水胁迫下多角度幼龄檀香图像颜色变化分析及含水率反演
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摘要
干旱与水淹胁迫是植物遭受的主要非生物胁迫,对植物的生理活动造成严重影响.本研究基于单反相机获取幼龄檀香的纵向和冠层叶片图像,使用分割算法提取叶片和颜色特征,然后讨论两种胁迫条件下多角度檀香叶片颜色变化及含水率反演.结果表明:干旱组在胁迫前期(前6 d)叶片亮度降低,绿色分量增加,之后叶片亮度增加,绿色分量降低;水淹组叶片在整个胁迫周期亮度持续降低,黄色分量增加;对照组则与干旱组的变化趋势类似,但拐点出现的时间较晚.当叶片含水率在50%～70%时,随着含水率的增加,彩色图像的红(R)、绿(G)、蓝(B)通道值均会减小;但当叶片含水率小于40%时,会出现R通道值大于G通道值的现象.在使用极限学习机反演含水率时,校正后的颜色分量对拟合优度及预测精度均有所提高.纵向图像更适合用来反演叶片的含水量,决定系数和平均绝对百分比误差分别为0.8352和2.3%;而冠层图像对叶片等效水厚度的表达更准确,上述指标分别为0.7924和9.3%.
Drought and waterlogging are two main abiotic stresses for plants, with serious impacts on plant physiological activities. In this study, the vertical and canopy leaf images of young sandalwood were obtained by SLR camera, with leaf segmentation algorithm being used to extract leaves and color features. We examined the color change of sandalwood leaves and water content inversion in different angles under two stress conditions. The results showed that leaf brightness decreased while the green component increased in the early stage(the first six days) of drought stress. After that, the brightness began to increase and green component began to decrease. Under water stress, the brightness of leaves decreased and yellow component increased in the whole stress cycle. The changes of control group was similar to that of the drought group, but the inflection point appeared later. Under the range of 50% to 70% for water content of leaves, the value of R, G, B channel of color images would decrease with the increases of water content. When the water content of leaves was less than 40%, the R channel value was larger than the G channel value. When using the extreme learning machine to retrieve the water content index, the corrected color components improved the fitness and the prediction accuracy. The vertical image was more suitable for retrieving water content of leaves, with the error of determination coefficient and average absolute percentage being 0.8352 and 2.3%, respectively. The canopy images were more accurate in expressing the equivalent water thickness of blades, with the above indices of 0.7924 and 9.3%, respectively.
Drought and waterlogging are two main abiotic stresses for plants, with serious impacts on plant physiological activities. In this study, the vertical and canopy leaf images of young sandalwood were obtained by SLR camera, with leaf segmentation algorithm being used to extract leaves and color features. We examined the color change of sandalwood leaves and water content inversion in different angles under two stress conditions. The results showed that leaf brightness decreased while the green component increased in the early stage(the first six days) of drought stress. After that, the brightness began to increase and green component began to decrease. Under water stress, the brightness of leaves decreased and yellow component increased in the whole stress cycle. The changes of control group was similar to that of the drought group, but the inflection point appeared later. Under the range of 50% to 70% for water content of leaves, the value of R, G, B channel of color images would decrease with the increases of water content. When the water content of leaves was less than 40%, the R channel value was larger than the G channel value. When using the extreme learning machine to retrieve the water content index, the corrected color components improved the fitness and the prediction accuracy. The vertical image was more suitable for retrieving water content of leaves, with the error of determination coefficient and average absolute percentage being 0.8352 and 2.3%, respectively. The canopy images were more accurate in expressing the equivalent water thickness of blades, with the above indices of 0.7924 and 9.3%, respectively.
引文
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[19] Pan H-X (潘华贤),Cheng G-J (程国建),Cai L (蔡磊).Comparison of the extreme learning machine with the support vector machine for reservoir permeability prediction.Computer Engineering & Science (计算机工程与科学),2010,31(2):131-134 (in Chinese)
[20] Guo W-C (郭文川),Wang M-H (王铭海),Gu J-S (谷静思),et al.Identification of bruised kiwifruits during storage by near infrared spectroscopy and extreme learning machine.Optics and Precision Engineering (光学精密工程),2013,21(10):2720-2727 (in Chinese)
[21] Wang YG,Cao FL,Yuan YB.A study on effectiveness of extreme learning machine.Neuro Computing,2011,74:2483-2490
[22] Ding H (丁红),Zhang Z-M (张智猛),Dai L-X (戴良香),et al.Effects of water stress and nitrogen fertilization on peanut root morphological development and leaf physiological activities.Chinese Journal of Applied Ecology (应用生态学报),2015,26(2):450-456 (in Chinese)
[23] Chen P (陈平),Meng P (孟平),Zhang J-S (张劲松),et al.Effects of drought stress on growth and water use efficiency of two medicinal plants.Chinese Journal of Applied Ecology (应用生态学报),2014,25(5):1300-1306 (in Chinese)
[24] Xie X-H (谢小红),Wei H (魏虹),Li C-X (李昌晓),et al.Hyperspectral characteristics of Chinese wingnut (Pterocarya stenoptera C.DC.) leaves under flooding stress.Journal of Southwest University (西南大学学报),2011,33(4):93-98 (in Chinese)
[25] Zhang F (张峰),Zhou G-S (周广胜).Research progress on monitoring vegetation water content by using hyperspectral remote sensing.Chinese Journal of Plant Ecology (植物生态学报),2018,42(5):517-525 (in Chinese)
[26] Mirzaie M,Darcishizadeh R,Shakiba A,et al.Compa-rative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements.International Journal of Earth Observation and Geoinformation,2014,26:1-11
[27] Peng J (彭杰),Xiang H-Y (向红英),Wang J-Q (王家强),et al.Inversion models of soil water content using hyperspectral measurements in field of the arid region farmland.Agricultural Research in the Arid Areas (干旱地区农业研究),2013,31(2):241-246 (in Chinese)
[2] Tan S-D (谭淑端),Zhu M-Y (朱明勇),Zhang K-R (张克荣),et al.Response and adaptation of plants to submergence stress.Chinese Journal of Ecology (生态学杂志),2009,28(9):1871-1877 (in Chinese)
[3] Van Bodegom PM,Sorrell BK,Oosthoek A,et al.Sepa-rating the effects of partial submergence and soil oxygen demand on plant physiology.Ecology,2008,89:193-204
[4] Jackson MB.Ethylene-promoted elongation:An adaptation to submergence stress.Annals of Botany,2008,101:229-248
[5] Zhang Q (张强),Yao Y-B (姚玉璧),Li Y-H (李耀辉),et al.Research progress and prospect on the monitoring and early warning and mitigation technology of meteorological drought disaster in Northwest China.Advances in Earth Science (地球科学进展),2015,30(2):196-213 (in Chinese)
[6] Zhao H (赵鸿),Wang R-Y (王润元),Shang Y (尚艳),et al.Progress and perspectives in studies on responses and threshold of major food crops to high temperature and drought stress.Journal of Arid Meteoro-logy (干旱气象),2016,34(1):1-12 (in Chinese)
[7] Rampino P,Pataleo S,Geredi C,et al.Drought response in wheat:Physiological and molecular analysis of resistant and sensitive genotypes.Plant,Cell & Environment,2006,29:2143-2152
[8] Zhu X-C (朱西存),Jiang Y-M (姜远茂),Zhao G-X (赵庚星),et al.Hyperspectral estimating leaf water contents based on spectral index in apple.Chinese Agricultural Science Bulletin (中国农学通报),2014,30(4):120-126 (in Chinese)
[9] Wang J (王洁),Xu R-S (徐瑞松),Ma Y-L (马跃良),et al.Methods and research developments for retrival of vegetable water content by remote sensing.Remote Sensing Information (遥感信息),2008(1):100-105 (in Chinese)
[10] Liu X (刘璇),Zhang Y (张晔),Teng Y-D (滕艺丹),et al.Estimation of vegetation water content based on Bi-inverted Gaussian fitting spectral feature analysis using hyperspectral data.Remote Sensing Technology and Application (遥感技术与应用),2016,41(5):302-304 (in Chinese)
[11] Petty CC,Curcio JA.The near infrared absorption spectrum of liquid water.Journal of the Optical Society of America,1951,41:302-304
[12] Dobrowski SZ,Pushnik JC,Zarco-Tejada PJ,et al.Simple reflectance indices track heat and water stress-induced changes in study-state chlorophyll fluorescence at the canopy scale.Remote Sensing of Environment,2005,97:403-414
[13] Holben BN,Schutt JB,Mc Murtrey III J.Leaf water stress detection utilizing thematic mapper bands 3,4 and 5 in soybean plants.International Journal of Remote Sensing,1983,4:289-297
[14] Zhang H-W (张海威),Zhang F (张飞),Zhang X-L (张贤龙),et al.Inversion of vegetation leaf water content based on spectral index.Spectroscopy and Spectral Analysis (光谱学与光谱分析),2018,38(5):1540-1546 (in Chinese)
[15] Fang M-H (方美红),Ju W-M (居为民).An inversion model for remote sensing of leaf water content based on the leaf optical property.Spectroscopy and Spectral Analysis (光谱学与光谱分析),2015,35(1):167-171 (in Chinese)
[16] Hu Z-Z (胡珍珠),Pan C-D (潘存德),Pan X (潘鑫),et al.Estimation models for water content of walnut leaves based on spectral moisture index.Scientia Silvae Sinicae (林业科学),2016,52(12):39-49 (in Chinese)
[17] Xu D-Q (徐道青),Liu X-L (刘小玲),Wang W (王维),et al.Hyper-spectral characteristics and estimation model of leaf chlorophyll content in cotton under waterlogging stress.Chinese Journal of Applied Ecology (应用生态学报),2017,28(10):3289-3296 (in Chinese)
[18] Gond V,De Pury DG,Veroustraete F,et al.Seasonal variations in leaf area index,leaf chlorophyll,and water content:Scaling-up to estimate fAPAR and carbon balance in a multilayer,multispecies temperate forest.Tree Physiology,1999,19:673
[19] Pan H-X (潘华贤),Cheng G-J (程国建),Cai L (蔡磊).Comparison of the extreme learning machine with the support vector machine for reservoir permeability prediction.Computer Engineering & Science (计算机工程与科学),2010,31(2):131-134 (in Chinese)
[20] Guo W-C (郭文川),Wang M-H (王铭海),Gu J-S (谷静思),et al.Identification of bruised kiwifruits during storage by near infrared spectroscopy and extreme learning machine.Optics and Precision Engineering (光学精密工程),2013,21(10):2720-2727 (in Chinese)
[21] Wang YG,Cao FL,Yuan YB.A study on effectiveness of extreme learning machine.Neuro Computing,2011,74:2483-2490
[22] Ding H (丁红),Zhang Z-M (张智猛),Dai L-X (戴良香),et al.Effects of water stress and nitrogen fertilization on peanut root morphological development and leaf physiological activities.Chinese Journal of Applied Ecology (应用生态学报),2015,26(2):450-456 (in Chinese)
[23] Chen P (陈平),Meng P (孟平),Zhang J-S (张劲松),et al.Effects of drought stress on growth and water use efficiency of two medicinal plants.Chinese Journal of Applied Ecology (应用生态学报),2014,25(5):1300-1306 (in Chinese)
[24] Xie X-H (谢小红),Wei H (魏虹),Li C-X (李昌晓),et al.Hyperspectral characteristics of Chinese wingnut (Pterocarya stenoptera C.DC.) leaves under flooding stress.Journal of Southwest University (西南大学学报),2011,33(4):93-98 (in Chinese)
[25] Zhang F (张峰),Zhou G-S (周广胜).Research progress on monitoring vegetation water content by using hyperspectral remote sensing.Chinese Journal of Plant Ecology (植物生态学报),2018,42(5):517-525 (in Chinese)
[26] Mirzaie M,Darcishizadeh R,Shakiba A,et al.Compa-rative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements.International Journal of Earth Observation and Geoinformation,2014,26:1-11
[27] Peng J (彭杰),Xiang H-Y (向红英),Wang J-Q (王家强),et al.Inversion models of soil water content using hyperspectral measurements in field of the arid region farmland.Agricultural Research in the Arid Areas (干旱地区农业研究),2013,31(2):241-246 (in Chinese)
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