近20年京津唐地区不透水面变化的遥感监测

不透水面是衡量城市化程度的重要指标之一,对京津唐城市群的不透水面进行深入研究,可以量化城市群扩张过程及其影响,对该区域多城市协调发展及规划布局具有重要意义。本文结合高分辨遥感影像、生长季及落叶季的Landsat TM遥感影像和夜间灯光数据等,采用分类和回归树（Classification and rRegression Tree, CART）算法,构建了适于京津唐地区不透水面盖度提取的技术方案,获取了京津唐地区1995-2016年共5期地表不透水面盖度专题信息,并分析了地表不透水面的时空演变规律,结论为：① 适于京津唐地区不透水面盖度提取的CART算法的最佳输入变量组合为：生长季和落叶季的Landsat TM图像以及对应的夜间灯光数据;其次为生长季Landsat TM遥感图像和夜间灯光数据组合方案。利用该组合方案,ISP估算输出结果的交叉验证精度R值可以达到约0.85,可以满足地表不透水面纵向对比分析的需要。② 从地表不透水面总面积数量值来看,1995-2016年京津唐主体城市区域整体上呈增长趋势,其中2011-2016年地表不透水面积增加愈加明显;③ 从地表不透水面盖度值的高低来看,1995-2016年京津唐中、高盖度不透水面的占比都是在不断增长的,低盖度不透水面占比存在少量下降现象,且京、津、唐3城市的主体城区各阶段变化差异较大,反映出了各城市扩张具有各自不同的时空演变特征。     

Google Earth Engine平台支持下的赣南柑橘果园遥感提取研究

赣南地区是中国柑橘主产区,柑橘种植产业经数十年发展已具较大规模。本文利用Google Earth Engine平台,使用2140景Landsat影像进行像元级融合,重构目标年份季节最小云量影像集,构建多维分类特征集,利用随机森林分类算法,实现了1990、1995、2000、2005、2010和2016年赣南柑橘果园的分布制图。结果表明：利用Google Earth Engine平台可实现大量遥感影像数据的高效处理;最小云量影像合成方法能够有效解决多云多雨地区高质量光学影像获取困难的问题;以最小云量影像合成构建的数据集,使用随机森林分类算法能够有效提取赣南柑橘果园,分类平均总体精度和Kappa系数分别为93.15%和0.90,分类效果良好;赣南柑橘果园面积由1990年9.77 km²扩大为2016年2200.34 km²,2005年以后呈大规模扩张趋势,果园分布由零星分布,逐步形成连片化的聚集分布特点,柑橘果园用地的主要来源为林地、灌丛和耕地。     

Classification of inorganic natural fine-grained soils in Korea based on modified plasticity chart

Classification of fine-grained soils is typically conducted using plasticity charts. The typically used plasticity chart proposed by Casagrande was questioned by Polidori proposing different classification criterion in separating clayey and silty soils. Using natural clayey and silty soils sampled from four different coastal sites in Korea, applicability of both Casagrande’s and Polidori’s plasticity charts was evaluated. Classification results of Korean natural soils based on the Casagrande’s and Polidori’s plasticity charts did not match well with those based on the soils’ behavior reported in the previous publication. The disagreement in classification of Korean natural fine-grained soils may result from disregard of considerable silt fraction effect on plastic and liquid limits for Polidori’s chart. Consequently, revised proposal of Polidori’s plasticity chart was tentatively made for further classification of fine-grained soils suitable for Korean natural soils by accounting the effect of silt fraction on soil classification.     

深海沉积物分类与命名的参数指标和主成分分析

对于南海中部(118个表层沉积物样,水深82~4 420 m)、东部(106个表层沉积物样,水深700~4 508 m)海域的表层沉积物的粒度资料按小于200 m,200~2 000 m,大于2 000 m水深段对水深、平均粒径、黏土含量进行统计分析,结果表明从陆架到陆坡再到深海,平均粒径和黏土含量随水深增加呈非常有规律的变化;把大于2 000 m水深区域再细分为大于2 500 m,大于3 000 m,大于3 500 m,结果表明平均粒径和黏土含量随水深增加几乎无变化,在南海中部水深大于2 000 m海域平均粒径为3.39~3.54μm,黏土平均含量为54.91%~55.47%;在南海东部水深大于2 000 m海域平均粒径为3.25~3.37μm,黏土平均含量为53.91%~54.56%。研究表明2 000 m水深具有划分深海沉积物的指示意义。南海中部水深大于2 000 m海域黏土平均含量为55.19%,平均粒径为3.39μm;在南海东部水深大于2 000 m海域黏土平均含量为53.91%,平均粒径为3.37μm;在南海中部、东部水深大于2 000 m海域平均粒径均小于4μm,黏土平均含量均大于50%,表明深海沉积物粒度特征是平均粒径小于4μm和黏土平均含量大于50%。黏土含量是非生物组分的代表和划分深海沉积物类型的一个独立参数,钙质生物和硅质生物组分是另外两个独立参数。南海东部海域表层沉积物中55种元素总含量为47.50%,硅、铝、钛、钠、钾、磷、钙、镁、铁、锰十种主元素含量为47.03%,其他45种元素含量为0.47%,虽然沉积物来源复杂、成因不同,但沉积物化学主成分并不复杂,主要由前10种主元素和氧元素组成。沉积物主元素铝、钙、硅分别富集于黏土、钙质沉积、硅质沉积中。通过建立沉积物生源组分与碳酸钙、三氧化二铝、二氧化硅的量化关系,可把碳酸钙、生物二氧化硅作为钙质生物和硅质生物的两个替代参数。     

Ecological classification of European transitional waters in the North-East Atlantic eco-region

A new methodology to classify European North-East Atlantic transitional waters into ecological types has been developed based on the most important hydrological and morphological features that are likely to determine the ecology of aquatic systems in transitional waters. Hydrological indicators help identifying if a transitional water area is dominated by fresh or sea water and/or by intertidal or subtidal areas, while morphological indicators allow an estimation of the complexity of the transitional water and the diversity of the habitats involved. Twelve transitional waters of the southern Bay of Biscay were classified using this methodology and the five hydro-morphological types obtained were validated with benthic macro-invertebrate data. Transitional waters with a complex morphology showed the highest values of species diversity, while those with a smaller tidal influence showed lower species diversity. The ‘Scrobicularia’ and ‘Abra’ assemblages, previously identified in the study area, were found to be related to different types of transitional waters. The ‘Abra’ assemblage only appeared in estuaries with a complex morphology and dominated by tidal influences, while the ‘Scrobicularia’ assemblage was detected in all the transitional waters except for a single coastal lagoon. This classification of transitional waters may therefore be useful to establish the biological reference conditions needed for European Directives.     

Capabilities of the bathymetric Hawk Eye LiDAR for coastal habitat mapping: A case study within a Basque estuary

The bathymetric LiDAR system is an airborne laser that detects sea bottom at high vertical and horizontal resolutions in shallow coastal waters. This study assesses the capabilities of the airborne bathymetric LiDAR sensor (Hawk Eye system) for coastal habitat mapping in the Oka estuary (within the Biosphere Reserve of Urdaibai, SE Bay of Biscay, northern Spain), where water conditions are moderately turbid. Three specific objectives were addressed: 1) to assess the data quality of the Hawk Eye LiDAR, both for terrestrial and subtidal zones, in terms of height measurement density, coverage, and vertical accuracy; 2) to compare bathymetric LiDAR with a ship-borne multibeam echosounder (MBES) for different bottom types and depth ranges; and 3) to test the discrimination potential of LiDAR height and reflectance information, together with multi-spectral imagery (three visible and near infrared bands), for the classification of 22 salt marsh and rocky shore habitats, covering supralittoral, intertidal and subtidal zones. The bathymetric LiDAR Hawk Eye data enabled the generation of a digital elevation model (DEM) of the Oka estuary, at 2 m of horizontal spatial resolution in the terrestrial zone (with a vertical accuracy of 0.15 m) and at 4 m within the subtidal, extending a water depth of 21 m. Data gaps occurred in 14.4% of the area surveyed with the LiDAR (13.69 km²). Comparison of the LiDAR system and the MBES showed no significant mean difference in depth. However, the Root Mean Square error of the former was high (0.84 m), especially concentrated upon rocky (0.55–1.77 m) rather than in sediment bottoms (0.38–0.62 m). The potential of LiDAR topographic variables and reflectance alone for discriminating 15 intertidal and submerged habitats was low (with overall classification accuracy between 52.4 and 65.4%). In particular, reflectance retrieved for this case study has been found to be not particularly useful for classification purposes. The combination of the LiDAR-based DEM and derived topographical features with the near infrared and visible bands has permitted the mapping of 22 supralittoral, intertidal and subtidal habitats of the Oka estuary, with high overall classification accuracies of between 84.5% and 92.1%, using the maximum likelihood algorithm. The airborne bathymetric Hawk Eye LiDAR, although somewhat limited by water turbidity and wave breaking, provides unique height information obscured from topographic LiDAR and acoustic systems, together with an improvement of the habitat mapping reliability in the complex and dynamic coastal fringe.     

基于随机森林的光子计数激光雷达点云滤波

新一代星载激光雷达卫星ICESat-2首次采用了微脉冲光子计数激光雷达技术,由于单光子探测的灵敏性导致数据在大气和地表下层产生了大量噪声,因此对光子计数激光雷达点云数据实现信号和噪声的分离是开展进一步应用研究的前提和基础。本文选择美国俄勒冈州和弗吉尼亚州2个研究区,采用MATLAS数据,根据光子点云数据的特点构造了12个光子点云特征,对所构造的特征利用随机森林进行变量筛选,用机器学习方法对光子点云进行分类,并将建立好的模型推广到整个研究区。研究结果表明,本文构建的分类器分类总精度达到了96.79%,Kappa系数为0.94,平均生产者精度和用户精度分别为97.1%和96.8%。在相对弱噪声、平坦地形区域和强噪声、复杂地形区域都取得较好的分类结果。本文结果显示了基于少量样本通过机器学习的方法构建模型,可以推广到较大范围区域的光子点云分类应用中。     

高分六号遥感卫星新增波段下的树种分类精度分析

高分六号卫星具有覆盖广、多种分辨率、波段多的优势,能为遥感解译提供更丰富的信息。为探究高分六号卫星新增波段在森林树种识别上的应用,本文以覆盖根河市阿龙山林业局的一期高分六号宽幅影像为数据源,基于特征优化空间算法（Feature Space Optimization,FSO）和最大似然分类法,分别利用高分六号的前4个波段和所有波段（8波段）的光谱、纹理等特征进行了森林树种分类,并逐一添加新增波段特征确定了各波段的贡献率排名。结果表明：在加入了优选出的均匀性纹理、均值纹理和角二阶矩纹理3种纹理特征后,前4波段和8波段的分类精度比只基于光谱特征时的精度分别高出13.23%和24.63%;利用8波段信息比只利用前4波段在基于光谱特征上的精度高11.88%,在基于光谱+纹理特征上则高23.24%;基于8波段光谱+纹理特征的树种分类精度最高,达到68.74%,新增4波段的贡献率排名为B6>B5>B8>B7,说明新增红边波段对于本次树种分类试验的贡献率最高,能为北方树种识别提供有效帮助。     

桂林-阳朔地区DEM地形特征与岩性相关性分析及分类研究

地形地貌是岩性解译的重要信息,地形因子作为描述DEM数字曲面几何特征的定量指标参数,可用来定量化表达不同岩性所在地区地形地貌特征。本文以桂林-阳朔地区为研究区,研究地形因子数学、地质意义,建立岩性与地形因子组合间的定量关联,进而实现岩石类型划分。本文基于ASTERGDEM提取坡度、起伏度等12个地形因子,在分析各个地形因子地质意义基础上,通过聚类分析及方差分析的多元统计分析方法,研究各岩性地形因子特性及其关联性,建立研究区岩性之间的定量差异;此外,利用因子分析方法研究岩性分类过程中的主导因素,确定适宜岩性分类方法以实现定量化岩性分类。实验结果表明：不同岩性、不同地形地貌的地形因子（组合）之间具有显著差异,基于因子分析得到的宏观地形复杂度指数（MTI）以及微观曲率指数（MCI）对岩石类型的分类精度达77.36%。研究表明,地形复杂度等地形因子可用于岩性分类,采用因子分析方法可获取反映地形地貌宏观、微观特征的定量指标,且岩性分类效果良好。     

Extending self-organizing maps for supervised classification of remotely sensed data

An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.