سابِق|تال

Combination of NDSI and NDFSI for snow cover mapping in a mountainous and forested region

  • role： First author第一作者
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

    Cold & Arid Region Environmental & Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China

  • Email：wangxiaoy@lzu.edu.cn
  • Introduction：王晓艳(1973—　)，女，讲师，研究方向为积雪遥感研究，E-mail：wangxiaoy@lzu.edu.cn
WANG Xiaoyan12,  
  • Affiliation：

    Cold & Arid Region Environmental & Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China

    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

WANG Jian23,  
  • Affiliation：

    Cold & Arid Region Environmental & Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China

LI Hongyi2,  
  • Affiliation：

    Cold & Arid Region Environmental & Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China

HAO Xiaohua2

ملخص

Snow is one of the important parts of the crysphere, because its high reflectivity and low thermal conductivity can directly affect the ground and air temperatures, albedo of the Earth’s surface, and soil moisture. In mid-latitude arid and semi-arid mountainous regions, seasonal melt are mainly supplies the spring runoff. The changes of snow distribution in mountainous regions exert an important influence on the fluctuation in river runoffs during the snowmelt season. The currently used snow products in mountainous and forested regions exhibit low accuracy when simulating mountainous hydrological processes. Thus, this research aims to develop a highly accurate snow cover mapping method for complex environments, such as mountainous and forested regions.Normalized Difference Snow Index (NDSI) based on the spectral characteristics of snow is extensively used in mapping snow cover at the global scale. However, NDSI presents a small value and discrete distribution in snow-covered forests. Thus, it cannot effectively identify snow in forested areas. Meanwhile, Normalized Difference Forest Snow Index (NDFSI) is based on the combination of the spectral features of snow and forest and defined as NDFSI=(ρnir–ρswir)/(ρnir+ρswir). In this study, a snow-cover mapping method that combines the NDSI and NDFSI was used for snow extraction in the taiga forest of Altay Mountains from the Landsat OLI image acquired in spring. First, the NDSI was used in all the pixels. The pixels with NDSI of >0.4 and ρnir of >0.11 were classified as snow, whereas those with NDSI of >0.4 but ρnir of ≤0.11 were classified as water. In this step, most snow without shielding can be recognized, and forest snow is usually not recognized because of the shielding by the forest crown. Second, NDFSI was used in pixels with NDSI of ≤0.4, and the pixels with NDFSI exceeding 0.4 were classified as forest with snow. Pixels with NDFSI of less than 0.4 were defined as forest without snow.The snow extraction result shows that snow in the no-forest region can be extracted using a reasonable NDSI threshold value. However, the snow in the forested region cannot be recognized with NDSI alone. In NDSI, the snow-covered area is underestimated in the forested-region. Thus, NDSI and NDFSI were combined, and the snow extraction result was evaluated using DEM data, temperature inversion result, and GF-1 image. Result showed that the average altitude of the forest with snow is 1611 m, and the average altitude of the forest without snow is 1278 m. The reason is that snow in lower altitudes melts earlier than snow in higher altitudes during the snowmelt season. The average temperature of forest with snow and forest without snow are 6.8 ℃ and 15.3 ℃, respectively. These temperatures are consistent with those obtained from the field. In the GF-1 image with 2 m spatial resolution can indicate the presence of snow in a forest. According to the obtained GF-1 image, the snow extraction in this study is highly accurate, and thus most snow in forest can be extracted correctly.NDFSI outperforms NDSI in the extraction of snow in forested areas. The accuracy of snow-cover mapping in complex mountainous and forested environments can be improved considerably by combining NDSI and NDFSI. Moreover, this approach can be applied easily without using other auxiliary data, such as forest maps.

مفهوم

remote sensing;snow cover mapping;mountainous forest;NDSI;NDFSI

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