"资源一号"02C卫星影像土地利用变化检测能力分析

“资源一号”02C卫星自成功发射以来,被广泛应用于土地资源、矿产资源、地质环境调查,以及国土资源、地质灾害应急监测等应用领域。“资源一号”02C卫星是一颗填补我国高分辨率遥感数据空白的卫星,是根据国土资源主体业务需求定制的第一颗国产高分辨率业务卫星。本文以吉林省和浙江省某试验区“资源一号”02C卫星数据为例,对“资源一号”02C卫星数据融合影像进行了土地利用遥感监测变化信息检测能力的方法试验。根据试验区数据变化信息提取试验结果,对“资源一号”02C卫星影像在土地利用动态遥感监测中的变化检测能力和适用性开展了试验、分析和评价工作。     

珠三角村镇建成区的空间型态：特征、类型和产业影响因素

改革开放以来,珠三角地区的工业迅速发展,村镇土地利用快速非农化,其中以东莞、中山、佛山3市最为典型。论文以3市为案例,探讨2010年来珠三角村镇建成区空间型态。研究数据主要包括广东省2015年土地利用现状数据和2009—2015年间3市的土地利用数据。首先,通过斑块密度、空间离散度、空间均匀度、平均斑块面积、分形维数、景观破碎度、缓冲区分析、Moran's I指数这8个指标,分析建设用地的空间形态特征。其次,分析工业用地与非工业建设用地、建设用地与交通道路的空间分布关系,比较并总结3市的村镇建成区空间类型。最后,基于灰色关联分析法,探讨影响空间类型的产业发展因素。研究发现,东莞村镇建设用地呈现高度破碎均匀分布型,佛山呈现团状离散型,中山介于二者之间,为中度碎化分散型;3市的产业发展能较大程度上影响其形成不同的建成区空间型态。未来,基于产业经济作为村镇建成区空间特征变化的主因,珠三角可以结合粤港澳大湾区规划建设,通过调整产业结构、布局以及交通和土地利用,优化村镇空间布局。     

京津冀城市用地形态的双分形特征及其演化

区域城乡一体化的标志之一是城镇体系结构的一体化,这个过程可以从标度的角度进行描述和评价。分形是标度分析的重要方法,地理空间无尺度分布特征的典型参数是分维。论文以京津冀城镇体系为例,利用遥感图像的解译数据和人口普查数据开展分形分析、位序-规模分布分析和异速标度分析,用以解释1995—2013年间京津冀城镇体系演化的过程。结果表明,京津冀城镇体系及其演化的特征有：①京津冀空间结构和位序-规模分布都表现为自仿射双分形结构;②京津冀区域的城市人口-城区面积异速标度退化为假线性关系;③随着城镇体系的演化,自仿射的双分形结构逐步向自相似分形结构演化。由此得出结论：其一,京津冀城镇体系存在结构性的不协调因素。其空间结构和等级结构具有二元化特征,但演化方向却呈现内在结构一体化的显著趋势。其二,大城市用地不够集约。城市边缘区的无序扩张导致土地利用铺张浪费。地方政府和规划专家可以有意识地利用城镇体系演化的这种特征和趋势制定管理措施和优化规划方案。     

黄土丘陵沟壑区典型村域土地利用变化及对区域乡村转型发展的启示

深入开展乡村地域土地利用变化和聚落发展规律的研究对于探究乡村地域系统优化、推进城乡融合与乡村可持续发展具有重要意义。论文以延安市碾庄流域为例,基于ENVI和GIS等软件技术,结合Landsat卫星影像、马尔科夫链模型和实地调查,从土地利用转移与交换变化、建筑用地空间转移、社会响应等角度探讨了黄土丘陵沟壑区典型村域土地利用变化特征以及对区域乡村发展转型的影响。研究表明：① 在一系列生态工程措施作用下,碾庄流域生态覆被发生明显改观,主要表现为大量的坡耕地及稀疏生态用地转化为林地,其中,林地面积占比由32.34%(2009年)增加至50.88%(2018年);② 在社会经济发展和延安新城“边缘效应”的影响下,碾庄建设用地呈现向流域的东南和西南方向拓展的趋势,10 a间建设用地面积增加了近75%;③ 在人类活动的主导作用下,黄土丘陵沟壑区的乡村正在朝着生态化和集约化的方向转型发展。研究结果有望为深入理解黄土丘陵沟壑区乡村地域系统结构和优化提供新参考。     

Analog simulation of urban construction land supply and demand in Chang-Zhu-Tan Urban Agglomeration based on land intensive use

Journal of Geographical Sciences - Urban land intensive use is an important indicator in harmonizing the relationship between land supply and demand. The system dynamics (SD) can be used to...     

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.     

Modeling spatially non-stationary land use/cover change in the lower Connecticut River Basin by combining geographically weighted logistic regression and the CA-Markov model

Simulating land use/cover change (LUCC) and determining its transition rules have been a focus of research for several decades. Previous studies used ordinary logistic regression (OLR) to determine transition rules in cellular automata (CA) modeling of LUCC, which often neglected the spatially non-stationary relationships between driving factors and land use/cover categories. We use an integrated geographically weighted logistic regression (GWLR) CA-Markov method to simulate LUCC from 2001–2011 over 29 towns in the Connecticut River Basin. Results are compared with those obtained from the OLR-CA-Markov method, and the sensitivity of LUCC simulated by the GWLR-CA-Markov method to the spatial non-stationarity-based suitability map is investigated. Analysis of residuals indicates better goodness of fit in model calibration for geographically weighted regression (GWR) than OLR. Coefficients of driving factors indicate that GWLR outperforms OLR in depicting the local suitability of land use/cover categories. Kappa statistics of the simulated maps indicate high agreement with observed land use/cover for both OLR-CA-Markov and GWLR-CA-Markov methods. Similarity in simulation accuracy between the methods suggests that the sensitivity of simulated LUCC to suitability inputs is low with respect to spatial non-stationarity. Therefore, this study provides critical insight on the role of spatial non-stationarity throughout the process of LUCC simulation.     

Temporal Variations and Driving Factors of Cultivated Land Use Intensity in Shandong Province from 1980 to 2015

Based on the cost-income data of agricultural products and relevant statistical data, taking major grain crops and economic crops in the process of cultivated land use as study cases, we studied characteristics of the temporal variation of cultivated land use intensity and its composition in Shandong Province from 1980 to 2015, and then analyzed its main driving factors. The results showed that: (1) The total intensity of major crops in Shandong Province showed a rising trend from 919.73 Yuan ha^-1 in 1980 to 3285.06 Yuan ha^-1 in 2015, and the average annual intensity of economic crops was higher than that of grain crops. The labor cost and material cost of major crops both showed an increasing trend, but the material input was much higher than labor input for grain crops, while the labor input was much higher than material input for economic crops. (2) The labor intensity of major crops in Shandong Province showed a decreasing trend from 501.75 man-day ha^-1 in 1980 to 161.93 man-day ha^-1 in 2015. The labor intensity of grain crops was lower than that of economic crops and its decline rate was fast. On the contrary, the capital intensity showed an increasing trend from 518.33 Yuan ha^-1 in 1980 to 1159.95 Yuan ha^-1 in 2015. In the internal composition of capital intensity, the proportion of yield-increasing inputs such as seed, farmyard manure, fertilizer, pesticide and drainage and irrigation decreased gradually, while the proportion of labor-saving inputs such as agricultural machinery increased significantly. (3) The cultivated land use intensity in Shandong Province had significant negative correlations with the amount of agricultural labor and cultivated land area per capita. The primary direct driving factor was the net income per unit cost of major crops, but the time response lagged by 1-3 years. The main indirect driving factor was the reform of agricultural policy.     

我国瓯江口和珠江口凤鲚洄游模式和生境利用研究

Habitat use of the tapertail anchovy(Coilia mystus Linnaeus, 1758) from the Oujiang River Estuary and the Zhujiang(Pearl) River Estuary was studied by examining the environmental signatures of Sr and Ca in otoliths using electron probe microanalysis. Individuals from the Oujiang River had higher and varied Sr:Ca ratios(expressed as(Sr:Ca)×1 000, 3.83–13.0 average) in the otolith core regions, suggesting that they were born in brackish or sea waters, and that a freshwater habitat might not be necessary for egg hatching and larval growth.While, individuals from the Zhujiang River had lower Sr:Ca ratios(0.39–2.51 average) in the core regions,suggesting a freshwater origin. After hatching, anchovies from the Zhujiang River migrate downstream to the river estuary close to brackish water. Our results demonstrated varied habitat use for spawning during stages of early life history between the two populations, and suggested that such variations are promoting diversity of life history strategies of this species.     

耦合土地利用回归与人口加权模型的PM2.5暴露风险评估

PM_2.5已成为人群健康的重要威胁之一,科学精准的暴露评估是PM_2.5风险防控的前提,为提升PM_2.5暴露精准评估,本文利用土地利用数据、道路数据、气象数据等构建PM_2.5土地利用回归反演模型,实现了2013年12月1日-2014年2月8日（冬季）广佛都市区PM_2.5时空动态演变监测,在此基础上将PM_2.5反演结果与人口密度数据耦合,分别从PM_2.5污染浓度与人口加权PM_2.5浓度2个方面,评估广佛都市区PM_2.5污染暴露风险。研究结果表明：① 土地利用回归模型能够较好的反映研究区域内PM_2.5的空间分布特征,R²大于0.78;② 2013年12月1日-2014年2月8日,广佛都市区PM_2.5浓度平均值呈现波动变化趋势,研究时段内,最高平均浓度为97.91 μg/m³ （12月29日-1月11日）,最低平均浓度为53.40 μg/m³ （1月26日-2月8日）,全时段PM_2.5浓度超WHO健康标准的面积占比达99.8%;③ 广佛都市区PM_2.5的空间分布具有异质性规律,其高值区分别位于广州市天河区、越秀区、番禺区北部、花都区北部及佛山市禅城区、南海区中部、三水区中部,低值区主要位于广州市白云区、番禺区东南部及佛山市顺德区南部。人口加权暴露风险存在2个高值中心,分别位于广州市和佛山市的主城区;④ 耦合人口加权模型前后,广佛都市区PM_2.5暴露风险高风险区空间分布发生变化,未考虑人口加权模型时,广佛深高值区较为分散,主要位于南海区、天河区、越秀区、禅城区,考虑人口加权模型后,高值区更加集中于广州市和佛山市的主城区。