Testing the discrimination and detection limits of WorldView-2 imagery on a challenging invasive plant target

Invasive plants pose significant threats to biodiversity and ecosystem function globally, leading to costly monitoring and management effort. While remote sensing promises cost-effective, robust and repeatable monitoring tools to support intervention, it has been largely restricted to airborne platforms that have higher spatial and spectral resolutions, but which lack the coverage and versatility of satellite-based platforms. This study tests the ability of the WorldView-2 (WV2) eight-band satellite sensor for detecting the invasive shrub mesquite (Prosopis spp.) in the north-west Pilbara region of Australia. Detectability was challenged by the target taxa being largely defoliated by a leaf-tying biological control agent (Gelechiidae: Evippe sp. #1) and the presence of other shrubs and trees. Variable importance in the projection (VIP) scores identified bands offering greatest capacity for discrimination were those covering the near-infrared, red, and red-edge wavelengths. Wavelengths between 400 nm and 630 nm (coastal blue, blue, green, yellow) were not useful for species level discrimination in this case. Classification accuracy was tested on three band sets (simulated standard multispectral, all bands, and bands with VIP scores ≥1). Overall accuracies were comparable amongst all band-sets (Kappa = 0.71–0.77). However, mesquite omission rates were unacceptably high (21.3%) when using all eight bands relative to the simulated standard multispectral band-set (9.5%) and the band-set informed by VIP scores (11.9%). An incremental cover evaluation on the latter identified most omissions to be for objects <16 m². Mesquite omissions reduced to 2.6% and overall accuracy significantly improved (Kappa = 0.88) when these objects were left out of the confusion matrix calculations. Very high mapping accuracy of objects >16 m² allows application for mapping mesquite shrubs and coalesced stands, the former not previously possible, even with 3 m resolution hyperspectral imagery. WV2 imagery offers excellent portability potential for detecting other species where spectral/spatial resolution or coverage has been an impediment. New generation satellite sensors are removing barriers previously preventing widespread adoption of remote sensing technologies in natural resource management.     

遥感数据融合的进展与前瞻

数据融合是提升遥感影像应用能力的重要手段,一直是遥感信息处理与应用领域的研究热点。本文系统综述了遥感数据融合的进展与前瞻:首先对数据融合的层次与分类进行了总结和归纳,将遥感数据融合划分为同质遥感数据融合、异质遥感数据融合、遥感—站点数据融合、遥感—非观测数据融合4大类;在此基础上,重点针对时—空—谱光学遥感数据的融合,从多视超分辨率融合、多尺度融合、空—谱融合、时—空融合、时—空—谱一体化融合等方面进行了详细阐述;最后总结了遥感数据融合的前瞻研究方向,包括时—空—谱一体化融合的拓展、空天地观测数据的跨尺度融合、传感网环境下的在线融合、面向应用的融合方法等。     

生态敏感性限制下的城市建设用地适宜性评价

针对当前城市规划中对建设用地适宜性评价框架的不尽合理问题,以河南省西峡县为例构建了包含主导性和限制性逻辑的综合评价方案。利用遥感与地理信息系统相结合的方法,将生态敏感性作为限制性因素进行城市建设用地适宜性研究。研究区的分析结果显示:西峡县整体新增城镇建设区呈现南北区域不适宜而中间适宜的特征,而适宜和较适宜城市建设的用地主要分布在鹳河及其支流形成的河谷地带。但在中心城区外缘某些区域生态敏感性强,应禁止开发活动。研究结果有效体现出建设用地和生态用地的空间格局,对城市总体规划具有一定的参考价值。     

改进支持向量机的高分遥感影像道路提取

针对支持向量机受分类数的限制在高分辨率遥感影像中无法直接获取高精度道路网信息的问题,该文提出一种新的混合的基于支持向量机的方法:首先,利用模糊C均值聚类方法将输入的遥感影像分为3类,以减少支持向量机的错分现象;其次,运用支持向量机将不同类别的像素分为道路类和非道路类;最后,应用马尔科夫随机场对分类结果进行噪声去除,并采用形态学进行后处理,进而得到精确道路网信息。实验结果表明:该算法不仅能够从高分辨率遥感影像中提取出道路网,而且精度优于直接使用支持向量机算法以及对比算法。     

Advances and Trends in Atmospheric Measurement by Smartphones*

With the development and popularization of smartphones and embedded sensors, a non-professional atmospheric measurement method by using smartphones carried by the public has been proposed recently. Without extra dedicated instrument, this method has many advantages, such as low hardware cost, high spatio-temporal resolution, and wide coverage, and it can supplement the professional atmospheric measurement methods, which has broad applications in the meteorological operation, scientific research, public service, and other fields. At present, the research on the non-professional atmospheric measurement in China is limited. In order to make full use of this method, this paper briefly outlined the states of existing smartphones and embedded sensors, highlighted the measurement of precipitation, air temperature, pressure, aerosols, and radiation by smartphones. To promote the development of smartphones for atmospheric measurement, future research should focus on mechanism study, available sources exploration, data quality control, big data processing, joining and matching with operation, research and service, etc.     

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.     

Assessing soil salinity using WorldView-2 multispectral images in Timpaki,Crete, Greece

Salinization is one of the major soil degradation threats occurring worldwide. This study evaluates the feasibility of operational surface soil salinity mapping based on state-of-the-art Earth Observation (EO) products captured by sensors on-board WorldView-2 (WV2) and Landsat 8 satellites. The proposed methods are tested in Timpaki, south-central Crete,Greece, where brackish water irrigation puts soil health at risk of soil salinization. In all cases, EO products are calibrated against soil samples collected from bare soil locations. Results indicate a moderate correlation of observed EC_e values with the investigated remote sensing parameters. Regarding sensitivity to saline soil, the yellow band displays higher values. Comparison between methods used in the literature shows that those developed specifically for soil salinity, and especially index S5, perform better. The proposed ‘detection index’ and 3D PCA transformation methodology perform reasonably well in detecting areas with high EC_e values and provide a simple and effective operational alternative for saline topsoil detection and mapping.     

基于遥感的大兴安岭小扬气镇用地类型变化及生态环境状况评价

大兴安岭小扬气镇位于大兴安岭南部,属大小兴安岭森林生态功能区,森林覆盖率高,湿地资源丰富,曾是木材生产基地。为掌握小扬气镇用地类型现状及其变化,开展生态状况评价,利用多源卫星遥感数据,通过多尺度图像分割、决策树及目视解译等方法分别对该区1985年、1998年、2008年和2018年土地利用类型进行提取,计算生态环境状况指数(I_E),评价生态环境状况。结果表明: 小扬气镇土地利用类型以林地、沼泽、水域为主,三者占研究区总面积的97%以上; 沼泽草地主要由阔叶林转化而来,耕地主要由阔叶林、沼泽草地转化而来,草地主要转化为阔叶林、沼泽草地,阔叶林与沼泽草地之间的相互转化最为剧烈; 新增工矿仓储用地主要占用了原有阔叶林土地,新增住宅用地主要占用了原有的沼泽草地、阔叶林、森林沼泽等。总体来看,区内生态环境状况良好,在维护生态安全、促进当地绿色经济发展中起到了重要作用。     

GPS技术和遥感影像在编制重点文物地图中的应用——以制作武汉大学校园重点文物图为例

GPS技术具有实时导航、定位准确的特点,遥感影像能提供快速及时的地理信息,它们当前还未广泛应用于地图的制作,本文尝试将这两种技术结合应用来编制地图,进而探讨在制作过程中的数据处理、重点文物图的设计与编制等问题;并以制作武汉大学的重点文物图为例,来说明和分析这种制作方法所具有的显著特点和具体操作的可行性。     

SNOW WETNESS ESTIMATES OF VEGETATED TERRAIN FROM SATELLITE PASSIVE MICROWAVE DATA

The Special Sensor Microwave/Imager (SSM/I) radiometer is a useful tool for monitoring snow wetness on a large scale because water content has a significant effect on the microwave emissions at the snowpack surface. To date, SSM/I snow wetness algorithms, based on statistical regression analysis, have been developed only for specific regions. Inadequate ground-based snow wetness measurements and the non-linearity between SSM/I brightness temperatures (T_Bs) and snow wetness over varied vegetation covered terrain has impeded the development of a general model. In this study, we used a previously developed linear relationship between snowpack surface wetness (% by volume) and concurrent air temperature (°C) to estimate the snow wetness at ground weather stations. The snow condition (snow free, dry, wet or refrozen snow) of each SSM/I pixel (a 37 × 29 km area at 37.0 GHz) was determined from ground-measured weather data and the T_B signature. SSM/I T_Bs of wet snow were then linked with the snow wetness estimates as an input/output relationship. A single-hidden-layer back-propagation (backprop) artificial neural network (ANN) was designed to learn the relationships. After training, the snow wetness values estimated by the ANN were compared with those derived by regression models. Results show that the ANN performed better than the existing regression models in estimating snow wetness from SSM/I data over terrain with different amounts of vegetation cover.