[Spatiotemporal dynamic simulation on aboveground carbon storage of bamboo forest and its influence factors in Zhejiang Province, China.] / æµ™æ±Ÿçœç«¹æž—åœ°ä¸Šç¢³å‚¨é‡çš„æ—¶ç©ºåŠ¨æ€æ¨¡æ‹ŸåŠå½±å“å› ç´ .

Bamboo forests have an efficient carbon sequestration capacity and play an important role in responding to global climate change. However, the current estimation of bamboo carbon storage has some errors, leading to uncertainty in the spatiotemporal pattern of bamboo forest carbon storage. This study simulated aboveground carbon storage of Zhejiang Province, China, during 1984-2014 based on the combination of an improved BIOME-BGC (biogeochemical cycles) model and remote sensing data, with the accuracy being verified with forest resource inventory data. The spatio-temporal distribution and environmental factors of aboveground carbon storage were analyzed. The results showed that the simulated carbon storage was accurate, with average correlation coefficient (r), root mean square error (RMSE) and relative bias (rBIAS) being 0.75, 7.24 Mg C·hm-2 and -2.57 Mg C·hm-2, respectively. Generally, the aboveground carbon storage of bamboo forests in the whole province tended to increase from 1984 to 2014, the range of aboveground carbon density was 13.10-17.14 Mg C·hm-2, and that of the total aboveground carbon storage was between 9.94-17.19 Tg C. The high aboveground carbon storage of bamboo was mainly distributed in developed bamboo industry areas, such as Anji, Lin'an, and Longyou. The change of aboveground carbon storage in bamboo forest was significantly correlated with temperature, precipitation, radiation, CO2 concentration and nitrogen deposition, with higher partial correlation coefficients between precipitation and temperature and carbon storage.

[Retrieval of leaf area index of Phyllostachys praecox forest based on MODIS reflectance time series data.] / 基于MODIS时间序列反射率数据的雷竹林LAI反演.

Based on the MODIS surface reflectance data, five vegetation indices, including norma-lized difference vegetation index (NDVI), simple ratio index (SR), Gitelson green index (GI), enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI) were constructed as remote sensing variables, coupled with the seven original spectral reflectance bands of MODIS. Stepwise regression and correlation analysis were used to select the variables, and the stepwise regression and Back Propagation (BP) neural network models were constructed based on the measured LAI to retrieve the LAI time series data of Phyllostachys praecox (Lei bamboo) forest during the period from January 2014 to March 2017. The retrieval results were compared with MOD15A2 LAI products during the same period. The results showed that SR was the single variable selected for the stepwise regression model. The correlations of LAI with bands b1, b2, b3, b7 and five vegetation indices were significant, which could be used as input variables of BP neural network model. There was a significant correlation between the LAI estimated from BP neural network and measured LAI, with the R2 of 0.71, RMSE of 0.34, and RMSEr of 13.6%. R2 was increased by 10.9%, RMSE decreased by 5.6%, and RMSEr decreased by 12.3% compared with LAI estimated from stepwise regression method. R2 was increased by 54.5%, RMSE decreased by 79.3%, and RMSEr decreased by 79.1% compared with MODIS LAI. The LAI of Lei bamboo forest could be accurately retrieved using BP neural network method based on MODIS reflectance time series data, which would be a feasible method for rapid monitoring of LAI in Lei bamboo forest.

[RS estimation of inventory parameters and carbon storage of moso bamboo forest based on synergistic use of object-based image analysis and decision tree]. / ç»¼åˆé¢å‘å¯¹è±¡ä¸Žå†³ç­–æ ‘çš„æ¯›ç«¹æž—è°ƒæŸ¥å› å­åŠç¢³å‚¨é‡é¥æ„Ÿä¼°ç®—.

By synergistically using the object-based image analysis (OBIA) and the classification and regression tree (CART) methods, the distribution information, the indexes (including diameter at breast, tree height, and crown closure), and the aboveground carbon storage (AGC) of moso bamboo forest in Shanchuan Town, Anji County, Zhejiang Province were investigated. The results showed that the moso bamboo forest could be accurately delineated by integrating the multi-scale ima ge segmentation in OBIA technique and CART, which connected the image objects at various scales, with a pretty good producer's accuracy of 89.1%. The investigation of indexes estimated by regression tree model that was constructed based on the features extracted from the image objects reached normal or better accuracy, in which the crown closure model archived the best estimating accuracy of 67.9%. The estimating accuracy of diameter at breast and tree height was relatively low, which was consistent with conclusion that estimating diameter at breast and tree height using optical remote sensing could not achieve satisfactory results. Estimation of AGC reached relatively high accuracy, and accuracy of the region of high value achieved above 80%.

[Simulating of carbon fluxes in bamboo forest ecosystem using BEPS model based on the LAI assimilated with Dual Ensemble Kalman Filter]. / 双集合卡尔曼滤波LAI同化结合BEPS模型的竹林生态系统碳通量模拟.

LAI is one of the most important observation data in the research of carbon cycle of forest ecosystem, and it is also an important parameter to drive process-based ecosystem model. The Moso bamboo forest (MBF) and Lei bamboo forest (LBF) were selected as the study targets. Firstly, the MODIS LAI time series data during 2014-2015 was assimilated with Dual Ensemble Kalman Filter method. Secondly, the high quality assimilated MBF LAI and LBF LAI were used as input dataset to drive BEPS model for simulating the gross primary productivity (GPP), net ecosystem exchange (NEE) and total ecosystem respiration (TER) of the two types of bamboo forest ecosystem, respectively. The modeled carbon fluxes were evaluated by the observed carbon fluxes data, and the effects of different quality LAI inputs on carbon cycle simulation were also studied. The LAI assimilated using Dual Ensemble Kalman Filter of MBF and LBF were significantly correlated with the observed LAI, with high R2 of 0.81 and 0.91 respectively, and lower RMSE and absolute bias, which represented the great improvement of the accuracy of MODIS LAI products. With the driving of assimilated LAI, the modeled GPP, NEE, and TER were also highly correlated with the flux observation data, with the R2 of 0.66, 0.47, and 0.64 for MBF, respectively, and 0.66, 0.45, and 0.73 for LBF, respectively. The accuracy of carbon fluxes modeled with assimilated LAI was higher than that acquired by the locally adjusted cubic-spline capping method, in which, the accuracy of mo-deled NEE for MBF and LBF increased by 11.2% and 11.8% at the most degrees, respectively.

[Retrieval of crown closure of moso bamboo forest using unmanned aerial vehicle (UAV) remotely sensed imagery based on geometric-optical model].

This research focused on the application of remotely sensed imagery from unmanned aerial vehicle (UAV) with high spatial resolution for the estimation of crown closure of moso bamboo forest based on the geometric-optical model, and analyzed the influence of unconstrained and fully constrained linear spectral mixture analysis (SMA) on the accuracy of the estimated results. The results demonstrated that the combination of UAV remotely sensed imagery and geometric-optical model could, to some degrees, achieve the estimation of crown closure. However, the different SMA methods led to significant differentiation in the estimation accuracy. Compared with unconstrained SMA, the fully constrained linear SMA method resulted in higher accuracy of the estimated values, with the coefficient of determination (R2) of 0.63 at 0.01 level, against the measured values acquired during the field survey. Root mean square error (RMSE) of approximate 0.04 was low, indicating that the usage of fully constrained linear SMA could bring about better results in crown closure estimation, which was closer to the actual condition in moso bamboo forest.

[Retrieval of leaf area index of moso bamboo forest with Landsat Thematic Mapper image based on PROSAIL canopy radiative transfer model].

The PROSAIL canopy radiative transfer model was used to establish leaf area index (LAI) and canopy reflectance lookup-table for Moso bamboo forest. The combination of Landsat Thematic Mapper (TM) image and this model was then used to retrieve LAI. The results demonstrated that the sensitivity of the input parameters in the PROSAIL model decreased in order of LAI >chlorophyll content (C(ab)) > leaf structure parameters (N) > mean leaf angle (ALA) > equivalent water thickness (C(w)) > dry matter content (C(m)). The most sensitive factors LAI and C(ab) were then used to construct the LAI-canopy reflectance lookup-table. The LAI estimates from the PROSAIL model had good agreement with the reference data, with the coefficient of determination (R2) reached 0.90. The root mean square error (RMSE) and relative RMSE were 0.58 and 13.0%, respectively. However, the mean LAI estimate was higher than the observed value.

[Transferability of remote sensing-based models for estimating moso bamboo forest aboveground biomass].

Taking the moso bamboo production areas Lin'an, Anji, and Longquan in Zhejiang Province of East China as study areas, and based on the integration of field survey data and Landsat 5 Thematic Mappr images, five models for estimating the moso bamboo (Phyllostachys heterocycla var. pubescens) forest biomass were constructed by using linear, nonlinear, stepwise regression, multiple regression, and Erf-BP neural network, and the models were evaluated. The models with higher precision were then transferred to the study areas for examining the model's transferability. The results indicated that for the three moso bamboo production areas, Erf-BP neural network model presented the highest precision, followed by stepwise regression and nonlinear models. The Erf-BP neural network model had the best transferability. Model type and independent variables had relatively high effects on the transferability of statistical-based models.

[Effects of atmospheric calibration on remote sensing estimation of Moso bamboo forest biomass].

Landsat Thematic Mapper (TM) image was used to estimate Moso bamboo forest biomass, and six atmospheric calibration methods (FLAASH model, 6S model, and DOS1-4 models) were adopted to analysis the effects of atmospheric calibration on the remote sensing estimation of Moso bamboo forest biomass. All the six calibration methods could effectively reduce the atmospheric impacts on TM spectral responses. The relationships between NDVI and Moso bamboo forest biomass under the calibration by the six calibration methods were improved. Great differences were observed in the relationships of Moso bamboo forest biomass with NDVI, II, and MI when using the same calibration methods, suggesting that atmospheric calibration should be made for studying the biophysical significance of vegetation indices. The Landsat TM data corrected with DOS3 model had the highest correlation coefficient with Moso bamboo forest biomass, but there were no significant differences in the correlation coefficients after corrected with the six calibration methods, which indicated that atmospheric calibration might be not required if a single TM image was used for biomass estimation with multiple linear regression model.

[Using fractal dimensions of hyperspectral curves to analyze the healthy status of vegetation].

The reflectance spectral curves of leaves can reflect many information of vegetation growth, and its variation maybe means that the healthy status of vegetation will change. Many spectral feature parameters such as red edge position, height of green peak, depth of red band absorption, the area of red edge and some vegetation index have been used to describe this change. However, the change of vegetation healthy status is not some feature parameters, but a comprehensive variation of the whole curve. So, a comprehensive index maybe has more value to describe the change of hyperspectral curve of vegetation and indicates its healthy status. Fractal is an appropriate mathematical tool, and fractal dimension can be used to explain the comprehensive variation of a curve. Therefore, in the present study, fractal theory was used to analyze the healthy status of different vegetation. Firstly, analytical spectral devices (ASD) were used to measure the hyperspectral curves of different vegetations with different healthy status. Secondly, spectral curves were analyzed, and some parameters which can really reflect different healthy status were obtained. Finally, the fractal dimension of reflectance spectral curves inside a spectral band zone between 450 and 780nm was computed by variation method, and the relationship between fractal dimensions and spectral feature parameters was established. The research results showed that (1) the hyperspectral curves of vegetation have fractal feature, and their fractal dimensions gradually decrease with the health deterioration of leaves, (2) fractal dimension has positive correlation with the height of green peak, the depth of red band absorption and the area of red edge, (3) multivariate analysis showed that fractal dimensions have a significant linear relationship with the three spectral feature parameters just mentioned above. So, the fractal dimension of hyperspectral curve can serve as a new comprehensive parameter to analyze quantitatively the healthy status of vegetations.

[Study on relationships between total chlorophyll with hyperspectral features for leaves of Pinus massoniana forest].

In the present study, the authors built the relationships between the total chlorophyll and hyperspectral features of P. massoniana. The research results showed that (1) chlorophyll content has a good linear relationship with spectral reflectance around 527, 703, 1 364 and 1 640 nm, and this result is helpful for us to select some important bands when monitoring P. massoniana by remote sensing image; (2) all of the nine kinds of spectral feature parameters including red edge position, mean reflectance of red edge, mean reflectance around red edge position, red edge slope, red edge area, absorption depth of red band, green peak height, red edge normalized difference vegetation index and red edge vegetation stress index, have exponential function relationship (r = 0.5-0.7) with the total chlorophyll; (3) the total chlorophyll content can be predicted by multivariate model by the nine spectral feature parameters, and partial least-squares regression model have higher prediction accuracy than the traditional multivariate linear model. The model's root mean square (RMS) is 0.008 8, and mean absolute percentage error is 0.761 7%. During the growth of vegetation, biochemical parameters such as chlorophyll have vital function, for example, it can indicate the health status or pathological feature. So, the models mentioned just above will help us understand the ecological process of P. massoniana forest and provide valuable reference for monitoring P. massoniana and pine wood nematode disease by remote sensing technique.