Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots,UAV-LiDAR data and Sentinel-2 imagery

The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha⁻¹. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R² = 0.62 > 0.52, RMSE = 50.36 Mg ha⁻¹<56.63 Mg ha⁻¹). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R² = 0.62, RMSE = 51.03 Mg ha⁻¹). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.     

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.     

莱州湾大型底栖动物群落结构及其动态变化特征

本文以莱州湾2009年夏季(8月)、秋季(10月)及2010年春季(5月)、夏季(8月)4个季节大型底栖动物资料为基础,对莱州湾大型底栖动物的种类组成、丰度、生物量、优势种进行了研究,同时与历史资料进行对比,探讨了莱州湾大型底栖动物的群落结构特征及动态变化。4个航次中共鉴定出大型底栖动物272种,其中包括环节动物多毛类122种,软体动物46种,甲壳动物64种,棘皮动物18种,鱼类9种,其他类13种。调查海域平均丰度为(1102.56 ± 216.32) ind./m2, 多毛类在丰度上占绝对优势;平均生物量为(28.16 ± 8.45) g/m2,软体动物占据优势。丰度和生物量空间分布规律具有很强的相似性,低值区位于莱州湾西部黄河口邻近海域,高值区位于渤海中部海域。丰度和生物量季节变化明显,夏季最高,秋季其次,春季最低。多毛类不倒翁虫(Sternaspis sculata)、寡鳃齿吻沙蚕(Nephthys oligobranchia)、紫壳阿文蛤(Alvenius ojianus)等是莱州湾调查海域的优势种。通过与历史资料的对比发现,莱州湾大型底栖动物种类组成及优势种类出现小型化的趋势。     

天山雪岭云杉个体生物量分配及其变化规律的研究

为了探讨天山雪岭云杉林生物量在个体组织中的分配情况及其变化规律,在研究区进行了大量的野外测量,利用已有的雪岭云杉林估算方程,分析了天山雪岭云杉林生物量在各器官（干、枝、叶、皮、根）中的分配及其变化规律。结果表明：（1） 研究区雪岭云杉林的平均生物量为388.74 t·hm^-2,树木各器官中,干、枝、根、叶和皮分别占生物量的43.65%、28.60%、13.49%、11.08%和3.18%。（2）各径级生物量所占百分比为：33.53%（40~50 cm）、20.13%（20~30 cm）、19.59%（30~40 cm）、18.19%（50~60 cm）和2.05%（10~20 cm）;树木生物量在不同树高中的分配表现为：48.78%（20~30 m）＞35.27%（10~20 m）＞14.70%（30~40 m）＞1.25%（0~10 m）;地上和地下生物量的分配比例为：87.54%和12.46%,分别为340.30 t·hm^-2和48.44 t·hm^-2。（3） 随海拔升高,天山雪岭云杉林生物量呈“单峰”变化,在海拔2 100~2 400 m处达到最大值611.58 t·hm^-2;干、皮生物量所占比例随海拔升高而减小,枝生物量逐渐增加,叶、根生物量呈先减小后增加的趋势;径级20~30 cm、30~40 cm和50~60 cm的生物量随海拔升高均呈“单峰型”变化趋势,都在海拔2 100~2 400 m处达到最大;雪岭云杉林不同树高生物量随海拔的升高呈现的趋势不同。天山雪岭云杉林生物量和年均降水量随经纬度的升高均呈降低变化,研究区林分生物量自西向东总体呈现逐渐降低的趋势;林分密度、海拔和降水共同决定了森林生物量的大小及其变化规律,海拔2 100~2 400 m是本研究区雪岭云杉林生长的最适宜场所。结果可为雪岭云杉林生态系统的恢复和重建提供基础资料,对研究区进行综合管理与生态健康分析具有重要意义。     

Response of Microbial Communities in Soil to Multi-level Warming in a Highland Barley System of the Lhasa River

No studies have examined the effect of experimental warming on the microbial biomass and community composition of soil in agricultural ecosystem on the Qinghai-Tibet Plateau. Thus it is unclear whether the influences of experimental warming on microbial communities in soil are related to warming magnitude in croplands on this Plateau. This study performed warming experiment (control, low- and high-level) in a highland barley system of the Lhasa River in May 2014 to examine the correlation between the response of microbial communities in soil to warming and warming magnitude. Topsoil samples (0-10 and 10-20 cm) were collected on September 14, 2014. Experimental warming at both low and high levels significantly increased soil temperature by 1.02 ℃ and 1.59 ℃, respectively at the depth of 15 cm. Phospho lipid fatty acid (PLFA) method was used to determine the microbial community in soil. The low-level experimental warming did not significantly affect the soil’s total PLFA, fungi, bacteria, arbuscular mycorrhizal fungi (AMF), actinomycetes, gram-positive bacteria (G⁺), gram-negative bacteria (G^-), protozoa, the ratio of fungi to bacteria (F/B ratio), and ratio of G⁺ to G^- (G⁺/G^- ratio) at the 0-10 and 10-20 cm depth. The low-level experimental warming also did not significantly alter the composition of microbial community in soil at the 0-10 and 10-20 cm depth. The high-level experimental warming significantly increased total PLFA by 74.4%, fungi by 78.0%, bacteria by 74.0%, AMF by 66.9%, actinomycetes by 81.4%, G⁺ by 67.0% and G^- by 74.4% at the 0-10 cm depth rather than at 10-20 cm depth. The high-level experimental warming significantly altered microbial community composition in soil at the 0-10 cm depth rather than at 10-20 cm depth. Our findings suggest that the response of microbial communities in soil to warming varied with warming magnitudes in the highland barley system of the Lhasa River.     

Application of S-SEBI model for crop evapotranspiration using Landsat-8 data over parts of North India

Estimation and monitoring of crop evapotranspiration (ET_c) or consumptive water use over large-area holds the key to irrigation management plans and regional drought preparedness. The objective of this study was to estimate ET_c by applying the simplified-surface energy balance index (S-SEBI) model to Landsat-8 data for the 2014–2015 period in parts of North India. An average ET_c was estimated 2.72 and 2.47 in mm day^?1 with 0.22, 0.18 standard deviation and 0.11, 0.07 standard error for Kharif and Rabi crops, respectively. On validation part, a close relationship was observed between S-SEBI derived and scintillometer observed evaporative fraction with 0.85 correlation coefficient and 0.86 agreement index. The statistical analysis also endorses the results accuracy and reliability with 0.026 and 0.602, relative root-mean square errors and model efficiency for wheat crop, respectively. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of ET_c.     

三亚湾浮游动物数量分布及群落特征的季节变化

根据三亚湾2005年春、夏、秋和冬季航次的调查资料,分析了这4个季度的浮游动物的种类组成、优势种、群落结构、丰度和生物量的时空变化。经过鉴定共有终生性浮游动物125种,阶段性浮游动物17种,其中毛颚类幼虫和短尾溞状幼虫是各个季度都出现的优势种。三亚湾的浮游动物可以分为河口类群、暖水沿岸类群和广布暖水外海类群。全年浮游动物平均丰度为95.91ind·m-3,浮游动物平均生物量为69.22mg·m-3;浮游动物丰度的季节分布是夏>秋>春>冬,平均生物量的季节分布是秋>夏>春>冬,总体上一般是夏秋季高于冬春季。冬季浮游动物丰度与浮游植物细胞总密度之间没有相关性。     

长江口渔场、舟山渔场硅酸盐浓度分布与生物量关系的研究

根据1998年和2000年东海北部的营养盐调查资料和相应的历史资料,以及同期开展的虾类资源调查资料,研究了冬、夏季长江冲淡水的流向以及它对长江口渔场、舟山渔场硅酸盐分布规律和虾类生物量分布规律的影响。结果表明,长江冲淡水转向的原因可以归纳为4类,夏季长江冲淡水的流动界限由123°E,30．3°N到127．3°E,33°N的直线和由123°E,31．8°N到127．3°E,34．5°N的直线所围的区域。长江冲淡水给长江口渔场、舟山渔场提供了大量的硅酸盐,对提高该海区的初级生产力起到了积极的作用,有利于生物的繁衍生息,提高了生物量。最后,用该海区虾类的分布密度证实了由该水团所做出的对生物量的推论。     

流沙湾的底栖大型海藻调查

2008年3月～2009年1月调查了流沙湾潮间带及湾内(109°55'～109°59'E,20?22'～20°28'N)底栖大型海藻资源,定性、定量分析该海湾底栖大型海藻种类、生态分布、区系性质、群落结构及生物量的季节变化规律。共采集底栖大型海藻3门10目17科19属32种,其中红藻门4目9科9属15种,绿藻门4目6科7属14种,褐藻门2目2科3属3种,以亚热带藻种占绝对优势,达59.4%,温带成分21.9%,热带成分18.7%。采样海藻具印度-西太平洋海洋植物区系中国-日本亚热带海洋植物亚区特点。调查区域内底栖大型海藻群落组成的季节变化明显。     

春秋季山东南部近岸浮游细菌生态分布

分别于2007年4月(春季)和10月(秋季),对山东南部近岸海域进行了现场调查,研究了该海区浮游细菌丰度、生物量分布特征,探讨了它们与温度、溶解氧(DO)、总氮(N)、总磷(P)、硝酸盐(NO-3)、铵盐(NH+2)及活性磷酸盐(PO4-P)的相关性.结果表明:浮游细菌生物量具有一定的时间、空间分布特征,春季浮游细菌丰度及生物量要高于秋季,2个季节近岸细菌数量高于远岸区域;浮游细菌丰度及生物量与温度、DO、总P、NO-3、NH+2及PO4-P均呈显著相关关系(P<0.01),表明上述因子可能是该海域浮游细菌数量分布的主要限制因子.