Estimation Model Of Poplar Plantation Productivity With Hyperspectral Information And Remote Sensing

Forest is the main body of terrestrial ecosystem, which plays an important role to maintain the global carbon balance and adjust climate change. In the world, as the forest shrinks, it is an important tool to vigorously develop plantation, which can remission the short of forest resources, enhance the carbon sequestration potential and promote the sustainable development of human beings. In China, Poplar is one of the main tree species of plantation. It has the great potential in increasing economic timber and carbon sequestration. Productivity of vegetation is an important indicator to measure of the ability of carbon sequestration. Accuracy and rapidly It is significant to estimate the productivity of Poplar accuracy and rapidly.At present, the methods of estimation productivity are respectively biomass method, micrometeorological method and remote sensing. The remote sensing is a hot research technique due to its fastness, efficiency and other advantages. Light use efficiency model, because of the characteristic of clearly mechanism and simply computation, is widely used in the application of remote sensing for estimation productivity. In this model, there are two important parameters affected by the physiological ecology and meteorological environment, which is respectively Light use efficiency (LUE) and Fraction of absorbed photosynthetic active radiation (FAPAR). LUE equals maximum light use efficiency (LUEmax) multiplied by limiting factors (temperature, moisture and phenological). The calculation of FAPAR is based on Beer-law using leaf area. In the current study, LUEmax of the same type of vegetation is regarded as a fixed value, in Traditional remote sensing model. But, the value of LUEmax is differences under different conditions of vegetation and different growth stages. At the same time, the model of FAPAR is lack of mechanism factors and involves less influence factors. These lead to uncertainty of LUE, FAPAR estimation, and affect accuracy of productivity estimation.The information of different vegetation physiological-ecological parameters can be extracted by Hyperspectral technique through radiative transfer model and mathematical method. The technology contributes to realize quantification of LUEmax and FAPAR. It is certain feasibility to improve the accuracy of estimating vegetation productivity by remote sensing, using Hyperspectral technique. But, characteristics bands of specific vegetation are different. It can improve the estimation precision of the model that realizes the precise optimal band selection.In this study, the Poplar shelterbelts were used as the research object in the Huanghuaihai Plain. Hyperspectral extraction algorithm of the optimal band was improved, and Poplar physiological ecology parameter inversion models were constructed. At the same time, the relationship between LUEmax and physiological-ecological parameters as well as the relationship between FAPAR and physiological-ecological parameters were researched and determined. Then, the spectrum inversion model of Poplar Gross primary productivity (GPP) was constructed by based on the relationship LUEmax, FAPAR and GPP. Finally to calculate the GPP data, the GPP model of the study were validated by the GPP data which were calculated by the eddy covariance method., and were applied. It is aims of this study to provide theory basis and technical support for remote sensing estimation of Poplar GPP. It is very meaningful and useful to improve the GPP estimation accuracy by this study.This article object was the Poplar shelterbelt-forest in Huanghuaihai Plain. Start with the physiological ecology parameters of LUE and FAPAR, and research the meaning physiological ecology parameters in vegetation. Established the LUE and FAPAR model inversed by the physiological ecology parameters, and achieved precise inversion of LUE and FAPAR. At the same time, construct the high spectral estimation model of the physiological and ecological parameters. At last, use the high spectral estimation model of physiological and ecological parameters converse to become for LUE and FAPAR estimate by hyperspectral. To improve the light energy utilization model, to raise the potential of the prediction accuracy on light energy utilization estimation of productive forces. Combine with GPP data using Eddy covariance method (GPP_EC), improve the light utility efficiency model, to gain more accurately remote sensing model of light energy utilization. To get the following main findings and conclusions through all research. In the study, main conclusions are as follows:(1) Analyzing the advantages and disadvantages of Optimal Index Factor Method (OIF) and the Maximum correlation coefficient Method (MCC), in this study a characteristics bands extraction method of Hyperspectral data was built by using the entropy weight method combining Optimal Index Factor and Correlation Coefficient Method, named Optimal Index Factor and Correlation Coefficient Method (OIFC). And the OIFC was successfully applied to the extraction chlorophyll characteristic bands of Poplar leaf and wheat leaf,(2) In the study, using the cost function combining with PROSPECT model and the measured data and analyzed leaf mesophyll structure of Poplar, the average of the Poplar leaf mesophyll structure parameter is 1.65.Poplar chlorophyll content characteristic bands(570nm、630nm、865nm) were extracted by OIFC method. mRVI vegetation index was constructed by the characteristic bands, and analyzed and compared with commonly used vegetation index. The result shows that:both in the leaf and canopy scale, chlorophyll content estimation based on mRVI has the very high accuracy. The models of leaf scale and canopy scale have upper precision and are LCC=0.08x (13.6xmRVI-1.50)162 andCCC= 0.28xmRVI-0.19, respectively.(3) The reflectance data of Hyperspectral were obtained by measure and PROSPECT and PROSAIL model output. The relationship between reflectance and dry matter content was analyzed in leaf scale and canopy scale. The result shows that:The Equivalent Water Thickness and dry matter content has a good linear correlation. The best combination bands of Normalized Difference dry matter index (NDMI) were obtained by the method of normalized index calculation, in the Poplar leaf scale and canopy scale. They are combination bands of 1685nm and 1704nm, combination bands of 1551nm and 2143nm. Analyzing the NDMI of The best combination bands, the models of leaf scale (LM=1.022 xNDMI(1685，1704)-0.0007) was built. Because the dry matter content of canopy is affected by the leaf area, In order to obtain canopy scale dry matter content, so the models of canopy scale can be built based on the models of leaf scale by leaf area index. The model of canopy scale is CM=13165-16583xLAI+(-645+55.18xLAI)xe7.704NDMI(1551,2143).(4) Analyzing the sensitivity of the commonly used water vegetation index and the influence of leaf dry matter and chlorophyll content, GMVI/MSI was built by the ratio of Global Vegetation Moisture Index (GVMI) and Moisture Stress Index (MSI). Both in leaf scale and canopy scale, the models of equivalent water thickness (EWTleaf is leaf scale, EWTcanopy is canopy scale) were built based on GMVI/MSI. Analyzing precision of these models, it was result that both models have high precision. The models of leaf scale and canopy scale are EWTleaf=0.244-0.247xe-0.159MSI/GVMI and EWT=0.052×MSI/GVMI+0.008.(5) The study found that there is high correlation between LUEmax and the ratio of chlorophyll content and dry matter content. In the leaf scale, the estimation LUEmaxmodel built by the ratio of chlorophyll content and dry matter content, has high precision. Using the relationship between canopy and leaf, the estimation LUEmax model of canopy was built LUEmzx=a×CCDM+b. Through the analysis of the major contribution to FAPAR, and considering the photosynthesis is mainly from the chlorophyll absorption photosynthetic active radiation as well as FAPAR is mainly consists of the fraction of absorbed photosynthetic active radiation by chlorophyll (FAPARch1). Therefore, canopy estimation FAPAR model is constructed by the canopy scale chlorophyll content.(6) LUEmax+FAPARch1 model was built by modified estimation LUEmax model and modified estimation FAPARch1 model. The GPP of Poplar was estimated respectively by LUEmax+FAPARch1 model and VPM model. At the same time, the estimation GPP of MODIS products were chosen, in the same period and woodland. Finally, using MODIS spectral data to the estimation GPP from above models were tested by the measured values of GPP (GPP_EC) from the eddy covariance method. It shows that:there are high correlation between estimate the GPP of all models and GPP_EC. And the correlation coefficients (R2) are 0.87,0.80 and 0.72, respectively. The correlation coefficient of LUEmax+FAPARch1 model (R2=0.87) is the highest among all the correlation coefficients. It is clear that LUEmax+FAPARch1 model has relatively high measuring accuracy for estimation GPP.(7) Remote sensing data of MODIS and meteorological data were got from January 1, 2010 to December 31,2013, in the Poplar plantation. From 2010 to 2013 years, the total annual GPP of Poplar plantation, by using LUEmax+FAPARchl model, are 8.97 MgC·hm-1,9.65 MgC·hm-1,9.33 MgC·hm-1,9.04 MgC·hm-1.