References

1. 1.
   Almeida C T D, Galvão L S , Ometto J P H B., Jacon A D, de Souza Pereira F R, Sato L Y, Lopes A P, de Alencastro Graça P M L, de Jesus Silva C V and Ferreira-Ferreira J. 2019. Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms. Remote sensing of environment, 232: 111323
2. 2.
   Berni J, Zarco-Tejada P J, Suarez L, Fereres E. 2009. Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle. IEEE Trans. Geo. Remote Sensing, 47(3): 722-738
3. 3.
   Bian L M and Zhang H C. 2020. Application of phenotyping techniques in forest tree breeding and precision forestry. Scientia Silvae Sinicae, 56(6): 113-126
4. 4.
   Blanco V, Blaya Ros P J, Castillo C, Soto F, Torres R and Domingo R. 2020. Potential of UAS-Based Remote Sensing for Estimating Tree Water Status and Yield in Sweet Cherry Trees. Remote Sensing, 12(15): 2359
5. 5.
   Brestic M and Zivcak M. 2013. PSII Fluorescence Techniques for Measurement of Drought and High Temperature Stress Signal in Crop Plants: Protocols and Applications. Molecular Stress Physiology of Plants. Heidelberg: Springer Dordrecht: 87-131
6. 6.
   Buddenbaum H, Rock G, Hill J and Werner W. 2015. Measuring Stress Reactions of Beech Seedlings with PRI, Fluorescence, Temperatures and Emissivity from VNIR and Thermal Field Imaging Spectroscopy. European Journal of Remote Sensing, 48: 263-282
7. 7.
   Buddenbaum H, Stern O, Stellmes M, Stoffels J, Pueschel P, Hill J and Werner W. 2012. Field imaging spectroscopy of beech seedlings under dryness stress. Remote Sensing, 4(12): 3721-3740
8. 8.
   Cao C X, Xu M, He Q S and Zhang Y. 2009. The trend of applying multi-source remote sensing data into forest health studies. The Journal of Remote Sensing, 13(s1): 401-407
9. 9.
   Cao L, Shen G H, Dai J S and Xu J X. 2013. Status and prospects of the LiDAR-based forest biomass estimation. Journal of Nanjing Forestry University (Natural Sciences Edition), 37(3): 163-169. (曹林, 佘光辉, 代劲松,徐建新 (2013). 激光雷达技术估测森林生物量的研究现状及展望. 南京林业大学学报(自然科学版), 37: 163-169)
10. 10.
    Cao L P. 2015. The research progresson machine recognition of plant diseases and insect pests. Chinese Agricultural Science Bulletin, 31(20): 244-249
11. 11.
    Cao Q, Miao Y X, Wang H Y, Huang S Y, Cheng S S, Khosla R and Jiang R F. 2013. Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research, 154: 133-144
12. 12.
    Cardona A and Tomancak P. 2012. Current challenges in open-source bioimage informatics. Nature Methods, 9(7): 661-665
13. 13.
    Chen F, Su S C, Zhang B and Wang W H. 2014. A preliminaery study on nitrogen status diagnosis using digital image processing of hybrid hazel. Journal of Northwest Forestry University, 30(5): 111-114
14. 14.
    Chéné Y, Rousseau D, Lucidarme P, Bertheloot J, Caffier V, Morel P, Belin É and Chapeau-Blondeau F O. 2012. On the use of depth camera for 3D phenotyping of entire plants. Computers & Electronics in Agriculture, 82: 122-127
15. 15.
    Cheng L Z, Zhu X C, Gao L L, Wang L and Zhao G X. 2016. Hyperspectral estimation of phosphorus content for apple leaves based on the random forest model. Journal of Fruit Science, 33(10): 1219-1229
16. 16.
    Cheng T, Riaño D, Koltunov A, Whiting M L, Ustin S L and Rodriguez J. 2013. Detection of diurnal variation in orchard canopy water content using MODIS/ASTER airborne simulator (MASTER) data. Remote sensing of environment, 132: 1-12
17. 17.
    Cheng T, Riaño D and Ustin S L. 2014a. Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis. Remote sensing of environment, 143: 39-53
18. 18.
    Cheng T, Rivard B, Sánchez-Azofeifa A G, Féret J B, Jacquemoud S and Ustin S L. 2014b. Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 28-38
19. 19.
    Cheng T, Rivard B, Sánchez-Azofeifa G, Feng J and Calvo-Polanco M. 2010. Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sensing of Environment, 114(4): 899-910
20. 20.
    Cheng T, Rivard B and Sanchez-Azofeifa A. 2011. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sensing of Environment, 115: 659-670
21. 21.
    Choi S T, Park D S, Kang S M and Park S J. 2011. Use of a chlorophyll meter to diagnose nitrogen status of ‘Fuyu’persimmon leaves. HortScience, 46(5): 821-824
22. 22.
    Cohen Y, Alchanatis V, Prigojin A, Levi A, Soroker V and Cohen Y. 2012. Use of aerial thermal imaging to estimate water status of palm trees. Precision Agriculture, 13(1): 123-140
23. 23.
    Croft H, Chen J, Zhang Y and Simic A. 2013. Modelling leaf chlorophyll content in broadleaf and needle leaf canopies from ground, CASI, Landsat TM 5 and MERIS reflectance data. Remote Sensing of Environment, 133: 128-140
24. 24.
    Dawson T P, Curran P J and Plummer S E. 1998. LIBERTY-Modeling the effects of leaf biochemical concentration on reflectance spectra. Remote Sensing of Environment, 65(1): 50-60
25. 25.
    Di Nisio A, Adamo F, Acciani G and Attivissimo F. 2020. Fast detection of olive trees affected by xylella fastidiosa from UAVs using multispectral imaging. Sensors, 20(17): 4915
26. 26.
    Dube T and Mutanga O. 2015. Investigating the robustness of the new Landsat-8 operational land imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 12-32
27. 27.
    Dungey H S, Dash J P, Pont D, Clinton P W, Watt M S and Telfer E J. 2018. Phenotyping whole forests will help to track genetic performance. Trends in Plant Science, 23(10): 854-864
28. 28.
    Eitel J U, Gessler P E, Smith A M and Robberecht R. 2006. Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. Forest Ecology and Management, 229(1-3): 170-182
29. 29.
    Féret J B, Francois C, Asner G, Gitelson A, Martin R, Bidel L, Ustin S L, le Maire G and Jacquemoud S. 2008. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, 112(6): 3030-3043
30. 30.
    Féret J B, le Maire G, Jay S, Berveiller D, Bendoula R, Hmimina G, Cheraiet A, Oliveira J, Ponzoni F, Solanki T, De Boissieu F, Chave J, Nouvellon Y, Porcar-Castell A, Proisy C, Soudani K, Gastellu-Etchegorry J P and Lefèvre-Fonollosa M. 2018. Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning. Remote Sensing of Environment, 231: 110959
31. 31.
    Gamon J, Penuelas J and Field C. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1): 35-44
32. 32.
    Gao B C. 1996. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257-266
33. 33.
    Gara T, Darvishzadeh R, Skidmore A and Heurich M. 2019. Evaluating the performance of PROSPECT in the retrieval of leaf traits across canopy throughout the growing season. International Journal of Applied Earth Observation and Geoinformation, 83: 101919
34. 34.
    Gastellu-Etchegorry J P, Martin E and Gascon F. 2004. DART: a 3D model for simulating satellite images and studying surface radiation budget. International Journal of Remote Sensing, 25(1): 73-96
35. 35.
    Goodbody T R H, Coops N C, Tompalski P, Crawford P and Day K J K. 2017. Updating residual stem volume estimates using ALS- and UAV-acquired stereo-photogrammetric point clouds. International Journal of Remote Sensing, 38: 2938-2953
36. 36.
    Guo X, Zhu X, Li C, Wei Y, Yu X, Zhao G and Sun H. 2017. Hyperspectral inversion of potassium content in apple leaves based on vegetation index. Agricultural Sciences, 08(08): 825-836
37. 37.
    Guo Y, Li Z Y, Chen E X, Tian X and Ling F L. 2015. Estimating forest above-ground biomass in the upper reaches of heihe river basin using multi-spectral remote sensing. Scientia Silvae Sinicae, 51(1): 140-149
38. 38.
    Gutiérrez S, Wendel A and Underwood J. 2019. Ground based hyperspectral imaging for extensive mango yield estimation. Computers and Electronics in Agriculture, 157: 126-135
39. 39.
    Hu G S, Yin C J, Wan M Z, Zhang Y and Fang Y. 2020. Recognition of diseased pinus trees in UAV images using deep learning and AdaBoost classifier. Biosystems Engineering, 194: 138-151
40. 40.
    Huang H G. 2019. Progress and perspective of quantitative remote sensing of forestry. Journal of Beijing Forestry University, 41(12): 1-14
41. 41.
    Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada P J, Asner G P, François C and Ustin S L. 2009. PROSPECT+ SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment, 113: S56-S66
42. 42.
    Jay S, Bendoula R, Hadoux X, Féret J B and Gorretta N. 2016. A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy. Remote Sensing of Environment, 177: 220-236
43. 43.
    Kankare V, Liang X, Vastaranta M, Yu X, Holopainen M and Hyyppa J. 2015. Diameter distribution estimation with laser scanning based multisource single tree inventory. ISPRS Journal of Photogrammetry and Remote Sensing, 108: 161-171
44. 44.
    Kimberley M, Moore J and Dungey H. 2015. Quantification of realised genetic gain in radiata pine and its incorporation into growth and yield modelling systems. Canadian Journal of Forest Research, 45(12): 1676-1687
45. 45.
    Kuhlgert S, Austic G, Zegarac R, Osei-Bonsu I, Hoh D, Chilvers M I, Roth M G, Bi K, Teravest D and Weebadde P. 2016. MultispeQ Beta: a tool for large-scale plant phenotyping connected to the open PhotosynQ network. Royal Society Open Science, 3(10): 160592
46. 46.
    Li B, Liu R Y, Liu S H, Liu Q, Liu F and Zhou G Q. 2012. Monitoring vegetation coverage variation of winter wheat by low-altitude UAV remote sensing system. Transactions of the Chinese Society of Agricultural Engineering, 28(13): 160-165
47. 47.
    Li B Z, Li M X, Zhou X, Zhang L S and Zhang H Y. 2010. Hyperspectral estimation models for nitrogen contents of apple leaves. Journal of Remote Sensing, 14(4): 761-773
48. 48.
    Li M S, Tan Y, Pan J and Peng S K. 2006. Modeling forest aboveground biomass by combining the spectrum, textures with topographic features. Remote Sensing Information, 6: 6-9
49. 49.
    Li S, Ding X Z, Kuang Q L, Ata-Ul-Karim S T, Cheng T, Liu X J, Tian Y C, Zhu Y and Cao Q. 2018. Potential of UAV-based active sensing for monitoring rice leaf nitrogen status. Frontiers in Plant Science, 9: 1834
50. 50.
    Li S D. 2016. Smart Forestry: New journey of Forestry in China. Information Construction, 8: 23-25
51. 51.
    Li W T, Yang J B, Zhang J, Wang K J, Deng L, Lv Q, He S L, Xie R J, Zheng Y Q and Ma Y Y. 2018. The evaluation of chlorophyll content detection in the leaves of newhall navel orange based on different sensors. Scientia Agricultural Sinica, 51(6): 1057-1066
52. 52.
    Li X F, Han T T, Dong Y, Wu M and Shen X. 2011. Relationships between Spectral Reflectance and Pigment or Nitrogen Concentrations in Leaves of Padus virginiana ‘Schubert’. Scientia silvae Sinicae, 47(8): 75-81
53. 53.
    Li Z Y, Liu Q W, Pang Y. 2016. Review on forest parameters inversion using LiDAR. Journal of Remote Sensing, 20(5): 1138-1150
54. 54.
    Liu C, Sun P S and Liu S R. 2016. A review of plant spectral reflectance response to water physiological changes. Chinese Journal of Plant Ecology, 40(1): 80-91
55. 55.
    Liu G H, Huang J Q, Pan C X, Wang Z J, Zheng B S and Jin S H. 2011. Analysis of the nitrogen levels in leaves of hickory seedling by reflectance spectra. Scientia Silvae Sinicae, 47(1): 165-171
56. 56.
    Liu J G, Zhao C J, Yang G J, Yu H Y, Zhao X Q, Xu B and Niu Q L. 2016. Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform. Transactions of the Chinese Society of Agricultural Engineering, 32(24): 98-106
57. 57.
    Liu K, Shen X, Cao L, Wang G B and Cao F L. 2018. Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 465-482
58. 58.
    Liu L Z, Li M J, Lin Y F, Zhang Z Z and Zhang M M. 2019. A preliminary study on nitrogen diagnosis of tea plant based on image processing. Journal of Beihua University (Natural Science), 20(1): 114-120
59. 59.
    Liu Q W, Li S M, Li Z Y, Fu L Y and Hu K L. 2017. Review on the applications of UAV-based lidar and photogrammetry in forestry. Scientia Silvae Sinicae, 53(7): 134-148
60. 60.
    Liu X F, Lyu Q, He S L, Yi S L, Hu D Y, Wang Z T, Xie R J, Zheng Y Q and Deng L. 2016. Estimation of carbon and nitrogen contents in citrus canopy by low-altitude remote sensing, 9: 149-157
61. 61.
    Malenovský Z, Homolová L, Zurita-Milla R, Lukeš P, Kaplan V, Hanuš J, Gastellu-Etchegorry J P and Schaepman M E. 2013. Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer. Remote Sensing of Environment, 131: 85-102
62. 62.
    Mei M Y and Lei Y D. 2019. Analysis on Development Trend of China’s Plantation in New Era. World Forestry Research, 32(3): 73-77
63. 63.
    Mishra A, Karimi D, Ehsani R and Albrigo L G. 2011. Evaluation of an active optical sensor for detection of Huanglongbing (HLB) disease. Biosystems Engineering, 110(3): 302-309
64. 64.
    Montagnoli, A, Terzaghi, M, Fulgaro, N, Stoew, B, Wipenmyr, J, Ilver, D, Rusu, C, Scippa, G and Chiatante, D. 2016. Non-destructive phenotypic analysis of early stage tree seedling growth using an automated stereovision imaging method. Frontiers in Plant Science, 7(e0119): 1644
65. 65.
    Mu Y, Fujii Y, Takata D, Zheng B, Noshita K, Honda K, Ninomiya S and Guo W. 2018. Characterization of peach tree crown by using high-resolution images from an unmanned aerial vehicle. Horticulture Research, 5: 74
66. 66.
    Mutka, A M and Bart, R S. 2015. Image-based phenotyping of plant disease symptoms. Frontiers in Plant Science, 5: 734
67. 67.
    Näsi R, Honkavaara E, Paivi L S, Blomqvist M, Litkey P, Hakala T, Viljanen N, Tuula K, Tanhuanpää T and Holopainen M. 2015. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sensing, 7(11): 15467-15493
68. 68.
    North P R J. 1996. Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Transactions on Geoscience and Remote Sensing, 34(4): 946-956
69. 69.
    Pádua L, Marques P, Martins L, Sousa A, Peres E and Sousa J. 2020. Monitoring of chestnut trees using machine learning techniques applied to UAV-based multispectral data. Remote Sensing, 12(18): 3032
70. 70.
    Pan B, Zhao G X, Zhu X C and Wang N N. 2012. Estimation of phosphorus content in apple tree canopy based on hyperspectrum. Infrared, 33(6): 27-31
71. 71.
    Penuelas J, Pinol J, Ogaya R and Filella I. 1997. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18(13): 2869-2875
72. 72.
    Perry E, Goodwin I and Cornwall D. 2018. Remote sensing using canopy and leaf reflectance for estimating nitrogen status in red-blush pears. HortScience, 53(1): 78-83
73. 73.
    Pont D, Dungey H, Watt M, Morgenroth J and Stovold T. 2016. The use of LiDAR for phenotyping. In Forest Genetics for Productivity Conference, Rotorua, New Zealand. [2016-03-01]
74. 74.
    Quan X, He B, Yebra M, Yin C, Liao Z and Li X. 2017. Retrieval of forest fuel moisture content using a coupled radiative transfer model. Environmental Modelling and Software, 95: 290-302
75. 75.
    Rollin E M and Milton E J. 1998. Processing of high spectral resolution reflectance data for the retrieval of canopy water content information. Remote Sensing of Environment, 65(1): 86-92
76. 76.
    Salvatori E, Fusaro L and Manes F. 2016. Chlorophyll fluorescence for phenotyping drought-stressed trees in a mixed deciduous forest. Annali Di Botanica, 6: 39-49
77. 77.
    Santos D A E, Daniela V S, Letícia D A, Cristina S A, Carla D N and Schramm M M. 2018. Relationships between reflectance and absorbance chlorophyll indices with RGB (Red, Green, Blue) image components in seedlings of tropical tree species at nursery stage. New Forests, 50: 377-388
78. 78.
    Shen G F. 2001. Silviculture. Beijing: China Forestry Publishing: 94-97
79. 79.
    Shen X, Cao L, Coops N C, Fan H, Wu X Q, Liu H, Wang G B and Cao F L. 2020. Quantifying vertical profiles of biochemical traits for forest plantation species using advanced remote sensing approaches. Remote Sensing of Environment, 250: 112041
80. 80.
    Shen X, Cao L, Dong C, Sun Y, Wang G and Ruan H H. 2018. Prediction of forest structural parameters using airborne full-waveform LiDAR and hyperspectral data in subtropical forests. Remote Sensing, 10(11): 1729
81. 81.
    Sun G, Liu L Y, Zheng W G, Huang W J and Yin M. 2009. Development of a solar-induced chlorophyll fluorescence monitor based on fraunhofer line principle. Transactions of The Chinese Society of Agricultural Machinery, 40: 248-236
82. 82.
    Tester M and Langridge P. 2010. Breeding technologies to increase crop production in a changing world. Science, 327(5967): 818-822
83. 83.
    Wang J, Yin H L, Wang X Y and Feng Z K. 2015. Remote sensing factor optimization for forest volume estimation based on ZY-3 images. Journal of Central South University of Forestry and Technology, 35(12): 29-33
84. 84.
    Wang L, Zhao G X, Zhu X C, Wang R Y and Chang C Y. 2013. Satellite remote sensing retrieval of canopy nitrogen nutritional status of apple trees at blossom stage. Chinese Journal of Applied Ecology, 24: 2863-2870
85. 85.
    Wang Y and Yan Z Y. 2016. Detection system of chlorophyll content of cyclobalanopsis glauca using image processing technology. Journal of agricultural science and technology, 19(4): 59-64
86. 86.
    Wang Z, Wang T, Darvishzadeh R, Skidmore A K, Jones S, Suarez L, Woodgate W, Heiden U, Heurich M and Hearne J. 2016. Vegetation indices for mapping canopy foliar nitrogen in a mixed temperate forest. Remote Sensing, 8(6): 491
87. 87.
    Wang Z, Zhou L D, Li H, Jia J S. 2011. Predicting Nitrogen Concentrations in fresh peach leaf from hyper spectral remote sensing. Chinese Agricultural Science Bulletin, 27(04): 85-90
88. 88.
    Wang J H, Zhao C J, Guo X W, Huang W J and Tian Q J. 2000. Study on the water content of wheat leaves by the remote sensing. Acta Agriculturae Boreali-Sinica, 15: 68-72
89. 89.
    Wang J X, Huang H G, Lin Q N, Wang B and Huang C. 2019. Shoot beetle damage to Pinus yunnanensis monitored by infrared thermal imaging at needle scale. Chinese Journal of Plant Ecology, 43(11): 959-968
90. 90.
    Wang M M, Lin J Y, Lin Y and Li Y. 2017. Subalpine coniferous forest crown information automatic extraction based on optical UAV remote sensing imagery. Forest Resources Management, 4: 82-88
91. 91.
    Watt M, Buddenbaum H, Estarija H, Bown H, Gómez-Gallego M, Hartley R, Massam P, Wright L, Zarco-Tejada P and Leonardo E. 2020. Using hyperspectral plant traits linked to photosynthetic efficiency to assess N and P partition. ISPRS Journal of Photogrammetry and Remote Sensing, 169: 406-420
92. 92.
    Wu H G and Shi J. 2004. Monitoring Technique of Pine Caterpillars with TM Image. Journal of Remote Sensing, 8(2): 172-177
93. 93.
    Wu W B, Zhang Z B, Zheng L J, Han C Y, Wang X M, Xu J and Wang X R. 2020. Research progress on the early monitoring of pine wilt disease using hyperspectral techniques. Sensors, 20(13): 3729
94. 94.
    Xie Y, Zhang J, Chen X, Pang S, Zeng H and Shen Z. 2020. Accuracy assessment and error analysis for diameter at breast height measurement of trees obtained using a novel backpack LiDAR system. Forest Ecosystems, 7: 33
95. 95.
    Yang X G, Yu Y, Huang H J and Fan W Y. 2012. Journal of Infrared Millimeter Waves, 31(6): 536-543
96. 96.
    Yang Y Y, Ma L F, Shi Y Z, Ruan, J Y and Li X H. 2008. Evaluation of nitrogen status in tea plants by SPAD. 28(4): 301-308
97. 97.
    Ye X, Abe S and Zhang S. 2019. Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging. Precision Agriculture, 21: 198-225
98. 98.
    Yi S L, Deng L, He S L, Zheng Y Q and Mao S S. 2010. Scientia Agricultura Sinica, 43(4): 780-786
99. 99.
    Yi S L, Deng L, He S L, Zheng Y Q, Wang L, Zhao X Y and Niu TX.2011. Diagnosis model of Jincheng orange leaf chlorophyll content- based on digital image analysis technique. Journal of Agricultural Mechanization Research, 33: 110-115
100. 100.
     Zarco-Tejada P J, Rueda C and Ustin S L. 2003. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85(1): 109-124
101. 101.
     Zarco-Tejada P J, Camino C, Beck P S A, Calderon R, Hornero A, Hernández-Clemente R, Kattenborn T, Montes-Borrego M, Susca L, Morelli M, Gonzalez-Dugo V, North P R J, Landa B B, Boscia D, Saponari M and Navas-Cortes J A. 2018. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4: 432-439
102. 102.
     Zarco-Tejada, P J, Diaz-Varela, R, Angileri V and Loudjani P. 2014. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy, 55: 89-99
103. 103.
     Zarco-Tejada, P J, Guilléncliment, M L, Hernándezclemente, R, Catalina A, González M R and Martín P. 2013. Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agricultural and Forest Meteorology, 171-172: 281-294
104. 104.
     Zeng B B, Zhang X L, Lu C K, Bi H M and Zhu F E. 2008. Current situation and prospects of the research on remote sensing in the monitor of forest pests. Forest Pest and Disease, 27(2): 24-29
105. 105.
     Zhang F and Zhou G S. 2018. Research progress on monitoring vegetation water content by using hyperspectral remote sensing. Chinese Journal of Plant Ecology, 42: 517-525
106. 106.
     Zhang H, Pan J, Ju Y W and Liao Z F. 2014. Early Detection of Pine Wilt Disease in Pinus massioniana with Hyperspectral Data. Journal of Northeast Forestry University, 42(11): 115-119
107. 107.
     Zhang N, Zhang X, Guijun Y, Zhu C, Huo L and Feng H. 2018. Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images. Remote Sensing of Environment, 217: 323-339
108. 108.
     Zhang P, Yang L, Cong J and Shen H L. 2008. A disscusion on precision of silviculture technology. World Forestry Research, 21(1): 34-39
109. 109.
     Zhao C J. 2019. Big data of plant phenomics and its research progress. Journal of Agricultural Big Data, 1(2): 5-14
110. 110.
     Zhao J, Han T T, Zhang X X, Li X and Shen X. 2011. Preliminary study of whangkeumbae leaf nutrients status diagnosis by using digital image processing technique. Chinese Agricultural Science Bulletin, 27(13): 272-276
111. 111.
     Zhao T X, Guo B, An X M and Zhang W J. 2012. The study of stomatal conductance estimation of Populus deltoids Bartr.×Populus ussuriensis Kom. by infrared thermography. Chinese Agricultural Science Bulletin, 28(31): 65-70
112. 112.
     Zhao X, Burks T F, Qin J W and Ritenour M A. 2009. Digital microscopic imaging for citrus peel disease classification using color texture features. Applied Engineering in Agriculture, 25(5): 769-776
113. 113.
     Zhou J, Tardieu F, Pridmore T, Doonan J, Reynolds D, Hall N, Griffiths S, Cheng T, Zhu Y and Wang X E. 2018. Plant phenomics: history, present status and challenges. Journal of Nanjing Agricultural University, 41(4): 580-588 (周济, Tardieu, F., Pridmore, T., Doonan, J., Reynolds, D., Hall, N., Griffiths, S., 程涛, 朱艳, 王秀娥. 2018. 植物表型组学:发展、现状与挑战. 南京农业大学学报, 41(4): 580-588)
114. 114.
     Zhu C H, Qu S and Zhang X L. 2016. Dendrolimus tabulaeformis disaster monitoring and analysis of its influencing factors through remote sensing technology. Journal of Remote Sensing, 20(4): 653-664
115. 115.
     Zhu X C, Zhao G X, Dong F, Wang L, Lei T and Zhan B. 2009. Monitoring models for phosphorus content of apple flowers based on hyperspectrum. Chinese Journal of Applied Ecology, 20(10): 2424-2430
116. 116.
     Zhu X C, Zhao G X, Jiang Y M, Wang L, Chen H Y and Wang L. 2011. Estimation of SPAD value of apple leaf in different phenophase based on hyperspectral red edge parameters. Infrared, 32(12): 31-38
117. 117.
     Zhu X C, Zhao G X, Sui X Y, Lei T and Sun D G. 2010. Estimation of kalium content in apple flowers based on spectral analysis technique. Infrared, 31: 19-23
118. 118.
     Zhu X L and Liu D S. 2014. Accurate mapping of forest types using dense seasonal Landsat time-series. ISPRS Journal of Photogrammetry and Remote Sensing,96:1-11[10.1016/j.isprsjprs.2014.06.012]