Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

Anaí Caparó Bellido,Bradley C Rundquist

doi:10.3390/rs13112045

Abstract

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.

Highlights

While the spatial and temporal dynamics of snow cover are important variables in climatological and hydrological studies because of their relationships with fluxes of environmental energy and mass, they confound satellite-based efforts to monitor vegetation phenology, often the purpose of time-series analysis of remotely sensed data.Phenology is the science of recording natural events to identify changes in seasonal and annual cycles of plants and animals
Establishing an accurate and precise record of vegetation phenology across expansive geographic regions to validate satellite observations is the primary purpose of a system of terrestrial cameras comprising the PhenoCam Network [2,4,5,11]
The third and fourth scenarios, exploited the relationship between Monochromatic RGB+NIR (MoNIR) and the Blue band calculated by: Fractional Snow Cover (FSC) from PhenoCam data, we developed two additional scenarios

Summary

Introduction

While the spatial and temporal dynamics of snow cover are important variables in climatological and hydrological studies because of their relationships with fluxes of environmental energy and mass, they confound satellite-based efforts to monitor vegetation phenology, often the purpose of time-series analysis of remotely sensed data.Phenology is the science of recording natural events to identify changes in seasonal and annual cycles of plants and animals. Remote sensing makes possible frequent, repeat monitoring of vegetation canopies, especially in difficult-to-access locations. Researchers use indexes such as the Normalized. PhenoCams are fixed terrestrial webcams taking frequent, repeat oblique pictures that are used to monitor vegetation phenology and other changes in land surfaces, including snow cover dynamics [7,14]. These systems are low-cost [15], and there are 393 cameras currently located across the U.S [16]

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