土壤区划类型的参比与属地化研究

文章以详细的1981年土壤分区资料为基础,将以发生分类(GSCC)为基础的土壤类型名称分别参比成中国土壤系统分类(CST)名称和美国土壤系统分类(ST)名称。同时利用G IS空间分析技术,确定了中国8个土壤大区在地级市(盟)和各县、区、旗(市)的行政归属,并以江南低山丘陵和台湾北部地区为例,探讨了二级分区的行政归属。     

海洋表面膜特征的SAR图像探测

讨论并分析各种成因类型的海洋表面膜特征及其在海洋ＳＡＲ图像中影像特征。对渤海湾的ＳＡＲ图像中的表面膜特征进行检测并对这些表面膜特征成因进行了分类。     

基于最优尺度和随机森林算法的海岛土地利用遥感分类研究 ——以觉华岛及周边海岛为例

为了研究海岛土地利用分类,文章以辽宁省葫芦岛市觉华岛及周边海岛为例,利用北京二号卫星融合后的0.8 m高分辨率遥感数据,通过局部方差变化率曲线峰值确定了岛屿上各地类的最优分割尺度,顾及形状和紧致度参数。探讨了随机森林算法在提高海岛土地利用类型分类精度的可靠性。结果表明,当形状因子、紧致度因子和分割尺度3个参数组合为（0.5,0.6,180）时,在最优特征数为13,随机森林决策树为200时,整幅图分类精度表现最佳,总分类精度达到81.73%,总Kappa系数为0.798 0。各类别中：海岛植被常绿针叶林地最佳分割参数为（0.5,0.4,415）,Kappa系数为0.923 2。落叶阔叶林地最佳分割参数为（0.4,0.6,465）,Kappa系数为0.895 0。灌草地最佳分割参数为（0.5,0.4,230）,Kappa系数为0.889 0。海岛植被提取的生产者精度优于90.40%;海岸带中基岩海岸最佳分割参数为（0.5,0.6,520）,Kappa系数为0.909 2。粉砂淤泥质海岸最佳分割参数为（0.4,0.6,465）,Kappa系数为0.979 9。海岸带提取的生产者精度优于92.07%;耕地中有茬耕地和无茬耕地最佳分割参数为（0.5,0.6,180）,Kappa系数分别为0.858 7和0.915 3。耕地提取的生产者精度优于87.70%。由此可见,针对海岛每一种的土地利用类型,都会存在一个最优尺度与之相吻合,尚不存在单一的普适尺度满足所有地类。     

海洋碳汇经济价值核算研究

海洋碳汇经济价值核算是推动海洋碳汇市场交易、促进海洋环境保护和生态修复的前提和基础。本文探讨了海洋碳汇及海洋碳汇价值内涵,提出海洋碳汇经济价值分狭义与广义两种,重点分析了广义海洋碳汇价值的特点和构成,运用“总经 济价值法”核算广义海洋碳汇价值,构建了分类价值指标体系和分类核算方法。最后从海洋碳汇资源调查监测方法学、评估与核算标准体系、海洋增汇项目开发、海洋碳汇政策保障等角度提出海洋碳汇发展的具体建议。     

Abiotic Stream Classification as a Basis for a Surveillance Monitoring Network in Austria in Accordance with the EU Water Framework Directive

This study presents the 26 major surface water types established in Austria in accordance with the draft of the EU Water Framework Directive (WFD). These types are made up of so‐called aquatic landscape units and large rivers. The 17 aquatic landscape units were defined using a database in which all Austrian running waters with a catchment area greater than 10 km^[2] were described according to the following typological features: size of catchment area, altitude of catchment area and confluences, stream order, geology, zoogeographical regions (ecoregions), and subregions. At running waters with gauges, a classification according to flow regimes was carried out. Large rivers were defined as running waters with a stream order ⩾7 and/or a catchment area > 2500 km² and/or with an average flow >50 m³/s. These major types represent, inter alia, the basis for the establishment of a surveillance monitoring network as required by the WFD.     

Assessing the optimal preprocessing steps of MODIS time series to map cropping systems in Mato Grosso,Brazil

The adoption of new cropping practices such as integrated Crop-Livestock systems (iCL) aims at improving the land use sustainability of the agricultural sector in the Brazilian Amazon. The emergence of such integrated systems, based on crop and pasture rotations over and within years, challenges the remote sensing community who needs to implement accurate and efficient methods to process satellite image time series (SITS) in order to come up with a monitoring protocol. These methods generally include a SITS preprocessing step which can be time consuming. The aim of this study is to assess the importance of preprocessing operations such as temporal smoothing and computation of phenological metrics on the mapping of main cropping systems (i.e. pasture, single cropping, double cropping and iCL), with a special emphasis on the iCL class. The study area is located in the state of Mato Grosso, an important producer of agriculture commodities located in the Southern Brazilian Amazon. SITS were composed of a set of 16-day composites of MODIS Vegetation Indices (MOD13Q1 product) covering a one year period between 2014 and 2015. Two widely used classifiers, i.e. Random Forest (RF) and Support Vector Machine (SVM), were tested using five data sets issued from a same SITS but with different preprocessing levels: (i) raw NDVI; (ii) raw NDVI + raw EVI; (iii) smoothed NDVI; (iv) NDVI-derived phenometrics; (v) raw NDVI + phenometrics. Both RF and SVM classification results showed that the “raw NDVI + raw EVI” data set achieved the highest performance (RF OA = 0.96, RF Kappa = 0.94, SVM OA = 0.95, SVM Kappa = 0.93), followed closely by the “raw NDVI” and the “raw NDVI + phenometrics” datasets. The “NDVI-derived phenometrics” alone achieved the lowest accuracies (RF OA = 0.58 and SVM OA = 0.66). Considering that the implementation of preprocessing steps is computationally expensive and does not provide significant gains in terms of classification accuracy, we recommend to use raw vegetation indices for mapping cropping practices in Mato Grosso, including the integrated Crop-Livestock systems.     

Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data

Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas.     

Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery

We present a study of the polarimetric information content of dual-pol imaging modes and dual-pol imaging extended by polarimetric scattering models. We compare Wishart classifications both among the partial polarimetric datasets and against the full quad-pol dataset. Our emphasis is the inter-comparisons between the classification results based on dual-pol modes, compact polarimetric modes and scattering model extensions of the compact polarimetric modes. We primarily consider novel dual-pol modes, e.g. transmitting a circular polarization and receiving horizontal and vertical polarizations, and the pseudo-quad-pol data derived from polarimetric scattering models based on dual-pol data. We show that the overall classification accuracy of the pseudo-quad-pol data is essential the same as the classification accuracy obtained directly employing the underlying dual-pol imagery.     

利用Radarsat-2极化雷达数据探测湿地地表特征与分类

利用新型的Radarsat-2极化雷达数据,结合极化雷达目标分解方法提取鄱阳湖湿地不同地表类型的极化特征量,并进行了Wishart非监督和监督分类,取得了较高的精度.研究表明,Radarsat-2卫星的全天候极化雷达成像能力将在湿地监测和制图中有较大的应用潜力.     

东海海洋锋分类的初步探讨

基于笔者近年来对海洋锋的研究（１９９０；１９９２；１９９５）［１，５～７］，本文初次探讨了东海海洋锋的分类问题。同时，对各类海洋锋的海洋学特征及其形成机理做了分析比较。并给出了各种海洋锋的垂直结构模式图。