应用Hydrolight模式研究含油水体辐射传输特性的关键技术

辐射传输模式Hydrolight在国际水色遥感研究领域已被广泛用于解决水体光学的各种问题,也是进行石油类污染水体辐射传输特性研究的有效模型。结合Hydrolight模式的机理、应用现状及含油水体的特点,提出了将该模式应用于光学特性复杂的石油污染水体辐射传输特性研究时需要解决的关键技术,包括测定石油污染水体吸收系数和散射系数、建立石油污染吸收和散射特性随波长和深度变化的参数化模型、确定油物质的吸附后悬浮颗粒物体散射函数模型等方面。依据2018年8月和2016年8月分别在辽宁大连港和辽宁盘锦辽河油田等区域测定的含油水体数据,讨论了这些关键技术的解决方案。     

绿潮暴发对浮游动物群落结构影响研究

本调查于2016年和2017年的5月、8月,对日照近岸海域浒苔暴发区进行了4个航次的浮游动物断面调查。4个航次中,2016年和2017年5月航次获得的种类数要明显低于同年8月航次,而平均丰度和湿重生物量均高于同年8月航次。造成这种情况的原因可能是调查海域8月浒苔暴发结束,浒苔的消亡过程会大量消耗营养盐,导致水体中营养盐含量下降,进而影响到其他浮游植物和浮游动物的生长。应用PRIMER软件中的单变量分析得到群落种数(S)、丰富度(d)、香农-威纳指数(Shannon-Weaner index)(H′)和均匀度(J),从多样性指数分析可以看出,5月航次的生物多样性均劣于同年8月航次。不同年度相同季节航次中出现的优势种极其相似,而不同季节航次出现的优势种则大不相同。     

辽宁省海洋服务新业态培育与法律保障研究

海洋服务业已成为海洋经济发展的重要引擎,"十三五"时期是辽宁省海洋经济加快转型升级的关键期,要求培育新动能、发展新模式。因此,培育海洋服务新业态对辽宁省海洋经济发展具有重要意义。文章针对辽宁海洋服务业发展滞后,规模偏小、区域发展不平衡,同质化严重、科技含量低,层次相对低下、与其他产业融合不足,资源整合较差等问题,提出培育海洋服务新业态重要路径为:积极发展四大产业,提高经济规模总量;优化产业区域布局,形成错位发展;提高科技含量,提升海洋服务业层次;促进海洋服务业与其他行业融合协调,提升资源整合度。法律保障措施为:完善法规体系、严格项目审批、提高执法水平、健全应急管理。     

雅浦海沟南缘铁锰结核矿物与地球化学特征及其成因研究

本文采用X射线衍射、全岩地球化学和电子探针等测试方法,对雅浦海沟南部附近海域获得的铁锰结核样品进行了显微构造、矿物和地球化学分析,并探讨了其成因。结果表明:铁锰结核的显微构造主要包括平行纹层构造、柱状构造、叠层状构造和同心环状构造;显微构造和探针结果显示铁锰结核在生长初期处于底部海洋动力比较强烈的环境,后期生长环境逐渐趋于稳定;铁锰结核的矿物组分以水羟锰矿、钠水锰矿、石英和钙十字石为主;样品中Fe、Mn元素含量较高且含量比较接近,Cu、Co、Ni和REE相对富集,REE分布模式整体比较平缓并都出现较强的Ce正异常和重稀土元素亏损现象;文中两块铁锰结核都为水成成因,成矿物质主要来自同期的海水沉淀,同时也受一定海底火山物质和陆源风尘物质的影响。     

利用改进型MODIS火点探测算法实现河北省秸秆焚烧火点识别

为准确地了解河北省秸秆焚烧火点的空间分布,为秸秆焚烧监测的实现、禁烧工作的开展、环境质量改善提供支持.基于MODIS L1B数据、MODIS标准火点产品MOD14、全国秸秆焚烧火点日报数据为基础,采用改进型MODIS火灾探测算法,并通过IDL语言实现,得到秸秆焚烧火点空间分布信息,并进行空间与定量精度分析.研究表明:火点大部分位于河北省南部的一些地区,其中尤以邢台、石家庄、邯郸火点数量最为突出;该算法运算速度快,获取的秸秆焚烧火点数据具有一定检测精度和可靠性,对秸秆焚烧的监测具有一定的实用价值.     

Semantic segmentation of high spatial resolution images with deep neural networks

Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.     

Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District

In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.     

Improved Priority‐Flood method for depression filling by redundant calculation optimization in local micro‐relief areas

Depression filling is a critical step in distributed hydrological modeling using digital elevation models (DEMs). The traditional Priority‐Flood (PF) approach is widely used due to its relatively high efficiency when dealing with a small‐sized DEM. However, it seems inadequate and inefficient when dealing with large high‐resolution DEMs. In this work, we examined the relationship between the PF algorithm calculation process and the topographical characteristics of depressions, and found significant redundant calculations in the local micro‐relief areas in the conventional PF algorithm. As such calculations require more time when dealing with large DEMs, we thus propose a new variant of the PF algorithm, wherein redundant points and calculations are recognized and eliminated based on the local micro‐relief water‐flow characteristics of the depression‐filling process. In addition, depressions and flatlands were optimally processed by a quick queue to improve the efficiency of the process. The proposed method was applied and validated in eight case areas using the Shuttle Radar Topography Mission digital elevation model (SRTM‐DEM) with 1 arc‐second resolution. These selected areas have different data sizes. A comparative analysis among the proposed method, the Wang and Liu‐based PF, the improved Barnes‐based PF, the improved Zhou‐based PF, and the Planchon and Darboux (P&D) algorithms was conducted to evaluate the accuracy and efficiency of the proposed algorithm. The results showed that the proposed algorithm is 43.2% (maximum) faster than Wang and Liu's variant of the PF method, with an average of 31.8%. In addition, the proposed algorithm achieved similar performance to the improved Zhou‐based PF algorithm, though our algorithm has the advantage of being simpler. The optimal strategies using the proposed algorithm can be employed in various landforms with high efficiency. The proposed method can also achieve good depression filling, even with large amounts of DEM data.     

Sentinel-1A SAR影像及其在矿区地面沉降监测中的应用研究

详细介绍了Sentinel-1A SAR影像数据的基本参数、工作模式、应用领域等。利用2016.11.09—2017.03.09的5景Sentinel-1A C波段SAR影像数据进行矿区地面沉降监测试验。基于SARscape利用双轨D-In SAR技术进行差分干涉处理,得到了研究区地面沉降分布图,直观地再现了研究区在2016.11.09—2017.03.09的沉降分布、沉降量级等。结果表明济宁某矿区在监测期间地面相对稳定,未有大面积和大量级的地面沉降发生,4个干涉对监测到的最大沉降都未超过3 cm。     

基于Landsat遥感数据武汉地区植被覆盖度动态变化监测分析

旨在研究受人类经济活动影响的植被资源及其动态变化趋势的程度,采用基于归一化植被指数的像素二分模型,利用2002—2010年(武汉市每年8月)的卫星遥感数据,对武汉地区6年间的植被覆盖变化进行监测分析。结果表明,植被覆盖度变化与武汉GDP增长率有着紧密联系:2002—2007年,武汉经济较缓慢上升,植被覆盖度基本维持在55%左右;而2007—2008年武汉GDP总值增长率高达26.06%,但随之而来的是植被覆盖度由高峰期的61.68%下降到49.18%,是历年来的最低水准; 2008—2010年经济发展放缓,植被指数有小幅度回升。人类经济活动是影响植被资源的重要因素。