Off-road vehicles can have a devastating impact on vegetation and soil. Here, we sought to quantify, through a combination of field vegetation, bulk soil, and image analyses, the impact of off-road vehicles on the vegetation and soils of Rawdat Al Shams, which is located in central Saudi Arabia. Soil compaction density was measured in the field, and 27 soil samples were collected for bulk density analysis in the lab to quantify the impacts of off-road vehicles. High spatial resolution images, such as those obtained by the satellites GeoEye-1 and IKONOS-2, were used for surveying the damage to vegetation cover and soil compaction caused by these vehicles. Vegetation cover was mapped using the Normalized Difference Vegetation Index (NDVI) technique based on high-resolution images taken at different times of the year. Vehicle trails were derived from satellite data via visual analysis. All damaged areas were determined from high-resolution image data. In this study, we conducted quantitative analyses of vegetation cover change, the impacts of vehicle trails (hereafter "trail impacts"), and a bulk soil analysis. Image data showed that both vegetation cover and trail impacts increased from 2008 to 2015, with the average percentage of trail impacts nearly equal to that of the percentage of vegetation cover during this period. Forty-six species of plants were found to be present in the study area, consisting of all types of life forms, yet trees were represented by a single species, Acacia gerrardii. Herbs composed the largest share of plant life, with 29 species, followed by perennial herbs (12 species), grasses (5 species), and shrubs (3 species). Analysis of soil bulk density for Rawdat Al Shams showed that off-road driving greatly impacts soil density. Twenty-two plant species were observed on the trails, the majority of which were ephemerals. Notoceras bicorne was the most common, with a frequency rate of 93.33 %, an abundance value of 78.47 %, and a density of 0.1 in transect 1, followed by Plantago ovata.
In this paper we proposed a system for authentication using Palm Vein based on using principle component analysis (PCA) for feature extraction. Palm vein images of dorsa captured using infrared camera. PCA is applied to generate vector of features that represent the highest detailed variant information. A matching process is then applied to find the best match from the data base to recognize and authenticate the person. Experiments show that our system able to recognize human with accuracy 85% in real-time based on supervised recognition.
Problem statement: Segmentation of 3D range images is widely used in computer vision as an essential pre-processing step before the methods of high-level vision can be applied. Segmentation aims to study and recognize the features of range image such as 3D edges, connected surfaces and smooth regions. Approach: This study presents new improvements in segmentation of terrestrial 3D range images based on edge detection technique. The main idea is to apply a gradient edge detector in three different directions of the 3D range images. This 3D gradient detector is a generalization of the classical sobel operator used with 2D images, which is based on the differences of normal vectors or geometric locations in the coordinate directions. The proposed algorithm uses a 3D-grid structure method to handle large amount of unordered sets of points and determine neighborhood points. It segments the 3D range images directly using gradient edge detectors without any further computations like mesh generation. Our algorithm focuses on extracting important linear structures such as doors, stairs and windows from terrestrial 3D range images these structures are common in indoors and outdoors in many environments. Results: Experimental results showed that the proposed algorithm provides a new approach of 3D range image segmentation with the characteristics of low computational complexity and less sensitivity to noise. The algorithm is validated using seven artificially generated datasets and two real world datasets. Conclusion/Recommendations: Experimental results showed that different segmentation accuracy is achieved by using higher Grid resolution and adaptive threshold.
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