Watershed-scale mapping of fractional snow cover under conifer forest canopy using lidar
Watershed-scale mapping of fractional snow cover under conifer forest canopy using lidar
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DOI：
10.1016/j.rse.2018.11.037
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发表时间：
2019-03
影响因子：
13.5
通讯作者：
T. Kostadinov;R. Schumer;M. Hausner;K. Bormann;R. Gaffney;K. McGwire;T. Painter;S. Tyler;A. Harpold
中科院分区：
文献类型：
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作者：
T. Kostadinov;R. Schumer;M. Hausner;K. Bormann;R. Gaffney;K. McGwire;T. Painter;S. Tyler;A. Harpold
积雪的分布对于预测生态水文过程和支撑美国西部内华达州山脉的山区供水至关重要。许多重要的供水区被山地森林覆盖，这对融雪的数量和时间有重大影响。对雪-森林相互作用的现场观测的空间覆盖范围有限，使用光学传感器（如中分辨率成像分光仪）的遥感无法观测树冠层以下的积雪。在这项研究中，我们开发并验证了一种基于激光雷达的方法来检测积雪覆盖下的树冠，研究了部分积雪覆盖面积（fSCA）如何随地形变化，在开放与树冠下的地区，并开发了一个校正因子，可用于改善卫星衍生的fSCA产品。我们开发了我们的新方法，使用三个雪激光雷达飞越，并验证了它与原位分布式温度传感器（温度传感器）在Sagehen溪流域的观测在塞拉利昂内华达州，加州，美国。激光雷达分类的验证显示出85- 96%的良好一致性，包括高一致性和大量的回报在树冠下的位置。激光雷达的fSCA观测结果显示，在树冠下比在开放的位置，这是一致的相对温暖的温度和更大的长波辐射的雪消失。然而，与预期相反，具有高太阳照射（即高西南）的区域在树冠下表现出更高的fSCA。结果表明，k因子（林冠下fSCA与开阔fSCA之比）随西南方向和海拔而系统变化。使用该因子校正研究领域fSCA表明，k = 1的典型假设可能导致高估的偏倚高达~0.05（以fSCA为单位）。然而，在10和100 m的单个像素内，fSCA过度预测偏差对于较高的fSCA值可以是25-30%。虽然不确定性将减少使用更高的雪激光雷达点密度，我们的方法显示出希望，以改善典型的假设，雪消失是相同的，在树冠下，并在开放（k= 1）。我们的激光雷达为基础的方法在不同的地点与不同的气候，地形和植被结构的未来应用具有双重潜力，以扩大在复杂地形的雪森林相互作用的理解，并提高业务fSCA产品。
The distribution of snow cover is critical for predicting ecohydrological processes and underpins mountain water supplies in ranges like the Sierra Nevada in the Western United States. Many key water supply areas are covered by montane forests, which have substantial effects on the amount and timing of snowmelt. In-situ observations of snow-forest interactions have limited spatial coverage and remote sensing using optical sensors (e.g. MODIS) cannot observe snow cover below the canopy. In this study, we developed and verified a lidar-based method to detect snow cover under canopy, investigated how fractional snow covered area (fSCA) varies with topography in open versus under canopy areas and developed a correction factor that could be used to improve satellite-derived fSCA products. We developed our new method using three snow-on lidar overflights and verified it with in-situ distributed temperature sensor (DTS) observations at Sagehen Creek watershed in the Sierra Nevada, California, USA. DTS validation of lidar classifications showed excellent agreement at 85–96%, including high agreement and large number of returns in under canopy locations. The lidar-derived fSCA observations generally showed earlier snow disappearance under the canopy than in open positions, which is consistent with relatively warm temperatures and greater longwave radiation. However, in contrast to expectations, areas with high solar exposure (i.e. high southwestness) exhibited higher fSCA under the canopy. Results indicated that thekfactor (the ratio of under canopy fSCA to open fSCA) varied systematically with southwestness and elevation. Using this factor to correct the study domain fSCA indicated that the typical assumption thatk= 1 could lead to an up to ~0.05 bias (in fSCA units) towards overestimation. However, within 10 and 100-m individual pixels the fSCA overprediction bias can be 25–30% for higher fSCA values. Although uncertainty would be reduced using higher snow-on lidar point densities, our method shows promise to improve the typical assumption that snow disappearance is identical in under the canopy and in the open (k= 1). Future applications of our lidar-based method at different sites with varying climate, topography and vegetation structure has the dual potential to expand understanding of snow-forest interactions in complex terrain and improve operational fSCA products.