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首页> 外文会议>Pixels, Objects, Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century >A COMPARISON OF OBJECT-BASED AND PIXEL-BASED APPROACHES TO ESTIMATE LIDAR-MEASURED FOREST CANOPY HEIGHT USING QUICKBIRD IMAGERY
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A COMPARISON OF OBJECT-BASED AND PIXEL-BASED APPROACHES TO ESTIMATE LIDAR-MEASURED FOREST CANOPY HEIGHT USING QUICKBIRD IMAGERY

机译：基于对象和基于像素的方法的比较Quickbird图像估计激光乐射森林冠层高度的方法

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Canopy surface height (CSH) is a significant forest biophysical parameter to estimate above-ground biomass and carbon content. High- spatial resolution optical remotely sensed data have shown promising results to delineate various forest biophysical properties; though few studies have evaluated the accuracy of forest height information from such data. In this study, we compare several strategies using high-resolution Quickbird imagery to estimate CSH measured from small-footprint lidar data in a forest scene. Two main approaches were tested: 1) geographic object-based image analysis (GEOBIA), where the areal units are the objects from a segmentation-derived partition, which are akin to forest patches; and 2) pixel-based, where the areal units used to estimate CSH are the cells of a grid-shaped partition, which are akin to square field plots. Multiple linear regression models between within areal unit spectral response and lidar-measured CSH were developed for these two types of approaches using various areal unit sizes (AUSs). The best results (derived from the optimal AUSs) illustrated a better fitting model employing the GEOBIA approach (R~(2) velence 0.605, RMSE velence 2.86 m) than the pixel-based approach (R~(2) velence 0.544, RMSE velence 2.97 m). To develop more representative models when using their optimal AUSs, texture (i.e., standard deviation, skewness and kurtosis) and tree-ray-shadow geometry were investigated and applied to GEOBIA and pixel-based approaches. For the GEOBIA approach, the addition of texture and tree-ray-shadow geometry explained more variance of lidar-measured CSH by 5 percent and 10 percent respectively. The best performance (R~(2) velence 0.739, RMSE velence 2.60 m) was achieved using the combination of all three types of variables. For the pixel-based approach, only slight improvements were made with the best result (R~(2) velence 0.577, RMSE velence 2.88 m) achieved using all types of variables in the regression analysis. The comparisons in this study illustrate the potential of using meaningful image-objects instead of traditional fixed-size square grids to achieve higher accuracies in estimating the vertical structure of tree canopies.
机译：冠层表面高度（CSH）是一种重要的森林生物物理参数，用于估计地上生物质和碳含量。高空间分辨率光远程感测数据显示出有前途的结果，以描绘各种森林生物物理性质;虽然很少有研究已经评估了这种数据的森林高度信息的准确性。在这项研究中，我们使用高分辨率Quickbird图像比较了几种策略，以估计从森林场景中的小型覆盖范围数据测量的CSH。测试了两种主要方法：1）基于地理对象的图像分析（Geobia），其中区域单位是来自分段衍生分区的对象，它们类似于森林斑块;和2）基于像素的，其中用于估计CSH的区域单元是网格形隔板的电池，其类似于方形场图。在使用各种区域尺寸（澳元）的这两种方法开发了在区域谱响应和激光雷达测量CSH之间的多个线性回归模型。最佳结果（来自最佳areuss）的结果示出了采用Geobia方法的更好的拟合模型（R〜（2）velence 0.605，Rmse velence 2.86 m）而不是基于像素的方法（R〜（2）velence 0.544，Rmse velence 2.97米）。在使用最佳奥斯的最佳奥斯，纹理（即标准偏差，偏斜和峰值）和树射线几何形状，并应用于巨大和基于像素的方法时，开发更多代表性模型。对于巨大方法，纹理和树射线几何形状的增加分别解释了激光雷达测量CSH的变化分别为5％和10％。使用所有三种类型的变量的组合，实现了最佳性能（R〜（2）velence 0.739，RMSE velence 2.60 m）。对于基于像素的方法，使用在回归分析中使用所有类型的变量实现的最佳结果（R〜（2）velence 0.577，Rmse Velence 2.88M）仅进行略微改进。本研究中的比较说明了使用有意义的图像对象而不是传统的固定尺寸方形网格的潜力，以实现估计树木檐篷的垂直结构的更高精度。

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