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.

中文翻译：

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东北森林自适应积雪识别算法


东北地区是主要雪区之一，森林覆盖率超过40%。森林积雪识别通常是一个具有挑战性的问题，并且由于归一化差异积雪指数较低，SNOMAP算法往往会低估森林区域的积雪量。本文提出了一种基于Landsat业务陆地成像仪的积雪识别改进方法。其中一项改进包括使用标准化差异森林积雪指数（NDFSI）来区分积雪森林和无雪森林。根据归一化植被指数差异设定不同森林类型NDFSI的阈值。另一方面，高纬度的东北地区，冬季太阳高度角很低；因此，在当前的SNOMAP算法中，阴影区域的雪因其近红外反射率较低而通常被归类为液态水。然后，另一项改进是引入从热红外波段检索的陆地表面温度，以区分阴影区域的液态水和雪。我们应用该改进方法对大兴安岭、小兴安岭和长白山地区不同季节的森林面积进行了评价。总分类准确率达到97.5%，引入遗漏误差和委托误差的像素点主要分布在茂密的森林阴影区域。该改进方法保留了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问题。