LESS: Large-scale remote sensing data and image simulation framework over Vegetated Areas

2020 ◽  
Author(s):  
Jianbo Qi ◽  
Donghui Xie
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Large Scale ◽  
Computational Cost ◽  
Field Measurements ◽  
Airborne Lidar ◽  
Graphic User Interface ◽  
Inversion Algorithm ◽  
Modeling Framework ◽  
Tracing Method

<p>Three-dimensional (3D) radiative transfer (RT) modeling and simulation of the transport of radiation through earth surfaces is a challenging and difficult task. The difficulties lie in the complexity of the landscapes and also the intensive computational cost of 3D RT simulations. Current models usually work with abstract landscape elements to reduce complexity or only consider relatively small realistic scenes. In this study, a new 3D RT modeling framework (called LESS) is proposed. It employs a forward photon tracing method to simulate bidirectional reflectance factor (BRF) or flux-related data (e.g., downwelling radiation) and a backward path tracing method to generate sensor images (e.g., fisheye images) or large-scale (e.g. 1 km<sup>2</sup>) spectral images from visible to thermal infrared band. In this framework, a graphic user interface (GUI) and a set of tools are also provided to help to construct the landscape and set parameters, e.g., extracting tree crowns from airborne LiDAR data, which makes it more accessible to common users. The accuracy of LESS is evaluated with other models and field measurements in terms of directional BRF and pixel-wise comparisons. It shows that the accuracy of LESS is consistent with the reference models from RAMI model inter-comparison website (http://rami-benchmark.jrc.ec.europa.eu/HTML/Home.php) as well as field measurements. LESS has also been extended to simulate atmosphere, LiDAR and in-situ sensors. It provides as a useful tool for studying the radiative transfer process over complex forest canopies from leaf to canopy scales. The simulated datasets can be used as benchmarks for validating other physical remote sensing inversion algorithm and developing parameterized models for retrieving bio-geophysical variables of canopy. LESS can be accessed from http://lessrt.org.</p>

Numerical modeling of mesospheric mountain wave propagation observed in Southern Argentina during the SOUTHTRAC campaign

2020 ◽  
Author(s):  
Tyler Mixa ◽  
Andreas Dörnbrack ◽  
Bernd Kaifler ◽  
Markus Rapp
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Numerical Models ◽  
Airborne Lidar ◽  
Tierra Del Fuego ◽  
Polar Night ◽  
Research Aircraft ◽  
Complex Propagation ◽  
The Antarctic

<p>We present numerical simulations of a deep orographic gravity wave (GW) event observed by the ALIMA airborne lidar on 11-12 September 2019 over Southern Argentina. The measurements are taken from the 2019 SOUTHTRAC Campaign, employing a comprehensive suite of remote sensing and in-situ instruments onboard the HALO research aircraft to study the stratospheric GW hotspot over Tierra del Fuego and the Antarctic Peninsula. Wind conditions on 11-12 September exhibit local and large-scale directional shear from the ground to the polar night jet, creating a complex propagation environment supporting multiple orientations of GW propagation and strong potential for local GW breaking and secondary GW generation. Using high resolution numerical models, we simulate the 3D evolution of the orographic GW field to analyze<span> the remote sensing and in-situ measurements from the event.</span></p>

scholarly journals Accelerated Simulations of Chemical Reaction Systems using the Stochastic Simulation Algorithm on GPUs

2020 ◽  
Author(s):  
James C. Pino ◽  
Martina Prugger ◽  
Alexander L. R. Lubbock ◽  
Leonard A. Harris ◽  
Carlos F. Lopez
Keyword(s):  
Stochastic Simulation ◽  
Graphics Processing Units ◽  
Large Scale ◽  
Computational Cost ◽  
Stochastic Simulation Algorithm ◽  
Gillespie Algorithm ◽  
Simulation Algorithm ◽  
Modeling Framework ◽  
Cellular Processes ◽  
Didactic Material

1AbstractStochasticity due to fluctuations in chemical reactions can play important roles in cellular network-driven processes. Although the Stochastic Simulation Algorithm (SSA, aka Gillespie Algorithm) has long been accepted as a suitable method to solve the time-dependent chemical master equation, its computational cost is prohibitive for large scale complex networks such as those found in cellular processes. Here we present GPU-SSA, an implementation of the SSA formalism utilizing Graphics Processing Units for use in Python using the PySB modeling framework. We show that the GPU implementation of SSA can achieve significant speedup compared to parallel CPU or single-core CPU implementations. We further include supplementary didactic material to demonstrate how to incorporate GPU-SSA workflows for interested readers.

scholarly journals Reconciling Ground-Based and Space-Based Estimates of the Frequency of Occurrence and Radiative Effect of Clouds around Darwin, Australia

2014 ◽  
Vol 53 (2) ◽  
pp. 456-478 ◽  
Author(s):  
A. Protat ◽  
S. A. Young ◽  
S. A. McFarlane ◽  
T. L’Ecuyer ◽  
G. G. Mace ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Radiative Forcing ◽  
Large Scale ◽  
Radiative Heating ◽  
Orthogonal Polarization ◽  
Frequency Of Occurrence ◽  
Radiative Fluxes ◽  
Radiative Impact ◽  
Satellite Radar

AbstractThe objective of this paper is to investigate whether estimates of the cloud frequency of occurrence and associated cloud radiative forcing as derived from ground-based and satellite active remote sensing and radiative transfer calculations can be reconciled over a well-instrumented active remote sensing site located in Darwin, Australia, despite the very different viewing geometry and instrument characteristics. It is found that the ground-based radar–lidar combination at Darwin does not detect most of the cirrus clouds above 10 km (because of limited lidar detection capability and signal obscuration by low-level clouds) and that the CloudSat radar–Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) combination underreports the hydrometeor frequency of occurrence below 2-km height because of instrument limitations at these heights. The radiative impact associated with these differences in cloud frequency of occurrence is large on the surface downwelling shortwave fluxes (ground and satellite) and the top-of-atmosphere upwelling shortwave and longwave fluxes (ground). Good agreement is found for other radiative fluxes. Large differences in radiative heating rate as derived from ground and satellite radar–lidar instruments and radiative transfer calculations are also found above 10 km (up to 0.35 K day−1 for the shortwave and 0.8 K day−1 for the longwave). Given that the ground-based and satellite estimates of cloud frequency of occurrence and radiative impact cannot be fully reconciled over Darwin, caution should be exercised when evaluating the representation of clouds and cloud–radiation interactions in large-scale models, and limitations of each set of instrumentation should be considered when interpreting model–observation differences.

Spectroscopy and Radiative Transfer of Planetary Atmospheres

2017 ◽  
Author(s):  
Kelly Chance ◽  
Randall V. Martin
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Satellite Remote Sensing ◽  
Field Measurements ◽  
Atmospheric Science ◽  
Planetary Atmospheres ◽  
Planetary Science ◽  
Atmospheric Composition ◽  
Systematic Observation ◽  
New Instrumentation

This book develops both spectroscopy and radiative transfer for planetary atmospheric composition in a rigorous and quantitative sense for students of atmospheric and/or planetary science. Spectroscopic field measurements including satellite remote sensing have advanced rapidly in recent years, and are being increasingly applied to provide information about planetary atmospheres. Examples include systematic observation of the atmospheric constituents that affect weather, climate, biogeochemical cycles, air quality on Earth, as well as the physics and evolution of planetary atmospheres in our solar system and beyond. Understanding atmospheric spectroscopy and radiative transfer is important throughout the disciplines of atmospheric science and planetary atmospheres to understand principles of remote sensing of atmospheric composition and the effects of atmospheric composition on climate. Atmospheric scientists need an understanding of the details, strength and weaknesses of the spectroscopic measurement sources. Those in remote sensing require an understanding of the information content of the measured spectra that are needed for the design of retrieval algorithms and for developing new instrumentation.

scholarly journals STUDY ON VORTEX CURRENT IN STRAIT WITH REMOTE-SENSING

1980 ◽  
Vol 1 (17) ◽  
pp. 157 ◽  
Author(s):  
Sotoaki Onishi ◽  
Tsukasa Nishimura
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Boundary Conditions ◽  
Mathematical Models ◽  
Large Scale ◽  
Sea Water ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Sensing Technology ◽  
Pollution Problem

With rapid increases of industrial activity in present time, water pollution in the coastal environment has "become an urgent problem to cope with. This problem is especially serious in enclosed bays or inland seas. Hydrodynamic character of the strait connecting the inland sea to the open ocean must be understood well in order to analyse the diffusion of pollutants in the Inland sea, because its character determine boundary conditions in the mathematical models of the water pollution problem. So far however, it is seemed that the main efforts exerted by coastal engineers have concentrated mainly on the development of mathematical models, lacking satisfactory knowledge of the boundary conditions through field measurements. One reason of this state is resulted from the fact that the relating phenomena in the field are of too large scale, in general, to perform the field measurements. Connecting with this point, the authors present in this paper, that remote-sensing technology is very useful to get information of the hydrodynaniical phenomena occurring in the water body around the strait. To show the above, the authors selected as an object of the study , Naruto Strait in the Seto Inland Sea, which is world famous for the existence of rapid tidal currents and dynamic vortices. Remote-sensing data both from the airplanes and from a space satellite Landsat are analysed with the aid of theoretical considerations and hydraulic mo-del tests to disclose the behavior of the vortices of various scales and the roles of them in the sea water mixing phenomena at the strait.

scholarly journals A Lightweight Object Detection Framework for Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 683
Author(s):  
Lang Huyan ◽  
Yunpeng Bai ◽  
Ying Li ◽  
Dongmei Jiang ◽  
Yanning Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Real Time ◽  
Large Scale ◽  
Feature Fusion ◽  
Computational Cost ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Low Level

Onboard real-time object detection in remote sensing images is a crucial but challenging task in this computation-constrained scenario. This task not only requires the algorithm to yield excellent performance but also requests limited time and space complexity of the algorithm. However, previous convolutional neural networks (CNN) based object detectors for remote sensing images suffer from heavy computational cost, which hinders them from being deployed on satellites. Moreover, an onboard detector is desired to detect objects at vastly different scales. To address these issues, we proposed a lightweight one-stage multi-scale feature fusion detector called MSF-SNET for onboard real-time object detection of remote sensing images. Using lightweight SNET as the backbone network reduces the number of parameters and computational complexity. To strengthen the detection performance of small objects, three low-level features are extracted from the three stages of SNET respectively. In the detection part, another three convolutional layers are designed to further extract deep features with rich semantic information for large-scale object detection. To improve detection accuracy, the deep features and low-level features are fused to enhance the feature representation. Extensive experiments and comprehensive evaluations on the openly available NWPU VHR-10 dataset and DIOR dataset are conducted to evaluate the proposed method. Compared with other state-of-art detectors, the proposed detection framework has fewer parameters and calculations, while maintaining consistent accuracy.

scholarly journals A Noise-Resilient Online Learning Algorithm for Scene Classification

2018 ◽  
Vol 10 (11) ◽  
pp. 1836 ◽  
Author(s):  
Ling Jian ◽  
Fuhao Gao ◽  
Peng Ren ◽  
Yunquan Song ◽  
Shihua Luo
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Reproducing Kernel ◽  
Computational Cost ◽  
Scene Recognition ◽  
Classification Algorithm ◽  
Classification Algorithms ◽  
Scene Classification ◽  
Online Classification ◽  
Noisy Labels

The proliferation of remote sensing imagery motivates a surge of research interest in image processing such as feature extraction and scene recognition, etc. Among them, scene recognition (classification) is a typical learning task that focuses on exploiting annotated images to infer the category of an unlabeled image. Existing scene classification algorithms predominantly focus on static data and are designed to learn discriminant information from clean data. They, however, suffer from two major shortcomings, i.e., the noisy label may negatively affect the learning procedure and learning from scratch may lead to a huge computational burden. Thus, they are not able to handle large-scale remote sensing images, in terms of both recognition accuracy and computational cost. To address this problem, in the paper, we propose a noise-resilient online classification algorithm, which is scalable and robust to noisy labels. Specifically, ramp loss is employed as loss function to alleviate the negative affect of noisy labels, and we iteratively optimize the decision function in Reproducing Kernel Hilbert Space under the framework of Online Gradient Descent (OGD). Experiments on both synthetic and real-world data sets demonstrate that the proposed noise-resilient online classification algorithm is more robust and sparser than state-of-the-art online classification algorithms.

Water remote sensing eutrophication inversion algorithm based on multilayer convolutional neural network

2020 ◽  
Vol 39 (4) ◽  
pp. 5319-5327
Author(s):  
Feng Lei ◽  
You Yu ◽  
Daijun Zhang ◽  
Li Feng ◽  
Jinsong Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Rapid Development ◽  
Water Environment ◽  
Inversion Method ◽  
Environment Monitoring ◽  
Inversion Algorithm ◽  
Monitoring Methods

In recent years, with the rapid development of satellite technology, remote sensing inversion has been used as an important part of environmental monitoring. Remote sensing inversion has been prepared for large-scale water environment monitoring in the watershed that is difficult for the traditional water environment monitoring methods. This paper will discuss some shortcomings of traditional remote sensing inversion methods, and proposes a remote sensing inversion method based on convolutional neural network, which realizes large-scale remote sensing smart and automatic inversion monitoring of the water environment. The results show that the method is practical and effective, and can achieve high recognition accuracy for water blooms.

scholarly journals A novel methodology using MODIS and CERES for assessing the daily radiative forcing of smoke aerosols in large scale over the Amazonia

2014 ◽  
Vol 14 (22) ◽  
pp. 31515-31550
Author(s):  
E. T. Sena ◽  
P. Artaxo
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Radiative Transfer ◽  
Biomass Burning ◽  
Satellite Remote Sensing ◽  
Radiative Forcing ◽  
Large Scale ◽  
High Temporal Resolution ◽  
Modis Aod ◽  
The Impact

Abstract. A new methodology was developed for obtaining daily retrievals of the direct radiative forcing of aerosols (24h-DARF) at the top of the atmosphere (TOA) using satellite remote sensing. For that, simultaneous CERES (Clouds and Earth's Radiant Energy System) shortwave flux at the top of the atmosphere (TOA) and MODIS (Moderate Resolution Spectroradiometer) aerosol optical depth (AOD) retrievals were used. This methodology is applied over a large region of Brazilian Amazonia. We focused our studies on the peak of the biomass burning season (August to September) from 2000 to 2009 to analyse the impact of forest smoke on the radiation balance. To assess the spatial distribution of the DARF, background scenes without biomass burning impacts, were defined as scenes with MODIS AOD < 0.1. The fluxes at the TOA retrieved by CERES for those clean conditions (Fcl) were estimated as a function of the illumination geometry (θ0) for each 0.5° × 0.5° grid cell. The instantaneous DARF was obtained as the difference between clean Fcl (θ0) and the polluted mean flux at the TOA measured by CERES in each cell (Fpol (θ0)). The radiative transfer code SBDART (Santa Barbara DISORT Radiative Transfer model) was used to expand instantaneous DARFs to 24 h averages. With this methodology it is possible to assess the DARF both at large scale and at high temporal resolution. This new methodology also showed to be more robust, because it considerably reduces statistical sources of uncertainties in the estimates of the DARF, when compared to previous assessments of the DARF using satellite remote sensing. The spatial distribution of the 24h-DARF shows that, for some cases, the mean 24h-DARF presents local values as high as −30 W m−2. The temporal variability of the 24h-DARF along the biomass burning season was also studied and showed large intraseasonal and interannual variability. In an attempt to validate the radiative forcing obtained in this work using CERES and MODIS, those results were compared to coincident AERONET ground based estimates of the DARF. This analysis showed that CERES-MODIS and AERONET 24h-DARF are related as DARFCERES-MODIS24 h = (1.07 ± 0.04)DARFAERONET24 h −(0.0 ± 0.6). This is a significant result, considering that the 24h-DARF retrievals were obtained by applying completely different methodologies, and using different instruments. The instantaneous CERES-MODIS DARF was also compared with radiative transfer evaluations of the forcing. To validate the aerosol and surface models used in the simulations, downward shortwave fluxes at the surface evaluated using SBDART and measured by pyranometers were compared. The simulated and measured downward fluxes are related through FBOAPYRANOMETER = (1.00 ± 0.04)FBOASBDART −(20 ± 27), indicating that the models and parameters used in the simulations were consistent. The relationship between CERES-MODIS instantaneous DARF and calculated SBDART forcing was satisfactory, with DARFCERES-MODIS = (0.86 ± 0.06)DARFSBDART −(6 ± 2). Those analysis showed a good agreement between satellite remote sensing, ground-based and radiative transfer evaluated DARF, demonstrating the robustness of the new proposed methodology for calculated radiative forcing for biomass burning aerosols. To our knowledge, this was the first time satellite remote sensing assessments of the DARF were compared with ground based DARF estimates.

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