Abstract
Forest pests , wilting disease, wood cutting and phenological changes can affect vegetation health . The traditional method for pigment extraction followed by spectrophotometric determination or high-performance liquid chromatography (HPLC) will have to destroy the measured leaves with high costs and a long processing time. Using hyperspectral EO-1 Hyperion remote sensor imagery and a spectral model of leaf pigment reflectance , we examined the two forest health related important parameters, anthocyanin and carotenoid , in the Baiyun Mountain national forest park , China . The remote sensing -derived outcome was validated through in situ sample analyses of canopy leaves. The result shows that the concentrations of anthocyanin and carotenoid indicating the vegetation stress can be quantified using the reflectance index derived from hyperspectral remote sensor imagery. The index has the potential to indicate the regional forest vegetation health . Furthermore, the forest phenological information can be retrieved when multi-temporal hyperspectral images are available.
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References
Chinese Jingwei Network (2009). http://www.huaxia.com/gdtb/zjny/rwjg/2009/09/1593568.html. Accessed 9 Jan 2018
Cui HS, Jiang Y, Lin MZ, Jiang T, Ji ST (2015) Forest naturalness evaluation in Baiyun Mountain of Guangzhou. J Subtrop Resources Environ 10(1):18–26
Dobrota C, Lazar L, Baciu C (2015) Assessment of physiological state of Betula pendula and Carpinus betulus through leaf reflectance measurements. Flora—Morphol Distrib Funct Ecol Plants 216:26–34
Dolling A, Nilsson H, Lundell Y (2017) Stress recovery in forest or handicraft environments—an intervention study. Urban For Urban Green 27:162–172
Gamon J, Surfus J (1999) Assessing leaf pigment content and activity with a reflectometer. New Phytol 143:105–117
Gitelson A, Zur Y, Chivkunova O, Merzlyak M (2002) Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem Photobiol 75:272–281
Hu DQ, Su X (2000) Damage analysis of air pollution on the forest of Baiyun Mountain. Ecol Sci 19(3):67–72
Li YZ, Wei CH, Yi YH, Lu CC (2003) Investigation of insect species and control strategy of forest pests in Baiyunshan scenic spot, Guangzhou. J South China Agric Univ (Nat Sci Ed) 24(1):p34–p41
Lin MZ, Ji ST, Zhao JL, Fang BZ, Xie GW (2015) Comprehensive assessment of environmental quality of Baiyun Mountain scenic area. Ecol Sci 34(2):42–50
Ma ZB, Cheng Y (1984) Chemical determination of anthocyanin content on apple fruit surface. Chin Fruit Trees 4:49–51
Masek JG, Hayes DJ, Mj Hughes, Healey SP, Turner DP (2015) The role of remote sensing in process-scaling studies of managed forest ecosystems. For Ecol Manage 355:109–123
Peng SL, Fang W (1995) Dynamics of community composition structure of secondary evergreen broad leaved forest in Baiyunshan of Guangzhou. Chin Bull Botany 12(S2):p49–p54
Pu RL, Gong P (2004) Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping. Remote Sens Environ 91:212–224
Research Systems Inc. (2005) Envi User’s Guide
Sims D, Gamon J (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81(2–3):337–354
Su ZY, Chen BG, Gu YK, Xie ZS, Zeng SC (1996) Vegetation and main phytocommunity types of Baiyun Shan spot, Guangzhou. J South China Agr Univ 18(2):23–29
Su Z, Chen BG, Gu YK, Xie ZS, Zeng SC (2001) Species richness and diversity of forest communities in Baiyunshan, Guangzhou. J South China Agric Univ 22(3):5–8
Sun B, Mi J, Xie ZZ, Zhong F, Huang JP (1997) Spatial characteristic and future structure of urban forest in Guangzhou. Urban Environ Urban Ecol 2:50–54
Tong YA, Zhou HJ (1982) Fruit tree nutrition diagnosis. Agricultural Press, Beijing, pp p149–p150
Ye XL, Wen CY (2010) Study on the construction planning of biological protection forest belt in Baiyun Mountain Scenic Area. Prot Forest Sci Technol 5(98):101–104
Yu MM, Xie ZS (2011) Study on soil permeability capability of five forest types in Baiyunshan scenic spot of Guangzhou. Res Soil Water Conserv 18(1):153–156
Zeng F, Li XW, Chen HF (2013) Analysis on the characteristics of the planting design in typical scenic area of Guangzhou Baiyun Mountain. Landsc Plant Study Appl 1:49–54
Zeng SC, Xie ZS, Gu YK, Su ZY, Chen BG, Lin SH (2002) The biomass and water-holding capacities of some forest communities of Baiyunshan Scenic Spot, Guangzhou. J South China Agric Univ (Nat Sci Ed) 23(4):41–44
Zhou X, Huang WJ, Kong WP, Ye HC, Casa R (2017) Assessment of leaf carotenoids content with a new carotenoid index: development and validation on experimental and model data. Int J Appl Earth Obs Geoinf 57:24–35
Acknowledgements
This research was partly funded by Guangdong Province’s Science & Technology Plan Project (2016A020223011, 2018B030311059, 2015B070701020), and GDAS’ Special Project of Science and Technology Development (2017GDASCX-0101), Production-Education-Research Collaborative Innovation Major Project of Guangzhou Municipality (201604020117) and Guangzhou Yuexiu District Science and Technology Project (2016-GX-059).
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Chen, S., Chen, W., Liu, J. (2019). Hyperspectral Remote Sensing of Vegetation Health at the Baiyun Mountain National Forest Park, China. In: Yang, X., Jiang, S. (eds) Challenges Towards Ecological Sustainability in China. JFGES 2017. Springer, Cham. https://doi.org/10.1007/978-3-030-03484-9_6
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DOI: https://doi.org/10.1007/978-3-030-03484-9_6
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