2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models

One of the challenges of remote sensing and computer vision lies in the three-dimensional (3-D) reconstruction of individual trees by using automated methods through very high-resolution (VHR) data sets. However, a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions. In this paper, we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models (DSMs). The symmetry of the citrus trees in a DSM is handled by an orientation-based radial symmetry transform which is computed in a unique way. Next, we propose an efficient strategy to accurately build influence regions of each tree, and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy. We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds. Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes. Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.     

Empirical assessment of rockfall and debris flow risk along the Karakoram Highway,Pakistan

Natural Hazards - The Karakoram Highway links north Pakistan with southwest China. It passes through unique geomorphological, geological and tectonic setting. This study focused 200-km-long section...     

Effect of Drain Pipes on Uplift Force and Exit Hydraulic Gradient and the Design of Gravity Dams Using the Finite Element Method

Geotechnical and Geological Engineering - The effects of diameter and location of drain pipes on the uplift force and exit hydraulic gradient for a gravity dam are investigated. A numerical model...     

An effective stress-based DSC model for predicting the coefficient of lateral soil pressure in unsaturated soils

Acta Geotechnica - In this study, an analytical model is developed to establish a framework for predicting the coefficient of lateral soil pressure in unsaturated soils. To this end, the disturbed...     

陆表自然和人文要素相互作用——“全球变化及应对”重点专项研究进展

在国家重点研发计划支持下,项目提出了陆表不均一性检测和订正的新方法,解决了渐变型不均一性检测和订正的难题,构建了中国地表太阳辐射、气温、地温、风速和降水等参数均一化站点和格点数据集,修订了关于中国地表风速变化趋势、增温格局及其形成机制的结论。融合多源数据,构建并验证了千米级、流域级或县域级的电厂、人口、生物质能、取水量、氮排放、二氧化碳排放等影响自然系统的关键人文要素历史和未来预估数据集。构建了未来关键人文要素情景,研制了碳中和目标下甲烷和氧化亚氮排放情景和用于驱动全球模式的未来情景,预估了中国碳中和战略的实施对全球变暖的减缓作用,发现中国碳中和对远期和中期全球变暖的减缓作用显著。给出了中国各省份水体氮排放安全阈值及超越时间,阐明了中国粮食产量与氮施肥的关系,提出了在保障粮食安全的前提下减少水体氮排放的有效途径,指出重构城乡养分循环体系是同时保障粮食安全和恢复水质的必要途径。发现全球饱和水汽压差的年际变化与大气二氧化碳浓度上升速率的年际变化显著相关,阐明了饱和水汽压差变化在调控生态系统生产力中的重要角色以及多因素耦合作用在生态系统生产力变化中的复杂影响。建议更全面细致地评估中国各种碳中...     

3D Static Modeling and Petrographic Aspects of the Albian/Cenomanian Reservoir,Komombo Basin,Upper Egypt

Natural Resources Research - The Albian/Cenomanian reservoir is one of the two main reservoirs composing the petroleum system in the Komombo Basin. However, these reservoirs have not previously...     

Using uncertainty and sensitivity analysis for finding the best rainfall-runoff model in mountainous watersheds(Case study: the Navrood watershed in Iran)

In watersheds that have not sufficient meteorological and hydrometric data for simulating rainfall-runoff events, using geomorphologic and geomorphoclimatic characteristics of watershed is a conventional method for the simulation. A number of rainfall-runoff models utilize these characteristics such as Nash-IUH, Clark-IUH, Geomorphologic Instantaneous Unit Hydrograph(GIUH), Geomorphoclimatic Instantaneous Unit Hydrograph(GcIUH), GIUH-based Nash(GIUH-Nash) and GcIUH-based Clark(GcIUH-Clark). But all these models are not appropriate for mountainous watersheds. Therefore, the objective of this study is to select the best of them for the simulation. The procedure of this study is: a) selecting appropriate rainfall-runoff events for calibration and validation of six hybrid models, b) distinguishing the best model based on different performance criteria(Percentage Error in Volume(PEV); Percentage Error in Peak(PEP); Percentage Error in Time to Peak(PETP); Root Mean Square Error(RMSE) and Nash-Sutcliffe model efficiency coefficient(ENS)), c) Sensitivity analysis for determination of the most effective parameter at each model, d) Uncertainty determination of different parameters in each model and confirmation of the obtained results by application of the performance criteria. For application of this procedure, the Navrood watershed in the north of Iran as a mountainous watershed has been considered. The results showed that the ClarkIUH and GcIUH-Clark are suitable models for simulation of flood hydrographs, while other models cannot simulate flood hydrographs appropriately. The sensitivity analysis shows that the most sensitive parameters are the infiltration constant rate and time of concentration in the Clark-IUH model. Also, the most sensitive parameters include the infiltration constant rate and storage coefficient in the GcIUHClark model. The Clark-IUH and GcIUH-Clark models are more sensitive to their parameters. The Latin Hypercube Sampling(LHS) on Monte Carlo(MC) simulation method was used for evaluation of uncertainty of data in rainfall-runoff models. In this method 500 sets of data values are produced and then the peak discharge of flood hydrographs for each produced data set is simulated with rainfall-runoff models. The uncertainty of data changes the value of simulated peak discharge of flood hydrograph. The uncertainty analysis shows that the observed peak discharges of different rainfall-runoff events are within the range of values of simulated by the six hybrid rainfall-runoff models and IUH that inputs of these models were the produced data sets. The range of the produced peak discharge of flood hydrographs by the Clark-IUH and GcIUH-Clark models is wider than those of other models.     

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.     

Fisher Matrix-based Predictions for Measuring the <Emphasis Type="Italic">z</Emphasis> = 3.35 Binned 21-cm Power Spectrum using the Ooty Wide Field Array (OWFA)

We use the Fisher matrix formalism to predict the prospects of measuring the redshifted 21-cm power spectrum in different k-bins using observations with the upcoming Ooty Wide Field Array (OWFA) which will operate at 326.5 MHz. This corresponds to neutral hydrogen (HI) at z = 3.35, and a measurement of the 21-cm power spectrum provides a unique method to probe the large-scale structures at this redshift. Our analysis indicates that a 5σ detection of the binned power spectrum is possible in the k range 0.05 ≤ k ≤ 0.3 Mpc^?1 with 1000 hours of observation. We find that the signal- to-noise ratio (SNR) peaks in the k range 0.1?0.2 Mpc^?1 where a 10σ detection is possible with 2000 hours of observations. Our analysis also indicates that it is not very advantageous to observe beyond 1000 h in a single field-of-view as the SNR increases rather slowly beyond this in many of the small k-bins. The entire analysis reported here assumes that the foregrounds have been completely removed.     

On the D́ust content of nearby galaxies

By inspection of photographs of the Hubble Atlas of Galaxies the frequency distribution of the sizes of dark cloud complexes in the four nearby galaxies NGC 3031, 5128, 5194 and 5457 is derived and the total amount of dust in these systems is estimated. The frequency distribution of the clouds is nearly the same in all the considered galaxies and may be approximated by n(R) ∼ e^−kR, k ≈︂ 0.050 pc⁻¹. The total amount of dust yields to roughly 10⁶M⊙.