首套全球30 m分辨率土地覆被产品区域尺度精度评价——以河南省为例

以河南省为研究区,对全球首套30 m分辨率土地覆盖产品GlobleLand30进行区域尺度精度评价。首先,以中国1\begin{array}{c}:\end{array}10万土地利用数据（CHINA-2010）为参考,分析两种产品的空间一致性;而后,通过Google Earth样本分析GlobleLand30在空间不一致区域的制图精度;最后,利用野外实地考察样本对GlobleLand30进行总体精度评价,并从土地覆被复杂度、高程等方面分析影响精度的原因,结果表明：① GlobleLand30与CHINA-2010空间一致性达80.20%。两种产品对耕地、林地、人工地面一致性高,对草地、水体、灌木、湿地、未利用土地的一致性低。② 在空间不一致区域,GlobleLand30的总体分类正确率略低于CHINA-2010,但两者对不同地类的优势不同。③ 经野外实地考察验证可知,GlobleLand30的总体精度达83.33%。④GlobleLand30与CHINA-2010的空间一致性随土地覆被复杂度的增加而降低,并在高程过渡带较低。     

多源遥感土地覆被产品在欧洲地区的一致性分析

土地覆被的空间分布格局及其动态变化对于全球变化、区域可持续发展等研究具有重要意义,卫星遥感是唯一能够快速获取大尺度区域土地覆被信息的方法。基于GLOBCOVER2005、GLOBCOVER2009、GLC2000、MODIS2000等4种全球卫星遥感土地覆被产品,研究其在欧洲地区的一致性。结果表明：① 4种产品对于欧洲土地覆被构成特征的刻画基本一致,即以耕地、林地为主,以草地、水体、灌丛等其他类型为辅;② 4种产品对耕地、林地识别的混淆程度最低、一致性最好,对草地、灌丛、裸地识别的混淆程度最高、一致性最差;③ 欧洲有75%的土地具有较高的一致性。斯堪的纳维亚半岛东侧及北欧地区、中欧—东欧大平原及巴黎盆地等地区的一致性最好,斯堪的纳维亚半岛西侧、科拉半岛、伯朝拉河—新地岛、伊比利亚半岛以及伏尔加河流域下游等地区的一致性最差;④ 4种产品两两比较时,参考精度大致在38.56%~77.65%之间。GLOBCOVER2009/GLOBCOVER2005组合的参考精度最高,反映出土地覆被变化所引起的误差远小于不同制作机构、不同数据源、不同判读方法所引起的制作误差。     

多源数据融合的中国滑雪场空间活力评价研究

随着北京2022年冬奥会及冬残奥会的成功举办,“三亿人参与冰雪运动”成为现实,有效带动了中国滑雪场建设和发展。后冬奥时代,如何推动中国滑雪场从量升到质变成为重要议题。为有效回答从“量”到“质”转变的问题,需针对滑雪场构建科学合理的综合评价体系。基于空间活力概念,从场所、活动和人的交互联系出发,提出滑雪场吸引力、滑雪活动活跃度和滑雪者体验感3项测度指标,采用层次分析法和熵权法构建复合视角下的滑雪场空间活力评价体系和框架;利用统计、位置、客流、用户评分等多源数据,通过时间序列波动率模型、核密度分析、可达性计算模型和信息熵等方法分析中国滑雪场空间活力特征。结果表明：(1)中国滑雪场吸引力、滑雪活动活跃度、滑雪者体验感空间分异特征明显,分别呈现京冀和广东省“双核”引领、北高南低、东高西低的分布特征;(2)中国滑雪场空间活力呈高值集聚,低值分散的空间分异特征,其中高活力滑雪场主要集中在北京-张家口、吉林省、新疆阿勒泰三大区域,而低活力滑雪场呈全国分散性分布;(3)中国滑雪场平均空间活力位列前五的省/直辖市为北京市、河北省、广东省、吉林省和辽宁省,而江苏省、安徽省、江西省、浙江省和湖北省的滑雪场平...     

基于XGBoost的多源降水数据融合方法研究

气象站点观测降水难以精确反映降水时空分布与变化,而雷达降水存在复杂地形区域精度不高等问题。为了最大限度发挥两者的优势,文章以广东省北部山区为研究区域,选择2018-08-26—30一次暴雨过程为研究对象,结合地形、与海岸线距离、植被指数、经纬度等地表辅助参量,分析地面站点降水与地表辅助参量、雷达降水的相关关系,利用XGBoost算法与克里金插值方法,构建地面-雷达日降水数据融合模型,得到了空间分辨率为1 km的日降水融合数据集。此外,采用多元线性回归（LM）与克里金插值方法,实现了地面-雷达日降水数据的融合,并利用地面降水数据分别对XGBoost与LM日降水融合性能进行精度验证。结果表明：1）地面降水与雷达降水存在显著的正相关,地面降水与地表辅助参量之间的相关性随时间变化;2）XGBoost预测精度整体上高于LM预测结果;经模型残差校正后,XGBoost融合模型的精度整体上优于LM融合模型,这是因为XGBoost方法在捕捉地面降水与地表辅助参量、雷达降水之间关系性能上优于LM方法。     

基于多源地理数据融合的黑河中游土地多功能性时空格局与权衡研究

土地多功能利用是提高土地利用效率、缓解人地矛盾、促进区域可持续发展的重要途径。研究构建了“生产-生活-生态”功能的土地多功能性评价指标体系,基于3 km×3 km格网,融合了遥感、统计、POI等多源地理数据,利用投影寻踪模型对黑河中游土地多功能性进行评价,通过变量相关分析和双变量局部空间自相关分析,揭示了土地多功能权衡与协同关系,并采用RGB三通道合成与二阶聚类分析相结合进行土地功能分区。结果表明:(1)黑河中游在不同利用方式下呈现不同功能,在走廊平原区土地功能以生产功能为主导,生活功能和生态功能为辅。生产功能在走廊绿洲农业区优势明显,生活功能高值区集中于城镇等基础设施条件较好的地区,祁连山、龙首山发挥着生态屏障作用。(2)研究时段内,土地多功能性稳中增强。土地多功能性与一级功能的空间格局保持稳定,土地功能间协调性普遍增强,不同功能在空间上日益重叠。(3)按照主导利用方向,土地可划分为以农业生产与城镇空间为主体的重点开发区、优化开发区和适度开发区,以及以生态空间为主的生态屏障区、生态缓冲区和生态修复区。(4)土地多功能利用仍有提升空间,尤其要加强对非主导功能的关注,协调功能间的固有矛盾;统筹推进生态修复,关注自然资本增值,从构建区域绿水青山的格局挖掘土地价值新的增长点。     

全球土地覆被时空变化与中国贡献

20世纪下半叶以来全球土地覆被发生了剧烈变化,人类活动对土地覆被变化的影响成为“人类世”最为显著的特征之一。科学评估全球土地覆被变化的时空过程和新态势,分析中国在其中扮演的角色和地位并提出优化路径,成为中国在可持续发展领域应对全球百年未有之大变局的核心任务之一。本文基于多源土地覆盖数据,运用地理空间分析方法定量刻画了全球土地覆被变化的时空演化过程,从景观尺度分析了地类间的转化关系以及全球“变绿”和森林覆盖度的变化趋势,揭示了中国对全球土地覆被时空变化的贡献。结果表明,1992—2015年全球土地覆被经历了显著变化,全球土地覆被变化度在南美洲中部、撒哈拉以南的非洲、中亚、东南亚和东亚等地形成显著的热点区。中国森林覆盖率从1990年的12.98%增至2020年的23.34%,湿地面积增长1908 km²,为维护全球生态安全贡献了力量,同时在城市用地增长、草地和其他用地减少等方面也有一定的限制作用。与全球其他国家不同,中国城市扩张占用耕地面积居全球第一位,高达7.3万km²。1999—2019年全球叶面积指数存在全球性的显著提高趋势。中国以仅占全球6.6%的植被面积,贡献了全球20%左右的叶面积增加量,引领了全球“变绿”过程。1990—2020年全球森林覆盖度变化呈现出空间集聚性。中国森林面积增长62.84万km²居全球前列,其中西南林区和秦巴山区是林地增长的主要区域,长三角、粤港澳大湾区和内蒙古东部部分地区是森林覆盖度降低的主要区域。中国未来应进一步提升经济社会发展与生态保护的均衡协调度,持续推进美丽中国建设,为全球生态安全和可持续发展贡献更大力量和更多经验。     

多源数据在城市体检中的有机融合与应用——以北京市为例

多源数据是当前信息时代下进行城市研究的重要基础和支撑,也是地理学和城市规划学重点研究方向。城市体检作为城市高质量发展背景下的新命题,多源数据在城市体检中的有机融合与科学应用对城市问题的深入剖析及其影响机制的探究中至关重要。从梳理主观和客观两类数据在现有城市研究中的应用出发,结合了2019年、2020年全国城市体检和2018年、2019年、2020年北京城市体检实践工作,探讨了多源数据在城市体检不同空间尺度、不同时间尺度和不同体检维度的应用,辨析了不同数据间的优劣势,提出了多源数据有机融合的方式,并以“城市停车问题”进行案例分析。以期为城市体检中数据应用方式的改进方向提出建议,为城市体检的研究发展提供思路。     

利用TM数据提取干旱区土地覆被信息的方法比较

以柴达木盆地香日德绿洲作为研究实验区,对该区域ETM遥感数据经过空间分辨率融合、主成分分析等方法进行空间信息增强及专题信息增强处理,组合最佳视觉背景图像,分别在不同背景图像上选择训练样本,利用最大似然法监督分类方法(MLC)、多空间尺度分层聚类(SSHC)和基于知识的模糊聚类方法(KFC)等分类器,分别用各自训练样本初始化各类别信息特征值,形成类别特征值模式库,分别以此为基础对待分样本进行分类,对初分类的结果经过类别合并、碎斑滤除以及重新编码赋色等分类后处理,得到最终分类结果及分类精度评价结果。从所获数据可以得出如下结论：从总体精度和Kappa值可知,SSHC和．KFC分类方法所获结果精度较高,总体精度比MLC分类结果约高于3％,SSHC之结果精度略高于KFC之结果;SSHC、KFC和MLC三种分类方法对该区域地表覆被信息的提取分类中,SSHC分类方法对耕地、石砾地、河滩和荒漠分类结果较好,KFC分类方法对耕地、沙地、河滩和荒漠分类结果较好,MLC分类方法对耕地、河滩和荒漠分类结果较好,三种分类方法对耕地、河滩和荒漠等三种地类的分类精度较高,用户精度都在80％以上,而对沙地和石砾地的分类结果其用户精度大都低于80％。     

多源信息的集成与融合及其在遥感制图中的优化利用

多源信息的集成和融合是地球信息科学领域的一大热点问题。它的意义和必要性与地球信息本身的特征、采集信息的手段特征及信息处理平台或系统的特点三方面紧密相联系。本文从不同传感器信息的集成和融合、遥感信息与非遥感地学信息的集成和融合、不同格式的 GIS数据的集成和复合三个方面研究了多源信息集成和融合的方法、前沿技术和应用领域,进而以黄土高原土壤侵蚀遥感调查和制图任务为例,介绍了多源信息集成和融合技术在该项目中的优化应用实例,包括技术流程分析、信息源分析、多源信息在土壤侵蚀遥感调查和制图中的融合方法、从遥感图像解译信息到 GIS数据库的转换技术等。     

广州市土地覆被格局异质性的尺度与等级特征

基于SPOT遥感数据源形成广州市土地覆被类型图,在中心城区选取互相垂直的南北和东西两条样带,用Fortran编程计算各样带在125m、250m、500m和1000m 4个幅度下的景观多样性指数,以及Moran I、Geary C系数和半变异函数。再将样带分别自北向南和自西向东等间距划分,形成一系列面积相等的区域,计算各区域以及样带整体上的多样性指数的尺度方差。结果表明,土地覆被景观多样性在不同的研究幅度下都存在正的空间自相关性,并具有方向性。尺度方差结果显示,尺度方差与尺度和幅度都有关,随着研究尺度增大,尺度方差都呈下降之势,而随幅度增大,尺度方差并非单调变化。如125m和250m两种幅度时,方差随着尺度增大而减少。尺度方差结果进一步揭示研究样带上土地覆被存在多尺度等级结构,并且具有方向性特点,同时也反映尺度方差不失为景观异质性研究的一种有效方法。     

The spatial local accuracy of land cover datasets over the Qiangtang Plateau,High Asia

We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.

     

Comparison of land cover maps using fuzzy agreement

A generic problem associated with different land cover maps that cover the same geographical area is the use of different legend categories. There may be disagreement in many areas when comparing different land cover products even though the legend shows the same or very similar land cover class. To capture the uncertainty associated with both differences in the legend and the difficulty in classification when comparing two land cover maps, expert knowledge and a fuzzy logic framework are used to map the fuzzy agreement. The methodology is illustrated by comparing the Global Land Cover 2000 data set and the MODIS global land cover product. Overall accuracy measures are calculated, and the spatial fuzzy agreement between the two land cover products is provided. This approach can be used to improve the overall confidence in a land cover product, since areas of severe disagreement can be highlighted, and areas can be identified that require further attention and possible re-mapping.     

Assessing the applicability of OpenStreetMap data to assist the validation of land use/land cover maps

The validation of land use/land cover (LULC) maps is usually performed using a reference database consisting of a sample of points or regions to which the ‘real’ class is assigned. This assignment is usually performed by specialists using photointerpretation (PI) of high-resolution imagery and/or field visits, which are time consuming and expensive processes. The aim of this article is to assess if the data available in the collaborative project OpenStreetMap (OSM) may be used as a source of data to assist the creation of these reference databases, reducing the time spent and costs associated with their generation. For this aim, two case studies were used, where the validation of the Global Monitoring for Environment and Security Urban Atlas (UA) was performed. The used methodology requires the harmonization of the data available in OSM with the UA nomenclature, and the subsequent creation of a LULC map from the OSM data. This map was then compared to UA to assess the similarity of the regions mapped in both. To test the usefulness of OSM data to assess the accuracy of UA, a sample of points was created and two reference databases generated, one assigning the data extracted automatically from OSM to the points where these data were available, and PI for the remaining points, and the other using only PI. The accuracy assessment of UA for the two case studies was then made building confusion matrixes and computing accuracy indicators. The results showed that for the two study areas, only low percentages of points had to be photo interpreted in the first reference database (respectively, 12% and 2% for the two study areas), decreasing the work load considerably. The results obtained with both reference databases are comparable for level 1 classes. For level 2 classes, worse results were obtained for some classes, showing that the OSM data used are not enough to create reliable reference data.     

2000-2010年亚欧大陆中低纬度海岸带土地利用/覆盖变化及驱动力分析

土地利用/覆盖变化是全球变化研究的重要问题,而海岸带则是该领域研究的热点区域。以三套土地利用/覆盖数据（MCD12Q1、CCI-LC和GlobeLand30）为基础,采用基于一致性分析和模糊集合理论的数据融合方法,获取2000年和2010年亚欧大陆中低纬度海岸带土地利用/覆盖分类信息,进而分析土地利用/覆盖变化特征及驱动因素。结果表明：十年间亚欧大陆中低纬度海岸带土地利用/覆盖变化方式主要以耕地萎缩和林地扩张为主,其次是湿地扩张,再次是草地和裸地萎缩,最后是灌木地和人造地表扩张;土地利用/覆盖类型之间的相互转换面积较小,仅占研究区总面积的4.22%,其中分布面积占优势的变化类型为耕地–林地–草地相互转换、灌木地–裸地相互转换、林地转为湿地以及林地转为灌木地等。地形因素、气候分异等自然驱动力深刻影响着土地利用/覆盖变化的宏观格局,而人口压力增大、经济高速发展、政策的颁布与实施等人文驱动力则是推动十年间亚欧大陆中低纬度海岸带土地利用/覆盖变化的主要原因。     

地理学视角下土地利用/覆被分类发展探讨

随着对地观测和互联网技术的发展,地理大数据时代正在到来,其多尺度、长时序、多模态等海量“超”覆盖数据为土地利用/覆被（Land Use/Land Cover, 简称LULC）分类及变化检测带来巨大的机遇,支撑着新时代人、地两大系统相互作用关系的认知和实践。然而,多数地理学者认为地理学基本原理与核心思想并未因为大数据的到来而发生本质性变化。所以,从地理学基本原理角度理解LULC分类的发展,尤其在地理大数据时代的发展方向,不失为一条可行的途径。为此,本文从区域、尺度、综合三方面的地理学基本原理视角将LULC分类技术的发展划分为地球观测数据匮乏阶段、人类行为数据融合阶段以及地理大数据“超”覆盖阶段分别探讨分析,以期主动把握LULC分类技术及应用的未来发展趋势。研究结果显示：在地球观测数据匮乏阶段,LULC分类多以类型还不丰富的遥感数据源,在空间分辨率较低的像元尺度上,进行以地表覆被状态为主的分类;发展到人类行为数据融合阶段,LULC分类在城市区域率先出现了对地观测数据和人类行为数据相融合,在街区尺度上进行以空间功能异质性划分、识别为主导的城市功能区分类;在地理大数据“超”覆盖阶段,LULC分类将实现多尺度协同、面向全空间的功能异质性划分,并在主体功能的基础上融合“社会-经济-自然”多维定量属性,本文称之为“空间场景”。希望本文的探讨能够为地理大数据时代LULC分类的新技术发展和新产品应用提供有益启示。     

The climatic impacts of land use and land cover change compared among countries

Land use and land cover change (LULCC) strongly influence regional and global climate by combining both biochemical and biophysical processes. However, the biophysical process was often ignored, which may offset the biogeochemical effects, so measures to address climate change could not reach the target. Thus, the biophysical influence of LULCC is critical for understanding observed climate changes in the past and potential scenarios in the future. Therefore, it is necessary to identify the mechanisms and effects of large-scale LULCC on climate change through changing the underlying surface, and thus the energy balance. The key scientific issues on understanding the impacts of human activities on global climate that must be addressed including: (1) what are the basic scientific facts of spatial and temporal variations of LULCC in China and comparative countries? (2) How to understand the coupling driving mechanisms of human activities and climate change on the LULCC and then to forecasting the future scenarios? (3) What are the scientific mechanisms of LULCC impacts on biophysical processes of land surface, and then the climate? (4) How to estimate the contributions of LULCC to climate change by affecting biophysical processes of land surface? By international comparison, the impacts of LULCC on climate change at the local, regional and global scales were revealed and evaluated. It can provide theoretical basis for the global change, and have great significance to mitigate and adapt to global climate changes.     

综合地理数据与MODIS物候特征的区域土地覆盖制图(英文)

The study developed a feasible method for large-area land cover mapping with combination of geographical data and phenological characteristics, taking Northeast China (NEC) as the study area. First, with the monthly average of precipitation and temperature datasets, the spatial clustering method was used to divide the NEC into four ecoclimate regions. For each ecoclimate region, geographical variables (annual mean precipitation and temperature, elevation, slope and aspect) were combined with phenological variables derived from the moderate resolution imaging spectroradiometer (MODIS) data (enhanced vegetation index (EVI) and land surface water index (LSWI)), which were taken as input variables of land cover classification. Decision Tree (DT) classifiers were then performed to produce land cover maps for each region. Finally, four resultant land cover maps were mosaicked for the entire NEC (NEC_MODIS), and the land use and land cover data of NEC (NEC_LULC) interpreted from Landsat-TM images was used to evaluate the NEC_MODIS and MODIS land cover product (MODIS_IGBP) in terms of areal and spatial agreement. The results showed that the phenological information derived from EVI and LSWI time series well discriminated land cover classes in NEC, and the overall accuracy was significantly improved by 5.29% with addition of geographical variables. Compared with NEC_LULC for seven aggregation classes, the area errors of NEC_MODIS were much smaller and more stable than that of MODIS_IGBP for most of classes, and the wall-to-wall spatial comparisons at pixel level indicated that NEC_MODIS agreed with NEC_LULC for 71.26% of the NEC, whereas only 62.16% for MODIS_IGBP. The good performance of NEC_MODIS demonstrates that the methodology developed in the study has great potential for timely and detailed land cover mapping in temperate and boreal regions.     

The Norwegian area frame survey of land cover and outfield land resources

There is a growing demand for reliable information about land cover and land resources. The Norwegian area frame survey of land cover and outfield land resources (AR18X18) is a response to this demand. AR18X18 provides unbiased land cover and land resource statistics and constitutes a baseline for studying changes in outfield land resources in Norway and a framework for a national land resource accounting system for the outfields. The area frame survey uses a systematic sampling technique with 0.9 km² sample plots at 18 km intervals. A complete wall-to-wall land cover map of an entire plot surveyed is obtained in situ by a team of fieldworkers equipped with aerial photographs. The use of sample plots with extended coverage (0.9 km²) ensures that the survey also deals with local variation, thus strengthening the estimates well beyond simple point sampling. The article documents the methodology used in the survey, followed by a discussion of issues raised by the choice of methodology. These issues include the problem of calculating uncertainty and a confidence interval for the estimates, the focus on common rather than rare land cover categories, and the prospect of downscaling the results in order to obtain statistics for subnational regions.