Identification of gaps in mangrove forests with airborne LIDAR

Mangrove forests change frequently due to disturbances from tropical storms, frost, lightning, and insects. It has been suggested that the death and regeneration of trees in small gaps due to lightning may play a critical role in mangrove forest turnover; however, the large-scale quantification of spatial pattern and areas of gaps is lacking for investigating this issue. Airborne light detection and ranging (LIDAR) technology provides an effective way for identifying gaps by remotely obtaining direct measurements of ground and canopy elevations. A method based on an alternative sequential filter and black top-hat mathematical morphological transformation was developed to extract gap features. Comparison of identified gap polygons with raw LIDAR measurements and field surveys shows that the proposed method successfully extracted gap features in mangrove forests in Everglades National Park. There are 400–500 lightning gaps per square kilometer in mangrove forests at the study sites. The distribution of gap sizes follows an exponential form and the area of gaps with sizes larger than 100 m² account for 55–61% of the total area of gaps. The area of gaps in the mangrove forest in Everglades National Park is about 4–5% of the total forest area and the average gap formation rate is about 0.3% of the total forest area per year, indicating that lightning gaps play an important role in mangrove forest dynamics.     

Quality assessment of SRTM C- and X-band interferometric data: Implications for the retrieval of vegetation canopy height

The Shuttle Radar Topography Mission distinguished itself as the first near-global spaceborne mission to demonstrate direct sensitivity to vertical vegetation structure. Whether this sensitivity is viewed as exploitable signal or unwanted bias, a great deal of interest exists in retrieving vegetation canopy height information from the SRTM data. This study presents a comprehensive application-specific assessment of SRTM data quality, focusing on the characterization and mitigation of two primary sources of relative vertical error: uncompensated Shuttle mast motion and random phase noise. The assessment spans four test sites located in the upper Midwestern United States and examines the dependence of data quality on both frequency, i.e., C-band vs. X-band, and the number of acquired datatakes. The results indicate that the quality of SRTM data may be higher than previously thought. Novel mitigation strategies include a knowledge-based approach to sample averaging, which has the potential to reduce phase noise error by 43 to 80%. The strategies presented here are being implemented as part of an ongoing effort to produce regional- to continental-scale estimates of vegetation canopy height within the conterminous U.S.     

An empirical InSAR-optical fusion approach to mapping vegetation canopy height

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km² in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service-Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r = 0.88) was achieved for the prototype mapping zone.     

Analysis of saturated free fatty acids from pollen by HPLC with fluorescence detection

A sensitive method for the determination of free fatty acids using 2‐(2‐(anthracen‐10‐yl)‐1H‐naphtho[2,3‐d]imidazol‐1‐yl) ethyl‐p‐toluenesulfonate (ANITS) as tagging reagent with fluorescence detection has been developed. ANITS could easily and quickly label fatty acids in the presence of the K₂CO₃ catalyst at 90 °C for 40 min in N,N‐dimethylformamide solvent. From the extracts of rape bee pollen samples, 20 free fatty acids were sensitively determined. Fatty acid derivatives were separated on a reversed‐phase Eclipse XDB‐C₈ column by HPLC in conjunction with gradient elution. The corresponding derivatives were identified by post‐column APCI/MS in positive‐ion detection mode. ANITS‐fatty acid derivatives gave an intense molecular ion peak at m/z [M+H]⁺; with MS/MS analysis, the collision‐induced dissociation spectra of m/z [M+H]⁺ produced the specific fragment ions at m/z [M–345]⁺ and m/z 345.0 (here, m/z 345 is the core structural moiety of the ANITS molecule). The fluorescence excitation and emission wavelengths of the derivatives were λ_ex = 250 nm and λ_em = 512 nm, respectively. Linear correlation coefficients for all fatty acid derivatives are >0.9999. Detection limits, at a signal‐to‐noise ratio of 3 : 1, are 24.76–98.79 fmol for the labeled fatty acids.     

A neural network-based scheme coupled with the RPV model inversion package

A hybrid scheme for the inversion of the Rahman-Pinty-Verstraete (RPV) model is presented. It combines the inversion technique described by Lavergne et al. (Lavergne, T., Kaminski, T., Pinty, B., Taberner, M., Gobron, N., Verstraete, M.M., Vossbeck, M., Widlowski, J.L., Giering, R. (2007). Application to MISR land products of an RPV model inversion package using adjoint and Hessian codes. Remote Sensing of Environment, 107, 362-375.) and a multilayer backpropagation feedforward neural network. The RPV inversion package is applied to a sample set of pixels within the satellite scene. Subsequently the pairs of bidirectional reflectance factors (BRF) and model parameters estimated from the sample set of pixels are used to train the neural network. Since the mathematical formulation of the RPV model is embedded in these training data variables, the neural network can efficiently retrieve the model parameters for the whole satellite scene. This scheme has been tested for a MISR L2 BRF scene, MISR L1B2-derived BRF data corresponding to two different dates and a mosaic of MISR L2 BRF scenes acquired over Southern Africa covering a large extent of Miombo woodland. The results show this strategy retrieves the RPV model parameters and uncertainties with high accuracy and considerable speed over large areas.     

基于计算机图形和多孔介质的树冠孔隙率反演

树冠孔隙率(GF)决定了光和气流穿过树冠与植被元素相互作用进行光合作用和蒸腾的传输情况,在多孔介质理论启发下,受计算机图形学的启发,将树冠中的植被元素定量为固体矩阵,将树冠中的缝隙作为孔隙来指导GF计算。通过从地面激光扫描数据中提取单个叶片,并采用拟合算法去除噪声和描绘叶片边缘,创新地定义了叶片卷曲和下垂程度的等效厚度,并从拟合点中计算出来。然后,用自适应尺寸分配的六边形棱镜包住每片叶子的扫描点,用圆柱体模型拟合每个枝干,以计算树冠的等效叶子和枝干体积。结果显示,分割的叶片数为紫薇(1193片)、樱花(2540片)以及含笑(379片),估计的GF分别为：紫薇0.990,樱花树0.976,含笑0.978。     

不同氮素水平对直立穗型水稻品种群体光合特性的影响

在田间条件下测定了两种氮素水平下直、弯穗型水稻品种的群体光合特性。结果表明：参试的两个水稻品种群体光合速率（CAP）的生育期变化为单峰曲线,在籽粒灌浆期,直立穗型品种CAP下降幅度慢于弯曲穗型品种;增施氮肥可提高CAP,并延长其高值持续期;直立穗型品种不同叶位单叶净光合速度（PN）最大值差异不明显,增施氮肥可延缓PN高值持续期,但对提高PN影响不大,于水稻开花期及乳熟期田间高氮区群体饲喂^14CO2,测定结果表明：直立穗型品种冠层中项部三片叶的光合速率最高,其光合量占单株总光合量的87.4%;弯曲穗品种测定结果与直立穗型品种相似。直立穗型品种抽穗后剑叶叶绿素含量、光合速率、比叶重、单株根伤流量和冠层生态条件大都优于弯曲穗型品种,这可能是直立患得患失同穗后CAP下降较缓慢的主要原因。     

森林水文效应的综合分析

引用森林水文实验站和国家水文基本站网中的森林流域和非森林流域的实测资料,进行同步期的对比分析.对于试验小流域或径流场,总结出林冠截留、树干径流和林地拦蓄的一些统计结果,并归纳得到可供利用的计算公式.对于大面积森林流域,主要从基本水文站网中,森林覆盖率明显不同而其它自然地理条件类似的测站进行对比分析,当干旱指数小于1.2的地区,森林使年径流增加,当干旱指数大于1.2的地区,森林使年径流减少.     

森林林冠对降雨量截留的实验分析

以实测资料为基础，分析了林冠对降雨量的截留量。     

跨高速铁路施工安全防护棚架设计

结合工程实例,对跨高速铁路施工防护棚架的设计和施工方案进行了研究,并对荷载、防护支架结构、防护支架拆除后钢管立柱的稳定性作了计算分析,证明了该防护棚架设计的合理性。