scholarly journals Динаміка розмірів сільськогосподарських полів як функція їх розмірів та форми

2007 ◽  
Vol 7 (3) ◽  
pp. 14-31
Author(s):  
O.V. Zhukov ◽  
V.O. Sirovatko ◽  
N.O. Ponomarenko
Keyword(s):  
Regression Analysis ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Field Size ◽  
Agricultural Fields ◽  
Distribution Law ◽  
Administrative Area ◽  
Log Normal ◽  
Shape Characteristics ◽  
Shape And Size

<p>We estimated the size and shape characteristics of agricultural fields within the administrative area and identified patterns of the margin trends from 1950-1960 till the present time. Here we considered large-scale soil maps for the area of Vasilkovsky district of the Dnepropetrovsk region, which were drawn up in 1950-1960. To assess the landscape metric we used FRAGSTATS program which allow to make conformity assessment of the observed distributions of field sizes regards the normal, exponential, log-normal, gamma, Weibull, and Pareto distributions. We also used Box-Cox transformation to convert the experimental data into the normal distribution law for the further application of the transformed data in regression analysis. We estimated that the area of agricultural fields ranged from 1.20 to 269.00 hectares during the period of large-scale mapping in 1950-1960. The variation limits of the field sizes based on the results of remote sensing data and in our time they are 2,.5-266.57 hectares. Area of the fields in different periods strongly correlate and are statistically significant (<em>r</em> = 0.98, <em>p</em> = 0.00). Field sizes currently associated with the field sizes in the 50-60 years of linear regression. Shape parameters and field sizes significantly correlated, therefore, to establish the main trends of varying shape and size of fields, as well as for non-multicollinearity variables for regression analysis, we performed a multivariate factor analysis. An important aspect of the structuring of the agri-landscape is the location of settlements and, therefore, the fields distance from them. In results obtained indicate that the processes increase and decrease the size of fields in agricultural production are determined by various factors. Aspects of the shape and size of the fields associated with the dynamics of the processes that lead to variations in field areas. Fields that have shown a tendency to change their size, have different characteristics of forms and size from the stable fields. Typically, variable field size is smaller and more complex shapes.</p>

scholarly journals Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours

2020 ◽  
Vol 12 (7) ◽  
pp. 1205 ◽  
Author(s):  
Matthias P. Wagner ◽  
Natascha Oppelt
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Reference Data ◽  
Active Contours ◽  
Extraction Process ◽  
Agricultural Landscapes ◽  
Human Interaction ◽  
Field Size ◽  
Agricultural Fields ◽  
Remote Sensing Imagery

Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing field maps and observation systems rely on historic administrative maps or labor-intensive field campaigns. These are often expensive to maintain and quickly become outdated, especially in regions of frequently changing agricultural patterns. However, exploiting openly available remote sensing imagery (e.g., from the European Union’s Copernicus programme) may allow for frequent and efficient field mapping with minimal human interaction. We present a new approach to extracting agricultural fields at the sub-pixel level. It consists of boundary detection and a field polygon extraction step based on a newly developed, modified version of the growing snakes active contours model we refer to as graph-based growing contours. This technique is capable of extracting complex networks of boundaries present in agricultural landscapes, and is largely automatic with little supervision required. The whole detection and extraction process is designed to work independently of sensor type, resolution, or wavelength. As a test case, we applied the method to two regions of interest in a study area in the northern Germany using multi-temporal Sentinel-2 imagery. Extracted fields were compared visually and quantitatively to ground reference data. The technique proved reliable in producing polygons closely matching reference data, both in terms of boundary location and statistical proxies such as median field size and total acreage.

scholarly journals Cross-Sectional Association between Hypercholesterolemia and Knee Pain in the Elderly with Radiographic Knee Osteoarthritis: Data from the Korean National Health and Nutritional Examination Survey

2021 ◽  
Vol 10 (5) ◽  
pp. 933
Author(s):  
Byung Woo Cho ◽  
Du Seong Kim ◽  
Hyuck Min Kwon ◽  
Ick Hwan Yang ◽  
Woo-Suk Lee ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Knee Pain ◽  
Logistic Regression Analysis ◽  
National Health ◽  
Large Scale ◽  
Metabolic Diseases ◽  
The Elderly ◽  
Cross Sectional ◽  
Ordinal Logistic Regression Analysis

Few studies have reported the relationship between knee pain and hypercholesterolemia in the elderly population with osteoarthritis (OA), independent of other variables. The aim of this study was to reveal the association between knee pain and metabolic diseases including hypercholesterolemia using a large-scale cohort. A cross-sectional study was conducted using data from the Korea National Health and the Nutrition Examination Survey (KNHANES-V, VI-1; 2010–2013). Among the subjects aged ≥60 years, 7438 subjects (weighted number estimate = 35,524,307) who replied knee pain item and performed the simple radiographs of knee were enrolled. Using multivariable ordinal logistic regression analysis, variables affecting knee pain were identified, and the odds ratio (OR) was calculated. Of the 35,524,307 subjects, 10,630,836 (29.9%) subjects experienced knee pain. Overall, 20,290,421 subjects (56.3%) had radiographic OA, and 8,119,372 (40.0%) of them complained of knee pain. Multivariable ordinal logistic regression analysis showed that among the metabolic diseases, only hypercholesterolemia was positively correlated with knee pain in the OA group (OR 1.24; 95% Confidence Interval 1.02–1.52, p = 0.033). There were no metabolic diseases correlated with knee pain in the non-OA group. This large-scale study revealed that in the elderly, hypercholesterolemia was positively associated with knee pain independent of body mass index and other metabolic diseases in the OA group, but not in the non-OA group. These results will help in understanding the nature of arthritic pain, and may support the need for exploring the longitudinal associations.

scholarly journals Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

2015 ◽  
Vol 19 (9) ◽  
pp. 3845-3856 ◽  
Author(s):  
F. Todisco ◽  
L. Brocca ◽  
L. F. Termite ◽  
W. Wagner
Keyword(s):  
Soil Moisture ◽  
Soil Loss ◽  
Large Scale ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Loss Estimation ◽  
Balance Model ◽  
Estimation Accuracy ◽  
Soil Losses

Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha−1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

scholarly journals Retrieving Forest Canopy Elements Clumping Index Using ICESat GLAS Lidar Data

2021 ◽  
Vol 13 (5) ◽  
pp. 948
Author(s):  
Lei Cui ◽  
Ziti Jiao ◽  
Kaiguang Zhao ◽  
Mei Sun ◽  
Yadong Dong ◽  
...  
Keyword(s):  
Large Scale ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Alternative Methods ◽  
High Signal ◽  
Laser Altimeter ◽  
Waveform Data ◽  
Forest Park ◽  
Clumping Index ◽  
The Impact

Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical reflectance data. However, these reflectance-based estimates have well-known limitations, such as the mere use of a linear relationship between the normalized difference hotspot and darkspot (NDHD) and CI, uncertainties in bidirectional reflectance distribution function (BRDF) models used to calculate the NDHD, and coarse spatial resolutions (e.g., hundreds of meters to several kilometers). To remedy these limitations and develop alternative methods for large-scale CI mapping, here we explored the use of spaceborne lidar—the Geoscience Laser Altimeter System (GLAS)—and proposed a semi-physical algorithm to estimate CI at the footprint level. Our algorithm was formulated to leverage the full vertical canopy profile information of the GLAS full-waveform data; it converted raw waveforms to forest canopy gap distributions and gap fractions of random canopies, which was used to estimate CI based on the radiative transfer theory and a revised Beer–Lambert model. We tested our algorithm over two areas in China—the Saihanba National Forest Park and Heilongjiang Province—and assessed its relative accuracies against field-measured CI and MODIS CI products. We found that reliable estimation of CI was possible only for GLAS waveforms with high signal-to-noise ratios (e.g., >65) and at gentle slopes (e.g., <12°). Our GLAS-based CI estimates for high-quality waveforms compared well to field-based CI (i.e., R2 = 0.72, RMSE = 0.07, and bias = 0.02), but they showed less correlation to MODIS CI (e.g., R2 = 0.26, RMSE = 0.12, and bias = 0.04). The difference highlights the impact of the scale effect in conducting comparisons of products with huge differences resolution. Overall, our analyses represent the first attempt to use spaceborne lidar to retrieve high-resolution forest CI and our algorithm holds promise for mapping CI globally.

Anatomical Predictors of Endoleaks or Migration After Endovascular Aneurysm Sealing

2018 ◽  
Vol 25 (6) ◽  
pp. 719-725 ◽  
Author(s):  
Kim van Noort ◽  
Johannes T. Boersen ◽  
Aleksandra C. Zoethout ◽  
Richte C. L. Schuurmann ◽  
Jan M. M. Heyligers ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Large Scale ◽  
High Volume ◽  
Control Group ◽  
Neck Length ◽  
Aortic Neck ◽  
Distal Migration ◽  
Aneurysm Growth ◽  
Instructions For Use ◽  
Neck Diameter

Purpose: To identify preoperative anatomical aortic characteristics that predict seal failures after endovascular aneurysm sealing (EVAS) and compare the incidence of events experienced by patients treated within vs outside the instructions for use (IFU). Methods: Of 355 patients treated with the Nellix EndoVascular Aneurysm Sealing System (generation 3SQ+) at 3 high-volume centers from March 2013 to December 2015, 94 patients were excluded, leaving 261 patients (mean age 76±8 years; 229 men) for regression analysis. Of these, 83 (31.8%) suffered one or more of the following events: distal migration ⩾5 mm of one or both stent frames, any endoleak, and/or aneurysm growth >5 mm. Anatomical characteristics were determined on preoperative computed tomography (CT) scans. Patients were divided into 3 groups: treated within the original IFU (n=166), outside the original IFU (n=95), and within the 2016 revised IFU (n=46). Categorical data are presented as the median (interquartile range Q1, Q3). Results: Neck diameter was significantly larger in the any-event cohort vs the control cohort [23.7 mm (21.7, 26.3) vs 23.0 mm (20.9, 25.2) mm, p=0.022]. Neck length was significantly shorter in the any-event cohort [15.0 mm (10.0, 22.5) vs 19.0 mm (10.0, 21.8), p=0.006]. Maximum abdominal aortic aneurysm (AAA) diameter and the ratio between the maximum AAA diameter and lumen diameter in the any-event group were significantly larger than the control group (p=0.041 and p=0.002, respectively). Regression analysis showed aortic neck diameter (p=0.006), neck length (p=0.001), and the diameter ratio (p=0.011) as significant predictors of any event. In the comparison of events to IFU status, 52 (31.3%) of 166 patients in the inside the original IFU group suffered an event compared to 13 (28.3%) of 46 patients inside the 2016 IFU group (p=0.690). Conclusion: Large neck diameter, short aortic neck length, and the ratio between the maximum AAA and lumen diameters are preoperative anatomical predictors of the occurrence of migration (⩾5 mm), any endoleak, and/or aneurysm growth (>5 mm) after EVAS. Even under the refined 2016 IFU, more than a quarter of patients suffered from an event. Improvements in the device seem to be necessary before this technique can be implemented on a large scale in endovascular AAA repair.

Do Housing Vacancies Induce More Crime? A Spatiotemporal Regression Analysis

2019 ◽  
Vol 66 (11) ◽  
pp. 1579-1605 ◽  
Author(s):  
Xiaojin Chen ◽  
Patrick Rafail
Keyword(s):  
Regression Analysis ◽  
Collective Efficacy ◽  
Large Scale ◽  
Spillover Effect ◽  
Property Crime ◽  
Drug Offense ◽  
Community Based Programs ◽  
The City ◽  
Housing Vacancy ◽  
Neighborhood Level

This study aims to investigate the longitudinal associations between patterns of housing vacancies, neighborhood social disorder, and crime in the city of New Orleans. Using large-scale administrative and contextual data collected from the year 2012 to 2018, our spatiotemporal regression analysis provides empirical evidence for the salient effects of housing vacancy on neighborhood level of property crime and violence. In addition, the spillover effect of housing vacancy is observed on the neighborhood level of drug offense, property crime, and violence. These results potentially identify vacant properties as a modifiable target for intervention to reduce urban crime and suggest that community-based programs aiming to enhance informal social control and collective efficacy may be as important as broken window policing programs.

The Impact of Extraversion and Sensation Seeking on Tourist Role

2015 ◽  
Vol 43 (1) ◽  
pp. 75-84 ◽  
Author(s):  
Cheng-Yu Li ◽  
Shiao-Yuan Lu ◽  
Bi-Kun Tsai ◽  
Keh-Yuan Yu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Sensation Seeking ◽  
Large Scale ◽  
Personality Variables ◽  
Tourism Marketing ◽  
Large Scale Survey ◽  
And Behaviors ◽  
The Impact

In recent years, personality variables, such as extraversion and sensation seeking, have been used to investigate tourist preferences and behaviors. For this study, we classified tourist roles into three types: the familiarized mass tourist, the organized mass tourist, and the independent tourist. We investigated the impact of extraversion and sensation seeking on tourist roles in a large-scale survey of Taiwanese citizens (N = 1,249) aged 20 years and older. Using logistic regression analysis, the results indicated that sensation seeking was a significant predictor of tourist role, but extraversion was not. Compared to familiarized mass tourists, people who are sensation-seeking are more likely to become independent tourists rather than organized mass tourists. We provide suggestions for tourism marketing.

LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes

2019 ◽  
Vol 221 ◽  
pp. 695-706 ◽  
Author(s):  
Jianbo Qi ◽  
Donghui Xie ◽  
Tiangang Yin ◽  
Guangjian Yan ◽  
Jean-Philippe Gastellu-Etchegorry ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Image Simulation ◽  
Simulation Framework ◽  
Sensing Data

scholarly journals Rhizosphere Bacteria Exhibit Narrower Environmental Adaptation in Proso Millet Than in Foxtail Millet and Sorghum in Agricultural Fields

2021 ◽  
Author(s):  
Lixin Tian ◽  
Feifei Zhang ◽  
Pengliang Chen ◽  
Panpan Zhang ◽  
Zhijun Gao ◽  
...  
Keyword(s):  
Community Assembly ◽  
Bacterial Communities ◽  
Large Scale ◽  
Foxtail Millet ◽  
Environmental Adaptation ◽  
Rhizosphere Bacteria ◽  
Agricultural Fields ◽  
Proso Millet ◽  
Rhizosphere Community ◽  
Rhizosphere Bacterial Communities

Abstract It is of great ecological significance to understand how the assembly processes of soil microbe communities respond to environmental change. However, the assembly processes of the rhizosphere bacterial communities in three minor grain crops (i.e., foxtail millet, proso millet, and sorghum) across agro-ecosystems are rarely investigated. Here, we investigated the environmental thresholds and phylogenetic signals for ecological preferences of rhizosphere bacterial communities of three minor grain crop taxa across complex environmental gradients to reflect their environmental adaptation. Additionally, we reported environmental factors affecting their community assembly processes based on a large-scale soil survey in agricultural fields across northern China using high-throughput sequencing.. The results demonstrated a narrower range of environmental thresholds and weaker phylogenetic signals for the ecological traits of rhizosphere bacteria in proso millet than in foxtail millet and sorghum fields, while proso millet rhizosphere community was the most phylogenetically clustered. The null model analysis indicated that homogeneous selection belonging to deterministic processes governed the sorghum rhizosphere community, whereas dispersal limitation belonging to stochastic processes was the critical assembly process in the foxtail and proso millet. Mean annual temperature was the decisive factor for adjusting the balance between stochasticity and determinism of the foxtail millet, proso millet, and sorghum rhizosphere communities. A higher temperature resulted in stochasticity in the proso millet and sorghum communities. For the foxtail millet community, the deterministic assembly increased with an increase in temperature. These results contribute to the understanding of root-associated bacterial community assembly processes in agro-ecosystems on a large scale.

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