Results of a CLM4 Land Surface Simulation over China Using a Multisource Integrated Land Cover Dataset

In this study, the high-accuracy multisource integrated Chinese land cover （MICLCover） dataset was used in version 4 of the Community Land Model （CLM4） to assess how the new land cover information affected land surface simulation over China. Compared to the default land cover dataset in CLM4, the MICL data indicated lower values for bare soil （14.6% reduction）, nee- dleleaf tree （3.6%）, and broadleaf tree （1.9%）; higher values for shrub cover （1.8% increase）, grassland （9.9%）, cropland （5.0%）, glaciers （0.5%）, lakes （1.6%）, and wetland （1.1%）; and unchanged for urban areas. Two comparative CLM4 simulations were conducted for the 33-yr period from 1972 to 2004, one using the MICL dataset and the other using the default dataset. The results revealed that the MICL data produced a 0.3% lower mean annual surface albedo over China than the original data. The largest contributor to the reduced value was semiarid regions （2.1% reduction）. The MICL-data albedo value agreed more closely with observations （MODIS broad- band black-sky albedo products） over arid and semiarid regions than for the original data to some extent. The simulated average sensible heat flux over China increased by only 0.1 W m 2 owing to the reduced values in arid and semiarid regions, as opposed to increases in humid and semihumid regions, while an increased latent heat flux of I W m-2 was reflected in almost identical changes over the whole region. In addition, the mean annual runoff simulated by CLM4 using MICL data decreased by 6.8 mm yr-1, primarily due to large simulated decreases in humid regions.     

A Dipole Pattern of Summer Precipitation over Mid-high Latitude Asia and Related Snow Cover Anomalies in the Preceding Spring

A dipole pattern of summer precipitation over the mid-high latitudes of Asia, which is characterized by opposing summer precipitation variations between the Mongolian and Northeast China （MNC） region and the West Siberian Plain （WSP）, is found to be clear and stable on both interdecadal and interannual scales during 1981- 2011. Spring snow cover anomalies over a small region within the WSP and the Heilongjiang River （HR） region are closely related to the variation of this dipole mode during the subsequent summer, and they can therefore be considered as forecasting factors. Our statistical results imply a potential process explaining the relationship between the spring snow anomalies and the summer rainfall dipole. Corresponding to the snow anomalies, Rossby waves propagate along a path from the WSP region, via the Mongolian Plateau, to the Stanovoy Range during summer. At the same time, Rossby-wave energy divergences and convergences along this path maintain and reinforce an anomalous cyclone and anticyclone pairing over the Asian continent, which is significantly linked to opposite summer precipitation anomalies between the MNC and WSP regions. Numerical experiments are need- ed to further confirm the above conjecture and demonstrate the detailed physical mechanisms linking the spring snow cover anomalies and summer precipitation dipole.     

Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies

A computational canopy volume (CCV) based on airborne laser scanning (ALS) data is proposed to improve predictions of forest biomass and other related attributes like stem volume and basal area. An approach to derive the CCV based on computational geometry, topological connectivity and numerical optimization was tested with sparse-density, plot-level ALS data acquired from 40 field sample plots of 500–1000 m² located in a boreal forest in Norway. The CCV had a high correspondence with the biomass attributes considered when derived from optimized filtrations, i.e. ordered sets of simplices belonging to the triangulations based on the point data. Coefficients of determination (R²) between the CCV and total above-ground biomass, canopy biomass, stem volume, and basal area were 0.88–0.89, 0.89, 0.83–0.97, and 0.88–0.92, respectively, depending on the applied filtration. The magnitude of the required filtration was found to increase according to an increasing basal area, which indicated a possibility to predict this magnitude by means of ALS-based height and density metrics. A simple prediction model provided CCVs which had R² of 0.77–0.90 with the aforementioned forest attributes. The derived CCVs always produced complementary information and were mainly able to improve the predictions of forest biomass relative to models based on the height and density metrics, yet only by 0–1.9 percentage points in terms of relative root mean squared error. Possibilities to improve the CCVs by a further analysis of topological persistence are discussed.     

A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data

Light Detection and Ranging (Lidar) can generate three-dimensional (3D) point cloud which can be used to characterize horizontal and vertical forest structure, so it has become a popular tool for forest research. Recently, various methods based on top-down scheme have been developed to segment individual tree from lidar data. Some of these methods, such as the one developed by Li et al. (2012), can obtain the accuracy up to 90% when applied in coniferous forests. However, the accuracy will decrease when they are applied in deciduous forest because the interlacing tree branches can increase the difficulty to determine the tree top. In order to solve challenges of the tree segmentation in deciduous forests, we develop a new bottom-up method based on the intensity and 3D structure of leaf-off lidar point cloud data in this study. We applied our algorithm to segment trees in a forest at the Shavers Creek Watershed in Pennsylvania. Three indices were used to assess the accuracy of our method: recall, precision and F-score. The results show that the algorithm can detect 84% of the tree (recall), 97% of the segmented trees are correct (precision) and the overall F-score is 90%. The result implies that our method has good potential for segmenting individual trees in deciduous broadleaf forest.     

Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data

Land cover classification of finer resolution remote sensing data is always difficult to acquire high-frequency time series data which contains temporal features for improving classification accuracy. This paper proposed a method of land cover classification with finer resolution remote sensing data integrating temporal features extracted from time series coarser resolution data. The coarser resolution vegetation index data is first fused with finer resolution data to obtain time series finer resolution data. Temporal features are extracted from the fused data and added to improve classification accuracy. The result indicates that temporal features extracted from coarser resolution data have significant effect on improving classification accuracy of finer resolution data, especially for vegetation types. The overall classification accuracy is significantly improved approximately 4% from 90.4% to 94.6% and 89.0% to 93.7% for using Landsat 8 and Landsat 5 data, respectively. The user and producer accuracies for all land cover types have been improved.     

Assessing integration of intensity,polarimetric scattering,interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification

Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type.     

The Observed and Simulated Major Summer Climate Features in Northwest China and Their Sensitivity to Land Surface Processes

Northwest China （NWC） is a typical arid and semi-arid region. In this study, the main summer climate features over NWC are presented and the performance of an atmospheric general circulation model （NCEP GCM/SSiB） over this region is evaluated. Satellite-derived vegetation products are applied in the model. Based on comparison with observational data and Reanalysis II data, the model generally captures major features of the NWC summer energy balance and circulation. These features include： a high surface tem- perature center dominating the planetary boundary layer; widespread descending motion; an anticyclone （cyclone） located in the lower and middle （upper） troposphere, covering most parts of central NWC; and the precipitation located mainly in the high elevation areas surrounding NWC.
The sensitivity of the summer energy balance and circulation over NWC and surrounding regions to land surface processes is assessed with specified land cover change. In the sensitivity experiment, the degradation over most parts of NWC, except the Taklimakan desert, decreases the surface-absorbed radiation and leads to weaker surface thermal effects. In northern Xinjiang and surrounding regions, less latent heating causes stronger anomalous lower-level anticyclonic circulation and upper-level cyclonic circulation, leading to less summer precipitation and higher surface temperature. Meanwhile, the dry conditions in the Hexi Corridor produce less change in the latent heat flux. The circulation change to the north of this area plays a domi- nant role in indirectly changing lower-level cyclonic conditions, producing more convergence, weaker vertical descending motion, and thus an increase in the precipitation over this region.     

Statistical Characteristics of Convective Initiation in the Beijing-Tianjin Region Revealed by Six-Year Radar Data

Characteristics of convective initiation (CI) in the Beijing-Tianjin region during the warm season of2008-2013 are examined. A total of 38877 CI cases are identified by a thunderstorm identification, tracking, analysis, and nowcasting algorithm. CI cases are evaluated in the context of associated terrain, weather systems, and land cover properties. The spatial distribution of all CI cases shows that there are dense CI activities around the 200-m elevation, which means that convective storms are more easily triggered over foothills. From 1500-1800 to 0300-0600 BT (Beijing Time), the high-occurrence CI region tends to propagate southeastward (i.e., from mountains to plains, then to ocean). Among the four local weather systems, the Mongolian cold vortex has the highest CI frequency while the after-trough system has the lowest CI frequency. For the land cover relationships with CI, the urban land cover has the highest CI density and the forest-type land cover has the second highest CI density; these two types of land cover are more conducive to CI formation.     

Google Earth Engine平台下秦岭地区植被覆盖时空变化分析

秦岭地区植被覆盖动态变化对其生态环境有重要影响。本文利用Google Earth Engine云平台,选取1986—2019年Landsat TM/OLI地表反射率数据,结合像元二分模型估算秦岭地区植被覆盖度(FVC);通过年际变化斜率、变异系数、Hurst指数等评价指标,对FVC的时空变化、稳定性和持续性变化进行分析。此外,探究FVC与气温、降雨的耦合关系,并分析土地利用变化对FVC的影响。结果表明:34年间,秦岭地区FVC整体上呈现良好的状况,中高等及以上植被覆盖区达73.11%;FVC由1986年的62.86%增长到2019年的70.01%,植被活动在不断增强;FVC的变异系数均值为0.34,标准差为0.45,其稳定性与其空间分布呈高度自相关性;秦岭地区的植被覆盖变化受气候变化和人为因素的共同影响。     

赣南白面石铀矿田成因再认识

赣南白面石铀矿田经上世纪60~80年代勘查,发现和探明了白面石、龙坑、双坑、马荠塘4个铀矿床和黄泥湖铀矿点,成为中国重要的铀资源基地。该矿田产于EW向南岭铀成矿带东段的白面石沉积-火山盆地内。该盆地的基底为印支期白面石花岗岩体;盖层为中侏罗统菖蒲组。盖层的底部为第一层砂岩,其上由基性-酸性双峰式火山岩组合与5层碎屑岩夹层组成。铀矿主要赋存在盖层底部的第一层砂岩中,其次是产在第一层玄武岩与第一层砂岩的接触带,有少量产在基底花岗岩顶部的风化壳中。关于该铀矿田的成因,有砂岩型沉积说、同生沉积后生富集说、成岩成矿热液叠加说、岩浆热液说,等等。为了厘定矿床成因类型,以确立今后的找矿方向,笔者对该矿田的资料进行了重新整理,应用Minesight软件选择典型的白面石铀矿床建立了三维地质模型,并收集了最新的同位素定年和岩石地球化学分析资料。研究表明,白面石铀矿田存在2次成矿（160~156 Ma和99~86 Ma）,其主成矿作用与第一层玄武岩的覆盖,在空间上相伴（铀矿体主要赋存于第一层砂岩及其与玄武岩的接触带）、时间上相近（成矿时间为160~156 Ma,玄武岩成岩时间为173 Ma）、成生上相关（矿化具有明显的中-低温热液蚀变）,为火山热盖成因类型,后期又有大量脉体活动,在岩脉两侧的砂岩层内又有热液型铀矿化叠加,从而形成了火山热盖及热液叠加的复成因矿床。该矿田的成矿条件是：富铀的基底,砂岩的沉积,洼沟的地形,岩浆的热盖,脉岩的入侵。