An Adaptive Snow Identification Algorithm in the Forests of Northeast China
An Adaptive Snow Identification Algorithm in the Forests of Northeast China
复制标题
东北森林自适应积雪识别算法
DOI：
10.1109/jstars.2020.3020168
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发表时间：
2020-08
影响因子：
5.5
通讯作者：
Wang Jian
中科院分区：
文献类型：
--
作者：
Wang Xiaoyan;Chen Siyong;Wang Jian
东北地区是我国主要的冰雪覆盖区之一，森林覆盖率超过40%。森林积雪识别是一个具有挑战性的问题，SNOMAP算法往往低估了积雪覆盖量的森林地区较低的归一化差异雪指数。本文提出了一种改进的基于陆地卫星业务成像仪的积雪识别方法。一个改进包括使用归一化差异森林积雪指数（NDFSI）来区分积雪覆盖和无雪森林。根据归一化差异植被指数，确定了不同森林类型的NDFSI阈值。另一方面，在高纬度的东北地区，冬季太阳高度很低，阴影区的积雪由于其较低的近红外反射率，在现有的SNOMAP算法中通常被归类为液态水。然后，另一个改进是引入地表温度，这是从热红外波段检索，以区分液态水和雪在阴影区。应用改进后的方法对大兴安岭、小兴安岭和长白山地区不同季节的森林面积进行了评价。总分类精度达到97.5%，引入遗漏误差和委托误差的像元主要分布在茂密的森林阴影区。这种改进方法保留了SNOMAP算法在非森林地区的计算简单性和有效性，并改善了森林地区和阴影地区积雪覆盖的低估。
Northeast China is one of the primary snow-covered regions, and its forest coverage is over 40%. Forest snow identification is usually a challenging problem, and the SNOMAP algorithm tends to underestimate the amount of snow cover in forest regions for the lower normalized difference snow index. In this article, an improved method of the snow-cover identification based on the Landsat operational land imager is proposed. One improvement includes using the normalized difference forest snow index (NDFSI) to discriminate between snow-covered and snow-free forests. The threshold value of the NDFSI in different forest types is set according to the normalized difference vegetation index. On the other hand, the sun elevation is very low in winter in Northeast China with high latitude; as a result, the snow in shadow areas is usually classified as liquid water for its low near-infrared reflectance in the current SNOMAP algorithm. Then, another improvement is introducing the land surface temperature, which is retrieved from the thermal infrared band to distinguish liquid water from snow in shadow areas. We applied this improved method to evaluate forest areas in the Daxinganling, Xiaoxinganling, and Changbai Mountain areas in different seasons. The total classification accuracy reached 97.5%, and the pixels that introduce omission error and commission error were mainly distributed in areas of dense forest shadows. This improved method retains the computational simplicity and effectiveness of the SNOMAP algorithm in nonforest areas and improves the underestimation of snow cover in forest regions and shadow areas.
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影响因子：
13.5
作者：
Czyzowska-Wisniewski, Elzbieta H.;van Leeuwen, Willem J. D.;Wisniewski, Wit T.
通讯作者：
Wisniewski, Wit T.
基于多索引技术的复杂山地森林环境积雪测绘
DOI：
10.1109/jstars.2018.2810094
发表时间：
2018
影响因子：
5.5
作者：
Wang Xiaoyan;Wang Jian;Che Tao;Huang Xiaodong;Hao Xiaohua;Li Hongyi
通讯作者：
Li Hongyi
DOI：
--
发表时间：
2002-07
期刊：
--
影响因子：
--
作者：
D. Hall;R. Solberg;G. Riggs
通讯作者：
D. Hall;R. Solberg;G. Riggs
影响因子：
4.9
作者：
R. Wu;B. Kirtman
通讯作者：
R. Wu;B. Kirtman
影响因子：
13.5
作者：
Sari J. Metsämäki;S. Anttila;Huttunen J. Markus;J. Vepsäläinen
通讯作者：
Sari J. Metsämäki;S. Anttila;Huttunen J. Markus;J. Vepsäläinen