自适应模糊规则分类方法及在TM土地覆盖分类中的应用研究

根据自组织网络和模糊逻辑推理,实现土地覆盖自适应模糊规则分类方法。该方法通过网络的节点和权值提取出模糊规则,调整网络中节点个数(即相应增加规则节点数)和权值向量,使模糊规则自动生成,并利用模糊逻辑推理,完成TM土地覆盖分类。对拒分类的像元,自适应增加K值使其可分。该方法所得分类精度及Kapp系数与最大似然分类方法结果相比分别提高了2.7%和2.9%;与自组织网络相比,总精度相差不大,而Kapp系数低1%。实验证明,如何提取和表示非光谱知识,从而解决类别混淆等问题,是提高自适应模糊规则分类性能的关键     

黑河流域土地覆盖分类数据的建立及其影响的模拟

基于黑河流域1:100000土地覆盖分布图, 融合了1:1000000植被分类图, 按照美国地质调查局(USGS)提出的土地覆盖分类标准, 合成了与全球土地覆盖类型分类标准相同的空间分辨率为1 km黑河流域土地覆盖类型分布(简称综合分类).将综合分类数据与USGS全球土地覆盖类型数据在黑河流域范围内进行对比, 发现土地覆盖类型分布变化区域主要集中在流域中游的绿洲区, 许多绿洲在全球数据中体现为草地, 在综合分类中为灌溉农田, 同时综合分类数据中较全球数据增加了许多城镇用地.利用两套土地覆盖类型分布数据对2003年黑河流域中上游大气要素进行了模拟, 比较了不同土地覆盖类型分布对气温等大气要素的影响, 结果显示, 土地覆盖参数中地表反照率和比辐射率的空间分布与近地层大气要素和土壤温、湿场分布的空间相关性高;比辐射率和粗糙度变化对局地大气、土壤要素影响较大, 绿洲区土地覆盖类型大多由草地变为灌溉农田, 导致了气温升高, 其中不排除城市化的影响, 无论从特征参数变化与大气要素模拟值变化场的空间相关性还是特征参数变化引起的气温变化的关系看, 气温变化与比辐射率变化关系较密切.在黑河流域中上游可以通过增大灌溉农田比辐射率来提高气温模拟的准确性, 这一结论还需要更多的观测与模拟的验证.     

近15年洞头五岛海岸线时空变化分析

洞头五岛相连工程连接的5个海岛的变化是洞头区发展的缩影,2002—2017年洞头五岛形态处于快速发展阶段。文章收集了2002年、2010年和2017年3期Landsat系列卫星影像,结合2010年、2014年两期0.5m分辨率DOM和2017年实地调查数据,采用RS和GIS技术提取3个时相海岛岸线,并分析了其15年来的时空变化特征。研究表明:2002—2017年海岛不断外扩,五岛面积增加约1 290.8hm~2;岸线日趋平直,平均曲折度由2.7变化为2.2,长度减少约12 692m;自然岸线和人工岸线此消彼长,自然岸线保有率由90.3%降低到54.2%,新增人工岸线长约41 927m,类型以道路和海堤为主;2002—2010年海岛形态及岸线人工化速率较2010—2017年快;岸线外推区内土地利用率不断提高,填筑形成的土地逐渐转化为建筑物、交通用地、人工绿化用地和水系等城市设施。     

Land cover mapping at Alkali Flat and Lake Lucero,White Sands,New Mexico,USA using multi-temporal and multi-spectral remote sensing data

The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR–SWIR (0.4–2.5 μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen analysis between the various datasets helped to uncover the most optimum spatial–spectral–temporal and radiometric-resolution sensor characteristics for remote sensing based on monitoring of seasonal land cover, surface water, groundwater, and alluvial sediment input changes within the basin. The results demonstrated good agreement between ground truth data and XRD analysis of samples, and the results of Matched Filtering (MF) mapping method.     

Integrating optical satellite data and airborne laser scanning in habitat classification for wildlife management

Wildlife habitat selection is determined by a wide range of factors including food availability, shelter, security and landscape heterogeneity all of which are closely related to the more readily mapped landcover types and disturbance regimes. Regional wildlife habitat studies often used moderate resolution multispectral satellite imagery for wall to wall mapping, because it offers a favourable mix of availability, cost and resolution. However, certain habitat characteristics such as canopy structure and topographic factors are not well discriminated with these passive, optical datasets. Airborne laser scanning (ALS) provides highly accurate three dimensional data on canopy structure and the underlying terrain, thereby offers significant enhancements to wildlife habitat mapping. In this paper, we introduce an approach to integrate ALS data and multispectral images to develop a new heuristic wildlife habitat classifier for western Alberta. Our method combines ALS direct measures of canopy height, and cover with optical estimates of species (conifer vs. deciduous) composition into a decision tree classifier for habitat – or landcover types. We believe this new approach is highly versatile and transferable, because class rules can be easily adapted for other species or functional groups. We discuss the implications of increased ALS availability for habitat mapping and wildlife management and provide recommendations for integrating multispectral and ALS data into wildlife management.