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Using Canopy Height Model Obtained with Dense Image Matching of Archival Photogrammetric Datasets in Area Analysis of Secondary Succession

机译:在二次演替区域分析中使用结合档案摄影测量数据集的密集图像匹配获得的树冠高度模型

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One of the threats that has a significant impact on the conservation status and on the preservation of non-forest Natura 2000 habitats, is secondary succession, which is currently analyzed using airborne laser scanning (ALS) data. However, learning about the dynamics of this phenomenon in the past is only possible by using archival aerial photographs, which are often the only source of information about the past state of land cover. Algorithms of dense image matching developed in the last decade have provided a new quality of digital surface modeling. The aim of this study was to determine the extent of trees and shrubs, using dense image matching of aerial images. As part of a comprehensive research study, the testing of two software programs with different settings of image matching was carried out. An important step in this investigation was the quality assessment of digital surface models (DSM), derived from point clouds based on reference data for individual trees growing singly and in groups with high canopy closure. It was found that the detection of single trees provided worse results. The final part of the experiment was testing the impact of the height threshold value in elevation models on the accuracy of determining the extent of the trees and shrubs. It was concluded that the best results were achieved for the threshold value of 1.25–1.75 m (depending on the analyzed archival photos) with 10 to 30% error rate in determining the trees and shrubs cover.
机译:次生演替是对自然保护状况和非森林Natura 2000生境的保存产生重大影响的威胁之一,目前已使用机载激光扫描(ALS)数据对其进行了分析。但是,只有通过使用档案航拍照片来了解过去这种现象的动态,这通常是有关过去土地覆盖状况的唯一信息来源。最近十年开发的密集图像匹配算法提供了数字表面建模的新质量。这项研究的目的是通过使用航空图像的密集图像匹配来确定树木和灌木的范围。作为全面研究的一部分,对具有不同图像匹配设置的两个软件程序进行了测试。这项研究的重要一步是数字表面模型(DSM)的质量评估,该模型是从点云得出的,该数据是基于单个树的单个参考树和高树冠封闭组的参考数据得出的。发现单棵树的检测提供了较差的结果。实验的最后部分是测试高程模型中的高度阈值对确定树木和灌木范围的准确性的影响。结论是,在确定树木和灌木覆盖率时,阈值1.25–1.75 m(取决于分析的档案照片)可获得最佳结果,错误率10%至30%。

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