Integrated Use of Existing Global Land Cover Datasets for Producing a New Global Land Cover Dataset with a Higher Accuracy: A Case Study in Eurasia

It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.

Detection of landuse/landcover changes using remotely-sensed data

We evaluated the use of spatial sampling and satellite images to identify deforested areas in Wonju, South Korea. The changes in land cover were identified using a grid of sample points overlaid onto medium and high-resolution remote sensing （RS） satellite images. Deforestation identified in this way （hereafter, RSD） was compared to administrative data on deforestation. We also compared high-resolution satellite images （HR-RSD） and actual deforestation based on categories which were Intergovernmental Panel on Climate Change data. RSD generated by medium-resolution satellite images overesti- mated the amount of deforested area by 1.5-2.4 times the actual deforested area, whereas RSD generated by HR- RSD underestimated the amount of deforested area by 0.4-0.9 times the actual area. The highest degree of matching （90 %） was found in HR-RSD with a grid interval of 500 m and the accuracy of HR-RSD was the highest, at 67 %. The results also revealed that the largest cause of deforestation was the establishment of settlements followed by conversion to cropland and grassland. We conclude that for the identification of deforestation using satellite images, HR-RSD with a grid interval of 500 m is most suitable.

Land Cover Map Delineation, for Agriculture Development, Case Study in North Sinai, Egypt Using SPOT4 Data and Geographic Information System

Land cover map for a part of North Sinai was produced using the FAO—Land Cover Classification System (LCCS) of 2004. The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approach also has the advantage of facilitating the integration of Sinai land cover mapping products to be included with the regional and global land cover datasets. The total study area is 7450 km2 (1,773,842) feddans. The landscape classification was performed on SPOT4 data acquired in 2011 using combined multi-spectral bands of 20 meter spatial resolution. Geographic Information System (GIS) was used to edit the classification result in order to reach the maximum possible accuracy. GIS was also used to include all necessary information. The identified vegetative land cover classes of the study area are irrigated herbaceous crops, irrigated tree crops and rain fed tree crops. The non-vegetated land covers in the study area include: bare rock, bare soil, bare soil stony, bare soil very stony, bare soil salt crusts, loose and shifting sands and sand dunes. The water bodies were classified as artificial perennial water bodies (fish ponds and irrigated canals) and natural perennial water bodies as lakes (standing) and rivers (flowing). Artificial surfaces in the study area include linear and non-linear. The produced maps and the statistics of the different land covers are included in the following sub-sections.

Land cover mapping of deciduous forest regions using ETM+ data: a case study of Azerbaijan Province, Iran

Up to date information about the existing land cover patterns and changes in land cover over time is one of the prime prerequisites for the preparation of an integrated development plan and economic development program of a region. By using ETM＋ image data from 2002, we provided a land cover map of deciduous forest regions in Azerbaijan Province, Iran. Initial qualitative evaluation of the data showed no significant radiometric errors. Image classification was carried out using a maximum likelihood-based supervised classification method. In the end, we determined five major land cover classes, i.e., grass lands, deciduous broad-leaf forest, cultivated land, river and land without vegetation cover. Accuracy, estimated by the use of criteria such as overall accuracy from a confusion matrix of classification was 86% with a 0.88 Kappa coefficient. Such high accuracy results demonstrate that the combined use of spectral and textural characteristics increased the number of classes in the field classification, also with excellent accuracy. The availability and use of time series of remote sensing data permit the detection and quantification of land cover changes and improve our understanding of the past and present status of forest ecosystems.

Vegetation Cover Density and Land Surface Temperature Interrelationship Using Satellite Data, Case Study of Wadi Bisha, South KSA

The aim of this study is to identify the relationship between Vegetation Cover (VC) and the land Surface Temperature (LST), using satellite data of Wadi Bisha, south the Kingdome of Saudi Arabia (KSA). The Landsat 7 Thematic Mapper (ETM) thermal band (band 6) was used for calculating the (LST) values. The near-infrared (NIR) and red band (bands 3 and 4 respectively) were used for estimating the vegetation cover. ERDAS Imagine 9.3 and ArcGIS 10.2 were used in the current study. The results of the study show that the increase of vegetation cover (VC) coincides with decrease of (LST), while the decrease in vegetation cover is linked with increase of (LST). It was found that there was no vegetation observed in areas practiced the highest temperature of 49℃, while areas of lowest temperature of 28℃ were characterized by dense vegetation cover. Thus, a quite significant correlation is approved between the (VC) and the (LST), based on the validation of (50) locations. It was concluded that availability and continuity of Satellite remote sensing data was required for elaborating a continuous monitoring of vegetation cover conditions and mapping was recommended in Wadi Bisha. Operational monitoring is recommended to ensure the adoption of flexible land cover validation protocols.

Inventory of Atmospheric Pollutant Emissions from Burning of Crop Residues in China Based on Satellite-retrieved Farmland Data

The burning of crop residues emits large quantities of atmospheric aerosols.Published studies have developed inventories of emissions from crop residue burning based on statistical data.In contrast,this study used satellite-retrieved land-cover data(1 km×1 km)as activity data to compile an inventory of atmospheric pollutants emitted from the burning of crop residues in China in 2015.The emissions of PM10,PM2.5,VOCs,NOx,SO2,CO,and NH3 from burning crop straw on nonirrigated farmland in China in 2015 were 610.5,598.4,584.4,230.6,35.4,3329.3,and 36.1 Gg(1 Gg=109 g),respectively;the corresponding emissions from burning paddy rice residues were 234.1,229.7,342.3,57.5,57.5,1122.1,and 21.5 Gg,respectively.The emissions from crop residue burning showed large spatial and temporal variations.The emissions of particulate matter and gaseous pollutants from crop residue burning in nonirrigated farmland were highest in east China,particularly in Shandong,Henan,Anhui,and Sichuan provinces.Emissions from burning paddy rice residue were highest in east and central China,with particularly high levels in Shandong,Jiangsu,Zhejiang,and Hunan provinces.The monthly variations in atmospheric pollutant emissions were similar among different regions,with the highest levels observed in October in north,northeast,northwest,east,and southwest China and in June and July in central and south China.The developed inventory of emissions from crop residue burning is expected to help improve air quality models by providing high-resolution spatial and temporal data.

Forest Change and Its Effect on Biomass in Yok Don National Park in Central Highlands of Vietnam Using Ground Data and Geospatial Techniques

This paper assesses the changes in forest cover in Yok Don National Park of Vietnam between 2004 and 2010, and the implications of such changes on the biomass stocks of this national park. Remote sensing and GIS tools along with the ground truth data collected from the field were employed for classifying the forest types of the study area from SPOT HRV satellite imagery for years 2004 and 2010. The total area considered in this study is 115.5 thousand ha. Five different categories of forests were identified. The results demonstrated that between 2004 and 2010, the Evergreen broad leaved rich quality forest decreased by 11.2 thousand ha (3.5 Mega tons of biomass) and the Dry open dipterocarps medium quality forest decreased by 15.3 thousand ha (2.5 Mega tons of biomass). In that time period, the Evergreen broad leaved medium quality forest increased by 3.2 thousand ha (0.8 Mega tons of biomass), the Evergreen broad leaved poor quality forest increased by 2.5 thousand ha (0.24 Mega tons of biomass), and the Dry open dipterocarps poor quality forest increased by 3.2 thousand ha (0.69 Mega tons of biomass). Total biomass of the study area decreased by 4.3 Mega tons.

Assessment of Land Use Land Cover Change Detection Using Geospatial Techniques in Southeast Rajasthan

Change analysis acquires effective information in the form of maps and statistical data which becomes the central component in spatial planning, monitoring environmental changes, management and utilization of land. The present study makes an attempt to assess the changes in land use land cover using multi-temporal satellite data in south-east Rajasthan. These maps were derived from geocoded dia-positive False Color Composites (FCC’s) of IRS 1991, 2001, 2010 & 2018 using Arc GIS platform. The present study demonstrates the extension, approach and result of change analysis which might be helpful for decision making and sustainable growth. The landscape has been divided into 12 categories. Mining and its associated features were increased whereas forest and open scrub cover shows decreasing trend during the study period. The former increased by 23.82 km2 while the later shrunk by 26.08 km2. Most significant changes are also witnessed in settlement and industrial areas which shows increment by 8.8 km2 and 1.33 km2. Stone quarrying has destroyed arable land, natural vegetation cover, topsoil, subsoil and consequently the soil profile of the area. On the other hand cultivated land is increasing due to the conversion of uncultivated land and scrub cover with facilitation of irrigation and modern agricultural activities under different government schemes. The study shows that the area of 184.88 km2 has undergone significant spatial and temporal changes during the study period.

面向多源异质遥感影像地物分类的自监督预训练方法

近年来,深度学习改变了遥感图像处理的方法。由于标注高质量样本费时费力,标签样本数量不足的现实问题会严重影响深层神经网络模型的性能。为解决这一突出矛盾,本文提出了用于多源异质遥感影像地物分类的自监督预训练和微调分类方案,旨在缓解模型对于标签样本的严重依赖。具体来讲,生成式自监督学习模型由非对称的编码器-解码器结构组成,其中深度编码器从多源遥感数据中学习高阶关键特征,任务特定的解码器用于重建原始遥感影像。为提升特性表示能力,交叉注意力机制模型用于融合异源特征中的信息,进而从多源异质遥感影像中学习更多的互补信息。在微调分类阶段,预训练好的编码器作为无监督特征提取器,基于Transformer结构的轻量级分类器将学习到的特征与光谱信息结合并用于地物分类。这种自监督预训练方案能够从多源异质遥感影像中学习到刻画原始数据的高级关键特征,并且此过程不需要任何人工标注信息,从而缓解了对标签样本的依赖。与现有的分类范式相比,本文提出的自监督预训练和微调方案在多源遥感影像地物分类中能够取得更优的分类结果。

全球地表覆盖时空变化交互式知识地图集设计与表达方法

地图集是呈现地表覆盖知识的高效表达工具。然而现有地图集仅面向预定区划的知识内容展示,难以满足用户对不同兴趣区域的个性化需求,从而导致图集实用性较差。另外,传统地图集图幅版面布局主要以不同信息的拼合模式为主,难以体现知识所包含信息的关联性,从而不利于用户较快的理解知识内容。为满足地图应用的个性化和知识服务需求,本文基于三期GlobeLand30数据,进行近20年全球地表覆盖时空变化地图集编研工作。除了制作了从宏观角度反映全球、大洲和国家尺度的地表覆盖分布和变化图幅集,还采用用户交互式选择感兴趣区域的方式实时化生成地表覆盖时空变化知识,并提供用户选择不同的知识可视化表达形式和图-文双向交互功能,从而以更为个性化和易懂的方式展示地表覆盖时空变化知识内容。

全球30m地表覆盖遥感数据产品-Globe Land30

在国家863重点项目支持下,我国成功研制出全球30 m地表覆盖数据产品Globe Land30。该成果包括2000基准年和2010基准年两期,有耕地、森林、草地、灌木地、湿地、水体、苔原、人造地表、裸地和冰雪十大类型,第三方评价总体精度为83.50%。2014年9月22日,中国政府将其赠送给联合国使用,是中国向联合国提供的首个全球地理信息产品。该成果成为全球变化和可持续发展研究的重要科学数据,目前已有近120个国家的用户下载使用,推动了国际对地观测与地理信息的开放共享,彰显了中国负责任大国的形象。本文主要介绍了Globe Land30产品的技术创新、精度评价与成果应用。

中国土地利用/覆被重建数据集历史文献数据源的应用现状与前景

历史时期土地利用/覆被变化(LUCC)是全球变化研究的重要组成部分。中国丰富的历史文献资料是开展长时段LUCC数据集重建的关键数据源。从百年以上历史文献数据源应用的角度分析现有中国LUCC重建数据集,将历史文献中蕴含的土地利用数据源归纳为统计、列举、描述和图像4种主要类型,从各类数据源应用现状、制备方法等方面对现有重建数据集进行了重点回顾,归纳重建数据集应用历史文献的主要特点和存在问题,并从多重历史数据源综合利用、重建思路调整、利用现代信息技术提高数据源制备效率等方面展望了LUCC重建数据集改进历史文献应用方式、方法的未来发展趋势。

全球地表覆盖数据辅助多源影像融合提取城市不透水面

本文提出了一种利用GlobeLand30数据辅助多源数据融合进行城市不透水面自动提取的方法。首先基于波段映射和小波变换的影像融合方法,融合哨兵二号和高分二号影像,获得同时具有较高空间分辨率和光谱分辨率的融合影像,其具有丰富的光谱特征和空间特征,有利于提升复杂城市区域的不透水面和非不透水面区分能力。然后利用GlobeLand30数据的类别信息自动获取初始分类样本,基于融合影像的丰富光谱信息构建多种植被指数、水体指数和建成区指数,对初始分类样本进行优化。最后利用优化后的训练样本,使用光谱、地物指数等特征训练分类器,实现城市不透水面的自动准确提取。本文以济南市2019年的高分二号和哨兵二号影像为试验数据,在时相、分辨率与影像均不同的GlobeLand30全球地表覆盖数据辅助下获得了总体精度优于92%的不透水面提取结果,验证了本文方法的有效性。

松辽流域生态环境时空动态评价及驱动因素分析

目前,我国高度重视以流域为基础的生态文明建设。及时有效的评估流域生态环境质量时空变化并分析其驱动因素对于制定流域协同性保护以及以流域为基础的生态文明建设十分重要。本文基于以数据为关键要素的,依托于遥感产品得到的绿度指数、湿度指数、干度指数、热度指数四项指标运用主成分分析方法建立松辽流域生态环境评估模型,并探索生态环境质量变化情况及其原因。结果表明:①松辽流域的生态环境质量整体优良,呈“东北优,西南差”的空间分布。②2000-2020年,松辽流域生态质量总体呈变好局势。第一个十年生态质量得到巨大改善,第二个十年处于生态保护维护阶段。③从空间分布上看,生态评级较差及以下等级面积明显收缩,优良等级面积显著向外扩张。④人类的社会经济生产活动对于生态环境的质量评估有至关重要的作用,如何平衡社会经济生产与生态环境保护之间的关系是帮助生态环境恢复的关键。

新疆及周边中亚地区土地覆盖变化对地表反照率的影响

基于MODIS MCD12C1土地覆盖和MCD43C3地表反照率数据,分析2002—2021年新疆及周边中亚地区土地覆盖和地表反照率时空分布特征并探讨土地覆盖变化对地表反照率的影响。结果表明:(1)土地覆盖净变化以裸地和耕地减少、林地和草地增长为主要特征,总变化最大的是草地。塔里木盆地四周裸地转为草地变化明显,中亚中部干旱区草地灌丛化问题严重。(2)裸地转为草地、草地转为林地分别导致地表反照率减少0.006、增长0.009,草地灌丛化导致地表反照率增长0.012。草地转为裸地对地表反照率的影响最大,变化量为0.015,耕地和草地相互转变对地表反照率的影响最小,变化量<0.001。(3)2006—2011年地表反照率变化最明显,显著变化量达34.87%。(4)干旱区地表反照率变化显著于其他区域,且林地的地表反照率高于草地和耕地,导致三者间转变的地表反照率变化趋势区别于其他区域。

2000、2010和2020年中国广西土地利用/土地覆盖融合数据集

本研究选取全球/中国广泛应用的6大土地利用/土地覆盖遥感数据产品,包括欧空局全球土地覆盖产品(ESAGC)、ESRI全球10米土地利用/土地覆盖数据(ESRI-LULC)、清华大学高分辨率全球土地覆盖产品(FROM-GLC)、武汉大学中国土地覆盖数据集(CLCD)、全球30米高分辨率系统土地覆盖产品(GLC_FCS30)以及Globe Land30(GLC30),通过对多个数据产品进行重分类,提出基于一致性评估的多源数据产品融合方法,合成新的广西区域2020年度土地利用/土地覆盖数据产品,与地方统计年鉴数据相关系数大于0.94。研究进一步选用有长时间序列的三套数据(CLCD、GLC_FCS30和GLC30)融合形成广西2000、2010及2020年共三期土地利用/土地覆盖融合数据产品。本研究可以为区域土地利用/土地覆盖数据产品的融合应用提供方法基础,融合数据可以更好的支持广西区域的环境保护、农业生产规划和城市发展规划等相关研究。

基于Landsat TM/ETM数据的锡林河流域土地覆盖变化

根据1987年、1991年、1997年和2000年4期Landsat TM/ETM影像的土地利用/土地覆盖分类结果,运用地理信息系统空间分析方法,分析了内蒙古锡林河流域1987～2000年间各土地利用类型及草甸草原、典型草原、荒漠草原的数量变化和空间变化特征.分析结果显示,锡林河流域土地利用/土地覆盖变化的主要特征为草甸草原、典型草原面积的大幅减少和荒漠草原、农田和沙漠化土地面积的大幅增加及城镇的扩张.其中面积增加最大的是荒漠草原,增加了2328 km2;相当于1987年荒漠草原面积的56%.农出和城镇面积逐年增大,分别从1987年的114.3 km2和25.2 km2增加到2000年的332.1 km2和43.6 km2.面积减少最多的是羊草+丛生禾草、羊草+杂类草等优良高产温带典型草原类型,共减少2040km2.草甸草原面积亦呈逐年减少的趋势,从1987年的1103 km2减少到2000年375 km2,面积减少了65.9%.农出、沙化地及城镇等非草原土地利用类型面积增加了62.5%.

乌鲁木齐市房屋空置率估算

以乌鲁木齐市为例,分别提取住宅区域与非住宅区域灯光亮度值、房屋使用率最大平均灯光亮度值,通过住宅区域单位面积灯光亮度值与房屋使用率最大时平均灯光亮度的比值,构建房屋空置率估算模型,分析结果显示:乌鲁木齐市天山区、新市区平均房屋空置率较低,乌鲁木齐县、达坂城区、头屯河区平均房屋空置率较高,水磨沟区、沙依巴克区与米东区平均房屋空置率居中;2018年至2019年乌鲁木齐市房屋空置率整体呈现下降趋势.运用实地调查数据对估算进度验证,均方根误差为0.051,表明所提出的房屋空置率估算模型精度较高,可有效估算房屋空置率.结合人口、经济、季节因素,分析空置率时空分布原因,得到:新市区、天山区户籍人口数与房屋空置率成负相关;乌鲁木齐市经济呈增长速率与房屋空置率成负相关,但乌鲁木齐市经济仍欠发达,整体房屋空置率偏高;乌鲁木齐市夏秋两季日落较晚,冬季天气寒冷且2月正值春节假期,因此8月、10月的平均房屋空置率低于2月.

基于多源地理大数据我国县域人口估算方法研究

实时人口数据对于城市规划、资源管理和社会的可持续发展等方面至关重要。为了有效提升现有基于地理大数据的人口估算方法,研究全面对比分析不同开放地理数据的人口模拟性能,并发展综合遥感与新兴社交媒体用户数据,以实现区县级人口的高精度快速估算。文章以中国各区县为试验区,运用多元线性回归及地理加权回归方法,全面评估各类地理遥感数据对我国人口模拟的性能,采用的数据包括腾讯位置服务(LBS)数据、高德兴趣点数据(POI)、夜间灯光遥感数据和基于遥感所得的土地利用/覆盖数据等。研究结果显示,在预估人口分布方面,腾讯定位数据与兴趣点数据比遥感所得的土地利用/覆盖数据和夜间灯光卫星数据都要好,人口模拟精度分别为81.6%、70.8%、68.8%和63.0%。文章进一步综合运用多源地理数据,可实现85.4%总体人口模拟精度,研究结果和发现可为我国人口相关政策提供数据和技术支撑。

基于地质大数据技术对云南土壤重金属地质高背景区的划定

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