh nhân Covid-19

2022 ◽  
Author(s):  
Thanh Xuan Nguyen
Keyword(s):  
Serum Level ◽  
Respiratory Infections ◽  
Severe Pneumonia ◽  
Cross Sectional Study ◽  
Multiple Organ ◽  
High Rate ◽  
Rt Pcr ◽  
Wuhan City ◽  
Cross Sectional ◽  
The Relationship

TÓM TẮT Đặt vấn đề: Bệnh COVID-19 đa dạng từ không có triệu chứng đến có các triệu chứng nhẹ cho đến viêm phổi nặng, hội chứng suy hô hấp cấp tiến triển (ARDS), nhiễm khuẩn huyết suy đa tạng và tử vong. Người cao tuổi, người có bệnh mạn tính sẽ có nguy cơ diễn biến nặng nhiều hơn. Nghiên cứu này nhằm xác định nồng độ lactate và PCT ở những bệnh nhân Covid-19 và xét mối liên quan giữa lactate và PCT trên bệnh nhân Covid-19. Đối tượng và phương pháp: Nghiên cứu mô tả cắt ngang trên 126 bệnh nhân được chẩn đoán nhiễm Sars-Cov-2 bằng xét nghiệm RT PCR. Kết quả: Tuổi trung bình 55,98 ± 17,1 tuổi (4 - 98 tuổi). Bệnh nhân > 60 tuổi chiếm tỉ lệ cao nhất (42,8%). Trung vị PCT: 3,6 (95%CI:3,21 - 3,75) ng/ml; trung vị lactate 1,5 (95%CI:1,21 - 1,91) mmol/L; lactate có tương quan thuận và yếu với procalcitonin với r = 0,241; p < 0,001. Nồng độ procalcitonin > 0,1 ng/ml; lactate > 2 mmol/l ở bệnh nhân Covid-19 chiếm tỷ lệ cao với 89,7% và 39,7%. Kết luận: Chỉ điểm procalcitonin, lactate tăng cao ở bệnh nhân Covid-19. ABSTRACT ASSESSMENT OF SERUM LEVEL OF LACTATE AND PROCALCITONIN IN COVID-19 PATIENTS Background: Sars-CoV-2 has been identified as the cause of acute respiratory infections in Wuhan city, Hubei province, China, and has since spread worldwide. Sars-CoV-2 is capable of aerosol transmission in enclosed, crowded, and poorly ventilated spaces. COVID-19 illness ranges from asymptomatic to mild symptoms to severe pneumonia, acute respiratory distress syndrome (ARDS), sepsis, multiple organ failure, and death. This study aims to determine lactate and PCT levels in Covid-19 patients and examine the relationship between lactate and PCT in Covid-19 patients. Methods: A cross-sectional study was performed on 126 patients diagnosed with Sars-Cov-2 infection by RT-PCR. Results: Mean age was 55.98 ± 17.1 years (range: 4-98 years). Patients more than 60 years old were accounted for the highest rate (42.8%). Median PCT: 3.6 (95%CI:3.21 - 3.75) ng/ml; median lactate 1.5 (95%CI:1.21 - 1,91) mmol/L; lactate has a positive and weak correlation with procalcitonin with r = 0.241; p < 0.001. Procalcitonin concentration > 0.1 ng/ml; lactate > 2 mmol/l in patients with Covid-19 accounted for a high rate with 89.7% and 39.7%. Conclusion: Serum level of procalcitonin and lactate raise highly in Covid-19 patients. Keywords: Covid-19, procalcitonin, lactate.

scholarly journals Water stress coefficient determined by orbital remote sensing techniques

2020 ◽  
Vol 24 (12) ◽  
pp. 847-853
Author(s):  
Élvis da S. Alves ◽  
Roberto Filgueiras ◽  
Lineu N. Rodrigues ◽  
Fernando F. da Cunha ◽  
Catariny C. Aleman
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Irrigation Management ◽  
Simple Algorithm ◽  
Landsat 8 ◽  
Stress Coefficient ◽  
Use Efficiency ◽  
Remote Sensing Techniques ◽  
Water Stress Coefficient ◽  
The Relationship

ABSTRACT In regions where the irrigated area is increasing and water availability is reduced, such as the West of the Bahia state, Brazil, the use of techniques that contribute to improving water use efficiency is paramount. One of the ways to improve irrigation is by improving the calculation of actual evapotranspiration (ETa), which among other factors is influenced by soil drying, so it is important to understand this relationship, which is usually accounted for in irrigation management models through the water stress coefficient (Ks). This study aimed to estimate the water stress coefficient (Ks) through information obtained via remote sensing, combined with field data. For this, a study was carried out in the municipality of São Desidério, an area located in western Bahia, using images of the Landsat-8 satellite. Ks was calculated by the relationship between crop evapotranspiration and ETa, calculated by the Simple Algorithm for Evapotranspiration Retrieving (SAFER). The Ks estimated by remote sensing showed, for the development and medium stages, average errors on the order of 5.50%. In the final stage of maize development, the errors obtained were of 23.2%.

scholarly journals Remote sensing the vertical profile of cloud droplet effective radius, thermodynamic phase, and temperature

2011 ◽  
Vol 11 (18) ◽  
pp. 9485-9501 ◽  
Author(s):  
J. V. Martins ◽  
A. Marshak ◽  
L. A. Remer ◽  
D. Rosenfeld ◽  
Y. J. Kaufman ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Brightness Temperature ◽  
Vertical Profile ◽  
Water Cycle ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Cloud Microphysics ◽  
Condensation Nuclei ◽  
Thermodynamic Phase

Abstract. Cloud-aerosol interaction is a key issue in the climate system, affecting the water cycle, the weather, and the total energy balance including the spatial and temporal distribution of latent heat release. Information on the vertical distribution of cloud droplet microphysics and thermodynamic phase as a function of temperature or height, can be correlated with details of the aerosol field to provide insight on how these particles are affecting cloud properties and their consequences to cloud lifetime, precipitation, water cycle, and general energy balance. Unfortunately, today's experimental methods still lack the observational tools that can characterize the true evolution of the cloud microphysical, spatial and temporal structure in the cloud droplet scale, and then link these characteristics to environmental factors and properties of the cloud condensation nuclei. Here we propose and demonstrate a new experimental approach (the cloud scanner instrument) that provides the microphysical information missed in current experiments and remote sensing options. Cloud scanner measurements can be performed from aircraft, ground, or satellite by scanning the side of the clouds from the base to the top, providing us with the unique opportunity of obtaining snapshots of the cloud droplet microphysical and thermodynamic states as a function of height and brightness temperature in clouds at several development stages. The brightness temperature profile of the cloud side can be directly associated with the thermodynamic phase of the droplets to provide information on the glaciation temperature as a function of different ambient conditions, aerosol concentration, and type. An aircraft prototype of the cloud scanner was built and flew in a field campaign in Brazil. The CLAIM-3D (3-Dimensional Cloud Aerosol Interaction Mission) satellite concept proposed here combines several techniques to simultaneously measure the vertical profile of cloud microphysics, thermodynamic phase, brightness temperature, and aerosol amount and type in the neighborhood of the clouds. The wide wavelength range, and the use of multi-angle polarization measurements proposed for this mission allow us to estimate the availability and characteristics of aerosol particles acting as cloud condensation nuclei, and their effects on the cloud microphysical structure. These results can provide unprecedented details on the response of cloud droplet microphysics to natural and anthropogenic aerosols in the size scale where the interaction really happens.

On the Relationship Between Radar Backscatter and Radiometer Brightness Temperature From SMAP

2021 ◽  
pp. 1-16
Author(s):  
Jiangyuan Zeng ◽  
Pengfei Shi ◽  
Kun-Shan Chen ◽  
Hongliang Ma ◽  
Haiyun Bi ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Radar Backscatter ◽  
The Relationship
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