[Spatiotemporal dynamic simulation on aboveground carbon storage of bamboo forest and its influence factors in Zhejiang Province, China.].

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.

[Retrieval of leaf area index of Phyllostachys praecox forest based on MODIS reflectance time series data.].

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.

[RS estimation of inventory parameters and carbon storage of moso bamboo forest based on synergistic use of object-based image analysis and decision tree].

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%.

[Simulating of carbon fluxes in bamboo forest ecosystem using BEPS model based on the LAI assimilated with Dual Ensemble Kalman Filter].

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.

[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.

[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)).

[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.

[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.

[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.

[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.