Infrared Camera Based Plant Phenology And GPP

Phenology is the periodic events in the life cycle of plants,for example,leaf-out and senescence.One of the ubiquitous impacts of climate change on the terrestrial ecosystem is the shifting plant phenology,most notably in the spring.The response of phenology to global climate change often observed in temperate forests as earlier spring and later autumn,and the whole growing season length of the vegetation is prolonged.This change in return could potentially exert feedbacks to the climate system through affecting the length and magnitude of photosynthesis and net C02 exchanges,carbon and earth heat balance of ecosystem.The traditional records of vegetation phenology were obtained by manual phenological observations,which could be found hundreds or even thousands of years ago in China,Japan and Europe.In recent decades,with the development of science and technology,remote sensing data,such as MODIS(Moderate Resolution Imaging Spectroradiometer)and AVHRR(Advanced Very High Resolution Radiometer)data,can help us to measure vegetation phenological characteristics in large scale(250 m,8000 m or more coarse Resolution).The ground-based observation data and remote sensing data can help us get phenological change information,for example,the leaf-out date was advanced responding to the climate warming in recent decades.However,people always obtain the satellite remote sensing data with coarse spatial and temporal resolution,which should reduce the accuracy of phenological information,then the remote sensing data need the ground validation.Based on the limit of manual observation and satellite remote sensing observation,this paper would introduce a new method of phenology,near-surface remote sensing observation(phonological)camera,which implies a radioactive instrument or imaging sensors to quantify the seasonal variation the surface of optical properties of vegetation,and reflects the vegetation phenology change.This camera phenology observation has a certain amount of space integration(taking an entire forest canopy as a whole,rather than focusing on a particular individual)with a high spatial and temporal resolution.In recent years,the "near" remote sensing is developing rapidly,many researchers used digital images to acquire plant canopy optical properties to detect plant growth condition by the remote sensing technology,plant analysis and production analysis.Obviously,the near-surface remote sensing is more targeted and with higher resolution than satellite remote sensing,and the digital camera images processing was much easier than satellite images.However,most of the digital cameras are based on red,green and blue(RGB)bands,and many studies have shown that the greenness index extracted from RGB camera could not accurately reflect the status of vegetation growth and plant physiological and biochemical properties.Hence,a widely adopted vegetation index—Normalized Difference Vegetation Index(NDVI)based on red and infrared bands information-can be a robust metric for estimating photosynthetically active vegetation coverage,developmental status,and productivity.Therefore,this study used two digital camera(the RGB camera and infrared camera)to continuously monitor the seasonal change of deciduous forest,and compare which camera could more accurately capture the canopy leaf physiological properties(chlorophyll concentration and nitrogen content)and structure characteristic(leaf area index).And try to understand how environmental cues drive the shift of phenology.In addition,we developed a Bayesian model(Bayesian Change Point detection)to extract the timing of key phenological stages from the time-series of canopy greenness from digital repeat photography data.The results showed that camera-NDVI was useful to identify the key phenological events,such as leaf-out,leaf expansion process,greenness peak and leaf-off dates.The infrared camera provided a new tool with a wide range of wavebands to interpret the seasonality of vegetation structural and functional properties(leaf chlorophyll concentration,nitrogen status,and LAI)(Chapter 3).Actually,both aboveground and belowground parts are important components of terrestrial ecosystem carbon stocks.Root phenology plays a significant role in studying the C cycling responding to warming.Some researches pointed out that root production occupied 50-90%of primary production in temperate forest ecosystem and contributed to CO2/CH4 flux by root respiration.Leaf development should be affected by root activities by taking up water and nutrients from organic soil layer,in turn root growth relied on leaf for photosynthates and roots transport carbon and nutrient into deeper soil.Therefore,understanding the correlation between leaf and root phenology is critically important to accurately assess terrestrial carbon fluxes variation.In this study,we simultaneously measured the seasonality of leaf and root production using sequential imaging throughout the growing season in Harvard Forest in 2014,and then examined the correlation between leaf and root phenology(Chapter 4).The results showed that leaf and root phenology are not coincident,like forest leaf production typically precedes root production because the atmosphere warms faster than the soil;and the dynamics of leaf or root phenology was strongly influenced by air temperature or soil temperature,respectively.Thus,leaf phenology may not be a reliable descriptor of whole plant phenology in forest ecosystem,and integrating above-ground and below-ground phenology should provide a better understanding of ecosystem response to climate change.Chlorophyll fluorescence,as the by-product of photosynthesis,is emitted back to the atmosphere by the leaves.Recently,developments of remote sensing techniques made it possible to extract solar-induced fluorescence(SIF)from the satellite imagery,and a strong correlation between SIF and GPP estimation from other methods suggested the possibility to directly estimate photosynthesis from satellite.Ground-based experiments and field measurements also suggested SIF could be used to improve the estimation of GPP.However,the continuous measurements of the seasonal leaf chlorophyll fluorescence from leaf to ecosystem scale are still rare,which may affect our understanding of using fluorescence as a proxy for photosynthesis under different spatial scales throughout the growing season.So we examined seasonal relationships between ChlF and photosynthesis at the leaf,canopy,and satellite scales,and explored how leaf-level ChlF was linked with canopy-scale solar induced chlorophyll fluorescence(SIF)in a temperate deciduous forest at Harvard Forest(Chapter 5).Our results show that leaf ChlF captured the seasonal variations of photosynthesis,and there is a significant linear relationship between leaf ChlF and photosynthetic capacity across the growing season over different spatial scales,which reflected that ChlF observations provide a powerful proxy to estimate global C budget.Moreover,sensitivity of ChlF signals provides a useful tool to understand the dynamic processes of vegetation growth in terrestrial ecosystems.