川滇地区应变率场分布及其变化特征研究

基于GPS多期复测资料, 利用最小二乘配置方法计算川滇地区应变参数, 分析该区域应变率场分布及其变化特征并探讨其分布与强震关系。 研究结果表明: ① 各时段应变率场空间分布的明显变化应属于大于GPS资料误差的真实地壳构造形变信息; ② 最大剪应变率及第一、 第二剪应变率的结果反映了走滑断裂对区域变形的显著控制; ③ 主应变率, 东西、 南北向应变率场动态结果反映的汶川地震孕震的空间尺度较大; ④ 在本区大致反映北东向与北西向剪切变形的第一剪应变率、 东西向应变率、 南北向应变率及最大剪应变率与6级以上地震对应较好。     

基于差分法的半参数模型粗差估计

运用差分法,得到半参数模型中参数向量和非参数向量的估计,并且当粗差的位置已经确定时,可以利用基于最小二乘原理的粗差探测方法,估计出半参数模型中的观测量所含粗差。通过算例分析,证明了算法的有效性,最后提出了粗差定位与定值时应注意的问题。     

以坐标差为因子的导线平差

根据全站仪可以直接测量导线坐标的特点,以基本的测量平差知识为基础,探讨了以测量所得的坐标差为观测因子来建立平差函数模型,并依据间接平差原理进行平差的方法。平差结果和传统的平差方法的结果进行了比较,得出了这种平差方法的可行性。     

Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery

Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest (Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI_(i,j) = (R_i − R_j)/(R_i + R_j), where R_i and R_j = reflectance in any two bands) were evaluated using calibration (n = 33) and test (n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756–820 nm) and the water absorption feature centred at 1200 nm (1172–1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R² = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH map could be useful to forest management in many ways, e.g. thinning of coppice to promote diameter growth, to assess the effects of management on forest structure or to detect changes in the forest structure caused by anthropogenic and natural factors.     

扫描地形图中居民地街区提取和矢量化方法

提出了特征点提取和总体最小二乘拟合相结合的新方法,对扫描地图上的居民地街区进行提取和矢量化。本文首先对居民地街区进行轮廓跟踪得到初始的矢量数据,通过特征点提取进行数据压缩,再利用总体最小二乘方法对每条边线进行拟合,得到最终的矢量数据。此外,还提出了三个精度指标,对实验图中矢量数据的精度进行定量分析。结果表明,该方法能够提高居民地街区矢量化的准确性,消除冗余数据。     

Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants

Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350–2500 nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n = 62) and validation (n = 32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, R_v² = 0.54; root mean square error in validation, RMSE_v = 12.99 mg kg⁻¹; and residual prediction deviation in validation, RPD_v = 1.35) was slightly better than using the soil spectra (R_v² = 0.42, RMSE_v = 13.35 mg kg⁻¹, and RPD_v = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (R_v² = 0.63, RMSE_v = 11.94 mg kg⁻¹, RPD_v = 1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and 2290 nm, leaf spectral bands near 700, 890 and 900 nm in PLSR models were important wavelengths for soil arsenic prediction. Moreover, soil arsenic showed significantly positive correlations with soil organic matter (r = 0.62, p < 0.01) and leaf arsenic (r = 0.77, p < 0.01), and a significantly negative correlation with leaf chlorophyll (r = −0.67, p < 0.01). The results showed that the prediction of arsenic contents using soil and leaf spectra may be based on their relationships with soil organic matter and leaf chlorophyll contents, respectively. Although RPD of 1.47 was below the recommended RPD of >2 for soil analysis, arsenic prediction in agricultural soils can be improved by combining the leaf and soil spectra.     

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中国小城镇产业结构特征及影响因素研究

分析揭示了2015年中国小城镇产业结构特征与空间差异,并利用加权最小二乘法实证估计了中国小城镇非农产业占比的影响因素。研究得出中国小城镇非农产业占比呈正态分布,非农产业占比较高的区域主要集中在东部沿海、长江经济带和边境口岸地区。东部地区小城镇非农产业占比呈负偏态分布,中部地区呈正态分布,而西部和东北地区则呈现正偏态分布特征,产业结构相对落后。小城镇规模、所在县域经济发展水平与小城镇非农产业占比显著正相关。县域第二产业占比提高带来的拉动效应略高于第三产业,小城镇产业更多的是为县域第二产业发展提供配套支撑。地级城市（地区、自治州、盟）规模的扩大对辖区内小城镇产业结构升级产生的扩散效应强于虹吸效应。