基于HJ-1A卫星数据的高寒草地氮素评估-以青海省贵南县及玛沁县高寒草地为例

doi:10.11686/cyxb2015576

草业学报 ›› 2016, Vol. 25 ›› Issue (10): 11-20.DOI: 10.11686/cyxb2015576

基于HJ-1A卫星数据的高寒草地氮素评估-以青海省贵南县及玛沁县高寒草地为例

高金龙¹, 孟宝平¹, 杨淑霞¹, 冯琦胜¹, 崔霞², 梁天刚^{1, *}

1.草地农业生态系统国家重点实验室,兰州大学草地农业科技学院,甘肃兰州 730020;
2.兰州大学西部环境教育部重点实验室,甘肃兰州 730000

收稿日期:2015-12-28 出版日期:2016-10-20 发布日期:2016-10-20
通讯作者: E-mail:tgliang@lzu.edu.cn
作者简介:高金龙(1991-),男,甘肃永昌人,在读硕士。E-mail:rslabjinlong@163.com
基金资助:
国家自然科学基金项目(31372367,41401472)和青海省科技支撑项目(2013-N-146-4)资助

Estimation of nitrogen content of alpine grassland in Maqin and Guinan Counties, Qinghai Province, using remote sensing

GAO Jin-Long¹, MENG Bao-Ping¹, YANG Shu-Xia¹, FENG Qi-Sheng¹, CUI Xia², LIANG Tian-Gang^{1, *}

1.State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China;
2.National Laboratory of Western China’s Environmental System, Lanzhou University, Lanzhou 730000, China

Received:2015-12-28 Online:2016-10-20 Published:2016-10-20

摘要/Abstract

摘要： 基于HJ-1A HSI卫星遥感资料,结合2014年青海省玛沁县、贵南县研究区高寒草地野外实测数据,建立了高寒草地氮素含量的估测模型,并对模型进行了精度评价,筛选出最优反演模型,分析了研究区高寒草地氮素的空间分布。结果表明,1)高寒草地HSI的原始光谱反射率、一阶导数光谱反射率及去包络线光谱反射率与地面实测氮素含量有一定的相关性,特别是吸收特征波段1(750.95~791.95 nm)和吸收特征波段3(889.03~921.30 nm)与氮素含量具有显著的相关性;2)基于吸收特征波段1构建的波段深度指数BD_767.99可以较好的估测研究区高寒草地氮素含量,利用此变量拟合的线性回归模型可以解释研究区高寒草地氮素含量变化的44%,模型估测精度可达81.6%;3)贵南研究区氮素含量的空间变异较大,整体而言,西北部和西南部地区氮素含量较高,东北部地区氮素含量较低;玛沁研究区氮素含量的空间变异较小,草地氮素含量水平整体较低。

Abstract: Using data sourced from a hyper-spectrum imager (HSI) from an environmental mitigation satellite (HJ-1A) and ground observation during 2014 in Maqin and Guinan Counties, Qinghai Province, inversion models were established for estimating the nitrogen (N) content of alpine grassland. The optimal inversion model was selected and the spatial distribution map of grassland N was analyzed in the study area. The results showed that there was a correlation between N and the original spectral reflectance, the first-order differential spectral reflectance and the continuum removed spectral reflectance of the original hyper-spectral image, and significant correlations between grassland N content and absorption feature band one (750.95-791.95 nm) and absorption feature band three (889.03-921.3 nm) were also found. The band depth index BD767.99 was able to estimate N content with the greatest accuracy; the linear model was able to explain over 44% of the variation in N content. The spatial variation of N content in Guinan County was large; generally N content was higher in northwest and southwest than in the northeast. The spatial variation of grassland N content in Maqin County was low and grassland N content was also low.

高金龙, 孟宝平, 杨淑霞, 冯琦胜, 崔霞, 梁天刚. 基于HJ-1A卫星数据的高寒草地氮素评估-以青海省贵南县及玛沁县高寒草地为例[J]. 草业学报, 2016, 25(10): 11-20.

GAO Jin-Long, MENG Bao-Ping, YANG Shu-Xia, FENG Qi-Sheng, CUI Xia, LIANG Tian-Gang. Estimation of nitrogen content of alpine grassland in Maqin and Guinan Counties, Qinghai Province, using remote sensing[J]. Acta Prataculturae Sinica, 2016, 25(10): 11-20.

参考文献

[1] Xu P, Hu Z Z, Zhu J Z, et al . Grassland Resource Survey and Planning[M]. Beijing: China Agriculture Press, 2000.
[2] Dube T, Mutanga O. Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 101: 36-46.
[3] Gu Y, Wylie B K. Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations. Remote Sensing of Environment, 2015, 171: 291-298.
[4] Mutanga O, Skidmore A K, van Wieren S. Discriminating tropical grass ( Cenchrus ciliaris ) canopies grown under different nitrogen treatments using spectroradiometry. ISPRS Journal of Photogrammetry and Remote Sensing, 2003, 57(4): 263-272.
[5] Mutanga O, Skidmore A K, Kumar L, et al . Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain. International Journal of Remote Sensing, 2005, 26(6): 1093-1108.
[6] Mutanga O, Skidmore A K. Red edge shift and biochemical content in grass canopies. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 62(1): 34-42.
[7] Mutanga O, Skidmore A K. Hyperspectral band depth analysis for a better estimation of grass biomass ( Cenchrus ciliaris ) measured under controlled laboratory conditions. International Journal of Applied Earth Observation and Geoinformation, 2004, 5(2): 87-96.
[8] Ramoelo A, Cho M A, Mathieu R, et al . Estimating grass nutrients and biomass as an indicator of rangeland (forage) quality and quantity using remote sensing in Savanna ecosystems[C]//9th International Conference of the African Association of Remote Sensing and the Environment. Eljadida, Morocco: AARSE, 2012.
[9] Knox N M, Skidmore A K, Prins H H T, et al . Dry season mapping of savanna forage quality, using the hyperspectral Carnegie Airborne Observatory sensor. Remote Sensing of Environment, 2011, 115(6): 1478-1488.
[10] Kokaly R F, Clark R N. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 1999, 67(3): 267-287.
[11] Ramoelo A, Cho M A, Mathieu R, et al . Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data. International Journal of Applied Earth Observation and Geoinformation, 2014, 43: 43-54.
[12] Wang X, Liu S J, Jia H F, et al . Study on the nutrition of alpine meadow based on hyperspectral data. Spectroscopy and Spectral Analysis, 2012, 32(10): 2780-2784.
[13] Foulkes J N, White I A, Sparrow B D, et al . AusPlots-Rangelands monitoring site stratification and survey methods within TERN (Terrestrial Ecosystem Research Network)[R]. Discussion Paper, Terrestrial Ecosystem Research Network, Adelaide, 2011.
[14] Tong Q X, Zhang B, Zheng L F. Hyperspectral Remote Sensing-Theory, Technology and Application[M]. Beijing: Higher Education Press, 2006.
[15] Bai J W, Zhao Y C, Zhang B, et al . Study on the classification methods of the hyperspectral image based on the continuum removed. Computer Engineering and Applications, 2003, 39(13): 88-90.
[16] Pu R L, Gong P. Hyperspectral Remote Sensing and Application[M]. Beijing: Higher Education Press, 2000.
[17] Wang X Z, Huang J F, Li Y M, et al . The study on multi-spectral remote sensing estimation models about LAI of rice. Remote Sensing Technology and Application, 2003, 18(2): 57-65.
[18] Fu Y B. Main Physiological Indicators of Hyperspectral Inversion in Alfalfa Seed Production[D]. Vrumchi: Xinjiang Agricultural University, 2013.
[19] Ramoelo A, Skidmore A K, Cho M A, et al . Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 82: 27-40.
[20] Mutanga O. Hyperspectral Remote Sensing of Tropical Grass Quality and Quantity[D]. Enschede: Wageningen University, 2004: 111.
[21] Clevers J, Büker C. Feasibility of the red edge index for the detection of nitrogen deficiency[C]//Proceedings of the 5th International Colloquium - Physical Measurements and Signatures in Remote Sensing, Courchevel. France: European Space Agency, 1991.
[22] 许鹏, 胡自治, 朱进忠, 等.草地资源调查规划学[M]. 北京: 中国农业出版社, 2000.
[23] 王迅, 刘书杰, 贾海峰, 等. 基于高光谱数据的高寒草地营养状况的研究. 光谱学与光谱分析, 2012, 32(10): 2780-2784.
[24] 童庆禧, 张兵, 郑兰芬. 高光谱遥感: 原理, 技术与应用[M]. 北京: 高等教育出版社, 2006.
[25] 白继伟, 赵永超, 张兵, 等. 基于包络线消除的高光谱图像分类方法研究. 计算机工程与应用, 2003, 39(13): 88-90.
[26] 浦瑞良, 宮鹏. 高光谱遥感及其应用[M]. 北京: 高等教育出版社, 2000.
[27] 王秀珍, 黄敬峰, 李云梅, 等. 水稻叶面积指数的多光谱遥感估算模型研究. 遥感技术与应用, 2003, 18(2): 57-65.
[28] 付彦博. 制种紫花苜蓿主要生理指标高光谱反演[D]. 乌鲁木齐: 新疆农业大学, 2013.

基于HJ-1A卫星数据的高寒草地氮素评估-以青海省贵南县及玛沁县高寒草地为例

Estimation of nitrogen content of alpine grassland in Maqin and Guinan Counties, Qinghai Province, using remote sensing

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