干旱区研究

干旱区研究 >

2025 , Vol. 42 >Issue 1: 141 - 153

DOI: https://doi.org/10.13866/j.azr.2025.01.13

生态与环境

基于高光谱反射特征的荒漠类型可分性评价

  • 刘志飞 ,
  • 杨雪梅 ,
  • 王景瑞 ,
  • 黄轲盼 ,
  • 徐浩杰
展开
  • 1.兰州大学草种创新与草地农业生态系统全国重点实验室，农业农村部草牧业创新重点实验室，草地农业教育部工程研究中心，草地农业科技学院，甘肃 兰州 730020
    2.兰州文理学院旅游学院，甘肃 兰州 730010
    3.甘肃省治沙研究所，甘肃 兰州 730070
    4.兰州大学资源环境学院，甘肃 兰州 730000
    5.兰州大学寒旱区生态环境遥感研究中心，甘肃 兰州 730000
刘志飞（2001-），男，硕士研究生，主要从事干旱区生态环境遥感监测. E-mail: liuzhf2023@lzu.edu.cn
徐浩杰. E-mail: xuhaojie@lzu.edu.cn

收稿日期: 2024-08-14

  修回日期: 2024-10-21

  网络出版日期: 2025-01-17

基金资助

国家重点研发计划(2020YFA0608401);国家自然科学基金(32060373);甘肃省自然科学基金项目(22JR5RA766);甘肃省自然科学基金项目(23JRRA1048);中央高校基本科研业务费专项资金(lzujbky-2022-27)

收起

Class separability evaluation of desert types based on the hyperspectral reflectance characteristics

  • LIU Zhifei ,
  • YANG Xuemei ,
  • WANG Jingrui ,
  • HUANG Kepan ,
  • XU Haojie
Expand
  • 1. State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, School of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, Gansu, China
    2. Tourism School, Lanzhou University of Arts and Science, Lanzhou 730010, Gansu, China
    3. Gansu Desert Control Research Institute, Lanzhou 730070, Gansu, China
    4. School of Resources and Environment, Lanzhou University, Lanzhou 730000, Gansu, China
    5. Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, Gansu, China

Received date: 2024-08-14

  Revised date: 2024-10-21

  Online published: 2025-01-17

Fold

摘要

本研究采用裸土高光谱反射曲线细节所提取的特征变量评价不同荒漠类型可分性，选择石羊河下游盐漠、砾漠、泥漠和沙漠为研究对象，运用累积差值、一阶微分、包络线去除、植被指数计算、主成分分析法等，辨别不同荒漠类型的高光谱反射特征，提取关键分类变量，量化不同荒漠类型区分度。结果表明：（1） 各荒漠类型在446~600 nm和2150~2285 nm处存在差异明显的吸收谷。（2） Carter指数1、绿度指数（GI）、绿色归一化植被指数（GNDVIh2）等在不同荒漠类型间存在显著差异。（3） 改进叶绿素吸收指数（MCARI）、土壤调整植被指数（SAVI）、2265 nm与1790~1810 nm反射率在主成分指标构建中的权重值较大。（4） 各荒漠类型区分度：沙漠&盐漠>沙漠&泥漠>泥漠&盐漠>砾漠&盐漠>沙漠&砾漠>泥漠&砾漠。研究结果可为西北干旱区荒漠遥感监测提供地面验证和数据支持。

关键词： 荒漠土壤; 高光谱特征波段; 特征提取; 主成分分析; 类别可分性

本文引用格式

刘志飞 , 杨雪梅 , 王景瑞 , 黄轲盼 , 徐浩杰 . 基于高光谱反射特征的荒漠类型可分性评价[J]. 干旱区研究, 2025 , 42(1) : 141 -153 . DOI: 10.13866/j.azr.2025.01.13

Abstract

Few studies have used the characteristic variables extracted from the details of the hyperspectral reflectance curves of bare soil to evaluate the separability of various desert types. In this study, salt desert, gravel desert, mud desert, and desert in the lower reaches of the Shiyang River were used as the research objects, and cumulative difference, first-order differentiation, continuum removal, vegetation index calculation and principal component analysis were used to identify the hyperspectral reflectance features of various desert types, extract the key categorical variables, and quantify the degree of differentiation of various desert types. The results showed that (1) the absorption valleys at 446-600 nm and 2150-2285 nm differed significantly among the desert types. (2) the Carter index 1, Greenness Index, and Green NDVI hyper 2 differed significantly among the desert types. (3) The Modified Chlorophyll Absorption Ratio Index, Soil Adjusted Vegetation Index, and 2265 nm and 1790-1810 nm reflectance had larger weight values in constructing the principal component indexes; and (4) the differentiation index of each desert type: desert & salty desert>desert & muddy desert>muddy & salty desert>gravelly & salty desert>desert & gravelly desert>mud & gravelly desert. These findings provide ground verification and data support for the remote sensing monitoring of deserts in the northwest Arid Zone.

Key words： desert soil; hyperspectral feature band; feature extraction; principal component analysis; class separability

参考文献

[1] 惠静夷, 孙鹏, 甄石, 等. 浅谈土壤分类对地类认定的借鉴意义[J]. 国土资源, 2015(7): 46-47.
  [Hui Jingyi, Sun Peng, Zhen Shi, et al. An introduction to the significance of soil classification for land category identification[J]. Land & Resources, 2015(7): 46-47. ]
[2] 吴吉龙, 杨发相, 周杰, 等. 策勒河流域荒漠类型特征研究[J]. 干旱区地理, 2013, 36(5): 803-811.
  [Wu Jilong, Yang Faxiang, Zhou Jie, et al. Desert types and characteristics in the Qira River Basin[J]. Arid Land Geography, 2013, 36(5): 803-811. ]
[3] 李小雨, 贾科利, 魏慧敏, 等. 基于随机森林算法的土壤含盐量预测[J]. 干旱区研究, 2023, 40(8): 1258-1267.
  [Li Xiaoyu, Jia Keli, Wei Huimin, et al. Prediction of soil salt content based on the random forest algorithm[J]. Arid Zone Research, 2023, 40(8): 1258-1267. ]
[4] 夏媛媛, 冯全, 杨森, 等. 基于高光谱遥感的苹果园土壤水分估测研究[J]. 林业机械与木工设备, 2023, 51(4): 24-32.
  [Xia Yuanyuan, Feng Quan, Yang Sen, et al. Estimation of apple soil moisture based on hyperspectral remote sensing[J]. Forestry Machinery & Woodworking Equipment, 2023, 51(4): 24-32. ]
[5] 刘潜, 王梦迪, 郭龙, 等. 基于机载高光谱影像的农田尺度土壤有机碳密度制图[J]. 遥感学报, 2024, 28(1): 293-305.
  [Liu Qian, Wang Mengdi, Guo Long, et al. Mapping of soil organic carbon density at farmland scale based on airborne hyperspectral images[J]. National Remote Sensing Bulletin, 2024, 28(1): 293-305. ]
[6] 吴艳花, 赵恒谦, 毛继华, 等. 典型铅锌矿区土壤重金属含量高光谱反演模型研究[J]. 光谱学与光分析, 2024, 44(6): 1740-1750.
  [Wu Yanhua, Zhao Hengqian, Mao Jihua, et al. Study on hyperspectral inversion modeling of soil heavy metals in typical lead-zinc mining areas[J]. Spectroscopy and Spectral Analysis, 2024, 44(6): 1740-1750. ]
[7] 戴昌达. 中国主要土壤光谱反射特性分类与数据处理的初步研究//遥感文选[M]. 北京: 科学出版社, 1981.
  [Dai Changda. A Preliminary Study on the Classification and Data Processing of Spectral Reflectance Properties of Major Soils in China//Selected Papers on Remote Sensing[M]. Beijing: Science Press, 1981. ]
[8] 黄应丰, 刘腾辉. 华南主要土壤类型的光谱特性与土壤分类[J]. 土壤学报, 1995, 48(1): 58-68.
  [Huang Yingfeng, Liu Tenghui. Spectral characteristics of main types of soils in southern China and soil classification[J]. Acta Pedologica Sinica, 1995, 48(1): 58-68. ]
[9] 刘焕军, 张小康, 张新乐, 等. 面向土壤分类的高光谱反射特征参数模型[J]. 遥感学报, 2017, 21(1): 105-114.
  [Liu Huanjun, Zhang Xiaokang, Zhang Xinle, et al. Hyperspectral reflectance characteristics parameter extraction for soil classification model[J]. National Remote Sensing Bulletin, 2017, 21(1): 105-114. ]
[10] 陈芳, 魏怀东, 周兰萍, 等. 石羊河流域下游民勤县典型荒漠土壤光谱特征分析[J]. 中国农学通报, 2013, 29(5): 183-186.
  [Chen Fang, Wei Huaidong, Zhou Lanping, et al. Spectral characteristics of typical desert soil in Minqin County[J]. Chinese Agricultural Science Bulletin, 2013, 29(5): 183-186. ]
[11] 刘春晓, 吴静, 李纯斌, 等. 基于MODIS的甘肃省土壤遥感分类[J]. 草原与草坪, 2018, 38(6): 83-88.
  [Liu Chunxiao, Wu Jing, Li Chunbin, et al. MODIS data-based soil classification and mapping in Gansu Province[J]. Grassland and Turf, 2018, 38(6): 83-88. ]
[12] Lu Yanli, Bai Youlu, Yang Liping, et al. Hyper-spectral characteristics and classification of farmland soil in northeast of China[J]. Journal of Integrative Agriculture, 2015, 14(22): 2521-2528.
[13] Kovacevic Milos, Bajat Branislav, Gajic Bosko. Soil type classification and estimation of soil properties using support vector machines[J]. Geoderma, 2010, 154(3): 340-347.
[14] Pham Binh Thai, Nguyen Manh Duc, Nguyen-Thoi Trung, et al. A novel approach for classification of soils based on laboratory tests using Adaboost, Tree and ANN modeling[J]. Transportation Geotechnics, 2021, 27: 100508.
[15] 刘焕军, 张柏, 张渊智, 等. 基于反射光谱特性的土壤分类研究[J]. 光谱学与光谱分析, 2008, 28(3): 624-628.
  [Liu Huanjun, Zhang Bo, Zhang Yuanzhi, et al. Soil taxonomy on the basis of reflectance spectral characteristics[J]. Spectroscopy and Spectral Analysis, 2008, 28(3): 624-628. ]
[16] 刘焕军, 孟祥添, 王翔, 等. 反射光谱特征的土壤分类模型[J]. 光谱学与光谱分析, 2019, 39(8): 2481-2485.
  [Liu Huanjun, Meng Xiangtian, Wang Xiang, et al. Soil classification model based on the characteristics of soil reflectance spectrum[J]. Spectroscopy and Spectral Analysis, 2019, 39(8): 2481-2485. ]
[17] 曹晓明, 史建康, 冯益明, 等. 几种典型荒漠植物冠层光谱特征比较研究[J]. 西北林学院学报, 2021, 36(1): 45-53.
  [Cao Xiaoming, Shi Jiankang, Feng Yiming, et al. Comparative study on canopy spectral characteristics of several typical desert plants[J]. Journal of Northwest Forestry University, 2021, 36(1): 45-53. ]
[18] 陈政融, 杨雪梅, 唐进年, 等. 黄河源区玛曲县不同沙化程度高寒草地光谱特征分析[J]. 草业科学, 2023, 40(11): 2751-2762.
  [Chen Zhengrong, Yang Xuemei, Tang Jinnian, et al. Spectral characteristics of alpine grassland with different degrees of desertification in Maqu County, the source region of the Yellow River[J]. Pratacultural Science, 2023, 40(11): 2751-2762. ]
[19] 何挺, 王静, 林宗坚, 等. 土壤有机质光谱特征研究[J]. 武汉大学学报(信息科学版), 2006, 31(11): 975-979.
  [He Ting, Wang Jing, Lin Zongjian, et al. Spectral features of soil organic matter[J]. Geomatics and Information Science of Wuhan University, 2006, 31(11): 975-979. ]
[20] 李丹, 彭智平, 韩留生, 等. 基于土壤反射光谱特性的广东省稻田土壤快速分类[J]. 热带地理, 2015, 35(1): 29-34.
  [Li Dan, Peng Zhiping, Han Liusheng, et al. Rapid soil classification of paddy field in Guangdong Province based on visible and near infrared reflectance spectra[J]. Tropical Geography, 2015, 35(1): 29-34. ]
[21] 杨芳, 陈冬花, 李虎, 等. 新疆博州典型荒漠植被光谱反射特征分析[J]. 遥感信息, 2016, 31(3): 88-93.
  [Yang Fang, Chen Donghua, Li Hu, et al. Analysis on spectral reflectance characteristics of typical desert vegetation in Bozhou of Xinjiang[J]. Remote Sensing Information, 2016, 31(3): 88-93. ]
[22] 王志杰, 柳书俊, 彭海兰, 等. 基于包络线去除法的贵州省常见树种高光谱特征分析[J]. 山地农业生物学报, 2020, 39(5): 15-22.
  [Wang Zhijie, Liu Shujun, Peng Hailan, et al. Analysis of hyperspectral characteristics of common tree species in Guizhou Province based on envelope removal method[J]. Journal of Mountain Agriculture and Biology, 2020, 39(5): 15-22. ]
[23] 李喆, 郭旭东, 古春, 等. 高光谱吸收特征参数反演草地光合有效辐射吸收率[J]. 遥感学报, 2016, 20(2): 290-302.
  [Li Zhe, Guo Xudong, Gu Chun, et al. Application of spectral absorption characteristic parameters inversing FAPAR in natural grassland[J]. National Remote Sensing Bulletin, 2016, 20(2): 290-302. ]
[24] 单姝瑶. 祁连山国家公园生态承载力的评估与预测[D]. 兰州: 兰州大学, 2024.
  [Shan Shuyao. Assessment and Prediction of Ecological Carrying Capacity in Qilian Mountain National Park[D]. Lanzhou: Lanzhou University, 2024. ]
[25] Vasques G M, Dematte J A M, Viscarra Rossel, et al. Soil classification using visible/near-infrared diffuse reflectance spectra from multiple depths[J]. Geoderma, 2014, 223: 73-78.
[26] 王一谋, 高鹏恂. 河西走廊沙漠反射光谱分析[J]. 中国沙漠, 1984, 4(2): 33-38.
  [Wang Yimou, Gao Pengxun. The analysis of reflective spectrum of desert in Hexi Corridor[J]. Journal of Desert Research, 1984, 4(2): 33-38. ]
[27] 秦忠, 郭凯. 亚马雷克沙漠沙丘不同部位沙粒粒度及光谱特性研究[J]. 内蒙古水利, 2015(1): 18-19.
  [Qin Zhong, Guo Kai. Study on grain size and spectral characteristics of sand particles in different parts of Amarek Desert dunes[J]. Inner Mongolia Water Resources, 2015(1): 18-19. ]
[28] 魏怀东, 李亚, 张勃, 等. 甘肃河西地区荒漠化土地光谱特征研究[J]. 光谱学与光谱分析, 2019, 39(11): 3508-3513.
  [Wei Huaidong, Li Ya, Zhang Bo, et al. Spectral characteristics of desertification land in Gansu Hexi Corridor[J]. Spectroscopy and Spectral Analysis, 2019, 39(11): 3508-3513. ]
[29] 曾庆猛, 孙宇瑞, 严红兵. 土壤质地分类的近红外光谱分析方法研究[J]. 光谱学与光谱分析, 2009, 29(7): 1759-1763.
  [Zeng Qingmeng, Sun Yurui, Yan Hongbing. NIR spectral analysis for soil textural classification[J]. Spectroscopy and Spectral Analysis, 2009, 29(7): 1759-1763. ]
[30] 曹晓阳, 穆悦, 曹晓明, 等. 基于高光谱数据的戈壁地表砾石粒径反演研究[J]. 干旱区地理, 2017, 40(2): 397-404.
  [Cao Xiaoyang, Mu Yue, Cao Xiaoming, et al. Grain size retrieving of Gobi surface based on hyperspectral data[J]. Arid Land Geography, 2017, 40(2): 397-404. ]
[31] Alavipanah S K, Damavandi A, Mirzaei Saham, et al. Remote sensing application in evaluation of soil characteristics in desert areas[J]. Natural Environment Change, 2016, 2(1): 1-24.
Options
文章导航

/

〈 〉