Scale Characteristics and Effects on Spatial Variability of Soil Available Nutrients

2019 ◽  
Vol 35 (2) ◽  
pp. 221-230
Author(s):  
Gengxing Zhao ◽  
Chao Dong ◽  
Xiaona Chen ◽  
Baowei Su
Keyword(s):  
Spatial Variability ◽  
Spatial Autocorrelation ◽  
Soil Nutrients ◽  
Kriging Interpolation ◽  
Small Scale ◽  
Available Phosphorus ◽  
Multi Scale ◽  
Classical Statistics ◽  
Scale Characteristics ◽  
Patch Density

Abstract.The spatial variability of farmland soil nutrients on different scales is important for farming as it forms the basis for the efficient utilization of soil nutrients and precision fertilization. Survey points were distributed throughout the study area on three different scales (county, field, and block). Research on the scale effect of the spatial variability of available nitrogen (AN), available phosphorus (AP), and available potassium (AK) involved a combination of classical statistics, geostatistics, and Geographic Information System (GIS) techniques. Results indicated that the three kinds of nutrients presented moderate variation intensity on the three scales. All of the nutrients tested exhibited strong spatial autocorrelation, indicating that spatial variability was primarily affected by structural factors, including climate, soil type and topography. As the sampling scale decreased, the nutrients showing weak variation at the large scale exhibited great variation at the small scale; the spatial autocorrelation of these three nutrients first became greater and then weakened; the distance of the spatial autocorrelation shortened gradually. Furthermore, the patch density value of the soil nutrient map increased, which indicated that the distribution of nutrients tended to be more fragile. When combined, sampling methods on the multi-scale allowed us to obtain real and systematic soil information. This study explored scale characteristics and the effects of spatial variability with regards to the primary nutrients available on farmland and provided a theoretical basis to effectively understand the nutrient status of regional farmland and improve the efficacy of soil sampling. Keywords: Multi-scale, Geostatistics, Patch density, Fractal dimension, Kriging interpolation.

scholarly journals MCSM-Wri: A Small-Scale Motion Recognition Method Using WiFi Based on Multi-Scale Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4162
Author(s):  
Ma ◽  
Huang ◽  
Li ◽  
Huang ◽  
Ma ◽  
...  
Keyword(s):  
Environmental Changes ◽  
Recognition System ◽  
Letter Recognition ◽  
Small Scale ◽  
Motion Recognition ◽  
Multi Scale ◽  
Scale Characteristics ◽  
Scale Motion ◽  
Device Free ◽  
Fold Cross Validation

environmental perception technology based onWiFi, and some state-of-the-art techniques haveemerged. The wide application of small-scale motion recognition has aroused people’s concern.Handwritten letter is a kind of small scale motion, and the recognition for small-scale motion basedon WiFi has two characteristics. Small-scale action has little impact on WiFi signals changes inthe environment. The writing trajectories of certain uppercase letters are the same as the writingtrajectories of their corresponding lowercase letters, but they are different in size. These characteristicsbring challenges to small-scale motion recognition. The system for recognizing small-scale motion inmultiple classes with high accuracy urgently needs to be studied. Therefore, we propose MCSM-Wri,a device-free handwritten letter recognition system using WiFi, which leverages channel stateinformation (CSI) values extracted from WiFi packets to recognize handwritten letters, includinguppercase letters and lowercase letters. Firstly, we conducted data preproccessing to provide moreabundant information for recognition. Secondly, we proposed a ten-layers convolutional neuralnetwork (CNN) to solve the problem of the poor recognition due to small impact of small-scaleactions on environmental changes, and it also can solve the problem of identifying actions with thesame trajectory and different sizes by virtue of its multi-scale characteristics. Finally, we collected6240 instances for 52 kinds of handwritten letters from 6 volunteers. There are 3120 instances fromthe lab and 3120 instances are from the utility room. Using 10-fold cross-validation, the accuracyof MCSM-Wri is 95.31%, 96.68%, and 97.70% for the lab, the utility room, and the lab+utility room,respectively. Compared with Wi-Wri and SignFi, we increased the accuracy from 8.96% to 18.13% forrecognizing handwritten letters.

scholarly journals Spatial Variability and Geostatistical Analysis of Soil Properties in the Diversified Cropping Regions of Bangladesh Using Geographic Information System Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Md. Zulfikar Khan ◽  
Md. Rafikul Islam ◽  
Ahmed Bin Abdus Salam ◽  
Tama Ray
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Nutrient Management ◽  
Sampling Site ◽  
Geostatistical Analysis ◽  
Kriging Interpolation ◽  
Total N ◽  
Study Region ◽  
Site Specific ◽  
Classical Statistics

Mapping of soil properties is an important operation as it plays an important role in the knowledge about soil properties and how it can be used sustainably. The study was carried out in a local government area in Bangladesh in order to map out some soil properties and assess their variability within the area. From the study area, a total of 92 soil samples (0–20 cm) were collected from different cropping patterns at an interval of 2.2 × 2.2 km2 on a regular grid design. A portable global positioning system (GPS) was used to collect coordinates of each sampling site. Then, soil properties, that is, pH, electrical conductivity (EC), soil organic carbon (SOC), total nitrogen (Total N), and soil available nutrients (P, K, and S) were measured in the laboratory. After the normalization of data, classical statistics were used to describe the soil properties, and geostatistical analysis was used to illustrate the spatial variability of the soil properties by using kriging interpolation techniques in a GIS environment. Results show that the spatial distribution and spatial dependency level of soil properties can be different even within the small or large scale. According to cross-validation results, for most soil properties, the kriging interpolation method provided the least interpolation error. The generated maps of soil properties that indicate soil nutrient status over the study region could be helpful for farmers and decision-makers to enhance site-specific nutrient management. Also, these prototype maps would be helpful for future nutrient and fertilizer applications management, including a site-specific condition to not only reduce the cost of input management but also prevent any environmental hazard. It also demonstrates that the effectiveness of geostatistics and GIS techniques provided a powerful tool for this study in terms of regionalized nutrient management.

Study on Vortex Structures and Intermittency in Two-Oscillating Grid Turbulence With Viscoelastic Fluids Based on Wavelet Analysis

2017 ◽  
Author(s):  
Yue Wang ◽  
Yongyao Li ◽  
Weihua Cai ◽  
Lu Wang ◽  
Fengchen Li ◽  
...  
Keyword(s):  
Wavelet Coefficient ◽  
Vortex Structures ◽  
Small Scale ◽  
Grid Turbulence ◽  
Multi Scale ◽  
Frequency Components ◽  
Scale Characteristics ◽  
Cetyltrimethyl Ammonium ◽  
Oscillation Frequencies ◽  
Oscillating Grid

In this paper, two-oscillating grid turbulence with/without viscoelastic additives was performed experimentally by particle image velocimetry. Two classical drag-reducing additives-polymer (Polyacrylamide, PAM) and cationic surfactant (cetyltrimethyl ammonium chloride, CTAC) were chosen. The experiments were carried out under the classical concentration (25ppm) and three different grid oscillation frequencies. Two-dimensional wavelet transform was utilized to investigate multi-scale characteristics of vortex structures and intermittency based on wavelet coefficient. The results showed that at the same decomposition level, the existence of viscoelastic additives attenuates the high-frequency components of fluctuation velocity. The small-scale intermittency is remarkably inhibited by viscoelastic additives especially for scale parameter smaller than 24. Besides, CTAC additives show different effect from PAM additives. Therefore, turbulent drag reduction with additives also happens in two-oscillating grid turbulence without wall effect.

scholarly journals Spatial variability and geostatistical analysis of selected soil

2019 ◽  
Vol 54 (1) ◽  
pp. 55-66
Author(s):  
MZ Khan ◽  
MA Islam ◽  
M Sadiqul Amin ◽  
MMR Bhuiyan
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Soil Nutrients ◽  
Strong Dependence ◽  
Chemical Properties ◽  
Spatial Prediction ◽  
Accurate Estimation ◽  
Available Phosphorus ◽  
Suitable Alternative ◽  
Physico Chemical

A study was conducted to explore the spatial variability of major soil nutrients of Agricultural fields in South-western region of Bangladesh. From the study area, 40 surface soil samples were collected by a random sampling strategy using GPS. Then soil physico-chemical properties i.e., pH, electrical conductivity (EC), organic matter (OM), total nitrogen (TN) N, soil available nutrients (P, K and S) were measured in laboratory. After data normalization, classical and geo-statistical analyses were used to describe soil properties and spatial correlation of soil characteristics. Spatial variability of soil physico-chemical properties was quantified through semi-variogram analysis and the respective surface maps were prepared through ordinary Kriging. Spherical model fits well with experimental semi-variogram of pH, EC, OM, TN, available P, K and S. Soil pH, available phosphorus (Av P), potassium (Av K) and sulfur (Av S) have the moderate spatial dependence, with nugget/sill ratios of 41.13% to 72.21%. The others have the strong dependence with nugget/sill ratios of less than 25%. The spatial variability of estimating soil properties varies within range of 0.0142 for Av P to 0.0383 for Av S and this model can calculate the un-sampled within neighboring distance of about 12.65 m for Av S to 150.82 m for TN, respectively. Cross validation of kriged map shows that spatial prediction of soil nutrients using semi-variogram parameters is better than assuming mean of observed value for any un-sampled location. Therefore, it is a suitable alternative method for accurate estimation of chemical properties of soil in un-sampled positions as compared to direct measurement which has time and costs concerned. Bangladesh J. Sci. Ind. Res.54(1), 55-66, 2019

scholarly journals A new multi-scale backbone network for object detection based on asymmetric convolutions

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110113
Author(s):  
Xianghua Ma ◽  
Zhenkun Yang
Keyword(s):  
Object Detection ◽  
Image Features ◽  
Detection Accuracy ◽  
Mobile Platforms ◽  
Multi Scale ◽  
Backbone Network ◽  
Aspect Ratios ◽  
Pascal Voc ◽  
Scale Characteristics ◽  
Detection Speed

Real-time object detection on mobile platforms is a crucial but challenging computer vision task. However, it is widely recognized that although the lightweight object detectors have a high detection speed, the detection accuracy is relatively low. In order to improve detecting accuracy, it is beneficial to extract complete multi-scale image features in visual cognitive tasks. Asymmetric convolutions have a useful quality, that is, they have different aspect ratios, which can be used to exact image features of objects, especially objects with multi-scale characteristics. In this paper, we exploit three different asymmetric convolutions in parallel and propose a new multi-scale asymmetric convolution unit, namely MAC block to enhance multi-scale representation ability of CNNs. In addition, MAC block can adaptively merge the features with different scales by allocating learnable weighted parameters to three different asymmetric convolution branches. The proposed MAC blocks can be inserted into the state-of-the-art backbone such as ResNet-50 to form a new multi-scale backbone network of object detectors. To evaluate the performance of MAC block, we conduct experiments on CIFAR-100, PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO 2014 datasets. Experimental results show that the detection precision can be greatly improved while a fast detection speed is guaranteed as well.

scholarly journals Multi-Scale Ionospheric Anomalies Monitoring and Spatio-Temporal Analysis during Intense Storm

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 215
Author(s):  
Na Cheng ◽  
Shuli Song ◽  
Wei Li
Keyword(s):  
Magnetic Storm ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Ionospheric Disturbance ◽  
Ionospheric Scintillation ◽  
Small Scale ◽  
Electron Content ◽  
Total Electron ◽  
Multi Scale ◽  
Intense Storm

The ionosphere is a significant component of the geospace environment. Storm-induced ionospheric anomalies severely affect the performance of Global Navigation Satellite System (GNSS) Positioning, Navigation, and Timing (PNT) and human space activities, e.g., the Earth observation, deep space exploration, and space weather monitoring and prediction. In this study, we present and discuss the multi-scale ionospheric anomalies monitoring over China using the GNSS observations from the Crustal Movement Observation Network of China (CMONOC) during the 2015 St. Patrick’s Day storm. Total Electron Content (TEC), Ionospheric Electron Density (IED), and the ionospheric disturbance index are used to monitor the storm-induced ionospheric anomalies. This study finally reveals the occurrence of the large-scale ionospheric storms and small-scale ionospheric scintillation during the storm. The results show that this magnetic storm was accompanied by a positive phase and a negative phase ionospheric storm. At the beginning of the main phase of the magnetic storm, both TEC and IED were significantly enhanced. There was long-duration depletion in the topside ionospheric TEC during the recovery phase of the storm. This study also reveals the response and variations in regional ionosphere scintillation. The Rate of the TEC Index (ROTI) was exploited to investigate the ionospheric scintillation and compared with the temporal dynamics of vertical TEC. The analysis of the ROTI proved these storm-induced TEC depletions, which suppressed the occurrence of the ionospheric scintillation. To improve the spatial resolution for ionospheric anomalies monitoring, the regional Three-Dimensional (3D) ionospheric model is reconstructed by the Computerized Ionospheric Tomography (CIT) technique. The spatial-temporal dynamics of ionospheric anomalies during the severe geomagnetic storm was reflected in detail. The IED varied with latitude and altitude dramatically; the maximum IED decreased, and the area where IEDs were maximum moved southward.

scholarly journals Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 179
Author(s):  
Said Munir ◽  
Martin Mayfield ◽  
Daniel Coca
Keyword(s):  
Data Fusion ◽  
Spatial Variability ◽  
Urban Areas ◽  
Road Traffic ◽  
Dispersion Model ◽  
Low Cost ◽  
Spatial Modelling ◽  
City Centre ◽  
Small Scale ◽  
The City

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

Multi-scale characteristics of remote sensing lineaments

2019 ◽  
Vol 13 (2) ◽  
pp. 287-297
Author(s):  
Junlong Xu ◽  
Xingping Wen ◽  
Haonan Zhang ◽  
Dayou Luo ◽  
Lianglong Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Multi Scale ◽  
Scale Characteristics
Sign in / Sign up

Export Citation Format

Share Document