Comparison of UAS-Based Structure-from-Motion and LiDAR for Structural Characterization of Short Broadacre Crops

The use of small unmanned aerial system (UAS)-based structure-from-motion (SfM; photogrammetry) and LiDAR point clouds has been widely discussed in the remote sensing community. Here, we compared multiple aspects of the SfM and the LiDAR point clouds, collected concurrently in five UAS flights experimental fields of a short crop (snap bean), in order to explore how well the SfM approach performs compared with LiDAR for crop phenotyping. The main methods include calculating the cloud-to-mesh distance (C2M) maps between the preprocessed point clouds, as well as computing a multiscale model-to-model cloud comparison (M3C2) distance maps between the derived digital elevation models (DEMs) and crop height models (CHMs). We also evaluated the crop height and the row width from the CHMs and compared them with field measurements for one of the data sets. Both SfM and LiDAR point clouds achieved an average RMSE of ~0.02 m for crop height and an average RMSE of ~0.05 m for row width. The qualitative and quantitative analyses provided proof that the SfM approach is comparable to LiDAR under the same UAS flight settings. However, its altimetric accuracy largely relied on the number and distribution of the ground control points.

Literature Review on Cloud Database

Cloud computing have high interest from companies .since ,its inception With its services delivery model, cloud computing add technical and strategic business value to companies. This paper presents a systematic literature review to explore the current key issues related to cloud computing adoption.

EVALUATION OF CLOUDS, RADIATION, AND PRECIPITATION IN CMIP6 MODELS USING GLOBAL WEATHER STATES DERIVED FROM ISCCP-H CLOUD PROPERTY DATA

A clustering methodology is applied to cloud optical depth cloud top pressure (TAU-PC) histograms from the new, 1-degree resolution, ISCCP-H dataset, to derive an updated global Weather State (WS) dataset. Then, PC-TAU histograms from current-climate CMIP6 model simulations are assigned to the ISCCP-H WSs along with their concurrent radiation and precipitation properties, to evaluate model cloud, radiation, and precipitation properties in the context of the Weather States. The new ISCCP-H analysis produces WSs that are very similar to those previously found in the lower resolution ISCCP-D dataset. The main difference lies in the splitting of the ISCCP-D thin stratocumulus WS between the ISCCP-H shallow cumulus and stratocumulus WSs, which results in the reduction by one of the total WS number. The evaluation of the CMIP6 models against the ISCCP-H Weather States, shows that, in the ensemble mean, the models are producing an adequate representation of the frequency and geographical distribution of the WSs, with measurable improvements compared to the WSs derived for the CMIP5 ensemble. However, the frequency of shallow cumulus clouds continues to be underestimated, and, in some WSs the good agreement of the ensemble mean with observations comes from averaging models that significantly overpredict and underpredict the ISCCP-H WS frequency. In addition, significant biases exist in the internal cloud properties of the model WSs, such as the model underestimation of cloud fraction in middle-top clouds and secondarily in midlatitude storm and stratocumulus clouds, that result in an underestimation of cloud SW cooling in those regimes.

Using paleoclimate data to constrain cloud parameterizations in GISS-E2.1

<p>Much of the inter-model spread in equilibrium climate sensitivity (ECS) estimates is attributed to cloud and convective parameterizations which model cloud and water vapor feedbacks. These parameterizations also directly influence water isotopes, which may be retrieved not only from modern observations, but also a plethora of paleoclimate archives that represent a much broader range of variability than is available in modern measurements. And thus, these water isotope tracers can be used to constrain ECS by flagging unrealistic parts of the parameterization phase space via model biases in a perturbed parameterization ensemble (PPE) of paleoclimate simulations. In this proof-of-concept study, we evaluate a suite of isotope-enabled atmosphere-only GISS-E2.1 simulations, each with varying cloud and convective perturbations, against speleothem and ice core δ<sup>18</sup>O for the Last Glacial Maximum (LGM, 21000 years ago), mid-Holocene (MH, 6000 years ago) and pre-Industrial periods. The first-order spatial pattern of δ<sup>18</sup>O of precipitation (δ<sup>18</sup>O<sub>p</sub>) is in excellent agreement between proxy data and all parameterizations across all time periods. While the simulations generally capture large scale δ<sup>18</sup>O<sub>p</sub> patterns, the magnitude of change is consistently smaller in all simulations than those of the proxies, highlighting uncertainties in both models and proxies. Not a single set of parameterizations worked well in all climate states, indicating that improving future simulations requires determining all plausible parameter combinations critical in refining ECS. Further, it may be that certain parameterization choices represent certain types of variability better than others, and there may be a non-unique solution to ideal clouds/convection parameterization choices that is modulated by the question asked.</p>

Climate sensitivity and the Southern Ocean: the effect of the "too few, too bright" model cloud problem

<p>Equilibrium climate sensitivity (ECS) and transient climate response (TCR) are some of the most fundamental properties characterising the future climate. Progress in estimating climate sensitivity over the last three decades has been hampered by a large climate model spread of ECS and TCR estimates, and more recently by a large increase in ECS predicted by several models in the latest generation of the Climate Model Intercomparison Project 6 (CMIP6). Clouds have been identified as the major source of this uncertainty and the recent increase in estimated ECS. A "too few, too bright" model cloud problem has been found in several regions of the globe, including tropical latitudes and the Southern Ocean. Southern Ocean has also been a major focus of changes in model microphysics in an effort to simulate more realistic supercooled liquid clouds. Here, we focus on the too few, too bright problem in the Southern Ocean in CMIP6 models and its possible relation to climate sensitivity. We explore the possibility of applying new emergent constraints on climate sensitivity based on metrics of the too few, too bright problem. We use satellite and and ship-based observational datasets such as lidar and radiometer observations for constraining climate sensitivity and evaluation of clouds in this region across generations of CMIP models.</p>

Photoionization feedback in turbulent molecular clouds

ABSTRACT We present a study of the impact of photoionization feedback from young massive stars on the turbulent statistics of star-forming molecular clouds. This feedback is expected to alter the density structure of molecular clouds and affect future star formation. Using the AMUN-Rad code, we first generate a converged isothermal forced turbulent density structure inside a periodic box. We then insert an ionizing source in this box and inject photoionization energy using a two-temperature pseudo-isothermal equation of state. We study the impact of sources at different locations in the box and of different source luminosities. We find that photoionization has a minor impact on the 2D and 3D statistics of turbulence when turbulence continues to be driven in the presence of a photoionizing source. Photoionization is only able to disrupt the cloud if the turbulence is allowed to decay. In the former scenario, the presence of an H ii region inside our model cloud does not lead to a significant impact on observable quantities, independent of the source parameters.

AN EFFICIENT RESOURCE SELECTION AND ALLOCATION IN CLOUD COMPUTING USING ARTIFICIAL NUTRIENTS DISTRIBUTION MODEL

The recent emergence of cloud computing and its rapid advancement in recent time indicates a promising technology. However, the increasing number of providers with different policies has induced a challenge for customers to select providers that can efficiently satisfy their requirements. This research work is regarding resource selection and allocation in cloud computing using artificial nutrients distribution model. Cloud computing makes it possible for system administrators to allocate resources whenever it is required. It provides multiple servers that are expandable and can meet future needs without buying any physical computer equipment. Because there are lots of providers available commercially selection of resources from reliable provider has become difficult for cloud users. This research proposed a new intelligent model using the idea of nutrients distribution in human body to optimally select and allocate resources in cloud. This model enables users to efficiently select resources from the integrated providers as a single unit of resource pool. The model intelligently evaluates the available resources from different providers and expeditiously selects a resource of highest value for the customer. This research has designed an intelligent architecture, algorithm and the UML model for Resource Selection, Evaluation and allocation. The simulation showed that the overhead cost of searching from one provider to another as opposed to the existing methods is minimized. This model is of good quality and could obtain solution with a worthy efficiency by only making a single selection attempts as providers’ resources are interwoven to a single resource pool.

Biodegradation by bacteria in clouds: An underestimated sink for some organics in the atmospheric multiphase system

Water-soluble organic compounds represent a significant fraction of total atmospheric carbon. The main oxidants towards them in the gas and aqueous phases are OH and NO3 radicals. In addition to chemical solutes, a great variety of microorganisms (e.g. bacteria, viruses, fungi) has been identified in cloud water. Previous lab studies suggested that for some organics, biodegradation by bacteria in water is comparable to their loss by chemical processes. We perform model sensitivity studies over large ranges of biological and chemical process parameters using a box model with a detailed atmospheric multiphase chemical mechanism and biodegradation processes to explore the importance of biodegradation of organics in the aqueous phase. Accounting for the fact that only a small number fraction of cloud droplets (~ 0.0001–0.001) contains active bacteria cells, we consider only a few bacteria-containing droplets in the model cloud. We demonstrate that biodegradation might be most efficient for volatile organic compounds (VOC) with intermediate solubility (~ 104 ≤ KH(eff) [M atm−1] ≤ 106, e.g., formic and acetic acids). This can be explained by the transport limitation due evaporation of organics from bacteria-free droplets to the gas phase, followed by the dissolution into bacteria-containing droplets. For non-volatile organics (NVOC), such as dicarboxylic acids, the upper limit of organic loss by biodegradation can be approximated by the amount of organics dissolved in the bacteria-containing droplets (

Analisis Forensik Digital Storage pada Owncloud Drive

Penyimpanan data saat ini dapat menggunakan model Cloud. Cloud dapat di akses oleh beberapa user yang telah dibuatkan akun oleh admin. Semua aktivitas user terekam di history cloud. Tindak kejahatan dapat dilakukan dengan menghapus semua data penting yang ada pada Owncloud Drive yakni Nextcloud menggunakan salah satu admin dan menghapus histori pada cloud. Penelitian ini melakukan analis investigasi untuk mengethui siapa pelaku dan mengembalikan data yang sudah terhapus. Proses forensik digital storage pada owncloud drive (Nextcloud) dapat menggunakan metode dari National Institute of Standard and Technology (NIST) yang merupakan metode digital forensik yang digunakan secara umum oleh para peneliti di dunia. NIST dikembangkan untuk menyelesaikan simulasi kasus penyalahgunaan owncloud drive menggunakan skenario 5 akun yang terdiri dari satu admin dan user, pelaku melakukan kejahatan mengambil lalu menghapus data pada akun admin. Tahapan digital forensik NIST yakni pengumpulan data, pemeriksaan, analisis dan pelaporan. Berdasarkan proses investigasi yang dilakukan didapatkan laporan terkait pelaku kejahatan yang telah menghapus semua data penting yakni laporan keuangan, foto dokumentasi rahasia, dan beberapa data lainnya. Sebesar 75% data yang terhapus didapatkan kembali. Hasil penelitian ini juga didapatkan keterangan jejak pelaku kejahatan yang dibutuhkan sebagai barang bukti.

UAV Photogrammetry Accuracy Assessment for Corridor Mapping Based on the Number and Distribution of Ground Control Points

Unmanned aerial vehicle (UAV) photogrammetry has recently emerged as a popular solution to obtain certain products necessary in linear projects, such as orthoimages or digital surface models. This is mainly due to its ability to provide these topographic products in a fast and economical way. In order to guarantee a certain degree of accuracy, it is important to know how many ground control points (GCPs) are necessary and how to distribute them across the study site. The purpose of this work consists of determining the number of GCPs and how to distribute them in a way that yields higher accuracy for a corridor-shaped study area. To do so, several photogrammetric projects have been carried out in which the number of GCPs used in the bundle adjustment and their distribution vary. The different projects were assessed taking into account two different parameters: the root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2). From the different configurations tested, the projects using 9 and 11 GCPs (4.3 and 5.2 GCPs km−1, respectively) distributed alternatively on both sides of the road in an offset or zigzagging pattern, with a pair of GCPs at each end of the road, yielded optimal results regarding fieldwork cost, compared to projects using similar or more GCPs placed according to other distributions.