جستجوی مقالات مرتبط با کلیدواژه « unmanned aerial vehicle » در نشریات گروه « آب و خاک »

The Internet of Things is a new technology that is conceived as a global network of machines and devices that can interact with each other. The Internet of Things is also known as one of the most important fields of technology of the future and has been considered by many industries. This system mimics the characteristics of emotion, intelligence, and the ability to think, memorize, make decisions, and react to the physical environment from the human brain. Also, from three parts of different types of sensors, the communication network of information processing units and intelligent and data management and processing units have been formed. The study, conducted using a large library study and using credible international information, traces the IoT from the background to applications and examines all the small to large components of this on-farm approach. Then the application of this concept in irrigation, especially in automation and intelligent irrigation systems in the field was investigated. Finally, common tools such as in-field sensors, wireless sensor networks, satellites, and the use of an unmanned aerial vehicle as a means to achieve the goal of communication between the Internet of Things and intelligent farm irrigation systems, were introduced. Finally, the benefits, applications, and challenges of the present and future of the Internet of Things in irrigation were outlined.

Understanding the ecological and geomorphological processes of the spatial distribution of loess deposits helps us to find their interactions in the arid and semi-arid regions. The Iranian loess plateau with a unique landscape and complex topography also located in steppe vegetation with semi-arid climate. The aims of present study are point pattern analysis of hillside, stream, and their interactions using different summary statistics in the particular part of Iranian loess plateau. As there is a small distance between hillslope and complex topography in the study area, unmanned aerial vehicle (UAV) imagery used to prepare precise colored aerial photos for statistical analyses. Further, regarding to the mentioned purposes, this research has enough novelty compare with previous studies, and thus, it is a new step in studying the spatial pattern of loess facies; In other words, this study attempts to find the effective relationship between the hillside and streams in terms of the expansion of channel erosion in the study region.

The study area has dry Xeric soil moisture and Thermic soil temperature regimes. The UAV technique used to prepare the precise colorful images with highly spatial/temporal resolution to model the spatial patterns of hillside and streams density. The topographic attributes (primary and secondary) obtained from digital elevation model (DEM) applied with a spatial resolution of 20×20 cm. The univariate and bivariate point analyses (modelling) used for variable analyses in Spatstat package in R and Progammita software. Finally, Mark correlation function (MCF) used to investigate the question of reducing the size of density dependent.

The results of univariate g(r) and O-ring (r) showed that different hillside facing i.e. flat, north, south, east, and west have the aggregated pattern in all distances in the study area. These implied that the same directions are distributed more closely next to each other and their arrangements follow the special pattern. In addition, the interaction between streams and north-face using the bivariate g12(r) and O12(r) confirmed the positive interaction between streams and north-face in all distances in the study area. The MCF analysis also showed that the slope as the effective factor has positive interaction with streams, and steep slopes are more aggregated compare with the low slopes. This indicates which the steeper slopes are more likely to form streams than flat lands.

Generally, the streams probability form in the steep slopes more than flat areas due to their shear energy, which leads to more soil losses. Further, the soil loss on the steep slopes is more than flat terrain. Finally, UAV technologies are recommended in the study of spatial pattern of deposits for detailed observations, highly accurate data, and deciding natural resource managers to reduce soil erosion.

Leaf area index (LAI) is an important indicator of plant growth and yield. Therefore, monitoring the spatial and temporal distribution of LAI at agricultural farms could be a significant predictor of how well the various elements of farm management strategies such as irrigation scheduling and uniformity have been implemented. The purpose of this study is to outline how pictures taken by a modified digital camera can be used for estimating the LAI of a corn silage field. It focuses on how to utilize a combination of a simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains, to estimate LAI. In order to remove the sources of errors in the resulting images, procedures to perform image vignetting corrections, geometric distortions corrections, and elimination of radiometric bidirectional effects are suggested. Due to high spatial resolution of this imaging system (at the level of a few centimeters), separation of surfaces with and without plant cover was accomplished well. This separation process was also useful in determination of percentage of vegetation cover (crop density). The leaf area index had the highest correlation with the vegetation cover percentage (R2 = 0/919), and the NIR spectral band (R2 = 0/741). There was a high correlation between the two spectra of red and green with the NIR spectral band. This correlation indicates that with the presence of the NIR spectral band, the effect of red and green spectral bands on estimating leaf area index is insignificant. Therefore, a multivariable regression model was generated to estimate leaf area index as a function of only two parameters, namely vegetation cover percentage and spectral band NIR. The performance of the developed model was evaluated by comparing its predicted values of LAI with corresponding measured values. The adjusted coefficient of determination of this comparison was 96.6%, which indicates that 96.6% of the variation in the estimated leaf area index values is explained by the two variables (vegetation cover percentage and NIR spectral band) incorporated into the model.