基于MCR模型和DO指数的九江滨水城市生态安全网络构建

兼顾生态保护和经济增长的生态安全网络对引导滨水城市走可持续发展道路具有重要指导意义。本文以九江市为例,采用景观生态数据集和产业信息数据集,结合最小累积阻力模型（MCR）和连续空间的产业集聚测度指数（DO）构建了综合评价方法,分别对景观生态格局和经济生产空间进行分析,诊断二者的空间冲突确定战略节点并构建生态安全网络。结果表明：① 研究区包含重要生态源地29个,总面积为7323 km²;生态安全阻力高值区面积占39.69%,位于城市中部和东部连片区域,低值区则处于外围,且连通过渡区域较少,呈现空间上两极分化的“中心—外围”景观生态格局;② 经济生产空间临近水资源分布,呈现为小尺度集聚、大尺度分散的空间格局;主要产业集聚区的平均长度和数量均显示出重度污染行业>轻度污染行业>中度污染行业的空间关系;③ 识别出景观生态廊道总长685.57 km,选取了25个生态—经济战略节点,规划了18条总长424.53 km的重要绿带和26条总长662.46 km的一般绿带,共同构成了“蜂巢状”九江市生态安全网络格局。本文采用自然和经济条件相结合的综合分析视角,为生态安全格局的构建提供了多样化的实现途径。     

“一带一路”建设背景下重庆与东盟国家贸易格局演变和影响因素分析

“贸易畅通”是“一带一路”建设的重要内容,重庆作为中国西部地区内陆开放高地,贸易发展优势明显。运用贸易强度指数和HM指数,分析2001—2017年重庆与东盟国家贸易格局和贸易依赖程度,并分析影响重庆与东盟国家贸易格局的影响因素。结果表明：①重庆与东盟国家贸易联系紧密,增长速度快,贸易总额在“一带一路”沿线六大区域中始终保持领先地位;②马来西亚和泰国成为重庆与东盟国家中最重要的贸易伙伴,越南、新加坡、菲律宾和印度尼西亚紧随其后,而与缅甸、柬埔寨、老挝和文莱的贸易总额较少;③在进口方面,形成以马来西亚、泰国和越南为首的多元进口格局,泰国和越南进口伙伴地位上升。在出口方面,形成以马来西亚为首的多元出口新格局,越南出口地位下降;④在市场相互依赖程度方面,重庆出口对东盟国家特别是马来西亚和新加坡贸易市场的重要性程度上升,而对东盟国家市场依赖程度较小;⑤产业结构、贸易通道、文化交流和政策等是影响重庆与东盟国家贸易格局的重要影响因素。     

中国城市信息网络结构演变特征及驱动因素分析

信息流能够客观反映城市社会经济联系,对城市网络研究具有重要价值。基于2011、2014和2017年中国城市间信息关注度数据,利用社会网络分析法,从网络密度、中心度、城市联系等方面分析中国城市网络结构的演变特征及其驱动因素,研究表明：1）城市信息网络结构明显拓展但网络总体联系强度仍较低;省域内部信息联系强度高于跨区域联系,信息联系仍受行政区划的影响较大。2）网络节点中心度显著提高,热点区主要集中在东部沿海三大城市群;中西部部分区域核心城市如重庆、成都、郑州等成长为全国性核心城市,但中西部地区城市信息发展仍与东部地区差距较大。3）信息联系层级分布特征明显,并呈现局部区域联系紧密、区域一体化特征;核心网络由以北京、上海为核心的“轴-辐”结构演变为以“北京-上海-广深-成渝”为核心的“菱形”结构。4）信息化水平、经济发展基础、城市职能等互补性因素,网络营销、大事件效应等介入性因素及信息技术变革、城市群建设等高效性因素相互作用,共同促进信息网络结构优化重组。     

Post-earthquake economic resilience and recovery efficiency in the border areas of the Tibetan Plateau: A case study of areas affected by the Wenchuan Ms 8.0 Earthquake in Sichuan,China in 2008

Journal of Geographical Sciences - The border areas of the Tibetan Plateau and the neighboring mountainous areas have a high incidence of earthquakes with a magnitude greater than Ms 5.0, as well...     

Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery

Coffee berry necrosis is a fungal disease that, at a high level, significantly affects coffee productivity. With the advent of surface mapping satellites, it was possible to obtain information about the spectral signature of the crop on a time scale pertinent to the monitoring and detection of plant phenological changes. The objective of this paper was to define the best machine learning algorithm that is able to classify the incidence CBN as a function of Landsat 8 OLI images in different atmospheric correction methods. Landsat 8 OLI images were acquired at the dates closest to sampling anthracnose field data at three times corresponding to grain filling period and were submitted to atmospheric corrections by DOS, ATCOR, and 6SV methods. The images classified by the algorithms of machine learning, Random Forest, Multilayer Perceptron and Naive Bayes were tested 30 times in random sampling. Given the overall accuracy of each test, the algorithms were evaluated using the Friedman and Nemenyi tests to identify the statistical difference in the treatments. The obtained results indicated that the overall accuracy and the balanced accuracy index were on an average around 0.55 and 0.45, respectively, for the Naive Bayes and Multilayer Perceptron algorithms in the ATCOR atmospheric correction. According to the Friedman and Nemenyi tests, both algorithms were defined as the best classifiers. These results demonstrate that Landsat 8 OLI images were able to identify an incidence of the coffee berry necrosis by means of machine learning techniques, a fact that cannot be observed by the Pearson correlation.     

The potential of Pléiades images with high angle of incidence for reconstructing the coastal cliff face in Normandy (France)

To monitor chalk cliff face along the Normandy coast (NW France) which is prone to erosion, we tested the potential of cliff face 3D reconstruction using pairs of images with high angle of incidence at different dates from the agile Pléiades satellites. The verticality aspect of the cliff face brings difficulties in the 3D reconstruction process. Furthermore, the studied area is challenging mainly because the cliff face is north-oriented (shadow). Pléiades images were acquired over several days (multi-date stereoscopic method) with requested incidence angles until 40°. 3D reconstructions of the cliff face were compared using two software: ASP® and ERDAS IMAGINE®. Our results are twofold. Firstly, despite ASP® provides denser point clouds than ERDAS IMAGINE® (an average of 1.60 points/m² from 40° incidence angle stereoscopic pairs on the whole cliff face of Varengeville-sur-Mer against 0.77 points/m² respectively), ERDAS IMAGINE® provides more reliable point clouds than ASP® (precision assessment on the Varengeville-sur-Mer cliff face of 0.31 m ± 2.53 and 0.39 m ± 4.24 respectively), with a better spatial distribution over the cliff face and a better representation of the cliff face shape. Secondly, the quality of 3D reconstructions depends mostly on the amount of noise from raw images and on the shadow intensity on the cliff face (radiometric quality of images).     

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.     

Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data

Most studies have the achieved rapid and accurate determination of soil organic carbon (SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial distribution of SOC. To predict and map SOC at a regional scale, we obtained fourteen hyperspectral images from the Gaofen-5 (GF-5) satellite and decomposed and reconstructed the original reflectance (OR) and the first derivative reflectance (FDR) using discrete wavelet transform (DWT) at different scales. At these different scales, as inputs, we selected the 3 optimal bands with the highest weight coefficient using principal component analysis and chose the normalized difference index (NDI), ratio index (RI) and difference index (DI) with the strongest correlation with the SOC content using a contour map method. These inputs were then used to build regional-scale SOC prediction models using random forest (RF), support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. The results indicated that: 1) at a low decomposition scale, DWT can effectively eliminate the noise in satellite hyperspectral data, and the FDR combined with DWT can improve the SOC prediction accuracy significantly; 2) the method of selecting inputs using principal component analysis and a contour map can eliminate the redundancy of hyperspectral data while retaining the physical meaning of the inputs. For the model with the highest prediction accuracy, the inputs were all derived from the wavelength range of SOC variations; 3) the differences in prediction accuracy among the different prediction models are small; and 4) the SOC prediction accuracy using hyperspectral satellite data is greatly improved compared with that of previous SOC prediction studies using multispectral satellite data. This study provides a highly robust and accurate method for predicting and mapping regional SOC contents.     

基于无人机遥感和面向对象法的简单地物分类研究

近年来,全国各地进行了大范围的土地利用调查,随着无人机遥感技术越来越成熟,无人机影像分析技术已深入应用到土地利用调查中,其中最多的用途是地物分类。本文选择昭通市昭阳区某乡镇区域为研究区,对采集到的无人机影像进行预处理,生成对应的正射影像;基于多种可见光植被指数,计算每3种指数合并得到影像的OIF指数,确定最佳波段组合;采用基于规则和基于样本两种面向对象分类方法,提取房屋、道路、植被等简单地物及背景。分析结果:两种方法的提取精度均达到90%以上,基于规则的面向对象分类方法精度较高,但耗时较长;基于样本的面向对象方法耗时较短,精度相对较低。两种方法相结合的全自动分类提取是下一步研究的目标。