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

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.

鄱陽湖生態(tài)經(jīng)濟(jì)區(qū)由水庫、堤防、蓄滯洪區(qū)組成的防洪工程體系，已經(jīng)形成基本框架，但是這一體系尚不完善，體系運(yùn)用中還存在一些建設(shè)、政策等方面的問題。部分流域還沒有采取工程措施控制，這些地區(qū)產(chǎn)生的洪水依然威脅下游地區(qū)的防洪安全。堤防的防洪能力難以滿足生態(tài)經(jīng)濟(jì)區(qū)建設(shè)要求，還需要繼續(xù)提高。蓄滯洪區(qū)人口增長、經(jīng)濟(jì)發(fā)展，加上蓄滯洪區(qū)經(jīng)費(fèi)投入不足、標(biāo)準(zhǔn)低，安全設(shè)施少等原因，啟用蓄滯洪區(qū)難度大，損失嚴(yán)重?，F(xiàn)有的防洪體系與生態(tài)經(jīng)濟(jì)區(qū)內(nèi)的經(jīng)濟(jì)發(fā)展需求不相適應(yīng)。

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.

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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