An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data

Xiao-Yan Wang,Xiao-Hua Hao,Jian Wang,Zhi-Yong Jiang,Hong-Yi Li

doi:10.3390/rs71215882

Xiao-Yan Wang, Xiao-Hua Hao + Show 3 more

Open Access

https://doi.org/10.3390/rs71215882

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Journal: Remote sensing	Publication Date: Dec 18, 2015
Citations: 50	License type: CC BY 4.0

Affiliation: Lanzhou University, Chinese Academy of Sciences

Abstract

The Normalized Difference Snow Index (NDSI) is an effective index for snow-cover mapping at large scales, but in forested regions the identification accuracy for snow using the NDSI is low because of forest cover effects. In this study, typical evergreen coniferous forest zones on Qilian Mountain in the Upper Heihe River Basin (UHRB) were chosen as example regions. By analyzing the spectral signature of snow-covered and snow-free evergreen coniferous forests with Landsat Operational Land Imager (OLI) data, a novel spectral band ratio using near-infrared (NIR) and shortwave infrared (SWIR) bands, defined as (ρnir − ρswir)/(ρnir + ρswir), is proposed. Our research shows that this band ratio, named the normalized difference forest snow index (NDFSI), can be used to effectively distinguish snow-covered evergreen coniferous forests from snow-free evergreen coniferous forests in UHRB.

Highlights

Snow plays an important role for regional climate change, the quality of the environment, and the daily lives of humans
Tpahgee–rpeafgoere, we propose a new spectral band ratio with bands NIR and shortwave infrared (SWIR), as shown in Equation (2), called the Normalized Difference Forest Snow Index (NDFSI). snow in the forest
Vikhamar and Solberg proposed a method for subpixel mapping of snow cover in forests [10], and it was tested in flat terrain covered by spruce, pine and birch forests, close to the Jotunheimen region of south Norway

Summary

Introduction

Snow plays an important role for regional climate change, the quality of the environment, and the daily lives of humans. Estimates of snow-covered areas and the snow depth/water equivalent are important parameters for regional climate change studies, agriculture, and water resource management. Over the past several decades, satellite remote sensing has been widely used for monitoring snow cover because it enables observations of large and remote areas [5,6,7,8,9]. Among these remote sensing techniques, microwave images are difficult to interpret, and their low spatial resolution makes passive microwave sensors suitable primarily for global snow monitoring [10].

Study Area

Landsat OLI Images

Ancillary D8ata

Experiment and Verification

Discussion

Conclusions