收稿：2016-06-01，
录用：2016-09-01，
纸质出版：2017-03
移动端阅览
积雪是冰冻圈的重要组成部分，因其在可见光波段的高反射率、低导热率的特性以及大面积的覆盖，成为全球辐射平衡的重要决定因子。在中纬度的干旱和半干旱山区，季节性的冰雪融水是春季河川径流的主要补给水源，山区积雪分布的变化对融雪期河流径流量的波动具有重要影响。当前的积雪产品在下垫面为山区林地时会低估积雪面积，从而影响了山区水文过程模拟的精度。本文基于Landsat OLI影像，采用归一化差值积雪指数NDSI和归一化差值林地积雪指数NDFSI相结合的方法，对春季融雪期的阿尔泰山区泰加林地进行积雪识别，并采用海拔高度、温度、以及对应的高分数据对提取结果进行了定量分析。结果表明，采用NDSI进行积雪识别时，山区林地的积雪会被大量漏分；对林地像元采用NDFSI阈值法可以区分林地中是否有积雪分布。NDSI和NDFSI相结合的积雪识别方法操作简单，不需要提供森林分布图等辅助数据，可以有效提高山区林地复杂环境下积雪制图的精度。
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=(
ρ
nir
–
ρ
swir
)/(
ρ
nir
+
ρ
swir
). 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
ρ
nir
of
>
0.11 were classified as snow
whereas those with NDSI of
>
0.4 but
ρ
nir
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.
Barnett T P, Dümenil L, Schlese U, Roeckner E and Latif M. 1989. The effect of Eurasian snow cover on regional and global climate variations. Journal of the Atmospheric Science, 46(5): 661–685
Buus-Hinkler J, Hansen B U, Tamstorf M P and Pedersen S B. 2006. Snow-vegetation relations in a high arctic ecosystem: inter-annual variability inferred from new monitoring and modeling concepts. Remote Sensing of Environment, 105(3): 237–247
曹云刚, 刘闯. 2006. 一种简化的MODIS亚像元积雪信息提取方法. 冰川冻土, 28(4): 562–567
Cao Y G and Liu C. 2006. A simplified algorithm for extracting subpixel snow cover information from MODIS data. Journal of Glaciology and Geocryology, 28(4): 562–567
程平, 潘存德, 寇福堂, 巴扎尔别克•阿斯勒汗, 谭卫平. 2008. 新疆喀纳斯旅游区森林群落数量分类与排序. 新疆农业大学学报, 31(6): 1–7
Cheng P, Pan C D, Kou F T, Bazhaerbieke•Asiliehan and Tan W P. 2008. Numerical classification and ordination of forest communities in Kanas Tourist District, Xinjiang. Journal of Xinjiang Agricultural University, 31(6): 1–7
Czyzowska-Wisniewski E H, van Leeuwen W J D, Hirschboeck K K, Marsh S E and Wisniewski W T. 2015. Fractional snow cover estimation in complex alpine-forested environments using an artificial neural network. Remote Sensing of Environment, 156: 403–417
Dankers R and De Jong S M. 2004. Monitoring snow-cover dynamics in northern Fennoscandia with SPOT VEGETATION images. International Journal of Remote Sensing, 25(15): 2933–2949
冯学智, 李文君, 柏延臣. 2000. 雪盖卫星遥感信息的提取方法探讨. 中国图象图形学报, 5(10): 836–839
Feng X Z, Li W J and Bai Y C. 2000. Research on the methods of obtaining satellite snowcover information. Journal of Image and Graphics, 5(10): 836–839
Hall D K, Riggs G A and Salomonson V V. 1995. Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sensing of Environment, 54(2): 127–140
Hall D K, Riggs G A, Salomonson V V, DiGirolamo N E and Bayr K J. 2002. MODIS snow-cover products. Remote Sensing of Environment, 83(1/2): 181–194
Hall D K and Riggs G A. 2007. Accuracy assessment of the MODIS snow products. Hydrological Processes, 21(12): 1534–1547
黄晓东, 郝晓华, 王玮, 冯琦胜, 梁天刚. 2012. MODIS逐日积雪产品去云算法研究. 冰川冻土, 34(5): 1118–1126
Huang X D, Hao X H, Wang W, Feng Q S and Liang T G. 2012. Algorithms for cloud removal in MODIS daily snow products. Journal of Glaciology and Geocryology, 34(5): 1118–1126
Jones H G, Pomeroy J W, Walker D A and Hoham R W. 2001. Snow Ecology: An Interdisciplinary Examination of Snow-Covered Ecosystems. Cambridge: Cambridge University Press: 1–264
Klein A G, Hall D K and Riggs G A. 1998. Improving snow cover mapping in forests through the use of a canopy reflectance model. Hydrological Processes, 12(10/11): 1723–1744
梁继, 张新焕, 王建. 2007. 基于NDVI背景场的雪盖制图算法探索. 遥感学报, 11(1): 85–93
Liang J, Zhang X H and Wang J. 2007. Exploration for the algorithm of snow cover mapping based on NDVI background field. Journal of Remote Sensing, 11(1): 85–93
林金堂, 冯学智, 肖鹏峰, 李晖. 2011. 天山典型区卫星雪盖时空特征研究. 冰川冻土, 33(5): 971–978
Lin J T, Feng X Z, Xiao P F and Li H. 2011. Spatial and temporal characteristics of satellite snow cover in a typical area of Tianshan mountains. Journal of Glaciology and Geocryology, 33(5): 971–978
陆恒, 魏文寿, 刘明哲, 韩茜, 洪雯. 2011. 中国天山西部季节性森林积雪雪层温度时空分布特征. 地理科学, 31(12): 1541–1548
Lu H, Wei W S, Liu M Z, Han X and Hong W. 2011. Spatial and Temporal Distributions of snow temperature in forest of the western Tianshan mountains, China. Scientia Geographica Sinica, 31(12): 1541–1548
Metsämäki S, Vepsäläinen J, Pulliainen J and Sucksdorff Y. 2002. Improved linear interpolation method for the estimation of snow-covered area from optical data. Remote Sensing of Environment, 82(1): 64–78
Metsämäki S, Mattila O P, Pulliainen J, Niemi K, Luojus K and Böttcher K. 2012. An optical reflectance model-based method for fractional snow cover mapping applicable to continental scale. Remote Sensing of Environment, 123: 508–521
Metsämäki S J, Anttila S T, Markus H J and Vepsäläinen J M. 2005. A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model. Remote Sensing of Environment, 95(1): 77–95
Rittger K, Painter T H and Dozier J. 2013. Assessment of methods for mapping snow cover from MODIS. Advances in Water Resources, 51: 367–380
Salinan M, Pulliainen J, Metsämäki S, Kontu A and Suokanerva H. 2009. The behaviour of snow and snow-free surface reflectance in boreal forests: Implications to the performance of snow covered area monitoring. Remote Sensing of Environment, 113(5): 907–918
Vikhamar D and Solberg R. 2003a. Subpixel mapping of snow cover in forests by optical remote sensing. Remote Sensing of Environment, 84(1): 69–82
Vikhamar D and Solberg R. 2003b. Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data. Remote Sensing of Environment, 88(3): 309–323
王建. 1999. 卫星遥感雪盖制图方法对比与分析. 遥感技术与应用, 14(4): 29–36
Wang J. 1999. Comparison and analysis on methods of snow cover mapping by using satellite remote sensing data. Remote Sensing Technology and Application, 14(4): 29–36
汪凌霄, 肖鹏峰, 冯学智. 2012. 天山典型林带积雪的多角度遥感识别. 遥感学报, 16(5): 1044–1053
Wang L X, Xiao P F and Feng X Z. 2012. Retrieving snow information in typical forest zone of Tianshan mountains from multi-angle imaging spetroradiometer data. Journal of Remote Sensing, 16(5): 1035–1053
Wang X Y, Wang J, Jiang Z Y, Li H Y and Hao X H. 2015. An effective method for snow-cover mapping of dense coniferous forests in the upper Heihe River Basin using landsat operational land imager data. Remote Sensing, 7(12): 17246–17257
Warren S G. 1982. Optical properties of snow. Reviews of Geophysics, 20(1): 67–89
延昊. 2004. NOAA16卫星积雪识别和参数提取. 冰川冻土, 26(3): 369–373
Yan H. 2004. Detecting snow and estimating snowpack parameters from NOAA16-AVHRR data. Journal of Glaciology and Geocryology, 26(3): 369–373
相关作者
相关机构
京公网安备11010802024621
