Watershed-scale mapping of fractional snow cover under conifer forest canopy using lidar

REMOTE SENSING OF ENVIRONMENT

卷:222

Article

Kostadinov, Tihomir S.^1,2  Schumer, Rina²  Hausner, Mark²  Bormann, Kat J.³  Gaffney, Rowan⁴  McGwire, Kenneth⁵  Painter, Thomas H.³  Tyler, Scott⁴  Harpold, Adrian A.⁶ 

[1] Calif State Univ San Marcos, Dept Liberal Studies, San Marcos, CA 92096 USA

[2] Desert Res Inst, Div Hydrol Sci, Reno, NV USA

[3] CALTECH, Jet Prop Lab, Pasadena, CA USA

[4] Univ Nevada, Dept Geol Sci & Engn, Reno, NV 89557 USA

[5] Desert Res Inst, Div Earth & Ecosyst Sci, Reno, NV USA

[6] Univ Nevada, Dept Nat Resources & Environm Sci, Reno, NV 89557 USA

关键词: Fractional snow cover;    Snow disappearance;    Snow ablation;    Conifer forests;    Mountain forests;    Lidar;    Distributed temperature sensing;    Snow-vegetation interactions;    Canopy correction for fSCA;    Sagehen experimental watershed;    Sierra Nevada;   

DOI  :  10.1016/j.rse.2018.11.037

来源: Elsevier

【 摘 要 】

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 the k factor (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 that k = 1 could lead to an up to similar 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.

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