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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图像估计激光雷达测量的森林冠层高度

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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：冠面高度（CSH）是估计的地上生物量和碳含量显著森林的生物物理参数。高空间分辨率的光学遥感数据已显示有希望的结果来描绘各种森林生物物理特性;虽然很少有研究评估了由这些数据的森林高度信息的准确性。在这项研究中，我们比较了使用高分辨率QuickBird卫星影像来自小尺寸激光雷达在森林场景数据测量估计CSH几种策略。两种主要的方法进行了测试：1）的地理基于对象的图像分析（GEOBIA），其中面单元从一个分割衍生分区，它们是类似于森林补丁对象;和2）基于像素的，其中用于估计CSH的面单元是是类似于正方形田间小区的网格状分区的细胞，。面积单元光谱响应与激光雷达测量CSH内之间多元线性回归模型用于这两种类型的使用各种面积单元尺寸（AUSs）方法开发的。最好的结果（从最佳AUSs派生）所示采用比基于像素的方法（R〜（2）Velence的0.544的GEOBIA方法（R〜（2）Velence的0.605，RMSE Velence的2.86米）更好的拟合模型，RMSE韦伦采2.97米）。要使用它们的最佳AUSs，纹理时开发更代表性的车型（即，标准偏差，偏斜度和峰度）和树射线阴影几何进行了研究并应用于GEOBIA和基于像素的方法。对于GEOBIA方法中，除了纹理和树射线阴影的几何形状由5％和10％分别解释激光雷达测量的CSH的更方差。使用所有这三种类型的变量的组合达到最佳性能（R〜（2）0.739 Velence的，RMSE Velence的2.60米）。对于基于像素的方法中，只有轻微的改进，用最好的结果（R〜（2）0.577 Velence的，RMSE Velence的2.88米）由取得了使用所有类型的回归分析的变量。本研究中的比较示出了使用图像有意义的对象，而不是传统的固定大小的正方形网格在估计树冠的垂直结构来实现更高的精度的潜力。

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