Please wait a minute...
Journal of Arid Land  2019, Vol. 11 Issue (3): 332-344    DOI: 10.1007/s40333-019-0013-x
    
Hyper-spectral characteristics of rolled-leaf desert vegetation in the Hexi Corridor, China
Huaidong WEI1,2, Xuemei YANG2,3,*(), Bo ZHANG1, Feng DING2, Weixing ZHANG2, Shizeng LIU2, Fang CHEN2
1 College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China
2 State Key Laboratory Breeding Base of Desertification and Aeolian Sand Disaster Combating, Gansu Desert Control Research Institute, Lanzhou 730070, China
3 Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Download: HTML     PDF(522KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

Desert plants survive harsh environment using a variety of drought-resistant structural modifications and physio-ecological systems. Rolled-leaf plants roll up their leaves during periods of drought, making it difficult to distinguish between the external structures of various types of plants, it is therefore necessary to carry out spectral characteristics analysis for species identification of these rolled-leaf plants. Based on hyper-spectral data measured in the field, we analyzed the spectral characteristics of seven types of typical temperate zone rolled-leaf desert plants in the Hexi Corridor, China using a variety of mathematical transformation methods. The results show that: (1) during the vigorous growth period in July and August, the locations of the red valleys, green peaks, and three-edge parameters, namely, the red edge, the blue edge, and the yellow edge of well-developed rolled-leaf desert plants are essentially consistent with those of the majority of terrestrial vegetation types; (2) the absorption regions of liquid water, i.e., 1400-1500 and 1600-1700 nm, are the optimal bands for distinguishing various types of rolled-leaf desert plants; (3) in the leaf reflectance regions of 700-1250 nm, which is controlled by cellular structure, it is difficult to select the characteristic bands for differentiation rolled-leaf desert vegetation; and (4) after processing the spectral reflectance curves using a first-order differential, the envelope removal method, and the normalized differential ratio, we identify the other characteristic bands and parameters that can be used for identifying various types of temperate zone rolled-leaf desert plants, i.e., the 510-560, 650-700 and 1330-1380 nm regions, and the red edge amplitude. In general, the mathematical transformation methods in the study are effective tools to capture useful spectral information for species identification of rolled-leaf plants in the Hexi Corridor.



Key wordsrolled-leaf desert vegetation      spectral characteristics      mathematical transformation      vegetation identification      Hexi Corridor     
Received: 02 November 2017      Published: 10 June 2019
Corresponding Authors:
Cite this article:

Huaidong WEI, Xuemei YANG, Bo ZHANG, Feng DING, Weixing ZHANG, Shizeng LIU, Fang CHEN. Hyper-spectral characteristics of rolled-leaf desert vegetation in the Hexi Corridor, China. Journal of Arid Land, 2019, 11(3): 332-344.

URL:

http://jal.xjegi.com/10.1007/s40333-019-0013-x     OR     http://jal.xjegi.com/Y2019/V11/I3/332

[1] Adam E, Mutanga O.2009. Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6): 612-620.
[2] An R, Jiang D P, Li X X, et al.2014. Using hyperspectral data to determine spectral characteristics of grassland vegetation in central and eastern parts of Three-river Source. Remote Sensing Technology and Application, 29(2): 202-211. (in Chinese)
[3] Cao W, Shao Q Q, Yu X Y, et al.2013. Analysis of spectral characteristics of Inner Mongolia's temperature steppe in different use patterns. Acta Agrestia Sinica, 21(2): 243-252. (in Chinese)
[4] Fang M H, Ju W M, Zhan W F, et al.2017. A new spectral similarity water index for the estimation of leaf water content from hyperspectral data of leaves. Remote Sensing of Environment, 196: 13-27.
[5] Gai Y Y, Fan W J, Xu X R, et al.2011. Flower species identification and coverage estimation based on hyperspectral remote sensing data in Hulunbeir grasslasnd. Spectroscopy and Spectral Analysis, 31(10): 2778-2783. (in Chinese)
[6] Gates D M, Keegan H J, Schleter J C, et al.1965. Spectral properties of plants. Applied Optics, 4(4): 11-20.
[7] He L, An S Z, Jin G L, et al.2014. Analysis on high spectral characteristics of degraded Seriphidium transiliense desert grassland. Acta Agrestia Sinica, 22(2): 271-276. (in Chinese)
[8] Jin G L, He L, An S Z, et al.2014. Spectral features of eight desert range plants on degradation Seriphidium transiliense desert grassland. Pratacultural Science, 31(10): 1848-1858. (in Chinese)
[9] Jin J, Wang Q.2016. Hyperspectral indices based on first derivative spectra closely trace canopy transpiration in a desert plant. Ecological Informatics, 35: 1-8.
[10] Li X S.2008. Quantitative retrieval of sparse vegetation cover in arid regions using hyperspectral data. PhD Dissertation. Beijing: Chinese Academy of Forestry. (in Chinese)
[11] Liu N F, Budkewitsch P, Treitz P.2017. Examining spectral reflectance features related to Arctic percent vegetation cover: Implications for hyperspectral remote sensing of Arctic tundra. Remote Sensing of Environment, 192: 58-72.
[12] Mansour K, Mutanga O, Everson T, et al.2012. Discriminating indicator grass species for rangeland degradation assessment using hyperspectral data resampled to AISA Eagle resolution. ISPRS Journal of Photogrammetry and Remote Sensing, 70: 56-65.
[13] Mansour K, Mutanga O, Everson T.2013. Spectral discrimination of increaser species as an indicator of rangeland degradation using field spectrometry. Journal of Spatial Science, 58(1): 101-117.
[14] Meyer H, Lehnert L W, Wang Y, et al.2017. From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information? International Journal of Applied Earth Observation & Geoinformation, 55: 21-31.
[15] Mirzaie M, Darvishzadeh R, Shakiba A, et al.2014. Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements. International Journal of Applied Earth Observation and Geoinformation, 26: 1-11.
[16] Qian Y R, Yu J, Jia Z H, et al.2013. Extraction and analysis of hyper spectral data from typical desert grassland in Xinjiang. Acta Prataculturae Sinica, 22(1): 157-166. (in Chinese)
[17] Schmidt K S, Skidmore A K.2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment, 85(1): 92-108.
[18] Wei X H, Jin G L, Fan Y M, et al.2016. Species analysis and identification of spectral characteristics on Seriphidium transiliense desert grassland. Pratacultural Sciende, 33(10): 1924-1932. (in Chinese)
[19] Xia X W, Jin G L, An S Z, et al.2015. Spectral characteristics of typical plants in Seriphidium transiliense desert grassland under enclosure. Pratacultural Science, 32(6): 870-876. (in Chinese)
[20] Yang K, Shen W S, Bo L.2014. Research on spectral reflectance characteristics for Naqu typical grassland. Remote Sensing Technology and Application, 29(1): 40-45. (in Chinese)
[21] Yang X M, Liu S Z, Yang T B, et al.2016. Spatial-temporal dynamics of desert vegetation and its responses to climatic variations over the last three decades: a case study of Hexi region in Northwest China. Journal of Arid Land, 8(4): 556-568.
[22] Zarco-Tejada P J, Hornero A, Hernández-Clemente R, et al.2018. Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 137: 134-148.
[23] Zhao J, Chen X, Guli J, et al.2009. Spectral discrimination of desert vegetation in the Tarim River basin. Journal of Desert Research, 29(2): 270-278. (in Chinese)
[24] Zhao Z, Li X, Yin Y B, et al.2010. Analysis of spectral features based on water content of desert vegetation. Spectroscopy and Spectral Analysis, 30(9): 2500-2503. (in Chinese)
[25] Zhao Z.2011. Analysis of spectral features based on water content of Xinjiang desert plants. MSc Thesis. Xinjiang: Xinjiang Agricultural University. (in Chinese)
[26] Zhang C M, Zhang J M.2012. Research on the spectral characteristics of grassland in arid regions based on hyperspectral image. Spectroscopy and Spectral Analysis, 32(2): 445-448.
[27] Zhang F, Tashpolat T, Ding J L, et al.2012. Spectral reflectance characteristics of typical halophytes in the oasis salinization desert zone of middle reaches of Tarim River, China. Chinese Journal of Plant Ecology, 36(7): 607-617. (in Chinese)
[28] Zhang K, Guo N, Wang R Y, et al.2006. Research on spectral reflectance characteristics for desert meadow of Northwest China. Advances in Earth Science, 21(10): 1063-1069. (in Chinese)
[29] Zhou L P, Wei H D, Ding F, et al.2013. Analysis on spectral reflectance characteristics of desert plants in Minqin basin of Shiyang River. Journal of Arid Land Resources and Environment, 27(3): 121-125. (in Chinese)
[1] Xiangdong LI, Ming'an SHAO, Chunlei ZHAO, Xiaoxu JIA. Spatial variability of soil water content and related factors across the Hexi Corridor of China[J]. Journal of Arid Land, 2019, 11(1): 123-134.
[2] GAO Liming, ZHANG Yaonan. Spatio-temporal variation of hydrological drought under climate change during the period 1960–2013 in the Hexi Corridor, China[J]. Journal of Arid Land, 2016, 8(2): 157-171.