365932 Fine Scale Variability in Vegetation Cover over the Southern Great Plains Using High Resolution Satellite Images: A Case Study

Tuesday, 14 January 2020
Hall B1 (Boston Convention and Exhibition Center)
Duli Chand, PNNL, Richland, WA; and L. K. Berg, J. Tagestad, C. N. Long, A. A. Matthews, S. L. Tai, Z. Yang, and J. D. Fast

Land surface processes in earth system are strongly controlled by surface type and vegetation cover (VC). Understanding vegetation cover is useful for climate and environmental applications in Earth System models, where it can impact surface to atmosphere mass and radiation transfer, radiative forcing, boundary-layer process, crop and forest management, land cover changes from harvesting, deforestation, fires, and diseases. VC is also a good indicator of the start of the growing and harvesting season in the land surface component of the typical Earth system model. Here we present high resolution satellite image-analysis based approach for quantifying vegetation cover and linking it to surface albedo characteristics from airborne observations during the US Department of Energy’s ‘Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems’ (HI-SCALE) campaign over the Southern Great Plains (SGP) in 2016. We employ two approaches from high resolution (10-20 m) satellite images: Classification of surface cover using (i) RGB frames with contrast enhancement via a histogram equalization approach and (ii) use of multiple satellite bands to apportion vegetation from land surface. The satellite retrieved vegetation cover is used to link with surface albedo from aircraft, satellite and the Weather Research and Forecasting (WRF) model. Our initial analysis indicates that the vegetation cover retrieved by medium resolution (250-500 m) satellite underestimates values derived to the high-resolution satellite images over a 50 km SGP domain. A case study to quantify vegetation cover and link it to surface albedo, a key parameter for controlling the Earth’s radiation budget, will be discussed in the AMS presentation.
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