Watershed-scale mapping of fractional snow cover under conifer forest canopy using lidar
2019
Abstract The distribution of
snowcover is critical for predicting
ecohydrologicalprocesses 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
snowcover below the
canopy. In this study, we developed and verified a
lidar-based method to detect
snowcover under
canopy, investigated how fractional
snowcovered area (fSCA) varies with topography in open versus under
canopyareas and developed a correction factor that could be used to improve satellite-derived fSCA products. We developed our new method using three
snow-on
lidaroverflights and verified it with in-situ distributed temperature sensor (DTS) observations at Sagehen Creek watershed in the Sierra Nevada, California, USA. DTS validation of
lidarclassifications showed excellent agreement at 85–96%, including high agreement and large number of returns in under
canopylocations. The
lidar-derived fSCA observations generally showed earlier
snowdisappearance under the
canopythan in open positions, which is consistent with relatively warm temperatures and greater
longwaveradiation. 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
canopyfSCA 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 ~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
lidarpoint densities, our method shows promise to improve the typical assumption that
snowdisappearance is identical in under the
canopyand 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.
Keywords:
-
Correction
-
Source
-
Cite
-
Save
72
References
12
Citations
NaN
KQI