基于小流域抽样单元的中国FROM-GLC30数据精度评价

土地覆盖数据是全球环境变化相关研究和应用的重要数据基础,在诸多领域中被广泛运用。FROM-GLC30 2017数据是最新的全球高分辨率(30 m)公开土地覆盖数据集之一。土地覆盖数据集的精度是其在不同领域应用中的重要问题。本研究旨在探讨FROM-GLC30 2017数据集精度在全国范围内的空间分布,并比较不同土地覆盖类型精度的区域差异。本研究以亚米级高分辨率遥感影像为基础,对布设在中国范围内的6434个小流域抽样单元进行了人工目视解译,获得了土地覆盖参考数据。通过野外实地调查验证了参考数据,在此基础上对FROM-GLC30 2017数据集进行了精度评价。研究结果表明:(1) FROM-GLC30 2017数据集中各土地覆盖类型的面积比例基本符合我国的实际情况;(2)数据集在中国的总体精度为75.39%,在7大地理分区中,华东地区的总体精度最高,华南地区的总体精度最低;(3)在7种土地覆盖类型中,裸地、森林以及农田的精度相对较高,灌丛的精度最低。研究结果可为大区域土地覆盖数据精度评价研究提供理论支持,促进公开土地覆盖数据集的有效应用。

基于多源数据的地表覆盖制图与分析——以广东省广州市为例

地表覆盖数据是关于土地利用信息的重要来源,在地理国情监测、气候变化研究、生态环境评估等方面发挥着重要的作用。本文以广东省广州市作为研究区域,利用OSM数据结合FROM-GLC数据和Landsat-8遥感影像数据,对研究区域地表覆盖进行分类并完成地表覆盖制图。实验结果表明,利用OSM数据进行地表覆盖制图结果总体精度较高,具有可用性,但是OSM数据空白区域占比较大,有数据区域个别要素数量较少,在参考选取样本时,样本代表性不够高,部分类型的地表覆盖分类精度较低。

一种遥感影像快速计算水面率的方法

为了有效去除阴影噪声对遥感影像水体提取的干扰,快速准确计算大区域范围内的水面率,本文提出基于GF6(高分六号卫星)影像,利用UWI(Urban Water Index,城市水体指数)并结合FROM-GLC10(Finer Resolution Observa-tion and Monitoring of Global Land Cover 10,全球陆地覆盖精细化观测与监测10)数据,通过求交计算和小碎斑删除实现水体提取结果的精细化,有效消除了阴影等噪声对水体提取的干扰,实现水面率的快速计算。本文选择中山市作为试验区,试验区域范围广、地物背景复杂,综合对比本文算法、NDWI(Normalized Water Index,归一化水体指数)算法和TSUWI(Two-Sted Urban Water Index,城市二类水体指数)算法,从定性与定量角度分析,结果表明:本文算法提取的水体连续完整、漏提和误提较少,可有效去除阴影、建筑、道路等噪声干扰,在总体精度、Kappa系数、错分误差、漏分误差等指标均取得了较好结果。利用UWI与FROM-GLC10数据相结合计算水面率具有可行性,为工程化、大面积、快速计算水面率提供了一种新的思路。

基于调查数据的福建省3种30 m全球土地覆盖产品精度评价

以最近发布的3种30 m分辨率的全球土地覆盖类型产品(GlobeLand30(2020)、GLC—FCS30(2020)和FROM—GLC30(2017))为研究对象,通过野外调查获取的福建省8170个土地覆盖类型样本,基于混淆矩阵、误差组分(数量、交换、转移)和强度分析结合的方法评价3种产品的精度,并分析3种产品的精度随地形(高程、坡度、坡向)的变化特征.研究结果表明:GlobeLand30、GLC-FCS30、FROM—GLC30在福建省的总体精度分别为70.13%、77.01%、82.02%.3种产品的耕地、林地、水体和人造地表分类精度相对较高(≥60%);裸地、草地、灌木精度相对较低(≤31%).GlobeLand30的数量误差组分最大(21.02%),GLC—FCS30的交换误差最大(13.46%),FROM—GLC30的转移误差最大(8.89%);3种产品的耕地和裸地的数量误差组分强度高于整体的数量误差组分强度;灌木、水体和人造地表的交换误差组分强度高于整体的交换误差组分强度;水体的转移误差组分强度高于整体的转移误差组分强度.3种产品的耕地和林地的精度随DEM和坡度的变化比较明显,林地的精度随着DEM和坡度增加而增加,耕地随着坡度的增加而降低.

基于珞珈一号的岳麓区第二、三产业GDP公里网格空间化研究

现代化城市中,夜间活动一般需要黑暗环境中灯光照明的支持,从而使夜间灯光可以间接反映城市中的各类生产生活的活跃情况,而利用遥感手段从高空获得的夜间灯光遥感数据可以大面积地折射城市中的社会经济情况。对于研究城市中主要以非第一产业的GDP指标的二、三产业分布研究具有一定的参考价值。同时考虑到第二、三产业活动的特殊性,通过FROM-GLC10的土地利用数据可以过滤与第二、三产业无关的土地覆盖信息,从而为研究城市第二、三产业GDP分布提供更加合理的分析支持。本文通过对岳麓区不透水面的夜间灯光指数研究,完成对岳麓区第二、三产业GDP的公里格网化,实现更高精度的GDP格网化。

应用地表覆盖数据估算LiDAR内业工作量的方法研究

针对机载激光雷达内业生产中的图幅工作量估计问题,提出了一个基于10m和30m较优分辨率的全球地表覆盖观测和监测数据(FROM-GLC)来定量估计图幅工作量的方法。首先测评地类的典型图幅的处理工时,获得标准工时,之后计算图幅有效部分,进行图幅采样,获得图幅有效部分的地类构成,最后计算标准工时加权的单个图幅工时。本文利用一个机载激光雷达项目,分析了地类数据和采样间隔对单图幅级、作业员级和测区级工作量定量估计的影响,证明了方法的可行性,对内业总工时的估计误差小于6%。该方法可以推广到航空摄影测量等的内业生产。

当归挥发油对人肺腺癌GLC-82细胞增殖及细胞周期的影响

目的:探讨当归挥发油对人肺腺癌GLC-82细胞增殖及细胞周期的作用。方法:采用噻唑蓝(MTT)比色法检测当归挥发油对GLC-82细胞存活率的影响;倒置显微镜下观察细胞生长形态;流式细胞仪检测当归挥发油对GLC-82细胞周期的影响。结果:在6. 25～200μg/m L浓度范围内当归挥发油对GLC-82细胞增殖具有浓度依赖性抑制作用,当归挥发油对GLC-82细胞的I C50为113. 03μg/mL;倒置显微镜下观察发现阴性组细胞具有GLC-82细胞典型的形态特征,多边形,成片生长,随着当归挥发油浓度的升高,细胞数量减少,散在生长,形态发生变化,逐渐皱缩;经当归挥发油处理后GLC-82细胞表现出明显的G2期周期阻滞。结论:当归挥发油可抑制GLC-82细胞生长,引起细胞周期G2期的阻滞。

FROM-GC: 30 m global cropland extent derived through multisource data integration

We report on a global cropland extent product at 30-m spatial resolution developed with two 30-m global land cover maps(i.e.FROM-GLC,Finer Resolution Observation and Monitoring,Global Land Cover;FROM-GLC=agg)and a 250-m cropland probability map.A common land cover validation sample database was used to determine optimal thresholds of cropland probability in different parts of the world to generate a cropland/noncropland mask according to the classification accuracies for cropland samples.A decision tree was then applied to combine two 250-m cropland masks:one existing mask from the literature and the other produced in this study,with the 30-m global land cover map FROM-GLC-agg.For the smallest difference with country-level cropland area in Food and Agriculture Organization Corporate Statistical(FAOSTAT)database,a final global cropland extent map was composited from the FROM-GLC,FROM-GLC-agg,and two masked crop=land layers.From this map FROM-GC(Global Cropland),we estimated the global cropland areas to be 1533.83 million hectares(Mha)in 2010,which is 6.95 Mha(0.45%)less than the area reported by the Food and Agriculture Organization(FAO)of the United Nations for the year 2010.A country-by=country comparison between the map and the FAOSTAT data showed a linear relationship(FROM-GC=1.05*FAOSTAT-1.2(Mha)with R^(2)=0.97).Africa,South America,Southeastern Asia,and Oceania are the regions with large discrepancies with the FAO survey.

基于改进LCCD算法的高分六号WFV数据云检测研究

高分六号WFV是搭载在我国高分六号卫星上的高空间分辨率多光谱传感器,该传感器实现了高分辨率和宽覆盖的结合。精确识别高分六号WFV数据的云像元对于农业资源监测、林业资源调查以及防灾减灾等行业具有重要意义。基于全球土地覆盖产品FROM-GLC10数据,改进地表类型支持的云检测算法(LCCD算法),开展了高分六号WFV数据的云检测工作。以FROM-GLC10作为先验数据,充分考虑不同地表类型反射率的变化,在每种地表类型上分别采用不同的方法设置阈值。通过目视解译的方法对云检测结果进行精度评价,云正确率整体达到了92.46%,其中植被类、水体类、高亮地表类的云正确率分别为93.09%、95.60%和88.70%。结果表明,改进的基于地表类型的云检测算法有效提高了高分六号WFV数据云检测的精度。

多源土地覆被数据类别一致性及景观格局差异性——以京津冀区域为例

土地覆被遥感数据是大尺度景观格局研究的基础信息。然而不同的土地覆被数据来自于不同的科研团队,采用不同的数据源及分类方法,导致其对于景观格局研究结果具有差异性,因此定量分析土地覆被数据一致性及其对景观格局研究的影响具有重要意义。本研究以当前2套全球尺度30 m分辨率的土地覆被遥感数据GlobeLand30和FROMGLC为对象,从土地覆被类别及景观格局指数两个角度对比分析了二者在京津冀不同水土保持区的差异。结果表明:(1)在太行山东部山地丘陵水源涵养保土区,二者的面积一致性最大,一致性值为96.8%,在太行山西北部山地丘陵防沙水源涵养区二者的面积一致性最低,其数值仅为3.6%;(2)两数据集在津冀鲁渤海湾生态维护区空间的一致性最高,其空间一致性值为73.6%,在太行山西北部山地丘陵防沙水源涵养区空间的一致性最低,其值为25.17%;(3)在景观格局上,GlobeLand30数据集在京津冀6个生态功能区的复杂程度均小于FROM-GLC数据集,说明GlobeLand30数据景观多样性较低,地表景观类别均质性较强,而FROM-GLC数据的地表景观异质性较强,破碎化程度较高。

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Currently,the satellite data used to estimate terrestrial net primary productivity(NPP)in China are predominantly from foreign satellites,and very few studies have based their estimates on data from China’s Fengyun satellites.Moreover,despite their importance,the influence of land cover types and the normalized difference vegetation index(NDVI)on NPP estimation has not been clarified.This study employs the Carnegie–Ames–Stanford approach(CASA)model to compute the fraction of absorbed photosynthetically active radiation and the maximum light use efficiency suitable for the main vegetation types in China in accordance with the finer resolution observation and monitoring-global land cover(FROM-GLC)classification product.Then,the NPP is estimated from the Fengyun-3D(FY-3D)data and compared with the Moderate Resolution Imaging Spectroradiometer(MODIS)NPP product.The FY-3D NPP is also validated with existing research results and historical field-measured NPP data.In addition,the effects of land cover types and the NDVI on NPP estimation are analyzed.The results show that the CASA model and the FY-3D satellite data estimate an average NPP of 441.2 g C m^(−2) yr^(−1) in 2019 for China’s terrestrial vegetation,while the total NPP is 3.19 Pg C yr^(−1).Compared with the MODIS NPP,the FY-3D NPP is overestimated in areas of low vegetation productivity and is underestimated in high-productivity areas.These discrepancies are largely due to the differences between the FY-3D NDVI and MODIS NDVI.Compared with historical field-measured data,the FY-3D NPP estimation results outperformed the MODIS NPP results,although the deviation between the FY-3D NPP estimate and the in-situ measurement was large and may exceed 20%at the pixel scale.The land cover types and the NDVI significantly affected the spatial distribution of NPP and accounted for NPP deviations of 17.0%and 18.1%,respectively.Additionally,the total deviation resulting from the two factors reached 29.5%.These results show that accurate NDVI products and land cover types are important prerequisites for NPP estimation.

Evaluation of global land cover maps for cropland area estimation in the conterminous United States

Global land cover data could provide continuously updated cropland acreage and distribution information,which is essential to a wide range of applications over large geographical regions.Cropland area estimates were evaluated in the conterminous USA from four recent global land cover products:MODIS land cover(MODISLC)at 500-m resolution in 2010,GlobCover at 300-m resolution in 2009,FROM-GLC and FROM-GLC-agg at 30-m resolution based on Landsat imagery circa 2010 against the US Department of Agriculture survey data.Ratio estimators derived from the 30-m resolution Cropland Data Layer were applied to MODIS and GlobCover land cover products,which greatly improved the estimation accuracy of MODISLC by enhancing the correlation and decreasing mean deviation(MDev)and RMSE,but were less effective on GlobCover product.We found that,in the USA,the CDL adjusted MODISLC was more suitable for applications that concern about the aggregated county cropland acreage,while FROM-GLC-agg gave the least deviation from the survey at the state level.Correlation between land cover map estimates and survey estimates is significant,but stronger at the state level than at the county level.In regions where most mismatches happen at the county level,MODIS tends to underestimate,whereas MERIS and Landsat images incline to overestimate.Those uncertainties should be taken into consideration in relevant applications.Excluding interannual and seasonal effects,R 2 of the FROM-GLC regression model increased from 0.1 to 0.4,and the slope is much closer to one.Our analysis shows that images acquired in growing season are most suitable for Landsat-based cropland mapping in the conterminous USA.