基于机器学习的沟谷地貌识别模型对比——以黄土高原典型流域为例

探索沟谷地貌空间分布与环境控制特征之间的联系并构建沟谷地貌准确提取模型,对大尺度范围沟谷提取具有重要意义。基于人工提取黄土高原典型流域沟谷地貌样本,结合不同时期的Landsat8 OLI影像数据和DEM数据,建立随机森林模型确定黄土高原沟谷地貌提取最佳影像时期和最佳组合特征,基于最优模型参数,对比其与支持向量机和人工神经网络沟谷提取模型效果,验证模型泛化能力。结果表明:(1)黄土高原沟谷提取的最佳影像时期为12月,最佳组合特征集为Red、Blue、H、SWIR1、PNT、Coastal、GLCM4和NIR;(2)3种方法提取测试区域的沟谷空间分布一致,从定性和定量角度进行比较,随机森林模型提取效果最好,验证样区平均总体精度为80.48％,相较于支持向量机模型和人工神经网络模型分别提高4.00和8.63个百分比;(3)测试区域中,沟谷地貌面积约占总面积的56.91％,且呈现西北至东南方向逐渐集中的特点。研究表明随机森林模型在黄土高原地区高精度沟谷地貌识别研究中综合表现最佳,可大范围推广。     

Assessment of gully erosion susceptibility using different DEM-derived topographic factors in the black soil region of Northeast China

As a primary sediment source, gully erosion leads to severe land degradation and poses a threat to food and ecological security. Therefore, identification of susceptible areas is critical to the prevention and control of gully erosion. This study aimed to identify areas prone to gully erosion using four machine learning methods with derived topographic attributes. Eight topographic attributes (elevation, slope aspect, slope degree, catchment area, plan curvature, profile curvature, stream power index, and topographic wetness index) were derived as feature variables controlling gully occurrence from digital elevation models with four different pixel sizes (5.0 m, 12.5 m, 20.0 m, and 30.0 m). A gully inventory map of a small agricultural catchment in Heilongjiang, China, was prepared through a combination of field surveys and satellite imagery. Each topographic attribute dataset was randomly divided into two portions of 70% and 30% for calibrating and validating four machine learning methods, namely random forest (RF), support vector machines (SVM), artificial neural network (ANN), and generalized linear models (GLM). Accuracy (ACC), area under the receiver operating characteristic curve (AUC), root mean square error (RMSE), and mean absolute error (MAE) were calculated to assess the performance of the four machine learning methods in predicting spatial distribution of gully erosion susceptibility (GES). The results suggested that the selected topographic attributes were capable of predicting GES in the study catchment area. A pixel size of 20.0 m was optimal for all four machine learning methods. The RF method described the spatial relationship between the feature variables and gully occurrence with the greatest accuracy, as it returned the highest values of ACC (0.917) and AUC (0.905) at a 20.0 m resolution. The RF was also the least sensitive to resolutions, followed by SVM (ACC = 0.781–0.891, AUC = 0.724–0.861) and ANN (ACC = 0.744–0.808, AUC = 0.649–0.847). GLM performed poorly in this study (ACC = 0.693–0.757, AUC = 0.608–0.703). Based on the spatial distribution of GES determined using the optimal method (RF + pixel size of 20.0 m), 16% of the study area has very high level susceptibility classes, whereas areas with high, moderate, and low levels of susceptibility make up approximately 24%, 30%, and 31% of the study area, respectively. Our results demonstrate that GES assessment with machine learning methods can successfully identify areas prone to gully erosion, providing reference information for future soil conservation plans and land management. In addition, pixel size (resolution) is the key consideration when preparing suitable datasets of feature variables for GES assessment.     

一种基于U-Net的高分影像土地利用/覆盖变化检测方法

[目的] 介绍一种基于U-Net的高分影像的土地利用/覆盖变化检测方法,为该模型在遥感影像变化检测方面的应用提供理论支持。[方法] 采用U型神经网络对河南省禹州市两期高分一号影像和WHU building dataset建筑物变化检测数据集中的变化图斑进行自动检测试验,并与FCN和SegNet两种模型进行比较。[结果] 在两个数据集的验证样本中,U型神经网络模型的F₁值分别为0.699,0.66和0.673,均优于其他两种模型,并且漏检率较低,更加接近变化参考图。[结论] 采用U型神经网络对高分辨率遥感影像进行土地利用/覆盖变化检测是可行的,且能有较高的检测精度。     

CO2地质储存中构造地层圈闭/水动力储存的研究

为了研究在深部咸水层中,构造地层圈闭/水动力储存作用下CO2储存特征及迁移规律,此次研究以鄂尔多斯某CO2地质储存示范场地的深部咸水层为研究对象,利用测井数据,运用TOUGH2数值模拟软件建立模型,分析了深部咸水层中CO2注入速率变化、压力分布、CO2饱和度分布、CO2质量分数分布等方面的特征,研究结果表明:①在定压力注入条件下,随着注入时间的延长CO2的注入速率逐渐减小最后趋于稳定.②溶解态CO2所占总储量比例随时间增加而增加,安全性随之增高,但超临界态CO2依然是主要的储存形式.③CO2注入后以注入井为中心形成CO2晕,并随着深度增加范围减小,由内向外饱和度逐渐降低.     

Nine New and Five Known Polyketides Derived from a Deep Sea-Sourced Aspergillus sp. 16-02-1

Nine new C₉ polyketides, named aspiketolactonol (1), aspilactonols A–F (2–7), aspyronol (9) and epiaspinonediol (11), were isolated together with five known polyketides, (S)-2-(2′-hydroxyethyl)-4-methyl-γ-butyrolactone (8), dihydroaspyrone (10), aspinotriol A (12), aspinotriol B (13) and chaetoquadrin F (14), from the secondary metabolites of an Aspergillus sp. 16-02-1 that was isolated from a deep-sea sediment sample. Structures of the new compounds, including their absolute configurations, were determined by spectroscopic methods, especially the 2D NMR, circular dichroism (CD), Mo₂-induced CD and Mosher’s ¹H NMR analyses. Compound 8 was isolated from natural sources for the first time, and the possible biosynthetic pathways for 1–14 were also proposed and discussed. Compounds 1–14 inhibited human cancer cell lines, K562, HL-60, HeLa and BGC-823, to varying extents.     

动机在自我调节学习中的作用及其对我国教育的启示

通过对动机结构在自我调节学习中的作用的探讨，指出自我效能感、任务价值与目标定向是自我调节学习中的关键变量，在自我调节学习中的不可替代的作用。教育工作者在教育实践中应该有意识地对学生加强这三方面的训练。     

天津市农产品深加工科技发展及重点领域研究

在研究和分析了国内外农产品加工业的发展趋势及天津市农产品加工业的现状和产业基础的背景下,提出了天津市农产品深加工科技发展的思路、重点产品和重点技术     

在专业基础课教学中对教学方法的改革尝试

探讨了与计算机相关的专业基础课教学方法的改革。通过运用启发式教学和研究性学习，采用先进的教学手段和技术，对教育过程中教与学的思路进行了有益的尝试。实践证明，这不但调动了学生学习的主动性和创造性，而且提高了教学质量。