人为干扰对尼泊尔中部丘陵森林物种多样性、更新和碳密度的影响

全球近四分之三的森林正在遭受人为干扰,尼泊尔三分之二以上的森林受到不同类型的干扰。在社区森林中,当地社区在生计的各个方面都依赖于森林提供的生态系统服务,这些服务以各种方式干扰森林的自然条件和生态系统的功能。本研究在尼泊尔中部丘陵区的两个社区管理森林中,研究了对植物物种多样性、更新(幼苗和树苗)、生物量、土壤有机碳(SOC)和总碳密度的主要干扰因子。以树桩数、断苗、砍伐和放牧践踏作为主要人为干扰的衡量指标,从89个随机选取的250 m2的样地中收集了必要的数据,利用广义线性模型(GLM)对人为干扰的响应进行了分析。结果表明,森林砍伐对生物量和总碳密度平衡的影响最大。森林砍伐程度越高,森林碳储量越低。SOC对上述类型的人为干扰均无显著反应。木本物种丰富度和幼树数量随着树桩数量的增加而增加,说明中间干扰是有益的。然而,较高的砍伐强度降低了幼树密度。放牧/践踏是抑制幼苗生长的最显著干扰,在践踏强度较高的森林地区,幼苗和树苗数量较少。这些结果将为尼泊尔多目标森林管理以及如何降低其他地区类似社会经济环境中的人为干扰的影响提供指南。     

呋喃唑酮间接竞争ELISA(ciELISA)检测法的建立

为快速检测水产品中FZ残留ELISA ;将呋喃唑酮 (Furazolidone ,FZ)与牛血清白蛋白 (BSA)、人血清白蛋白 (HSA)联接 ,分别作为免疫原及包被原 ,建立了水产养殖动物组织中呋喃唑酮的ELISA筛选方法。实验结果表明 ,理想的呋喃唑酮———人血清白蛋白 (FZ- HSA )包被抗原的浓度为 1.2 5 μg/mL ,酶标二抗 (羊抗兔IgG HRP)的工作浓度为 1∶10 0 0 ,多抗 (FZ- PcAb)的工作浓度为 1∶3 2 0 0 ,可测定的最适范围为 10～ 10 0ng/mL ,最小检测限为 1ng/mL。本实验所建立的ELISA方法的孔间差异为 4.16% ,板间差异为 9.2 0 % ,实验重复性好。添加回收率试验测的虾肉样本品均回收率为 (94.86± 9.5 5 ) %。在实验浓度范围内 ,呋喃唑酮 (FZ)的抗血清与氯霉素 (Chloramphenicol)、磺胺二甲基嘧啶 (Sulfamethazine)、红霉素 (Erythromycin)、恩诺沙星 (Enrofloxacin)、甲氧苄啶 (Trimethoprim )等可能会因同时使用而导致共同残留的抗微生物药物未显示免疫交叉反应性 ,与结构相似或相近药物呋喃西林 (Furacilinum)的交叉反应性为 43 %。这表明所建立的方法有较高特异性和灵敏度 ,适合呋喃唑酮残留的快速筛选。整个测定时间为 5h。     

大豆残茬光谱响应特征研究

利用HR-768型便携式光谱仪,测定了不同大豆残茬覆盖度下的地面光谱,利用照相法获取对应的大豆残茬覆盖度。采用线性回归方法分析了单波段反射率、反射率一阶导数、归一化指数、比值指数与大豆残茬覆盖度的相关性,获取了不同覆盖度水平下大豆残茬的光谱响应特征,并结合MODIS、TM、HJ-1B星的波段响应函数建立了大豆残茬覆盖度最优估算模型。结果表明,在2050—2150nm和2250—2350nm两个波段范围内,大豆残茬与裸土的光谱差异最显著,可用于二者的区分;大豆残茬的光谱特征与玉米、小麦残茬的光谱特征相似,仅在920—967nm范围内存在特殊的吸收峰;以高光谱数据为数据源,941.6nm处的反射率、2151.8nm处反射率一阶导数、1461.3nm和2404.4nm反射率构建的归一化指数以及2247nm和608.6nm反射率构建的比值指数适宜用于作物残茬覆盖度估算,以宽波段数据为数据源,短波红外与红波段反射率构建的比值指数适宜用于大豆残茬覆盖度估算。     

Disaggregating and mapping crop statistics using hypertemporal remote sensing

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998–2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered “small scale maps”. These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.     

珠江口红树林湿地演变与现状的遥感分析

1 Introduction Half of the mangrove wetlands in China are situated in Guangdong province (Wang and Chen, 1998). The province used to have various types of wild mangrove forests along its coastal areas. The rapid expansion of urban areas and the fast deve…     

基于植被盖度和高度的不同退化程度高寒草地地上生物量估算

由于气候变化和不合理的人类活动,20世纪80年代以来青藏高原高寒草地发生严重退化。地上生物量是评价草地退化的直观指标。通常采用植被盖度和高度来估算草地地上生物量,但草地退化后,植被盖度和高度与地上生物量之间的关系是否会发生变化目前还不清楚,这影响着退化草地生物量估算的精度。通过多元回归分析研究了青藏高原中部和东北部高寒草甸、高寒草原在不同退化程度下植被盖度和高度与地上生物量的关系。结果表明：（1）高寒草甸与高寒草原地上生物量整体上及不同退化阶段都没有显著差异（P>0.05）。（2）随着退化程度的加剧植被盖度和高度对地上生物量的影响也发生改变,体现在未退化阶段地上生物量主要受植被高度影响,退化后主要受植被盖度影响。（3）无论是高寒草甸还是高寒草原分退化程度的回归模型估算结果都较不分退化程度模型估算的生物量更接近实测值。我们建议在退化高寒草地研究中采用盖度和高度估算生物量时,根据退化阶段采用不同的估算模型。     

大亚湾夏季表层浮游细菌生物量分布与环境变量的关系

根据2008年夏季(7月)在大亚湾进行的现场综合调查资料,分析表层海水浮游细菌生物量的水平分布特征,探讨细菌生物量与环境变量的关系。大亚湾夏季浮游细菌生物量变化范围为14.00～118.80μg·L^-1,海区平均值为55.23±29.43μg·L^-1。大亚湾夏季细菌生物量水平分布大体上呈现南部高于北部的趋势,在北纬22°35′以北和以南的海区均各自表现为东南部高于西北部的趋势。水深、硝酸盐、铵盐、亚硝酸盐和总碱度等5个参数与浮游细菌生物量之间存在着中等水平的相关性(0.15相似文献   

Estimating residual biomass of olive tree crops using terrestrial laser scanning

Agricultural residues have gained increasing interest as a source of renewable energy. The development of methods and techniques that allow to inventory residual biomass needs to be explored further. In this study, the residual biomass of olive trees was estimated based on parameters derived from using a Terrestrial Laser Scanning System (TLS). To this end, 32 olive trees in 2 orchards in the municipality of Viver, Central Eastern Spain, were selected and measured using a TLS system. The residual biomass of these trees was pruned and weighed. Several algorithms were applied to the TLS data to compute the main parameters of the trees: total height, crown height, crown diameter and crown volume. Regarding the last parameter, 4 methods were tested: the global convex hull volume, the convex hull by slice volume, the section volume, and the volume measured by voxels. In addition, several statistics were computed from the crown points for each tree. Regression models were calculated to predict residual biomass using 3 sets of potential explicative variables: firstly, the height statistics retrieved from 3D cloud data for each crown tree, secondly, the parameters of the trees derived from TLS data and finally, the combination of both sets of variables. Strong relationships between residual biomass and TLS parameters (crown volume parameters) were found (R² = 0.86, RMSE = 2.78 kg). The pruning biomass prediction fraction was improved by 6%, in terms of R², when the variance of the crown-point elevations was selected (R² = 0.92, RMSE = 2.01 kg). The study offers some important insights into the quantification of residual biomass, which is essential information for the production of biofuel.     

Performance of GNSS-R GLORI data for biomass estimation over the Landes forest

The Above-Ground Biomass (AGB) is a key parameter used for the modeling of the carbon cycle. The aim of this study is to make an experimental assessment of the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to forest AGB. This is based on the analysis of the data recorded during several GLORI airborne campaigns in June and July 2015, over the Landes Forest (France). Ground truth measurements of tree height, density and diameter at breast height (DBH), as well as AGB, were carried out for 100 maritime pine forest plots of various ages. The GNSS-R data were used to obtain the right-left (Γ_RL) and right-right (Γ_RR) reflectivity observables, which are geo-referenced in accordance with the known positions of relevant GPS satellites and the airborne receiver. The correlations between forest AGB and the GNSS-R observables yield the highest sensitivity at high elevation angles (70°-90°). In this case, for (Γ_RL) and the reflectivity polarization ratio (PR = Γ_RL/Γ_RR) estimated with a coherent integration time Tc = 20 ms, the coefficients of determination R² are equal to 0.67 and 0.51, with a sensitivity of −0.051 dB/[10⁶g (Mg) ha⁻¹], and −0.053 dB/[Mg ha⁻¹], respectively. The relationships between AGB and the observables are confirmed through the use of a 5-fold cross validation approach, with several different coherent integration times.     

基于Google Earth Engine与机器学习的省级尺度零散分布草地生物量估算

大区域草地地上生物量估算对草地资源利用管理及全球碳循环研究具有重要意义。为高效快速地估算大区域零散分布草地地上生物量,本文选取安徽省为研究区,在谷歌地球云引擎（Google Earth Engine）平台的支撑下,通过机器学习方法建立Landsat 8 OLI及其他辅助数据与地面实测草地地上生物量之间的联系,开展了草地零散分布地区省级尺度草地地上生物量高分辨率估算,并与传统的基于归一化植被指数（NDVI）回归模型进行了比较。研究结果表明,综合利用光谱与地形因子的机器学习方法,估算零散化分布草地地上生物量的精度可以达到65%以上,其中分类回归树（CART）模型R²=0.57,预测精度为68.60%,支持向量机（SVM）模型R²=0.59,预测精度为75.74%,而使用NDVI的回归分析产生的误差较大,R²=0.37,预测精度为57.51%,因此机器学习方法相对于传统基于NDVI的回归分析具有明显优势。另外,谷歌地球云引擎平台数据来源广泛、获取方便,可以高效地实现海量影像数据的预处理及计算分析,大大提升了工作效率,与地面调查数据的结合可实现更大区域乃至全国尺度上的零散分布草地地上生物量高分辨率遥感估算。