土地利用变化研究中的细胞自动机与灰色局势决策

把细胞自动机和灰色局势决策结合起来对土地利用变换机制进行模拟。实验证明,基于灰色局势决策规则的细胞自动机是对土地利用变换机制从宏观和微观角度进行模拟的有效方法。     

地球空间信息技术在新一轮国土资源大调查中的应用探讨

从分析全球变化和社会可持续发展研究的要求出发,提出地球空间信息技术的形成背景和应用基础。针对新一轮国土资源大调查的性质和任务,探讨了地球空间信息技术在新一轮国土资源大调查中的应用。     

地表覆盖遥感产品更新完善的研究动向

近年来,多尺度地表覆盖遥感产品的不断涌现,为环境变化研究、地球系统模拟、地理国(世)情监测和可持续发展规划等提供了重要科学数据。为更好地满足广大用户日益增长的应用需求,应对地表覆盖遥感产品进行持续更新完善,保持其时效性、增强时序性、丰富多样性。针对大面积地表覆盖遥感产品更新完善所面临的主要问题,介绍和评述了国内外有关研究动向,包括影像与众源信息相结合的更新、数据类型细化与完善、地表覆盖真实性验证,并作了简要展望。     

Classification of forest land attributes using multi-source remotely sensed data

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008–2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.     

新疆和田河流域土地利用/覆被变化及其驱动力分析

土地利用／覆盖变化及其驱动力分析是土地利用／覆盖研究的一个主要核心内容。利用1990年、1999年和2005年和田河流域3期土地利用数据,对和田河流域1990—2005年土地利用/覆被变化进行了定量的分析研究,同时利用相关分析和主成分分析探讨了土地利用/覆被变化的驱动力。研究结果表明:在15 a尺度上研究区土地利用/覆被变化总特征是耕地、林地、水域和建设用地面积增加,草地和未利用地面积减少;土地利用程度变化量为0.56%,流域土地利用处于发展时期;优势度没有发生明显的变化,未利用地一直最高。研究区土地利用/覆被变化的主要驱动力为人口因素、经济因素、富裕程度、政策因素、技术因素和水文因素等,各驱动因素以合力形式作用于土地利用/覆被使其变化,其中,人口因素在土地利用/覆被变化中起主导作用。     

洞庭湖区域雷暴大风分型及预报分析研究

使用S波段双偏振天气雷达资料,对2021年5月15日洞庭湖区一次强对流过程的多单体风暴阶段和飑线阶段分阶段进行了分析,并重点对前阶段的超级单体风暴I2进行了分时段分析,结果表明：(1) I2初始发展时段,水平反射率因子(Z_H)＞55 dBz区域、差分反射率因子(Z_DR)柱(＞2.5 dB)扩展至湿球零度层WBZ高度,对应区域差分相移率(K_DP)较大(＞1.7 °·km^-1)、相关系数CC在0.9~0.99,说明该时段降水相态是以夹杂着大雨滴的水凝物为主。(2) I2冰雹碰并增长时段,Z_H强度和发展高度急速增长,垂直液态水含量VIL出现明显跃增,Z_H强中心(＞60 dBz区域)扩展至-10℃高度以上,对应的Z_DR降至低于0,Z_DR柱(＞2.5 dB)扩展至-10℃高度;CC下降、K_DP出现“空洞”,说明该时段降水以固态粒子为主,且处于增大时段。(3) I2成熟降雹时段,Z_H强中心(＞60 dBz)底部降至WBZ高度以下、CC局部低至0.8、对应区域存在Z_DR负值区、K_DP空洞,说明冰雹下落的拖曳作用削弱上升气流的强度,预示冰雹即将落地。(4)飑线阶段和多单体风暴阶段不同,飑线阶段K_DP值异常偏大、Z_DR大于1。(5)飑线阶段的极端大风发生前,55 dBz强回波、27 m·s^-1速度大值区扩展至1 km以下,强回波区域K_DP明显偏大,与强下沉气流相对应。另外,高空降水粒子在下降过程中融化导致的强降水拖曳作用加剧了极端大风的产生。

     

浅谈现代测绘技术装备在国土资源管理中的应用

进入新世纪以来,随着计算机技术、信息技术等新技术的发展,测绘技术装备也得到迅速发展,现代测绘技术装备在自动化、高效化方面的能力越来越突出,获取的数据类型也更加多样化。利用现代测绘技术装备服务于国土资源管理已经是大势所趋。本文结合现代测绘技术装备在国土资源管理中的应用范例,给出了相应建议,旨在提高现代测绘技术装备的应用能力,为国土资源管理提供全方位的服务。     

宁镇扬丘陵区土地利用景观格局演变与模拟

针对CA模型不能长期预测和Markov模型不能反映空间格局变化的不足,本文运用CA-Markov耦合模型,结合动态度模型、景观格局指数及PCA法,定量揭示宁镇扬丘陵区镇江市的土地利用景观格局演变特征、驱动机制及变化趋势,为区域土地利用规划及调整提供参考。结果表明:镇江市土地利用景观格局时空演变愈加复杂;人口增长、经济发展、城镇化及农业技术进步是土地利用景观格局演变的主要驱动力;到2030年镇江市城区周边建设用地将增加25.58%,而耕地、林地等景观类型将不断锐减。     

地理国情普查项目地表覆盖数据成果的质量控制

地理国情普查项目是全新的测绘地理信息项目,文章结合生产阶段及检查验收过程中地表覆盖数据生产容易出现的质量问题,通过认真分析影响质量的因素,从管理要素、生产过程、两级检查3个方面谈了解决该问题的部分质量控制措施,以便于更好地提高地理国情普查项目成果的质量。     

Quantification of LST and CO2 levels using Landsat-8 thermal bands on urban environment

Land surface temperature (LST) is an important aspect in global to regional change studies, for control of climate change and balancing of high temperature. Urbanization is one of the influencing factors increasing land surface and atmospheric temperature, by the emission of greenhouse gases (e.g. CO₂, NO and methane). In the present study, LST was derived from Landsat-8 of multitemporal data sets to analyse the spatial structure of the urban thermal environment in relation to the urban surface characteristics and land use–land cover (LULC). LST is influenced by the greenhouse gases i.e. CO₂ plays an important role in increasing the earth’s surface temperature. In order to provide the evidence of influence of CO₂ on LST, the relationship between LST, air temperature and CO₂ was analysed. Landsat-8 satellite has two thermal bands, 10 and 11. These bands were used to accurately to calculate the temperature over the study area. Results showed that the strength of correlation between ground monitoring data and satellite data was high. Based on correlation values of each month April (R² = 0.994), May (R² = 0.297) and June (R² = 0.934), observed results show that band 10 was significantly correlating with air temperature. Relationship between LST and CO₂ levels were obtained from linear regression analysis. band 11 was correlating significantly with CO₂ values in each of the months April (R² = 0.217), May (R² = 0.914) and June, (R² = 0.934), because band 11 is closer to the 15-micron band of CO₂. From the results, it was observed that band 10 can be used for calculating air temperature and band 11 can be used for estimation of greenhouse gases.