Reconstructing Digital Terrain Models from ArcticDEM and WorldView-2 Imagery in Livengood, Alaska
Reconstructing Digital Terrain Models from ArcticDEM and WorldView-2 Imagery in Livengood, Alaska
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DOI：
10.3390/rs15082061
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发表时间：
2023-04
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影响因子：
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通讯作者：
Tianqi Zhang;Desheng Liu
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作者：
Tianqi Zhang;Desheng Liu
ArcticDEM为公众提供了一个前所未有的机会，可以访问覆盖泛北极表面的非常高空间分辨率的数字高程模型（DEM）。由于它是从光学卫星图像的立体对生成的，ArcticDEM代表了非地面区域的数字表面模型（DSM）和裸露地面的数字地形模型（DTM）的混合物。因此，在需要裸露地面高程的研究中，需要从ArcticDEM重建DTM，如水文过程建模，跟踪地表变化动态，估计植被冠层高度和相关的森林属性。在这里，我们提出了一个自动化的方法来估计DTM从ArcticDEM的两个步骤：（1）识别地面像素从WorldView-2图像使用高斯混合模型（GMM）与局部细化形态操作，（2）生成一个连续的DTM表面使用ArcticDEM在地面位置和空间插值方法（普通克里金（OK）和自然邻居（NN））。我们评估了我们的方法在三个森林研究地点，其特征在于不同的树冠覆盖和地形条件Livengood，阿拉斯加，机载激光雷达数据可用于验证。实验结果表明：（1）该方法能有效地识别地物像元，且均方根误差（RMSE）小得多（2）NN算法在DTM插值中的鲁棒性优于OK算法;（3）利用基于GMM的地面掩膜进行NN插值生成的DTM将Site-1、Site-2和Site-3的ArcticDEM的RMSE分别降低到0.648 m、1.677 m和0.521 m，分别本研究提供了一个可行的方法，从ArcticDEM获得高分辨率的DTM，这将是非常有价值的研究集中在北极生态系统，森林变化动力学，地球表面过程。
ArcticDEM provides the public with an unprecedented opportunity to access very high-spatial resolution digital elevation models (DEMs) covering the pan-Arctic surfaces. As it is generated from stereo-pairs of optical satellite imagery, ArcticDEM represents a mixture of a digital surface model (DSM) over a non-ground areas and digital terrain model (DTM) at bare grounds. Reconstructing DTM from ArcticDEM is thus needed in studies requiring bare ground elevation, such as modeling hydrological processes, tracking surface change dynamics, and estimating vegetation canopy height and associated forest attributes. Here we proposed an automated approach for estimating DTM from ArcticDEM in two steps: (1) identifying ground pixels from WorldView-2 imagery using a Gaussian mixture model (GMM) with local refinement by morphological operation, and (2) generating a continuous DTM surface using ArcticDEMs at ground locations and spatial interpolation methods (ordinary kriging (OK) and natural neighbor (NN)). We evaluated our method at three forested study sites characterized by different canopy cover and topographic conditions in Livengood, Alaska, where airborne lidar data is available for validation. Our results demonstrate that (1) the proposed ground identification method can effectively identify ground pixels with much lower root mean square errors (RMSEs) (<0.35 m) to the reference data than the comparative state-of-the-art approaches; (2) NN performs more robustly in DTM interpolation than OK; (3) the DTMs generated from NN interpolation with GMM-based ground masks decrease the RMSEs of ArcticDEM to 0.648 m, 1.677 m, and 0.521 m for Site-1, Site-2, and Site-3, respectively. This study provides a viable means of deriving high-resolution DTM from ArcticDEM that will be of great value to studies focusing on the Arctic ecosystems, forest change dynamics, and earth surface processes.