面向多类型土壤有机碳定量反演的天基高光谱探测参数研究

星载高光谱仪器的光谱通道以及光谱分辨率和信噪比等核心参数设置直接影响土壤有机碳定量反演精度。本研究开展了卫星载荷光谱分辨率、信噪比、光谱特征波段对不同土壤类型有机碳反演影响的研究,提出了基于大气传输模型、光谱分辨率分析模型、信噪比分析模型、特征波段的提取分析模型以及偏最小二乘回归反演模型的面向不同土壤类型有机碳监测的高光谱卫星“地面–大气–仪器–观测–反演”全链路仿真分析方法,实现了土壤类型、大气效应、仪器特性参数、反演方法的耦合影响分析。结果表明：①3种类型土壤有机碳反演的最佳光谱分辨率均在10~20 nm。②不同土壤类型对观测的信噪比需求不同。对于Phaeozem的有机碳监测,较另外两种土壤有更高的信噪比需求。③在不同特征波段提取分析方法下所需的最佳光谱分辨率和信噪比一致。不同类型土壤光谱数据提取出的特征波段不同,其中反演效果最佳的土壤类型为Chernozem,特征波段数为26个,R²=0.826 5,RMSE=3.438 9 g/kg。④反演模型与仪器特性参数无耦合关系,同一类型土壤不同反演算法的最佳光谱分辨率和信噪比需求一致。⑤Chernozem有机碳最佳反演参数需求为光谱分辨率15 nm,信噪比大于506.66,特征波段提取数为26个;Kastanozem有机碳最佳反演参数需求为光谱分辨率17 nm,信噪比大于331.42,特征波段提取数为22个;Phaeozem有机碳最佳反演参数需求为光谱分辨率15 nm,信噪比大于432.51,特征波段提取数为19个。     

区域尺度分布式水文模拟的时空分辨率

基于数据实验,就区域尺度分布式水文模拟中时空分辨率匹配问题进行讨论,就分布式水文建模中时空分辨率匹配问题提出建议。结果表明:低分辨率高程数据在区域水文要素空间离散过程中,导致边界错位、高程趋于集中、坡度变缓、河网稀疏失真等不良后果,在区域水文模拟研究中,应根据不同时间尺度对应的科学计算需求,选择适宜的空间分辨率数据进行模型平台构建。     

Do coarse resolution U.S. presettlement land survey records adequately represent the spatial pattern of individual tree species?

Presettlement land survey records (PLSRs) are a valuable and unique source of information for the reconstruction of presettlement forest patterns. The purpose of this study was to determine whether coarsely resolved PLSRs are adequate to characterize the spatial patterns of individual tree species over large areas. The General Land Office Survey records of the PLSRs of Minnesota were used and species selected in the analysis were based on their abundances and degrees of clustering. A geostatistical procedure was developed to analyze observations of bearing-tree point-locations, at progressively coarser resolutions from 1×1 mile to 24×24 miles, to create spatially continuous probability surfaces of species occurrences across the landscape. Statistical and visual analyses of the geostatistical predictions indicated that coarsely resolved PLSRs, as coarse as 24×24 miles, can adequately represent the spatial pattern of individual species over large areas. Mean errors in predictions increased as more coarsely resolved data were used, primarily in response to the decreased abundance of a species and minorly in response to the degree of spatial clustering of a species. The results indicate that coarsely resolved township-level data of 6×6 miles can be used for presettlement vegetation reconstruction of large areas of several counties.     

卫星遥感影像图在国家公益林核查工作中的应用

通过国家公益林核查工作中对遥感卫星影像图的实际应用，介绍了从遥感数据准备、影像图使用、解译效果等全部工作过程。阐明了数据选取的原则，数据合成依据，分析了应用效果，指出了存在的问题。为其他类似的资源调查提供了参考和借鉴。     

A new approach for rescaling land cover data

The resolution of satellite imagery must often be increased or decreased to fill data gaps or match preexisting project requirements. It is well known that a change in resolution introduces systematic errors of size, shape, location and amount of contiguous land cover types. Nevertheless, robust methods for rescaling landscape data are frequently required to assess patterns of landscape change through time and over large areas. We developed a new method for rescaling spatial data that allows map resolution (grain size) to be either increased or decreased while holding the total proportion of land cover types constant. The method uses a weighted sampling net of variable resolution to sample an existing map and then randomly selects from the frequency of cover types derived from this sample to assign the cover type for the corresponding location in the rescaled map. The properties of the sampling net had a variable effect on measures of landscape pattern with the characteristic patch size (S) the most robust metric and the number of clusters (A) the most variable. A comparison of up-scaled and down-scaled maps showed that this process is not symmetrical, producing different errors for increases versus decreases in grain size. Rescaling Landsat (30 m) imagery to the 10 m resolution of SPOT imagery for four National Park units within Maryland and Virginia resulted in errors due to rescaling that were small (1–2%) relative to the total error (∼11%) associated with these images. The new rescaling method is general because it provides a single method for increasing or decreasing resolution, can be applied to maps with multiple land cover types, allows grid geometry to be transformed (i.e., square to hexagonal grids), and provide a more consistent basis for landscape comparisons when maps must be derived from multiple sources of classified imagery.     

Modeling grain-size dependent bias in estimating forest area: a regional application

A better understanding of scaling-up effects on estimating important landscape characteristics (e.g. forest percentage) is critical for improving ecological applications over large areas. This study illustrated effects of changing grain sizes on regional forest estimates in Minnesota, Wisconsin, and Michigan of the USA using 30-m land-cover maps (1992 and 2001) produced by the National Land Cover Datasets. The maps were aggregated to two broad cover types (forest vs. non-forest) and scaled up to 1-km and 10-km resolutions. Empirical models were established from county-level observations using regression analysis to estimate scaling effects on area estimation. Forest percentages observed at 30-m and 1-km land-cover maps were highly correlated. This intrinsic relationship was tested spatially, temporally, and was shown to be invariant. Our models provide a practical way to calibrate forest percentages observed from coarse-resolution land-cover data. The models predicted mean scaling effects of 7.0 and 12.0% (in absolute value with standard deviations of 2.2 and 5.3%) on regional forest cover estimation (ranging from 2.3 and 2.5% to 11.1 and 23.7% at the county level) with standard errors of model estimation 3.1 and 7.1% between 30 m and 1 km, and 30 m and 10 km, respectively, within a 95% confidence interval. Our models improved accuracy of forest cover estimates (in terms of percent) by 63% (at 1-km resolution) and 57% (at 10-km resolution) at the county level relative to those without model adjustment and by 87 and 84% at the regional level in 2001. The model improved 1992 and 2001 regional forest estimation in terms of area for 1-km maps by 15,141 and 7,412 km² (after area weighting of all counties) respectively, compared to the corresponding estimates without calibration using 30 m-based regional forest areas as reference.     

超高效液相色谱串联高分辨四极杆飞行时间质谱法快速筛选鉴别猪血浆中的大环内酯类抗生素

采用超高效液相色谱串联高分辨四极杆飞行时间质谱法(UPLC-Q-TOP-MS)结合大环内酯类抗生素数据库,建立快速筛查鉴别猪血浆中常见大环内酯类抗生素的方法。猪血浆经甲醇提取,通过UPLC-Q-TOP-MS正离子模式全扫描分析,采用Agilent Extend-C18色谱柱(50×2.1 mm,1.8μm)分离,以乙腈-2 mmol/L乙酸铵水溶液(含0.1%甲酸)为流动相梯度洗脱,流速0.4 m L/min,柱温40℃,得到猪血浆中大环内酯类抗生素准分子离子峰精确质量数、主要碎片峰的精确质量数和保留时间后,结合大环内酯类抗生素数据库筛查,实现猪血浆中大环内酯类抗生素的快速定性识别。所建数据库包含26种常见猪血浆中大环内酯类抗生素的准分子离子峰精确质量数、主要碎片峰的精确质量数和保留时间,其中13种常见大环内酯类抗生素的检测限在1.00~8.00ng/g范围内。该方法分离度高、检测灵敏,可以作为快速筛查鉴别猪血浆中大环内酯类抗生素的有效方法。     

The influence of input data quality in determining areas suitable for crop growth at the global scale – a comparative analysis of two soil and climate datasets

The assessment of biophysical crop suitability requires datasets on soil and climate. In this study, we investigated the differences in topsoil properties for the dominant soil mapping units between two global soil datasets. We compared the ISRIC World Soil Information Center’s World Inventory of Soil Emissions Potential 5 by 5 arc min Soil Map of the World (ISRIC‐WISE 5by5 SMW ) with the Harmonized World Soil Database (HWSD) in 0.5 arc min. We also incorporated annual mean temperature and mean precipitation from two global climate datasets that were the WorldClim measurement‐based climate dataset and the Kiel Climate Model (KCM) modelled results of global climate from 1960 to 1990. We then applied a fuzzy logic approach using different combinations and resolutions of the datasets to determine the effects on the extent and distribution of suitable areas for 15 crops. We only used the spatially dominant soil class in the mapping units in the soil databases (resampled to the same resolution of 5 arc min), and we found that the estimates of topsoil properties (0–20 cm in ISRIC‐WISE and 0–30 cm in HWSD) of the seven analysed parameters were up to 40% lower in most of the HWSD than in the ISRIC‐WISE 5by5 SMW. Results from the KCM are 0.1 °C (1%) lower in mean global annual temperature and 20% higher in average global annual precipitation compared with the WorldClim data. The HWSD‐based runs resulted in 10% less crop‐suitable land than the ISRIC‐WISE 5by5 SMW‐based results. The KCM simulations predicted 1% less crop‐suitable land than the WorldClim model. Despite generalizations, our results demonstrate that discrepancies in crop suitable areas are largely due to the differences in the soil databases rather than to climate.     

Interpretation of lysimeter weighing data affected by wind

Weighing lysimeters are valuable devices for measuring water‐balance components with high temporal resolution and high accuracy. However, some older lysimeter facilities still operate with lever‐arm‐counterbalance weighing systems that are sensitive to disturbances, e.g., forces exerted by wind. Filtering and averaging are commonly used for processing noisy raw data. We studied some data of a lever‐arm weighing system and performed additional experiments in order to (1) determine the measurement accuracy of the current weighing scheme (facility, and measuring and averaging procedure) regarding wind effects, (2) describe the oscillation behavior, (3) test the mechanical performance of the system, and (4) adapt the averaging procedure with respect to improved interpretation of the weighing data. The measurement accuracy for a wind velocity < 5 m s^–1, measured in 10 m height, was ≈ ± 0.4 kg (equivalent to ± 0.14 mm); at a higher wind velocity, the accuracy was three times lower, but there was no linear relationship. Additional experiments showed that the weighing system is oscillating with more or less irregular amplitudes. A loading–unloading experiment delivered proper results of the measured loads. The mechanical system reacted immediately, and no directional effects were found. However, small changes of < 1 kg could hardly be determined due to the oscillations. A time series of raw data measured every 2 s served as basis for improving the averaging method. A moving average from 64 values was computed representing the currently used method, and serving as reference. With this procedure an accuracy of ± 0.38 kg could be reached. Averaging 150 values led to an accuracy of ± 0.28 kg (0.1 mm) for a wind velocity < 5 m s^–1.