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A non-intrusive image analysis technique for measurement of heterogeneity in grass species around tree vicinity in a green infrastructure

机译:一种非侵入式图像分析技术,用于绿色基础设施树附近的草地异质性

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摘要

Spatial heterogeneity of vegetation growth is important for maintenance of urban green space. It also governs the differential settlement of foundation of buildings and performance of biofiltration units. The objective of this study is to analyse the heterogeneity in vegetation density and shoot growth of a grass around a tree vicinity. A novel non-intrusive image analysis approach was designed and developed for quantifying heterogeneity in vegetation growth. A commercially available unmanned air vehicle (UAV; PHANTOM 3 STANDARD) was utilized to capture images. Vegetation density from these captured images were quantified using a public domain image processing program ImageJ. Atmospheric parameters were monitored by micro-climate monitoring system for interpreting vegetation growth. It is found that, at a given radial distance from stem of tree, vegetation density range is found more heterogenous than shoot growth. The basic assumption of symmetricity around tree vicinity as adopted in previous models for root water uptake is found to be not true. Variation of rainfall is one of the main reason causing heterogeneity in grass growth around tree vicinity. Heterogeneity in vegetation growth more prominent near the tree vicinity than away from it. An increase in vegetation density is found within 2 m radial distance in both sides of tree stem due to presence of shredded leaves from tree during winter.
机译:植被生长的空间异质性对于维护城市绿地来说是重要的。它还管辖建筑物基础和生物滤光装置的表现差异定居。本研究的目的是分析植被密度的异质性,并在树附近拍摄草的生长。设计并开发了一种新的非侵入式图像分析方法,用于量化植被生长中的异质性。使用市售的无人驾驶空气车辆(UAV;幻影3标准)来捕获图像。使用公共领域图像处理程序ImageJ量化来自这些捕获的图像的植被密度。通过微气候监测系统监测大气参数,用于解释植被生长。发现,在从树茎的给定径向距离处,发现植被密度范围比芽生长更加异。发现在以前的根水摄取模型中采用的树附近的对称性的基本假设是不是真的。降雨的变异是在树附近引起草生长的异质性的主要原因之一。植被生长的异质性在树附近更加突出而不是远离它。由于在冬季树木的树叶的存在,树干两侧的两侧径向距离内发现了植被密度的增加。

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