Weijie Yuan•Yi Yu•Yongde Yue•Ji Wang•Fengchun Zhang•Xiaohong Dang

Use of infrared thermal imaging to diagnose health of Ammopiptanthus mongolicus in northwestern China

Weijie Yuan1,2•Yi Yu1•Yongde Yue1•Ji Wang3•Fengchun Zhang4•Xiaohong Dang3

Population of the rare and endangered species Ammopiptanthus mongolicus(Maxim.)Cheng f.declined rapidly in China’s arid region and CentralAsia.There is an urgent need to protect this species,which is particularly important in maintaining biodiversity throughout the arid region of northwestern China.By analyzing the infrared thermal images based on plant-transpiration transfer coefficient(hat)and photosynthetic parameters,we made quantitative and accurate diagnoses of the plantgrowth and health status of A.mongolicus.Using an LI-COR6400 photosynthesis system,we measured the netphotosynthetic rate(Pn),stomatalconductance(Gs),and transpiration rate (Tr).Infrared thermal images obtained in the field were processed by ENVI4.8 software to calculate surface temperatures of the plant subjects.We found that the plant transpiration transfer coefficient of A.mongolicus was inthe order of old plants＞young plants＞intermediate-aged plants.Declining health levels of young,intermediate,and old plants were divided into three categories:＜0.4,0.4–0.7, and＞0.7.The coefficient showed a significant negative correlation with Tr,Gs,and Pn,indicating that they can simultaneously reflect the state of plant growth.By establishing hatand photosynthetic parameters in regression model Y=a-blnx,we can accurately diagnose plant growth and decline of plant health conditions.

Photosynthetic parameters·Plant stress· Stomatalconductance·Thermography·Three-temperature (3T)model·Plant-transpiration transfer coefficient

Introduction

Protection of rare and endangered species,such as Ammopiptanthus mongolicus(Maxim.)Cheng f.(Liu 1998),is a global issue and important in maintaining biodiversity. This plant is one of the most important species in the arid region in western China(Zhang and Jiang 1987)and the only broad-leaved evergreen shrub species there.This species has long attracted the research interests of plant physiologists,ecologists,and geographers because itis an ancient tertiary flora relict with significant scientific value (Wang and Yin 1991).

Due to its long-term evolution in a harsh environment,it possesses drought-and disease-resistance characteristics and is an excellent sand-fixation plant(Zhou et al.2001). However,this valuable plant has rapidly declined in recent years in the distribution area,as did its overall plant community health,to varying degrees in northwestern China.There is an urgent need to protect this rare andendangered plantand maintain its relatively stable habitats in the region.Effective protection of any plant community dominated by A.mongolicus depends upon efficient management based on correct and accurate diagnosis of plant health.The first step in rejuvenating the plants is to measure plant growth and environmental stresses.Current methods for assessing plant growth and environmental stress include measuring photosynthesis,transpiration, biomass,and growth potential and so on.However,these methods generally are complicated:they are time-consuming,can damage plant leaf or tissues,and may limit sample size.Infrared thermal imaging used in this study provides accurate,reliable,and non-damaging diagnosis of plant growth and degree of environmental stresses.

Infrared thermal imaging is a non-contact,non-destructive technology(Chaerle and Van der Straeten 2000), where a radiometer transforms the energy emitted from targetobjects in the infrared band into an electronic video signal and ultimately a visible image.The energy radiated from an object is mainly a function of its surface temperature.Infrared thermal imaging is a two-dimensional temperature measurement(Meola and Carlomagno 2004). In our application,the heat radiation energy is directly related to the surface temperature of the A.mongolicus.

Industry,agriculture,environmental protection,and scientificresearch haveemployed thistechniquewidely sincethe early 1980s.In plant science,it is used to study stomatal movement conductance,photosynthesis(Omasa and Takayama 2003),and—more recently—plant droughtresistance(Bettina et al.2010;Giuseppe et al.2011),salt stress (Jamesetal.2008),and stomatalmutation(Merlotetal.2002; Song et al.2006),and screening of plant genotypes(Jones 1999).However,little research has used this technology to diagnoseplanthealth in CentralAsiaand China’sarid regions.

Under normal environmental conditions,the surface temperature of a plant stays relatively stable through transpiration.Butwith externalstress,such as drought,the stomatalbehavior of a plantwillchange in porosity,as will other physiological parameters,such as conductance and transpiration intensity(Jones 1999).These changes usually alter the heat loss from the leaf surface,causing the leaf temperature to change(Chaerle and Van der Straeten 2000).Plant surface temperature varies as evapotranspiration,photosynthesis,and environmental factors change in response to differentexternalconditions.Despite this,plant surface temperature is often used to measure plant water status,stomatal opening,and transpiration attenuation as a means of monitoring water and environmental stress of plants(Chaerle and Van der Straeten 2000;Qiu et al. 2009).A recently proposed algorithm,the three-temperature(3T)model—air temperature,land surface temperature,and land reference temperature—estimates actual evapotranspiration and environmental quality(Qiu et al. 2006a,2009).It has the advantages of fewer parameters, simple calculation,easy operation,and accurate estimation, which was known as a very informative and a significant step towards using remote sensing to truly measure hydrologic processes(Qiu et al.2006b,c).

The 3T model incorporates five basic models:soil evaporation,soil-evaporation transfer coefficient(evaluated soil moisture and soil environmental quality),plant transpiration,plant-transpiration transfer coefficient(evaluation of water status of vegetation and vegetation environmental quality),and plant water-deficit coefficient. Using vegetation surface temperature withouttranspiration, we were able to calculate the amount of plant transpiration and the plant-transpiration transfer coefficient.

In this study,we used infrared thermalimaging to obtain field images of A.mongolicus and record canopy surface temperatures,in order to calculate the plant-transpiration transfer coefficient using the 3T model,analyze planttranspiration transfer coefficient correlations between plant photosynthetic parameters with ENVI,and diagnose the health of the plant.The results of the study provided valuable information for restoring the ecosystems dominated by A.mongolicus in northwestern China.

Materials and methods

Experimentalsite

This study was conducted in the West Ordos National Nature Reserve(WONNR)in western Inner Mongolia, China.The geographicalcoordinates of the study area are 106°44′59.7′′–107°43′12′′E,39°13′35′′–40°10′50′′N. WONNR lies in the warm temperate continentalmonsoon climate zone with plateau characteristics,such as large diurnal and seasonal temperature variations,dry climate, long hours ofsunshine,high solar radiation,and high wind speed.The mean annual,maximum,and minimum air temperatures are 8.2,39.4,and-35.7°C,respectively.The warmest month is July and coolest month is January. Maximum land surface temperature can reach 63.4°C and a yearly mean humidity of 13%.Average annualsunshine hours are 3046.1 h.Annual rainfall is 272.3 mm,mostly from June to August,with a mean rainfall of 173.9 mm, accounting for 63.9%of the annual precipitation.The annual evaporation rate is 2470.4 mm,which was nine times that of precipitation.Annual average wind speed is about 3.2 m/s.

Vegetation in the study area comprises xerophytic shrubs and semi-shrubs,dominated by A.mongolicus. Other species include Tetraena mongolica Maxim.,Zygop hyllumxant hoxylon Bunge,Helianthemum soongoricum Schrenk,Potaninia mongolica Maxim.,and Prunesmongolica.Our investigation of A.mongolicus in this area showed that it had rapidly declined in plant community health and was having difficulty regenerating in recent years.

Experimentaldesign

We selected a 50×50 m sample plotin a flatarea.Shrubs covered more than 75%of the plot and the distribution of plants was relatively concentrated and contiguous.We selected five A.mongolicus plantsfromeach ofthree age groups: young,intermediate-aged,and old.Newly geminated plants, emerging from recent stubble,were marked as young plants (H≤20 cm,30＜W≤60 cm).Plantswithoutstubble were marked as intermediate-aged(60＜H≤120 cm, 60＜W≤180 cm)orold(H＞120 cm,W＞180 cm),according to the heightand canopy ofthe plants.We measured the heightand canopy of plants three times(Table 1).Field measurements were made on September26 and 27,2012.

Data collection

Thermal infrared data were obtained using a Ti55FT IR Flex Cam thermal imager(FLUKE Co.,Washington, USA).The detector parameters of the instrument were focal plane array 320×240,25μpixel pitch vanadium oxide(VOX),and no refrigeration.The field of view (FOV)of the instrument was 23°×17°(horizontal×vertical),spatial resolution of instantaneous field of view(IFOV)was 1.3 mrad,and spectral band was 8–14μm.Temperature measurement range was-20 to 600°C with a resolution of 0.05°C.The screen operation of the instrument was thermal infrared light,visible light, or combined thermal infrared/visible light.In cloudless weather,we took photographs attwo-hour intervals,at9:00 am,11:00 am,1:00 pm,3:00 pm,and 5:00 pm.When taking photos,we had to keep the lens perpendicular to the vegetation canopy at a distance of 1 m.

We created a reference leaf(without transpiration)by cutting out the shape of a leaf from a card in a natural leaf color,which was placed on the upper canopy to avoid being shaded by other leaves.The inclination angle of the reference leaf was the same as real leaves(Fig.1).

Infrared thermal images obtained in the field were processed and analyzed to calculate surface temperatures of select plant leaves,using ENVI4.8 software.Air temperature,solar radiation,and other meteorological parameterswere measured with a DavisVantage Pro 2 weather station.Data were automatically recorded every two minutes.In addition,we also used a LI-COR6400 portable photosynthesis system(LI-COR Inc.,Nebraska,USA)to measure the netphotosynthetic rate(Pn,μmolCO2m-2s-1), stomatal conductance(Gs,mol H2O m-2s-1),and transpiration rate(Tr,mmol H2O m-2s-1)of the A.mongolicus. We selected three healthy leaves for each treatment and measured each leafthree times.Plantleafarea wasmeasured using image-scanning technology(Bond-Lamberty et al. 2003).

Calculation and data analysis

According to the 3T model equation,

T was the transpiration rate(MJ m-2d-1);Rnand Rnpwere non-canopy and canopy transpiration reference net radiation(MJ m-2d-1);Tcwas the canopy layer temperature with transpiration;Tpwas the reference canopy temperature without transpiration;and Tawas the air temperature(°C).

The plant transpiration transfer coefficient(hat)was calculated using the following formula:

where(hat)ranged from 0 to 1.The scope of the transpiration rate was from the minimum transpiration rate(0)to the maximum transpiration rate(potential transpiration). When Tc=Tp,hathad the maximum value(hat=1)and the corresponding plant transpiration had the minimum amountof evapotranspiration(0).Conversely,when the hathad the minimum value(hat=0),the corresponding planttranspiration had the maximum amount of potential transpiration.In other words,when plants did not suffer from water deficit or environmental stress,the transpiration transfer coefficient had a minimum value.When plants were under serious water stress,the transpiration transfer coefficient reached the maximum value.

Table 1 Statistical analysis of the growth index for selected Ammopiptanthus mongolicus in the experiment

Fig.1 Infrared thermal image(left)and visible color light image(right)of A.mongolicus

The plant transpiration transfer coefficient(hat)can be used as an index of crop water deficit to measure the evapotranspiration of plant and evaluate its water use potential.The main advantage of hatis its simplicity in infrared applications.The data were analyzed by SPSS 16.0 and Origin 8.0.

Results

Plant transpiration transfer coefficients(hat)

Tc1,Tc2,and Tc3 are canopy temperatures of young,intermediate-aged,and old plants,respectively.Both the canopy temperature(Tc1,Tc2,and Tc3)and airtemperature (Ta)showed similar patterns,where the highest value was reached atnoon during the measuring period(Fig.2a).For old plants,the highest canopy temperature came at 1:00 pm,and at 3:00 pm for young and intermediate-aged plants.This reflected hysteresis in accordance with the highest value of air temperature.The highest and lowest difference values between air and canopy temperatures were 11.47 and 0.59°C,respectively.Differentages of A. mongolicus plants showed different canopy temperatures. The daily average canopy temperature values of young, intermediate-aged,and old plants were 20.29,18.85,and 21.39°C,respectively.

Fig.2 Variations in a temperatures(Ta,Tp,Tc1,Tc2,Tc3)and b hatvalues ofdifferentgrowth and health status of A.mongolicus.Tc1,Tc2 and Tc3 are canopy temperatures of young plant,intermediate-aged plant and old plant,respectively

The lowest values for all plant transpiration transfer coefficients(hat)of young,intermediate-aged,and old plants came atnoon—opposite the trends ofthe canopy and reference canopy temperatures on both September 26 and 27(Fig.2b).The lowest value of hatof young, intermediate-aged,and old plants appeared at 11:00 am, 1:00 pm,and 1:00 pm,respectively,indicating that the plants suffered less water stress.The daily mean value of hatof young,intermediate-aged,and old plants was 0.62, 0.40,and 0.78,respectively,during the measuring period.

Photosynthetic parameters

The net photosynthetic rate(Pn,μmol CO2m-2s-1), stomatal conductance(Gs,mol H2O m-2s-1),and transpiration rate(Tr,mmol H2O m-2s-1)of A.mongolicus showed a clear diurnal pattern—a single peak curve (Fig.3;Table 2).Pn,Trand Gswere significantly different among the treatments.The patterns showed that intermediate-aged plants＞young plants＞old plants in Pn,Tr,and Gsat the measuring points.

鄱阳湖生态经济区由水库、堤防、蓄滞洪区组成的防洪工程体系，已经形成基本框架，但是这一体系尚不完善，体系运用中还存在一些建设、政策等方面的问题。部分流域还没有采取工程措施控制，这些地区产生的洪水依然威胁下游地区的防洪安全。堤防的防洪能力难以满足生态经济区建设要求，还需要继续提高。蓄滞洪区人口增长、经济发展，加上蓄滞洪区经费投入不足、标准低，安全设施少等原因，启用蓄滞洪区难度大，损失严重。现有的防洪体系与生态经济区内的经济发展需求不相适应。

The transpiration rates(Tr)of intermediate-aged and young plants reached maximum values around 1:00 pm and noon,respectively,with daily average values of 3.29 and 2.54 mmol H2O m-2s-1on September 26 and 2.80 and 2.17 mmol H2O m-2s-1on September 27(Fig.3a).The transpiration rate of old plants reached its maximum value at11:00 am,with a daily average of 1.76 mmol H2O m-2s-1on September 26 and 1.56 mmol H2O m-2s-1on September 27.

Fig.3 Variations in photosynthetic parameters(Tr,Gs,Pn)of different growth and health status of A.mongolicus

The stomatal conductance of the intermediate-aged and young plants reached their maximum around 11:00 am (Fig.3b).The daily average values were 0.16 and 0.13 mol H2O m-2s-1on September26 and 0.14 and 0.12 molH2O m-2s-1on September27.The stomatalconductance ofold plants reached their maximum values at 9:00 am,with a daily average of 0.09 mol H2O m-2s-1on September 26 and 0.08 mol H2O m-2s-1on September 27.

The net photosynthetic rate(Pn)of the intermediateaged and young plants reached maximum values around 11:00 am,with daily averages of 15.69 and 13.44μmol CO2m-2s-1on September 26 and 14.71 and 12.01μmol CO2m-2s-1on September 27;while the net photosynthetic rate ofthe old plants reached theirmaximum at9:00 am,with daily averages of 10.33μmol CO2m-2s-1on September 26 and 8.12μmol CO2m-2s-1on September 27(Fig.3c).

Correlation analysis and regression models

The correlation analysis of the plant transpiration transfer coefficients(hat)and corresponding photosynthetic parameters(Tr,Gs,and Pn)for young,intermediate-aged,and old plants is shown in Table 3.We found that the plant transpiration transfer coefficients were significantly negatively correlated with Tr,Gs,and Pnin all age classes (p＜0.01).The optimal regression equations between the planttranspiration transfer coefficients(hat)and their corresponding photosynthetic parameters(Tr,Gs,and Pn)for young,intermediate-aged,and old plantsisshown in Table 4.

Discussion

Age composition is an important indicator of plant population dynamics(Xie et al.1999).In order to avoid destroying rare and endangered species,size structure of individual plants is used to gauge plant age(Xie et al.1995;Yan etal.2001),with good results.A.mongolicus is a shrub species with more branches at its roots and no apparent trunk.Annual rings cannot be distinguished, making it difficult to use growth cone or diameter class to determine plantage.Previous studies(Hou etal.1994;Wei et al.2005;He et al.2006)have shown that the age of a plant correlates with height and crown breadth.Based on these findings,ourstudy selected heightand crown breadth to analyze A.mongolicus population dynamics.

Table 3 Correlation analysis between the planttranspiration transfer coefficients(hat)and their corresponding photosynthetic parameters

In general,the photosynthetic physiological characteristics of plants constantly change as they age.The photosynthetic capacity of young leaves is not well developed: they have small stomatal openings;immature mesophyll cells with a low rate of gas exchange with the outside world,underdeveloped chloroplasts,and fewer photosynthetic pigments;weak light-harvesting capacity;and low levels of photosynthetic enzymes(especially ribulose-1, 5-bisphosphate carboxylase/oxygenase,or Rubisco)(Pan 2012).As they develop,plantleaves gradually increase the number of stomata and their chlorophyll content,improve chloroplast structure,and strengthen electron transport and photosynthetic capacity(Greer and Halligan 2001;Chen and Tao 2003;Jiang et al.2005).When the area and thickness of leaves reach a maximum value,their photosynthetic rate also typically maximizes.As leaves become older,their stomatal conductance,photosynthetic electron transportcapacity,and Rubisco contentdecrease,and thus the photosynthetic capacity gradually weakens(Dai et al. 2004).

Table 4 Regression models for estimating photosynthetic parameters(Tr,Gs,Pn)by using the plant transpiration transfer coefficients(hat)

According to the 3T model,plant transpiration rates at different recessionary conditions were in the order of old plants＜young plants＜intermediate-aged plants,which was consistent with the order of photosynthesis measured with a LI-COR6400 system.Based on the mean values of the plant transpiration transfer coefficients,the recession levels ofthe young,intermediate-aged,and old plants were divided into three categories:＜0.4,0.4–0.7,and＞0.7.By establishing hatwith recession levels and the relationship between physiological and ecological indicators,we were able to identify the health conditions of A.mongolicus using infrared thermalimaging.

Earlier methods for assessing plant growth and degradation of plant health included determining biomass, growth potential,and othergrowth indicators,ormeasuring a combination of photosynthesis,transpiration,and other physiological indicators.These methods are time-consuming,requiring significant labor and resources.Infrared thermal imaging provides quick and precise high-resolution spatial information on plants and environments without damaging them(Leinonen and Jones 2004;Jones 2004).The‘‘three-temperature’’model allows for simple and accurate diagnosis of plantgrowth and degradation of health(in situ,without contact),and continuous observation of plant growth.Previous studies mostly focused on the use of infrared thermal imaging to record the temperature of plants and did not look at the relation between temperature values and photosynthetic parameters.

Conclusions

As a resultofthe data we gained through the use of infrared thermal imaging,we were able to draw four conclusions from ourstudy.First,the orderofplant-transpiration transfer coefficients of A.mongolicus was old plants＞young plants＞intermediate-aged plants.Recession levels of the young, intermediate,and old plants were divided into three categories:＜0.4,0.4–0.7,and＞0.7.Second,the diurnal variation of photosynthetic parameters was contrary to hat.The orderofthe photosynthetic parameters of A.mongolicus was intermediated-aged plants＞young plants＞old plants.In other words,the higher the planttranspiration transfer coefficients,the lower the netphotosynthetic rate(Pn),stomatalconductance(Gs),and leaftranspiration rate(Tr).Third, the plant transpiration transfer coefficient showed a significant negative correlation with Tr,Gs,and Pn,indicating that hatand Pn,Gs,and Trcan simultaneously reflectthe state of plant growth.By establishing hatand photosynthetic parameters in regression model Y=a-blnx(where Y is photosynthetic parameters Pn,Gs,and Tr;x is the planttranspiration transfercoefficient hat;and a,b are constants), we can accurately diagnose plantgrowth and degradation of plant health conditions.Fourth,our results with A.mongolicus prove thatinfrared thermalimaging is a practicable and usefultechnique for diagnosing planthealth.

AcknowledgmentsWe thank the West Ordos National Nature Reserve for its valuable assistance with manpower,materials,research,site selection,and other support.

Bettina B,Boris P,Mark T(2010)High-throughputshoot imaging to study droughtresponses.J Exp Bot 61:3519–3528

Bond-Lamberty B,Wang C,Grower ST(2003)The use of multiple measurement techniques to refine estimates of conifer needle geometry.Can J For Res 33:101–105

Chaerle L,Van der Straeten D(2000)Imaging techniques and the early detection of plant stress.Trends Plant Sci 5:495–501

Chen NL,Tao YH(2003)Study on photosynthetic property of Cucurbita maxima.Acta Bot Boreali-Occident Sin 23:976–981

Dai JM,Gao HY,Zou Q(2004)Changes in activity of energy dissipating mechanisms in wheat flag leaves during senescence. Plant Biol 6:171–177

Davies WJ,Zhang J(1991)Rootsignals and the regulation of growth and developmentofplants in drying soil.Ann Rev Plant Physiol Plant Mol Biol 42:55–76

Giuseppe R,Shamaila Z,Wolfram S,Ciro S,JillC,Jose LA,Joachim M(2011)Use of thermography for high throughputphenotyping of tropical maize adaptation in water stress.Comput Electron Agric 79(1):67–74

Greer DH,Halligan EA(2001)Photosynthetic and fluorescence light responses for kiwifruitleaves atdifferent stages of development on vines grown at two different photon flux densities.Aust J Plant Physiol 28:373–382

He HB,Zhang HJ,Jia GX(2006)Population structure and spatial distribution pattern of Ammopiptanthus mongolicus in Dengkou County,Inner Mongolia autonomous region.Sci Silvae Sin 42(10):13–18

Hou P,Yin LK,Yan C(1994)Study on biomass of Ammopiptanthus mongolicus.Arid Zone Res 11(1):16–22

Huo H,Wang CK(2007)Effects of canopy position and leaf age on photosynthesis and transpiration of Pinuskoraiensis.Chin J Appl Ecol 18(6):1181–1186

James RA,Caemmerer SV,Condon AG,Zwart AB,Munns R(2008) Genetic variation in tolerance to the osmotic stress componentof salinity stress in durum wheat.Funct Plant Biol 35:111–123

Jiang CD,Li PM,Gao HY,Zou Q(2005)Enhanced photoprotection at the early stages of leaf expansion in field-grown soybean plant.Plant Sci 168:911–919

Jones HG(1999)The use of thermography for quantitative studies of spatialand temporalvariation of stomatalconductance over leaf surfaces.Plant,Cell Environ 22:1043–1055

Jones HG(2004)Irrigation scheduling:advantages and pitfalls of plant-based methods.J Exp Bot 55:2427–2436

Leinonen I,Jones HG(2004)Combining thermaland visible imagery for estimating canopy temperature and identifying plant stress. J Exp Bot 55:1423–1431

Liu GH(1998)Study on the endangered reasons of Ammopitanthus mongolicus in the desertof Alashan.BullBot Res 18(3):341–345

Meola C,Carlomagno GM(2004)Recent advances in the use of infrared thermography.Meas Sci Technol 15:27–58

Merlot S,Mustilli AC,Genty B,North H,Lefebvre V,Sotta B, Vavasseur A,Giraudat J(2002)Use of infrared thermography to isolate Arabidopsis mutants defective in stomatal regulation. Plant J 30:601–609

Omasa K,Takayama K(2003)Simultaneous measurement of stomatal conductance,non-photochemical quenching,and photochemical yield of photosystem II in intact leaves by thermal and chlorophyll fluorescence imaging.Plant Cell Physiol 44:1290–1300

Pan RC(2012)Plant physiology.Higher education press,Beijing

Qiu GY,ShiP,Wang L(2006a)Theoreticalanalysisofa soilevaporation transfercoefficient.Remote Sens Environ 101:390–398

Qiu GY,Wang S,Wu X,Song XF(2006b)Three temperature model (3T)model—a method to estimate evapotranspiration and evaluate environmental quality based on surface temperature IV.Plant transpiration transfercoefficient.JPlant Ecol30(5):852–860

Qiu GY,Wang S,Wu X(2006c)Three temperature model(3T) model—a method to estimate evapotranspiration and evaluate environmental quality.J Plant Ecol 30(2):231–238

Qiu GY,Omasa K,Sase S(2009)An infrared-based coefficient to screen plantenvironmentalstress:concept,testand applications. Funct Plant Biol 36:990–997

Ren LL,Gao HY(2008)Effects of NaCl stress on induction of photosynthesis and PS II photochemistry efficiency of rumex K-1 leaves with different age.Acta Bot Boreali-Occident Sin 28(5):1014–1019

Sauter A,Davies WJ,Hartung W(2001)The long-distance abscisic acid signal in the drougted plant:the fate of the hormone on its way from rootto shoot.J Exp Bot 52(363):1991–1997

Schroeder JI,Kwak JM,Allen GJ(2001)Guard cell abscisic acid signaling and engineering drought hardiness in plants.Nature 410:327–330

Song YW,Kang YL,Liu H,Zhao XL,Wang PT,An GY,Zhou Y, Miao C,Song CP(2006)Identification and primary genetic analysis of Arabidopsis stomatalmutants in response to multiple stresses.Chin Sci Bull 51(21):2586–2594

Wang Y,Yin LK(1991)Measurementofsalttolerance oftwo species of Ammopitanthus mongolicus Maxim.Arid Zone Res 1:20–22

Wang B,Zhao GD,Li SN,Bai XL,Deng ZF(2005)Diurnal photosynthetic change characteristics of the dominant species Castanopsis fargesii and Castanopsis sclerophylla in evergreen broad-leaved forest in Dagangshan mountain,Jiangxi province. Acta Agric Univ Jiangxiensis 27(4):577–579

Wei QS,Wang JH,Li CL,Zhuang GH,Chen SK(2005)A preliminary study on the distribution patterns and characteristics of Ammopiptanthus mongolicus populations in different desert environments.Acta Phytoecol Sin 29(4):591–598

Xie ZQ,Chen WL,Jiang MX,Huang HD,Zhu RG(1995)A preliminary study on the population of Cathaya argyrophlla in Bamianshan mountain.Acta Bot Sin 37:58–65

Xie ZQ,Chen WL,Lu P,Hu D(1999)The demography and age structure of the endangered plant population of Cathaya argyrophlla.Acta Ecol Sin 19:523–528

Xu DQ(1990)Ecology,physiology and biochemistry of midday depression of photosynthesis.Plant Physiol Commun 6:5–10

Yan GQ,Zhao GF,Hu ZH,Yue M(2001)Population structure and dynamics of Larix chinenesis in Qinling mountain.Chin J Appl Ecol 12:824–828

Zhang T,Jiang ZR(1987)Study on introduction and cultivation test of Ammopitanthus mongolicus Maxim.J Desert Res 7(3):41–47

Zhou YJ,Liu CL,Feng JZ,Jia XH(2001)Advances of droughtresistance and frigid-resistance mechanism research on Ammopitanthus mongolicus.J Desert Res 21(3):312–316

13 March 2014/Accepted:26 May 2014/Published online:15 May 2015

©Northeast Forestry University and Springer-Verlag Berlin Heidelberg 2015

Project funding This work was supported by the nationalforestry nonprofitindustry research projects of China,‘‘Diagnosis of rare and endangered plants Ammopiptanthus mongolicus degradation and research of conservation technology’’(No.201304305).

The online version is available at http://www.springerlink.com.

Corresponding editor:Hu Yanbo.

✉Yi Yu yuyi@icbr.ac.cn

1International Centre for Bamboo and Rattan,Beijing 100102, China

2Forestry Experiment Center of North China,Chinese Academy of Forestry,Beijing 102300,China

3College of Ecology and Environmental Science,Inner Mongolia Agricultural University,Hohhot 010019,China

4Foreign Economic Cooperation Office,Ministry of Environmental Protection,Beijing 100035,China