首页> 外文会议>Asian conference on remote sensing;ACRS >ACCURACY ASSESSMENT OF GLOBAL TOPOGRAPHIC DATA (SRTM ASTER GDEM) IN COMPARISON WITH LIDAR FOR TROPICAL MONTANE FOREST
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ACCURACY ASSESSMENT OF GLOBAL TOPOGRAPHIC DATA (SRTM ASTER GDEM) IN COMPARISON WITH LIDAR FOR TROPICAL MONTANE FOREST

机译:热带山地森林的全球地形数据(SRTM和ASTER GDEM)与激光雷达的准确性评估

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Shuttle Radar Topographic Mission (SRTM) and ASTER Global Digital Elevation Model (GDEM) provide topographic data in a global scale which are freely available for users. The potential use of these datasets for many forestry applications is highly depending on the accuracy of the datasets. In this study, we evaluated the accuracy of SRTM and ASTER GDEM version 2 with high accuracy topographic data of Light Detection and Ranging (LiDAR) acquired using Riegl LMS-Q560 sensor. This study is conducted in tropical montane forest area of approximately 3,600 hectare, Malaysian Borneo. We resampled both the SRTM (90m resolution) and ASTER GDEM (30m resolution) with bilinear interpolation and cubic convolution method to one, two and five meter pixel resolutions. The evaluation was divided into two sites; site 1 (2,100 ha) and site 2 (1,500 ha). The SRTM (SD=9.0m-10.4m; RMSE=9.3m-10.6m) was found to produce better topographic data in comparison with ASTER GDEM (SD=16.9m-18.7m; RMSE=17.0m-19.3m). Our result revealed that resampling using cubic convolution performs only slightly better than bilinear interpolation method (when compared for all SD and RMSE values) for SRTM. Resampling to higher spatial resolution (i.e. 1m) did not influence significantly the performance (when compared for all mean, standard deviation and RMSE values).
机译:穿梭雷达地形任务(SRTM)和ASTER全球数字高程模型(GDEM)提供了全球范围内的地形数据,可供用户免费使用。这些数据集在许多林业应用中的潜在用途在很大程度上取决于数据集的准确性。在这项研究中,我们使用由Riegl LMS-Q560传感器获得的光检测和测距(LiDAR)的高精度地形数据,评估了SRTM和ASTER GDEM版本2的准确性。这项研究是在马来西亚婆罗洲约3600公顷的热带山地森林地区进行的。我们使用双线性插值和三次卷积方法将SRTM(90m分辨率)和ASTER GDEM(30m分辨率)重新采样到一,二和五米像素分辨率。评估分为两个地点:站点1(2,100公顷)和站点2(1,500公顷)。发现与ASTER GDEM(SD = 16.9m-18.7m; RMSE = 17.0m-19.3m)相比,SRTM(SD = 9.0m-10.4m; RMSE = 9.3m-10.6m)可产生更好的地形数据。我们的结果表明,对于SRTM,使用三次卷积的重采样仅比双线性插值方法(与所有SD和RMSE值相比)稍好。重新采样到较高的空间分辨率(即1m)不会显着影响性能(与所有均值,标准差和RMSE值相比)。

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