A dynamic relationship between the leaf phenology and rainfall regimes of Hawaiian tropical ecosystems: A remote sensing approach

Hypertemporal MODIS time series data provide a unique opportunity to investigate a dynamic relationship between leaf phenology and the climatic pattern of diverse, cloud‐prone Hawaiian ecosystems. Harmonic analysis summarized the complex greenness signals of Hawaiian tropical ecosystems into two main phenological wave forms – a moisture‐limited and a light‐limited type. Greenness maximums occurred during the wet season in dry and mesic ecosystems, and during the dry season in wet forests. The magnitude and periodicity of greenness fluctuations were also rainfall‐dependent. The annual greenness amplitude increased with increasing mean annual precipitation (MAP) in dry and mesic ecosystems. In wetter environments where MAP was greater than 3000 mm, however, annual greenness amplitude decreased with MAP. Annual greenness periodicity was stronger in drylands than in wet forests, and it weakened as annual precipitation increased. This result shows that rainfall is less important as a limiting factor in wet forests than it is in drylands. Therefore, leaf phenology is not governed by rainfall seasonality as forest wetness increases in the region.     

Latitudinal variation of leaf morphological traits from species to communities along a forest transect in eastern China

Comprehensive information on geographic patterns of leaf morphological traits in Chinese forests is still scarce. To explore the spatial patterns of leaf traits, we investigated leaf area (LA), leaf thickness (LT), specific leaf area (SLA), and leaf dry matter content (LDMC) across 847 species from nine typical forests along the North-South Transect of Eastern China (NSTEC) between July and August 2013, and also calculated the community weighted means (CWM) of leaf traits by determining the relative dominance of each species. Our results showed that, for all species, the means (± SE) of LA, LT, SLA, and LDMC were 2860.01 ± 135.37 mm², 0.17 ± 0.003 mm, 20.15 ± 0.43 m² kg^–1, and 316.73 ± 3.81 mg g^–1, respectively. Furthermore, latitudinal variation in leaf traits differed at the species and community levels. Generally, at the species level, SLA increased and LDMC decreased as latitude increased, whereas no clear latitudinal trends among LA or LT were found, which could be the result of shifts in plant functional types. When scaling up to the community level, more significant spatial patterns of leaf traits were observed (R ² = 0.46–0.71), driven by climate and soil N content. These results provided synthetic data compilation and analyses to better parameterize complex ecological models in the future, and emphasized the importance of scaling-up when studying the biogeographic patterns of plant traits.     

Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments

Hyperspectral remote sensing research was conducted to document the biophysical and biochemical characteristics of controlled forest plots subjected to various nutrient and irrigation treatments. The experimental plots were located on the Savannah River Site near Aiken, SC. AISA hyperspectral imagery were analysed using three approaches, including: (1) normalized difference vegetation index based simple linear regression (NSLR), (2) partial least squares regression (PLSR) and (3) machine-learning regression trees (MLRT) to predict the biophysical and biochemical characteristics of the crops (leaf area index, stem biomass and five leaf nutrients concentrations). The calibration and cross-validation results were compared between the three techniques. The PLSR approach generally resulted in good predictive performance. The MLRT approach appeared to be a useful method to predict characteristics in a complex environment (i.e. many tree species and numerous fertilization and/or irrigation treatments) due to its powerful adaptability.     

Effects of Crop Growth and Development on Land Surface Fluxes

In this study, the Crop Estimation through Resource and Environment Synthesis model (CERES3.0) was coupled into the Biosphere-Atmosphere Transfer Scheme (BATS), which is called BATS CERES, to represent interactions between the land surface and crop growth processes. The effects of crop growth and development on land surface processes were then studied based on numerical simulations using the land surface models. Six sensitivity experiments by BATS show that the land surface fluxes underwent substantial changes when the leaf area index was changed from 0 to 6 m2 m-2. Numerical experiments for Yucheng and Taoyuan stations reveal that the coupled model could capture not only the responses of crop growth and development to environmental conditions, but also the feedbacks to land surface processes. For quantitative evaluation of the effects of crop growth and development on surface fluxes in China, two numerical experiments were conducted over continental China: one by BATS CERES and one by the original BATS. Comparison of the two runs shows decreases of leaf area index and fractional vegetation cover when incorporating dynamic crops in land surface simulation, which lead to less canopy interception, vegetation transpiration, total evapotranspiration, top soil moisture, and more soil evaporation, surface runoff, and root zone soil moisture. These changes are accompanied by decreasing latent heat flux and increasing sensible heat flux in the cropland region. In addition, the comparison between the simulations and observations proved that incorporating the crop growth and development process into the land surface model could reduce the systematic biases of the simulated leaf area index and top soil moisture, hence improve the simulation of land surface fluxes.     

Assessing adaptability of planted trees using leaf traits: A case study with Robinia pseudoacacia L. in the Loess Plateau, China

Leaf trait patterns and their variations with climate are interpreted as an adaptive adjustment to environment.This study assessed the adaptability of planted black locust (Robinia pseudoacacia L.) based on the analysis of leaf traits and the comparison of its leaf traits with inter-specific ones existing in the same area.We measured some water and N use related leaf traits: leaf dry mass per unit area (LMA) and N,P and K concentrations based on both leaf area (Narea,Parea and Karea) and leaf mass (Nmass,Pm...     

Estimating leaf area index by inversion of reflectance model for semiarid natural grasslands

The study developed an integrated reflectance model combining radiative transfer and geometric optical properties in order to inverse leaf area index (LAI) of semiarid natural grasslands. In order to better link remote sensing information with land plants, and facilitate regional and global climate change studies, the model introduced a simple but important geometrical similarity parameter related to plant crown shapes. The model revealed the influences of different plant crown shapes (such as spherical, cylindrical/cuboidal and conic crowns) on leaf/branch angle distribution frequencies, shadow ground coverage, shadowed or sunlit background fractions, canopy reflectance, and scene reflectance. The modeled reflectance data agreed with the measured ones in the three Leymus chinensis steppes with different degradation degrees, which validated the reflectance model. The lower the degradation degree was, the better the modeled data agreed with the measured data. After this reflectance model was coupled with the optimization inversion method, LAI over the entire study region was estimated once every eight days using the eight-day products of surface reflectance obtained by multi-spectral Moderate-Resolution Imaging Spectroradiometer (MODIS) during the growing seasons in 2002. The temporal and spatial patterns of inversed LAI for the steppes with different cover degrees, swamps, flood plains, and croplands agreed with the general laws and measurements very well. But for unused land cover types (sands, saline, and barren lands) and forestlands, totally accounting for about 10% of the study region, the reasonable LAI values were not derived by inversing, requiring further revising of the model or the development of a new model for them. Supported by the National Natural Science Foundation of China (Grant No. 30500076), the National Basic Research Program of China (Grant No. 2007CB714407), and the President Foundation of Graduate University of Chinese Academy of Sciences (Grant No. YZJJ200205)     

叶片化学组分成像光谱遥感探测机理分析

利用地面光谱仪的测量数据 ,进行了成像光谱遥感探测叶片化学组分的机理性研究。采用多元逐步回归方法 ,分析了鲜叶片 7种化学组分含量与其光谱特性的统计关系 ,分别建立了反射率 ρ及其变化式 1/ρ、logρ和ρ的一阶导数Kρ 与化学组分含量的统计方程 ,并对这 4个指标的性能进行了比较和评价。结果表明 ,在 95 %的置信水平下 ,可以由叶片的精细光谱特征较好地反映出化学组分含量 ;特别是利用Kρ 作为因子 ,使置信水平提高到 99% ,尤以对粗蛋白质、N、K含量反映最好 ,R2 均达到 0 8以上 ,粗蛋白质可达 0 95 6 4,从而为进一步探讨在中国利用成像光谱遥感探测叶片化学组分奠定了基础     

西北地区陆地生态系统植被状态参数业务化遥感研究

植被指数(NDVI)和叶面积指数(LAI)是两个非常重要的陆地生态系统植被状态参数.我们首先利用最大值(MVC)合成方法使用先进遥感数据如MODIS、AVHRR3等得到旬合成植被指数(NDVI),然后利用最新的经验方法针对不同的陆地生态系统类型反演得到叶面积指数,重点研究了我国沙尘暴发生频率较高的我国西北地区植被覆被状态及其变化情况.植被指数能够反映区域,乃至全球范围植被年季状态,用于监测陆地生态系统植物光合作用活动及其变化.植被指数作为一个基础参数能够用于计算反演更高级别的陆地生态系统状态参数.叶面积指数直接影响植被的光合作用,蒸腾作用的变化和陆面过程的能量平衡状态.在沙尘暴预测研究中使用的起沙过程模型需要将叶面积指数作为一个关键输入变量,另外,绝大多数生态过程模型模拟碳、水循环时也都需要将叶面积指数作为一个非常重要的输入变量.我们总结了最新的叶面积指数经验反演方法,针对6钟不同的陆地生态系统类型应用不同经验模型计算得到了叶面积指数.     

人工增雪对冬麦冻害的影响

自１９７８年以来，在新疆北部沿天山一带对冬季层状云飞机播撒碘化银、干冰等冷云催化剂进行人工增雪作业，对１８个非播云年和１７个播云年的冬麦冻害做了对比，在播云期内冬小麦年平均冻害面积减少８０％以上。对３个主要因素（麦种改良、冬季气温升高和人工增雪）进行了分析，结果表明，人工播云是一个主要的因素。