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不透水面是城市区域中一种典型的土地覆盖类型,是衡量城市环境质量和城市化水平的重要标志之一。与传统基于像元级的遥感研究方法相比,不透水面百分比（Impervious Surface Percent,ISP）的估算可以进入像元内部,获得更准确的城市信息。本文应用Cubist模型树,对Landsat TM的原始波段变量（除热红外波段）,建立ISP估算的基础模型（Base Cubist-ISP）。通过基于模型树的集成学习优化算法和加入相邻时相影像的波段变量中值,以削弱噪声的影响。然后,优选热红外波段和各种衍生变量,并进行属性精简,继而应用集成学习算法得到的参数和精简后的变量建立ISP估算的优化模型（Optimal Cubist-ISP）。对广东省广州市海珠区的实验结果表明,Optimal Cubist-ISP模型估算不透水面的整体均方根误差（RMSE）为12.98%,决定系数（R2）为0.90,精度明显优于Base Cubist-ISP模型,RMSE降低约5.03%,ISP在透水面区域被高估和高密度不透水面区域被低估的现象得到改善。本文提出的基于Cubist模型树建立ISP遥感估算的模型及优化方法可以适用于城市区ISP的提取。 相似文献
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鼠害是影响草原生态健康的重要因素,了解小型啮齿动物种群密度时空分布特征,对精准的鼠害综合防治具有重要意义。以往对小型啮齿动物时空分布的研究多局限于静态的站点分布或小范围的种群密度时间变化分析,缺乏对较大时空尺度小型啮齿动物种群密度变化的分析。从已发表的文献中收集了天山北坡草地1982—2015年小尺度的有效洞口密度实地调查信息,同时结合环境因子数据,再根据海拔将研究区划分为≤900 m和>900 m 2类,运用Cubist模型和随机森林模型,分析有效洞口密度时空分布。结果表明：（1） 1982—2015年天山北坡海拔≤900 m地区的有效洞口密度总体呈增加趋势,而海拔>900 m的地区总体呈减少趋势。基于Cubist模型构建有效洞口密度与环境因子的模型拟合精度明显优于随机森林模型。（2） 植被状况、气象因子和放牧强度是天山北坡有效洞口密度时空分布主要的环境驱动因素。在天山北坡内海拔≤900 m和>900 m的地区中,有效洞口密度的驱动机制存在着显著差异。（3） 在海拔≤900 m地区,影响有效洞口密度时空分布主要是叶面积指数,而对于海拔>900 m地区为归一化植被指数。这可能是受到大沙鼠（Rhombomys opimus）和黄兔尾鼠（Eolagurus luteus）消耗不同类型植被的影响。 相似文献
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Modelling of piping collapses and gully headcut landforms:Evaluating topographic variables from different types of DEM 总被引：1，自引：0，他引：1
Alireza Arabameri Fatemeh Rezaie Subodh Chandra Pal Artemi Cerda Asish Saha Rabin Chakrabortty Saro Lee 《地学前缘(英文版)》2021,12(6):129-146
The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)tech-niques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable's importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model's result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area. 相似文献
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