Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover.     

Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000

The automatic extraction of information content from remotely sensed data is always challenging. We suggest a novel fusion approach to improve the extraction of this information from mono-satellite images. A Worldview-2 (WV-2) pan-sharpened image and a 1/5000-scaled topographic vector map (TOPO5000) were used as the sample data. Firstly, the buildings and roads were manually extracted from WV-2 to point out the maximum extractable information content. Subsequently, object-based automatic extractions were performed. After achieving two-dimensional results, a normalized digital surface model (nDSM) was generated from the underlying digital aerial photos of TOPO5000, and the automatic extraction was repeated by fusion with the nDSM to include individual object heights as an additional band for classification. The contribution was tested by precision, completeness and overall quality. Novel fusion technique increased the success of automatic extraction by 7% for the number of buildings and by 23% for the length of roads.     

Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery

Efficient forest fire management requires precise and up-to-date knowledge regarding the composition and spatial distribution of forest fuels at various spatial and temporal scales. Fuel-type maps are essential for effective fire prevention strategies planning, as well as the alleviation of the environmental impacts of potential wildfire events. The aim of this study was to assess and compare the potential of Disaster Monitoring Constellation and Landsat-8 OLI satellite images (Operational Land Imager), combined with Object-Based Image Analysis (GEOBIA), in operational mapping of the Mediterranean fuel types at a regional scale. The results showcase that although the images of both sensors can be used with GEOBIA analysis for the generation of accurate fuel-type maps, only the OLI images can be considered as applicable for regional mapping of the Mediterranean fuel types on an operational basis.     

煤矿井下碎软煤层瓦斯抽采钻孔钻进工艺

碎软煤层在我国煤矿区分布广泛,具有瓦斯含量高、压力大、渗透率低等特征,在碎软煤层中钻进存在喷孔、塌孔、排渣不畅等问题,导致碎软煤层钻进困难、孔内事故频发,进而影响成孔深度和成孔率,造成瓦斯治理盲区;尤其是随着我国煤矿开采深度的增加,碎软煤层瓦斯抽采孔工作量和成孔难度不断增大。针对碎软煤层瓦斯抽采对钻孔施工需求,研究开发了高转速螺旋钻进工艺、中风压空气钻进工艺、气体定向钻进工艺等实用、经济的碎软煤层高效钻进技术,破解了碎软煤层钻孔排渣护孔、轨迹控制和高效成孔等方面难题,实现了碎软煤层钻孔在服役周期内的长效利用,相关技术在安徽、贵州、山西等地区成功推广应用,达到高效、精准抽采的目的,为矿井安全生产提供了技术保障。     

ASTER mapping of gypsum deposits of Thumrait region of southern Oman

This study demonstrates the use of ASTER data for the mapping of gypsum deposits and associated geological formations that occurred in the Thumrait region of southern Oman. The measurement of spectra over samples of gypsum in the 1,300–2,500 nm wavelength using a PIMA spectrometer showed the presence of distinct absorptions at 1400–1600, 1750, 1940, 2,100, and 2,400 nm characteristics to O? H stretching, H₂O combinations, and S? O bending overtones and stretching, respectively. Studying the unique spectral absorption characters of gypsum samples, we developed a false color composite (FCC) and an image by decorrelation stretch using the spectral bands 7, 3, and 2 of ASTER. The results FCC showed the regions of gypsum occurrences, and the decorrelated image discriminated the gypsum occurrences from other geological formations of the area. The study of surface mineralogy of the region using the VNIR‐SWIR bands by the spectral angle mapper method showed the presence of sulfate, carbonate, and clay minerals of the geological formations in the study area. We compared the results of ASTER with the results obtained using spectral bands 12, 8, and 4 of Sentinel‐2A processed by the same methods. The study showed that the spectral bands of ASTER can be used for mapping the gypsum and associated geological formations.     

基于多源遥感影像的长春市城市建成区提取

夜光遥感影像记录的城市灯光与人类活动密切相关,已广泛应用于城市信息提取。珞珈一号作为新一代夜光遥感数据源,比以往的夜光数据具有更高的空间分辨率和光谱分辨率,可以更清晰地表达城市建成区范围和内部结构。本文利用珞珈一号夜光遥感影像,通过人类居住指数（human settlement index, HSI）、植被覆盖和建筑共同校正的城市夜光指数（vegetation and build adjusted nighttime light urban index, VBANUI）及支持向量机（support vector machine, SVM）监督分类3种方法对长春市城市建成区进行提取,并与利用NPP/VIIRS（suomi national polar-orbiting partnership/visible infrared imaging radiometer suite）夜光遥感影像、采用同样方法得到的结果对比。结果显示：本文提出的VBANUI提高了传统植被覆盖校正的城市夜光指数（vegetation adjusted nighttime light urban index, VANUI）的提取精度,使用珞珈一号夜光遥感影像通过VBANUI提取的城市建成区结果最优,其Kappa系数为0.80,总体分类精度为90.74%;使用珞珈一号和NPP/VIIRS夜光遥感影像通过HSI按最佳阈值提取城市建成区的Kappa系数分别为0.75和0.72,总体分类精度分别为88.27%和86.54%;复合数据的SVM监督分类法中Landsat-NDBI、Landsat-NDBI-VIIRS、Landsat-NDBI-LJ和Landsat-NDBI-LJlog的Kappa系数分别为0.602、0.627、0.643和0.681,总体分类精度分别为81.11%、81.52%、82.25%和84.48%。研究结果表明：3种提取方法下,均为使用珞珈一号夜光遥感影像的结果优于使用NPP/VIIRS夜光遥感影像的结果,证明相比于NPP/VIIRS夜光遥感影像,珞珈一号夜光遥感影像更适用于城市尺度的建成区范围提取。     

Who governs here? Informal resource extraction,state enforcement,and conflict in Ghana

Over the past two decades, “illegal” natural resource extraction has become a significant driver of environmental change and social conflict across the Global South. In response, numerous Sub-Saharan African states have engaged in governance reforms that heed calls to securitize – or, establish and consolidate state control over – natural resources. In Ghana, securitization has served to entrench the informal economy as domestic producers, marginalized in the process of reform, continue to utilize non-state institutions to maintain access. While the Ghanaian state has branded “illegal” resource extraction a major environmental, social, and national security concern, it has responded to this threat unevenly; it has violently enforced its authority in some contexts but remained relatively indifferent in others. This article explores the phenomenon of selective enforcement to explain patterns of violence that have emerged between state and society in response to both securitization and informality. Drawing on a multimethod approach, I find that natural resource governance authority remains fragmented across resource contexts, and that the configuration of authority and interests on the ground shapes the extent of state intervention. I propose a natural resource typology that identifies when the state is most likely to enforce its authority, and the degree of violent conflict likely to result. Ultimately, I contend that domestic patterns of enforcement are shaped primarily by: 1) competition with local power holders over resource entitlements and 2) global conservation and extraction priorities. While specific to Ghana, this argument can provide important insights into the relationship between informal extraction, state enforcement, and social conflict in other Global South contexts.     

基于镜像图的LRC和CRC偏差结合的人脸识别

为了提高人脸识别率及更好地显示人脸特征,本文提出了一种基于镜像图的LRC和CRC偏差结合的人脸识别方法.该方法首先生成一种镜像人脸,再通过融合原始人脸和镜像人脸形成新的混合训练样本,最后利用LRC和CRC偏差结合进行人脸识别.新方法增加了训练样本的数目,克服了由于光照和姿态等外部因素带来的影响.实验结果表明,镜像图与LRC和CRC偏差结合的人脸识别方法提高了人脸识别的准确性.     

基于图像处理的自动野外救援系统设计

针对野外救援的不及时性,结合人工智能等技术的应用,提出了一种新的自动野外救援系统.人类在野外和海上活动中受困后,除了依靠自身能力和装备进行求生之外,外部的有效及时救援是提高生存几率的关键.自动野外救援系统,由自动报警装置、手动报警装置实现报警,触发整个系统的运行.自动救援装置可以通过定位模块实现定位,通过执行器移动到随身装置附近实现救援,随身装置还可以发出信标导引信号;自动救援装置还可以通过传感器检测信标,通过相位检测技术等计算待救者的坐标并规划路径,实现更精准的定位与救援.     

文本特征提取的研究进展

文本理解是人工智能的一个重要分支,其技术推动了人与计算机之间在自然语言上的有效交互.为了让计算机准确地理解和感知文本数据,文本特征提取是最为基础和关键的步骤之一.基于此,本文介绍文本特征提取研究的发展历史,以及近年来主流特征提取的方法,并对未来的研究方向进行展望.首先,介绍语义最底层的词级表示;接着,总结在词级表示基础上衍生出的句级表示上的研究进展;随后,介绍比词级表示和句级表示更高层的篇分析;最后,通过文本特征提取的一个典型应用——问答系统的介绍,阐述文本特征提取的最新方法和技术在问答系统上的应用,并对未来的研究方向做了展望.