[1]俞正祥,韩广忠,蔡体久*,等.基于GF-1遥感数据的大兴安岭林区雪水当量反演[J].南京林业大学学报(自然科学版),2017,41(03):105-111.[doi:10.3969/j.issn.1000-2006.2017.03.016]
 YU Zhengxiang,HAN Guangzhong,CAI Tijiu*,et al.Comparison between GF-1 data and Landsat-8 data in inversing SWE of the forest in the northern of Daxing’an Mountain[J].Journal of Nanjing Forestry University(Natural Science Edition),2017,41(03):105-111.[doi:10.3969/j.issn.1000-2006.2017.03.016]
点击复制

基于GF-1遥感数据的大兴安岭林区雪水当量反演/HTML
分享到:

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

卷:
41
期数:
2017年03期
页码:
105-111
栏目:
研究论文
出版日期:
2017-05-31

文章信息/Info

Title:
Comparison between GF-1 data and Landsat-8 data in inversing SWE of the forest in the northern of Daxing’an Mountain
文章编号:
1000-2006(2017)03-0105-07
作者:
俞正祥1韩广忠2蔡体久1*琚存勇1朱宾宾1
1. 东北林业大学林学院,黑龙江 哈尔滨 150040;
2,大兴安岭松岭林业局,黑龙江 松岭 165012
Author(s):
YU Zhengxiang1 HAN Guangzhong2 CAI Tijiu1* JU Cunyong1 ZHU Binbin1
1. School of Forestry, Northeast Forestry University, Harbin 150040, China;
2. Songling Forestry Bureau of Daxing’an Mountain, Songling 165012, China
关键词:
雪水当量 遥感数据 GF-1 Landsat-8 大兴安岭北部
Keywords:
snow water equivalent remote sense data GF-1 Landsat-8 northern Daxing’an Mountain
分类号:
S715-3
DOI:
10.3969/j.issn.1000-2006.2017.03.016
摘要:
【目的】以Landsat-8卫星数据为对照,探索国产GF-1卫星数据对林区积雪特征的估测能力,实现基于国产卫星对东北林区雪水水文过程的监测。【方法】以大兴安岭北部林区为研究区,结合同期的地面雪水当量野外观测数据,利用偏最小二乘回归与BP神经网络两种方法建立线性与非线性雪水当量反演模型。通过平均均方根误差(E^-RMSE)、平均相对均方根误差(r^-RMSE)和平均估测精度这3个评价指标对所建模型进行对比评价。同时,利用两种遥感数据建立的最优模型对研究区内雪水当量分布特征进行反演,并对反演结果进行对比分析。【结果】基于GF-1数据所建立的线性与非线性模型性能均略低于以Landsat-8数据构建的模型,其中GF-1数据最优反演模型精度为80.3%,较Landsat-8反演模型低1.6%; 基于GF-1数据反演的雪水当量值与Landsat-8的基本相同; 两类遥感数据反演得到的雪水当量在空间分布特征上基本一致,均反映了雪水当量与地形、植被及土地利用类型的高度相关性; 由于山地林区植被和地形复杂,并且春季升温过程中地面积雪日消融速率快,GF-1数据以其高空间与时间分辨率上的优势能够更好地对研究区雪水水文过程进行监测。【结论】国产GF-1卫星能够替代Landsat-8卫星成为对大兴安岭北部林区雪水水文过程监测的遥感数据源。
Abstract:
【Objective】 With a large number of independently developed satellites launched, China has made tremendous advancements in remote sensing. The successful launch of the GF-1 satellite indicates that the observation of earth has entered the “high-resolution epoch” in China. This study was conducted to monitor the snow hydrological process of forests based on data from this Chinese satellite. 【Methods】The accuracy of forest snow characteristics based on the Chinese GF-1 satellite data was estimated by comparison with Landsat-8 satellite data. The research site was the forest of the northern Daxing’an Mountains. Based on the two methods of PLS(partial least squares)and BP(back propagation)neural networks, the linear and nonlinear SWE(snow water equivalent)inversion models were built using synchronous ground SWE field observation data. The models were evaluated through the indicators of E-RMSE, r-RMSE and average estimation accuracy. The best models based on the two types of remote sensing data were used to determine the inverse of the distribution characteristics of the SWE in the study area.【Results】The results showed that the accuracy of the models established using GF-1 data was slightly lower than that of those established using Landsat-8 data. The highest accuracy of a GF-1 model was 80.3%, which was 1.6% lower than that of the equivalent Landsat-8 model. The inverse SWE values based on the data of GM-1 were little higher than that of Landsat-8. The inversed SWE from the two types of remote sensing data showed the same spatial distributions, which showed that the SWE is highly correlated with topography, vegetation and land use type. Both terrain and vegetation are complicated in mountain forests. Snow ablation is relatively fast in spring. Owing to a higher spatial and time resolution than that of Landsat-8, the GF-1 satellite is more efficient for monitoring snow hydrological processes. 【Conclusion】Our results indicated that data from the GF-1 satellite can replace Landsat-8 data in monitoring snow hydrological processes in the northern Daxing’an Mountains.

参考文献/References:

[1] AULT T W, CZAJKOWSKI K P, BENKO T, et al. Validation of the MODIS snow product and cloud mask using student and NWS cooperative station observations in the Lower Great Lakes Region[J]. Remote Sensing of Environment, 2006, 105(4): 341-353. DOI:10.1016/j.rse.2006.07.004.
[2] DIETZ A J, KUENZER C, GESSNER U, et al. Remote sensing of snow:a review of available methods [J]. International Journal of Remote Sensing, 2012, 33(13): 4094-4134. DOI:10.1080/01431161.2011.640964.
[3] 武黎黎,李晓峰,赵凯,等. 东北典型林区雪深反演算法的验证与分析[J]. 地球信息科学学报, 2014, 16(2):320-327. DOI:10.3724/SP.J.1047.2014.00320. WU L L, LI X F, ZHAO K, et al. Validation and analysis of snow depth inversion algorithm in northeast typical forest based on the FY3B-MWRI data [J]. Journal of Geo-information Science, 2014, 16(2):320-327.
[4] 李新,车涛. 积雪被动微波遥感研究进展[J]. 冰川冻土, 2007, 29(3): 487-496. LI X, CHE T. A review on passive microwave remote sensing of snow cover [J]. Journal of Glaciology and Geocryology, 2007, 29(3): 487-496.
[5] 孙之文,施建成,蒋玲梅,等. 被动微波遥感反演中国西部地区雪深、雪水当量算法初步研究[J]. 地球科学进展, 2006, 21(12): 1363-1369. SUN Z W, SHI J C, JIANG L M, et al. Development of snow depth and snow water equivalent algorithm in western China using passive microwave remote sensing data [J]. Advances in Earth Science, 2006, 21(12): 1363-1369.
[6] 刘艳,张璞,李杨. 新疆北疆地区雪水当量遥感定量研究[J]. 红外与毫米波学报, 2011, 30(2): 115-119. LIU Y, ZHANG P, LI Y. Quantitative analysis of snow water equivalent in the region of northern Xinjiang [J]. Journal of Infrared and Millimeter Waves, 2011, 30(2): 115-119.
[7] 贾玉秋,李冰,程永政,等. 基于GF-1与Landsat-8多光谱遥感影像的玉米LAI反演比较[J]. 农业工程学报, 2015, 31(9): 173-179. DOI:10.11975/j.issn.1002-6819.2015.09.027 JIA Y Q, LI B, CHENG Y Z, et al. Comparison between GF-1 images and Landsat-8 images in monitoring maize LAI [J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(9): 173-179.
[8] 徐涵秋,唐菲. 新一代Landsat系列卫星:Landsat-8遥感影像新增特征及其生态环境意义[J]. 生态学报, 2013, 33(11): 3249-3257. DOI:10.5846/stxb201305030912 XU H Q, TANG F. Analysis of new characteristics of the first Landsat-8 image and their eco-environmental significance [J]. Acta Ecologica Sinica, 2013, 33(11):3249-3257.
[9] 郑璞,邓正栋,关洪军,等. 基于TM和ETM+数据的玛纳斯河流域积雪时空分布特征研究[J]. 气象科学, 2014, 34(1): 39-46. DOI:10.3969 /2012jms.0177. ZHENG P, DENG Z D, GUAN H J, et al. Spatial and temporal variation characteristics of snow based on TM and ETM+ data in Manas River Basin[J]. Journal of the Meteorological Sciences, 2014, 34(1): 39-46.
[10] 罗韦慧,满秀玲,田野宏,等. 大兴安岭寒温带地区森林流域溪流水化学特征[J]. 水土保持学报, 2013, 27(5): 119-124. DOI:10.13870/j.cnki.stbcxb.2013.05.045. LUO W H, MAN X L, TIAN Y H, et al. Hydrochemical characteristics of streams of forest watershed in cold-temperate zone of Greater Xing’an Mountains, China [J]. Journal of Soil and Water Conservation, 2013, 27(5): 119-124.
[11] 李奕,满秀玲,蔡体久,等. 大兴安岭山地樟子松天然林土壤水分物理性质及水源涵养功能研究[J]. 水土保持学报, 2011, 25(2): 87-91+96. DOI:10.13870/j.cnki.stbcxb.2011.02.009. LI Y, MAN X L, CAI T J, et al. Research on physical properties of soil moisture and water conservation of scotch pine forest in Da Xing’an Mountains [J]. Journal of Soil and Water Conservation, 2011, 25(2): 87-91+96.
[12] 郝建亭,杨武年,李玉霞,等. 基于FLAASH的多光谱影像大气校正应用研究[J]. 遥感信息, 2008, 23(1): 78-81. HAO J T, YANG W N, LI Y X, et al. Atmospheric correction of multi-spectral imagery ASTER [J]. Remote Sensing Information, 2008, 23(1): 78-81.
[13] 黄晓东,郝晓华,杨永顺,等. 光学积雪遥感研究进展[J]. 草业科学, 2012(1): 35-43. HUANG X D, HAO X H, YANG Y S, et al. Advances in snow-cover monitoring using optical remote sensing [J]. Pratacultural Science, 2012(1): 35-43.
[14] 徐婷,曹林,佘光辉. 基于Landsat-8 OLI的特征变量优化提取及森林生物量反演[J]. 遥感技术与应用, 2015, 30(2): 226-234. DOI:10.11873/j.issn.1004-0323.2015.2.0226. XU T, CAO L, SHE G H. Feature extraction and forest biomass estimation based on Landsat-8 OLI [J]. Remote Sensing Technology and Application, 2015, 30(2): 226-234.
[15] DOZIER J. Spectral signature of alpine snow cover from the Landsat Thematic Mapper[J]. Remote Sensing of Environment, 1989, 28: 9-22. DOI: 10.1016/0034-4257(89)90101-6.
[16] XIAO X, SHEN Z, QIN X. Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: a normalized difference snow and ice index[J]. International Journal of Remote Sensing, 2001, 22(13): 2479-2487. DOI:10.1080/01431160010002902
[17] 李奕,蔡体久,盛后财,等. 大兴安岭地区天然樟子松林降雪截留及积雪特征[J]. 水土保持学报, 2014, 28(5): 124-128. DOI:10.13870/j.cnki.stbcxb.2014.05.022 LI Y, CAI T J, SHENG H C, et al. Characteristics of the snow interception and the snowpack in scotch pine forest in Great Xing’an Mountains [J]. Journal of Soil and Water Conservation, 2014, 28(5): 124-128.
[18] JOST G, WEILER M, GLUNS D R, et al. The influence of forest and topography on snow accumulation and melt at the watershed-scale[J]. Journal of Hydrology, 2007, 347(1): 101-115. DOI:10.1016/j.jhydrol.2007.09.006.
[19] 刘琼阁,彭道黎,涂云燕. 基于偏最小二乘回归的森林蓄积量遥感估测[J]. 中南林业科技大学学报, 2014, 34(2): 81-84. DOI:10.13870/j.cnki.stbcxb.2014.05.022. LIU Q G, PENG D L, TU Y Y. Estimation of forest stock volume based on partial least squares regression [J]. Journal of Central South University of Forestry & Technology, 2014, 34(2): 81-84.
[20] 竞霞,黄文江,琚存勇,等. 基于PLS算法的棉花黄萎病高空间分辨率遥感监测[J]. 农业工程学报, 2010, 26(8): 229-235. DOI:10.3969/j.issn.1002-6819.2010.08.039. JING X, HUANG W J, JU C Y, et al. Remote sensing monitoring severity level of cotton verticillium wilt based on partial least squares regressive analysis [J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(8): 229-235.
[21] CHONG I G, JUN C H. Performance of some variable selection methods when multicollinearity is present[J]. Chemometrics and Intelligent Laboratory Systems, 2005, 78(1): 103-112. DOI:10.1016/j.chemolab.2004.12.011.
[22] 樊彦国,刘金霞. 基于神经网络技术的遥感水深反演模型研究[J]. 海洋测绘, 2015, 35(4): 20-23. DOI:10.3969 /j.issn.1671-044.2015.04.005. FAN Y G, LIU J X. Water depth remote sensing retrieval model based on artificial neural network techniques [J]. Hydrographic Surveying and Charting, 2015, 35(4): 20-23.
[23] BROWNE M W. Cross-validation methods[J]. Journal of Mathematical Psychology, 2000, 44(1): 108-132. DOI:10.1006/jmps.1999.1279.
[24] 庞勇,李增元. 基于机载激光雷达的小兴安岭温带森林组分生物量反演[J]. 植物生态学报, 2012, 36(10): 1095-1105. DOI:10.3724/SP.J.1258.2012.01095. PANG Y, LI Z Y. Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, northeastern of China [J]. Chinese Journal of Plant Ecology, 2012, 36(10): 1095-1105.
[25] 李明泽,谢雨,邸雪颖,等. 大兴安岭林区地表可燃物载量遥感估测模型[J]. 东北林业大学学报, 2014, 42(5): 60-63+82. DOI:10.13759/j.cnki.dlxb.20140522.011. LI M Z, XIE Y, DI X Y, et al. Remote sensing model for forest surface fuel loads in Daxing’an Mountains [J]. Journal of Northeast Forestry University, 2014, 42(5): 60-63+82.
[26] 李辉东,关德新,吴家兵,等. 长白山阔叶红松林冬季雪面蒸发特征[J]. 应用生态学报, 2013, 24(4): 1039-1046. DOI:10.13287/j.1001-9332.2013.0278. LI H D, GUAN D X, WU J B, et al. Characteristics of evaporation over broadleaved Korean pine forest in Changbai Mountains, northeast China during snow cover period in winter [J]. Chinese Journal of Applied Ecology, 2013, 24(4): 1039-1046.

相似文献/References:

[1]耿 君,涂丽丽,吕春光,等.基于RIA技术的中国植被净初级生产力在线估算与查询系统设计[J].南京林业大学学报(自然科学版),2015,39(03):167.[doi:10.3969/j.issn.1000-2006.2015.03.030]
 GENG Jun,TU Lili,LYU Chunguang,et al.Developing an online estimation and query system of net primary productivity of China based on RIA technology[J].Journal of Nanjing Forestry University(Natural Science Edition),2015,39(03):167.[doi:10.3969/j.issn.1000-2006.2015.03.030]

备注/Memo

备注/Memo:
收稿日期:2016-05-09 修回日期:2016-10-03
基金项目:国家自然科学基金项目(31370460); 东北林业大学学术名师支持计划(PFT-1213-21)
第一作者:俞正祥(nefu_yzx@163.com), 博士生。*通信作者:蔡体久(caitijiu1963@163.com),教授。
引文格式:俞正祥,韩广忠,蔡体久,等. 基于GF-1遥感数据的大兴安岭林区雪水当量反演[J]. 南京林业大学学报(自然科学版),2017,41(3):105-111.
更新日期/Last Update: 2017-05-20