农用地分等成果在土地利用总体规划中的应用

不同层次的农用地分等成果有着不同的应用方向。文章以土地利用总体规划为导向,以农用地利用等别与自然等别的高低延伸顺序与耕地最低需求量相结合,确定出基本农田保护的数量;以基本农田数量的105％～110％为基准,将县级利用等别图与自然等别图重叠的区域划定为基本农田保护区;交叉运用分等的土壤肥力图、气候生产潜力指数曲线图来选择作物适宜生长区和改良土地用养结构;以分等因素图作为限制因子的选择标准来划分土地限制类别区。     

基于成像高光谱数据的高低潮潮位线提取方法研究

以蓬莱西海岸西庄至栾家口岸段作为研究区,利用机载成像高光谱数据,对比航空垂直影像图和野外调查结果,通过分析砂质、黑泥砂质和黄泥砂质3种海岸类型的光谱特征,建立分类识别模型,完成了对3种海岸类型的分类处理,确定出湿滩和干滩范围,并进一步提取出高潮潮位线和低潮潮位线。     

利用极化SAR数据进行土地覆盖分类研究

基于相关矩阵特征向量的目标分解将地物回波复杂的散射过程分解成相互独立的三种单一散射分量:单向散射、双向散射和交叉散射,分别对应各自的目标相关矩阵。目标分解技术降低了散射回波之间的相关性,有利于分析地物散射机理,有助于提高分类精度。对荷兰F levoland地区全极化数据进行分解,经过试验和相关性分析,选用7种数据形成多参数数据组合,对其进行最大似然监督分类,同时进行常规三种极化加相位差的分类和基于复W ishart分布的最大似然分类,逐像元计算混淆矩阵,分析对比三种分类结果的精度,试验表明:相对于常规数据组合分类,基于复W ishart分布的监督分类可以小幅度提高分类精度,而利用目标分解得到多参数组合数据进行分类则有大幅度的提高。     

海洋经济红藻江蓠科的分类研究现状及存在的问题

江蓠科红藻是重要的经济海藻,用途十分广泛。然而,近年来受分子生物学技术引入等的影响,其分类学地位引起了极大争议。针对这个问题,本研究以争议比较大的江蓠属(Gracilaria)、拟江蓠(龙须菜)属(Gracilariopsis)和多穴藻属(Polycavernosa)为对象,总结归纳了其物种多样性、国内外研究进展、存在的问题、学者试图解答的问题以及部分自己的研究结果。以期为该类群的研究提供相对详细、客观的参考数据。     

Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data

Information on tree species composition is crucial in forest management and can be obtained using remote sensing. While the topic has been addressed frequently over the last years, the remote sensing-based identification of tree species across wide and complex forest areas is still sparse in the literature. Our study presents a tree species classification of a large fraction of the Białowieża Forest in Poland covering 62 000 ha and being subject to diverse management regimes. Key objectives were to obtain an accurate tree species map and to examine if the prevalent management strategy influences the classification results. Tree species classification was conducted based on airborne hyperspectral HySpex data. We applied an iterative Support Vector Machine classification and obtained a thematic map of 7 individual tree species (birch, oak, hornbeam, lime, alder, pine, spruce) and an additional class containing other broadleaves. Generally, the more heterogeneous the area was, the more errors we observed in the classification results. Managed forests were classified more accurately than reserves. Our findings indicate that mapping dominant tree species with airborne hyperspectral data can be accomplished also over large areas and that forest management and its effects on forest structure has an influence on classification accuracies and should be actively considered when progressing towards operational mapping of tree species composition.     

Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands,South Africa

Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively.     

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.     

电力巡线LiDAR点云电塔自动定位和提取算法

针对电力巡线机载激光雷达（LiDAR）激光点云电塔自动提取问题,提出了一种电塔自动定位和点云提取算法。首先,基于点云进行二维空间网格划分,利用网格点云高程偏差和方差特征提取潜在电塔网格;其次,基于电塔点云的高程连续特性完成电塔自动定位和点云粗提取;然后,利用点云分层密度信息和图像开运算,实现电塔精细提取;最后,利用轻小型无人机载激光雷达数据验证本文算法的有效性。试验结果表明,本文所提出的自动提取算法,能够有效解决LiDAR数据中电塔自动定位和点云提取问题,在LiDAR数据质量较差时仍能够取得良好效果,算法对于噪点数据具有较强的稳健性。本文所提出的电塔自动提取算法在LiDAR电力巡检数据处理中具有一定的应用价值。     

地表覆盖分类成果质量特征分析

自我国全面建成地理国情普查成果库之后,工作重心由全面普查变为重点监测。不论普查还是监测,准确地对地表覆盖进行分类一直是工作的重点和难点。在常态化监测阶段,把握地表覆盖分类成果的主要质量指标,归纳其诸如变化率、变化区域分布、变化类型,分析影响其成果质量的主要因素,对监测生产组织及质量控制具有非常重要的作用。