《园冶》中的时间 #

知应时间是藉以与自然节律同步,创造“天人合一”理想境域的必要途径,具有重要研究价值。本文梳理《园冶》中的气象、物候、风物等时间性要素;借助当代气象、物候资料,结合古代医学、民俗、文学类文献对其进行甄别,验证其科学性;凝练古代造园活动和园居生活中的遵时智慧,并对未来研究提出展望。     

高层建筑的生态设计手法--解读杨经文生态摩天楼的建筑实践

建筑能耗逐年上升．建筑节能在建筑设计的开始即加以考虑是生态建筑设计的关键。该文主要针对杨经文先生生态摩天楼的建筑实践作一些分析,阐述其在高层建筑中的生态设计手法。     

Letter to the editor

Pines are known to produce one seed crop each year and mature about 20 months after pollination. The entire life cycle may take two to three years. Recent observations, however, show that a greater number of crops may develop annually and that cone development may be faster. In Pinellas County, Florida, Pinus clausa and P. elliottii now have three seed crops annually, and consequently, more seeds during the year. Also, there is no dormant phase from pollination time to seed maturity in the primary (most abundant) crop of these species. The reason for this change in reproductive strategy is thought to be as a response to climatic changes, in particular, CO₂ levels. Pines seem to be reacting to increases of available atmospheric CO₂ that fluctuate seasonally. The gas may act as a biochemical switch for developmental stages and be a mechanism for controlling cone growth. Furthermore, there are definite phenological shifts forward in the primary seed crops of P. clausa (about 105 days) and P. elliottii (about 150 days) that present more opportunity for their hybridization. Pines are known to show sensitivity to their environment, and therefore, are climatic indicators of changes that are probably occurring in other economically valuable plants.     

The Evaluation of Efficiency of Color Metrics in Monitoring Robiuia PseudoacaciaPhenology based on RGB Images

The widely application of digital camera,especially the appearance of time|lapse camera,inspired the monitoring vegetation seasonal dynamic using time|series RGB imagery.Extract critical temporal stage of vegetation dynamic is the most useful aspect.Color index including Green Excess Index(ExG),Green Chromatic Coordinate(Gcc),Green Red Vegetation Index(GRVI),and Hue based on HSL(Hue) are the most widely used metrics.However,their efficiency for specific plants may differ.In this study,the efficiency of four color indices mentioned above were tested taking RGB imagery of Robiuia Pseudoacacia as data source.The critical timing point of plant growth and senescence reflected by greenness of leaves were used to pick out the most efficiency color index.The results showed that the best SOS(Start of the Seasonality),EOS(End of the Seasonality) can be extracted using ExG following the     

Cross-scalar satellite phenology from ground, Landsat, and MODIS data

Phenological records constructed from global mapping satellite platforms (e.g. AVHRR and MODIS) hold the potential to be valuable tools for monitoring vegetation response to global climate change. However, most satellite phenology products are not validated, and field checking coarse scale (≥ 500 m) data with confidence is a difficult endeavor. In this research, we compare phenology from Landsat (field scale, 30 m) to MODIS (500 m), and compare datasets derived from each instrument. Landsat and MODIS yield similar estimates of the start of greenness (r² = 0.60), although we find that a high degree of spatial phenological variability within coarser-scale MODIS pixels may be the cause of the remaining uncertainty. In addition, spatial variability is smoothed in MODIS, a potential source of error when comparing in situ or climate data to satellite phenology. We show that our method for deriving phenology from satellite data generates spatially coherent interannual phenology departures in MODIS data. We test these estimates from 2000 to 2005 against long-term records from Harvard Forest (Massachusetts) and Hubbard Brook (New Hampshire) Experimental Forests. MODIS successfully predicts 86% of the variance at Harvard forest and 70% of the variance at Hubbard Brook; the more extreme topography of the later is inferred to be a significant source of error. In both analyses, the satellite estimate is significantly dampened from the ground-based observations, suggesting systematic error (slopes of 0.56 and 0.63, respectively). The satellite data effectively estimates interannual phenology at two relatively simple deciduous forest sites and is internally consistent, even with changing spatial scale. We propose that continued analyses of interannual phenology will be an effective tool for monitoring native forest responses to global-scale climate variability.     

The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India

India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. The phenology of these natural vegetation types is often controlled by climatic condition. Estimating phenological variables will help in understanding the response of tropical and subtropical vegetation to climate change. The study investigated the annual and inter-annual variation in vegetation phenology in India using satellite remote sensing. The study used time-series data of the only available satellite measured index of terrestrial chlorophyll content (MERIS Terrestrial Chlorophyll Index) from 2003 to 2007 at 4.6 km spatial resolution. A strong coincidence was observed with expected phenological pattern, in particular, in inter-annual and latitudinal variability of key phenological variables. For major natural vegetation type the onset of greenness had greater latitudinal variation compared to the end of senescence and there was a small or no leafless period. In the 2003-04 growing season a late start for the onset of greenness was detected at low-to-mid latitudes and it was attributed to the extreme cold weather during the early part of 2003. The length of growing season varied from east to west for the major cropping areas in the Indo-Gangetic plain, for both the first and second crops. For the first time, this study attempted to establish a broad regional phenological pattern for India using remotely sensed estimation of canopy chlorophyll content using five years of data. The overall patterns of phenological variables detected from this study broadly coincide with the pattern of natural vegetation phenology revealed in earlier community level studies. The results of this study suggest the need for an organised network combining ground and space observation which is at presently missing in India.     

Land Use Intensity and Radiation Feedback in Beijing from 2000 to 2015

Many current studies focus on urban expansion and its heat island effect, but the impact of different land use intensity on radiant energy needs further analysis. Based on the land use data of 2000 and 2015 in Beijing, this study divided the land use of Beijing into five types according to the influence degree of human activities and vegetation resilience, namely, the old urban areas, urban expansion areas, unchanged cropland areas, mixed pixel areas with changed gridcells, and unchanged pure pixel areas. On this basis, we calculated Radiative Forcing (RF) due to the change of surface albedo and explored the relationship between RF and vegetation cover. The results showed that: (1) In pure pixel areas, natural vegetation had a lower albedo, and the corresponding RF was larger than the other four land use type areas. However, under the influence of human activities, RF in the four land use type areas showed an obvious increasing trend during the research period, and the increment was also larger than RF in PP areas. (2) Comparing with unchanged pure pixel, the EVI within the other four human-affected land type areas (old urban areas, urban expansion areas, mixed pixel, and unchanged cropland) decreased but the LOS extended. The combined effect of LOS and EVI contributed to the decreasing trend of surface albedo, which prompted the increase of RF. Our finding highlights that human activities often enhances RF by affecting the intensity of land use. This study has important reference value for analyzing the climate feedback of land use change from physical mechanism.     

基于多时相遥感观测的板栗林分布提取研究

板栗林在欧亚、北美等地广泛分布,具有良好的生态价值和经济效益。我国板栗产量居世界首位,是重要的经济树种。使用遥感影像建立板栗林空间分布提取方法能够为其科学管理和高效经营提供定量数据,但树种分类是遥感分类的难点,并且针对板栗林的遥感提取研究较少。以河北省宽城满族自治县为研究区,结合MODIS高时间分辨率特征和Landsat数据较高空间分辨率的特征,研究板栗林提取的最佳时相以及分类特征,并采用多时相观测基于支持向量机算法实现板栗林的提取。结果表明：①4月至6月各地类光谱差异最大,是板栗林提取的关键物候期;②蓝、绿、红、近红外和短波红外波段地表反射率是分类的有效波段,NDI、NDVI、NDWI、RSI和RVI等植被指数增强了植被信息,是板栗林提取的有效分类特征;③单一时相板栗林分类中,生长季前期6月精度最高,生长季后期9月次之,非生长季1月分类结果较差;④结合生长季6月、9月和非生长季1月遥感影像的分类精度最佳,板栗林制图和用户精度分别为89.90%和87.25%。与林业局板栗林面积统计数据相比,精度可达93.45%。     

Calibration of the phenology sub-model of APSIM-Oryza: Going beyond goodness of fit

Parameterizing the phenology of new crop varieties is a major challenge in crop modeling. Here we consider calibration of the phenology sub-model of the widely used crop model APSIM-Oryza, using commonly available varietal data. We show that the dynamic phenology sub-model can be well approximated by a static model, with three equations. It is then straightforward to estimate the parameters using any standard statistical software package. The approach is applied to four rice varieties from Sri Lanka. The software provides not only the best-fit parameters, but also uncertainty information about those parameters. This is essential for understanding how well the model will predict out of sample. Here the photoperiod sensitivity coefficient has large uncertainty, and so predictions for day lengths outside the data set are very unreliable. The uncertainty information is also used to show that in our case, doing more field trials would have very little effect on uncertainty.     

Determination of phenological dates in boreal regions using normalized difference water index

Monitoring and understanding plant phenology are important in the context of studies of terrestrial productivity and global change. Vegetation phenology, such as dates of onsets of greening up and leaf senescence, has been determined by remote sensing using mainly the normalized difference vegetation index (NDVI). In boreal regions, the results suffer from significant uncertainties because of the effect of snow on NDVI. In this paper, SPOT VEGETATION S10 data over Siberia have been analysed to define a more appropriate method. The analysis of time series of NDVI, normalized difference snow index (NDSI), and normalized difference water index (NDWI), together with an analysis of in situ phenological records in Siberia, shows that the vegetation phenology can be detected using NDWI, with small effect of snow. In spring, the date of onset of greening up is taken as the date at which NDWI starts increasing, since NDWI decreases with snowmelt and increases with greening up. In the fall, the date of onset of leaf coloring is taken as the date at which NDWI starts decreasing, since NDWI decreases with senescence and increases with snow accumulation. The results are compared to the results obtained using NDVI-based methods, taking in situ phenological records as the reference. NDWI gives better estimations of the start of greening up than NDVI (reduced RMSE, bias and dispersions, and higher correlation), whereas it does not improve the determination of the start of leaf coloring. A map of greening up dates in central Siberia obtained from NDWI is shown for year 2002 and the reliability of the method is discussed.