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地理研究 >

2017 , Vol. 36 >Issue 11: 2061 - 2074

DOI: https://doi.org/10.11821/dlyj201711003

羌塘高原研究

七套土地覆被数据在羌塘高原的精度评价

  • 刘琼欢 , 1, 2 ,
  • 张镱锂 , 1, 2 ,
  • 刘林山 1 ,
  • 李兰晖 1, 2 ,
  • 祁威 1
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  • 1. 中国科学院地理科学与资源研究所,陆地表层格局与模拟重点实验室,北京 100101
  • 2. 中国科学院大学,北京 100049
通讯作者:张镱锂（1962- ）,男,吉林长岭人,研究员,主要从事生物地理与自然地理综合研究。E-mail：

作者简介：刘琼欢（1990- ）,女,湖南永兴人,博士研究生,主要从事土地覆被变化研究。E-mail：

收稿日期: 2017-04-24

  要求修回日期: 2017-09-08

  网络出版日期: 2017-11-20

基金资助

国家科技基础性工作专项重点项目（2012FY111400）

中国科学院战略性先导科技专项（XDB03030500）

国家科技支撑计划（2012BAC06B00）

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Accuracy evaluation of the seven land cover data in Qiangtang Plateau

  • LIU Qionghuan , 1, 2 ,
  • ZHANG Yili , 1, 2 ,
  • LIU Linshan 1 ,
  • LI Lanhui 1, 2 ,
  • QI Wei 1
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  • 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2017-04-24

  Request revised date: 2017-09-08

  Online published: 2017-11-20

Copyright

《地理研究》编辑部
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摘要

基于羌塘高原8个一级土地覆被类型（包括10个二级土地覆被类型）的6851个样本点,采用混淆矩阵方法,从总体精度、制图精度和用户精度角度评价International Geosphere-Biosphere Program's Data and Information System Cover（IGBPDIS）、Global Land cover mapping at 30 m resolution（GlobeLand 30）、The MODIS Land Cover Type product（MCD12Q1）、Climate Change Initiative Land Cover（CCI-LC）和Global Land Cover 2000（GLC2000）等七套土地覆被数据产品在羌塘高原的精度。结果表明：① 七套数据产品的一级类型和二级类型总体精度普遍偏低,在相对较高的GlobeLand 30和CCI-LC数据中,一级类型总体精度分别为55.09%和53.92%,二级类型分别为46.55%和46.23%;② 草地、裸地和荒漠三个主要一级类型生产者精度最高的数据对应为：GLC 2000（46.19%）、MCD12Q1（39.20%）和IGBPDIS（84.44%）。而三个主要一级类型的用户精度均低于50%。其他覆被类型中,雪被与冰川类型用户精度最高的数据为CCI-LC（92.80%）,漏分比例为19.90%;③ 羌塘高原特殊的高原环境与土地覆被分类系统构成原则和标准是影响遥感解译数据精度的主要原因。

关键词： 土地覆被数据; 遥感解译; 精度评价; 羌塘高原; 青藏高原

本文引用格式

刘琼欢 , 张镱锂 , 刘林山 , 李兰晖 , 祁威 . 七套土地覆被数据在羌塘高原的精度评价[J]. 地理研究, 2017 , 36(11) : 2061 -2074 . DOI: 10.11821/dlyj201711003

Abstract

The land cover datasets in the Qiangtang Plateau (QP) have generally been considered as the fundamental data in the studies of local environmental and ecological issues. We evaluated the accuracy of seven land cover datasets in the QP, i.e. International Geosphere-Biosphere Program's Data and Information System Cover (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand 30), The MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), GlobCover 2009 (GlobCover) and University of Maryland (UMD). For that, the study used 6851 field samples with first and second level (8 and 10, respectively) land cover types. Three widely used parameters were derived to describe the error matrix of the map and also for the overall user's and producer's accuracy. The quantitative assessments of the map quality and classification accuracy for the available land cover maps will help to improve the overall accuracy of land cover mapping in future. The overall results of the assessment pointed out that the GlobeLand 30 and CCI-LC land cover map have higher accuracy than other data sets. However, they are also just 55.09% and 53.92% in first level assessment and 46.55% and 46.23% in the second level accuracy assessment. The best producer's accuracies of the three main land cover classes, e.g. alpine grassland, barren land and desert land in the QP were 46.19% in GLC 2000, 39.20% in MCD12Q1, and 84.44% in the IGBPDIS. The user's accuracy of the three first level land cover classes were less than 50%. In addition, the accuracy of the CCI-LC data was 92.8%, with omission error at 19.90% in the snow and ice cover. After analysis, we found that the discrepancy of classification system and the typical plateau environment in the QP are the main factors that result in a high level of inaccuracy of the land cover datasets.

Key words： land cover data; remote sensing interpretation; accuracy evaluation; Qiangtang Plateau; Tibetan Plateau

1 引言

土地覆被数据是环境、生态等众多科学研究的基础,尤其在土地资源可持续利用以及政府决策中发挥重要作用[1]。随着遥感和GIS技术的发展,涌现出大量以遥感影像为数据源的全球或区域范围的土地覆被产品。截至2016年,有20余套全球尺度及区域尺度免费共享土地覆被数据产品[2]。然而,由于分类系统、分类技术、影像获取时间和空间分辨率等不一致,不同数据应用于区域或者全球尺度时,其精度存在明显的差异[3,4],严重影响土地覆被数据在各个领域的有效利用[5,6]。因此,对已有土地覆被数据资料的精度进行分析非常必要[7-9]。
目前,部分学者采用相对评价方法,对比多种土地覆被数据在整体范围的面积和空间一致性及差异性[4,10]。如对于不同分辨率产品进行全球或区域土地覆被数据空间一致性比较[11,12]。也有学者以某套数据产品为参考,如GLC2000数据,对比评估其他全球土地覆被数据总体精度[13]。然而,大多学者以样本点为参考数据,评价和验证数套全球土地覆被数据精度[3,14-18]。因该方法可靠性高,在土地覆被数据精度评价中最为常见,在该方法中,样本点的选取、布设和大小是影响精度评价的关键因素[17]。
近年来,青藏高原的草地退化、冰川退缩、湖泊扩张等生态环境变化越来越受到科研工作者的关注[19-22]。作为青藏高原腹地,羌塘高原的土地覆被变化对自身区域内生态环境起着关键作用,是青藏高原生态环境变化研究的重要组成部分[23]。然而,由于羌塘高原自然环境极其恶劣,在该区域的科学考察较少,研究基础资料匮乏。已有的数值模拟和遥感反演数据的精度也大多缺乏地面观测的验证[23,24]。
本文基于横跨羌塘高原的6851个土地覆被样本点,从总体精度、用户精度和制图精度三个方面,对GlobCover 2009（GlobCover）、University of Maryland（UMD）、和IGBPDIS等七套大尺度土地覆被数据在羌塘高原的精度进行评估,并试图回答：在羌塘高原地区,所选的七套代表性土地覆被数据产品中,数据综合精度如何、哪套数据精度最高?对于主要的土地覆被类型,哪套数据最为合理?影响羌塘高原土地覆被数据精度的原因有哪些,如何改进?

2 研究方法与数据来源

2.1 土地覆被数据产品

已有的20余套全球土地覆被数据产品分别属于11个数据系列[2]。本文选用其中的七套。其中,有5个数据系列——GlobeLand30、CCI-LC、GlobCover、MODIS Land Cover和Global Land Cover 250 m China（GLC250 m_CN）——包含不同年份的数据共22套。有一个数据系列未共享（GLC250 m_CN）,故本文选用了其他4个数据系列（表1）中最新年份的数据（截至2016年12月公开发布）;文中选用的另外3套数据（表1）分别属于3个数据系列（UMD、IGBP DISCover和GLC 2000）,且在长时间序列的土地覆被变化研究中运用广泛。有2个数据系列是同一年份、不同空间分辨率,即Finer Resolution Observation and Monitoring-Global Land Cover（FROM-GLC）和International Satellite Land Surface Climatology Project（ISLSCP II）,该系列共12套数据,本文未选用;另1个数据系列（GeoWiki）,因其年份不确定,亦未选用。
Tab.1 Characteristics of seven land cover data sets

表1 七套土地覆被数据

数据名称 整体精度（%） 验证方法 传感器 分类方法 分辨率 年份 分类系统类型数 链接 参考文献
GLC 2000 68.6 Confidence values statistical sampling SPOT4 VEGETATION 非监督分类 1 km 1999-2000 FAO LCCS
(23 classes)		 http://bioval.jrc.ec.europa.eu/products/glc2000/products.php Bartholomé等[26]
IGBPDIS 66.9 Statistical sampling of validation working group AVHRR 非监督分类 1 km 1992-1993 USGS IGBP
(17 classes)		 http://edc2.usgs.gov/glcc/tabgoode_globe.php Loveland等[27]
UMD 65.0 Evaluated using other digital datasets AVHRR 非监督分类、
决策树分类		 1 km 1992-1993 Simplified IGBP (14 classes) http://www.landcover.org/data/landcover/index.shtml Hansen等[28]
MCD12Q1 74.8 Cross-validation MODIS 监督分类、决策树分类、神经网络 500 m 2013 IGBP
(17 classes)		 http://e4ftl01.cr.usgs.gov/MOTA/MCD12Q1.051/ Friedl等[29,30]
GlobCover 67.5 Statistical sampling expert's judgement MERIS FR 监督分类、
非监督分类		 300 m 2009 UN LCCS
(22 classes)		 http://due.esrin.esa.int/globcover/ Bontemps等[31]
CCI-LC 74.1 Sampling-based labeling approach MERIS Full and Reduced Resolution/ SPOT 非监督分类 300 m 2008-2012 UN LCCS
(22 classes)		 http://maps.elie.ucl.ac.be/CCI/viewer/index.php Belgium等[32]
GlobeLand 30 80.0 Knowledge-based interactive verification Landsat TM, ETM7, HJ-1A/b/ 基于像元、对象和知识规则分类 30 m 2010 11 classes http://www.globallandcover.com Chen等[33]

2.2 样本点数据

本文所用样本数据来自野外实地调查、基于空间采样的湖泊样点调查和冰川样点调查,共计6851个样本点（表2）。其中在2012-2014年羌塘高原土地覆被类型野外调查中,获取2991个样本数据,样本点主要分布在陆面交通条件较好的区域;基于谷歌地球影像和相片标记的湖泊样本点1157个,范围大小为2 km×2 km;冰川样本点数据源自寒区旱区科学数据中心中国第二次冰川编目数据集[25],计2703个,范围为2 km×2 km,样点的空间分布如图1所示。
Tab. 2 Class description of the filed sample in Qiangtang Plateau

表2 羌塘高原样本点概况

类型 样本点数量 类型定义 类型 样本点数量 类型定义
高寒草甸 777 由寒冷中生多年生草本植物为主的植物群落覆盖区域,本研究区主要指藏北嵩草草甸、小嵩草（高山嵩草）草甸覆盖区 湖泊 1157 指自然条件下形成的积水区常年水位以下的土地
高寒草原 1245 具有一定御寒能力的、旱生的多年生草本植物和小半灌木植物占优势的植物群落覆盖区域 沼泽湿地 80 指覆盖着水（淡水、半咸水或咸水）与草本或木本植物的广阔区域,是介于陆地和水体之间的过渡带
稀疏植被 29 分布在连续植物覆盖的植被以上至永久雪线之间的、由适应严寒生境的寒旱生或寒冷中旱生多年生轴根性杂类草或以垫状植物或地衣苔藓等构成的盖度在5%~40%的植被区域。如蚤缀、点地梅垫状植被分布区域 居民建设用地 35 指被建筑物覆盖的土地类型
半灌木或矮半灌木荒漠 662 半灌木、矮半灌木（驼绒藜、木亚菊、蒿）荒漠、垫状驼绒藜荒漠广泛分布区域 裸地 125 指裸地、沙地、岩石、盐碱地,植被覆盖度不超过10 %
河流 38 指自然形成的沿着地表长条状槽形洼地 雪被与冰川 2703 指常年由积雪或者冰覆盖的土地类型
合计 6851

注：野外样本点选择标准：参照植被类型调查方法,选取土地覆被类型均一、周邻一致性较好、代表性较强的位置;该点位空域代表范围依土地覆被类型分布特征而变化,如草原、荒漠等大面积类型,可表示以点位为圆心,半径为500~1000 m的区域;除部分小面积类型,如河流点位表示的范围不确定外,其他类型样本点位至少可表示半径为30~50 m的区域。

Fig.1 Spatial distribution of the filed investigated sample points in Qiangtang Plateau

图1 羌塘高原样本点空间分布

2.3 数据处理与方法

2.3.1 土地覆被数据预处理 七套土地覆被数据的投影统一为“Albers_Conic_Equal_Area”。数据的空间分辨率保持不变,具体原因为：通过重采样的方法,如面积占优法、最近邻法等[12],其结果会降低数据本身的质量,且数据产品分辨率范围跨度大（30 m~1 km）,难以选择折中且对数据影响小的统一分辨率;其次,由于羌塘高原特殊的地理环境和植被特征,从草地、荒漠、裸地主要土地覆被类型中筛选出的代表性样本点,能保持周围类别单一,成片分布范围大于最大像元的大小（1 km）,基本不会影响像元大小造成的数据评价结果精度。因此,在不影响样本点对数据评价结果的条件下,为了尽可能减少对数据本身精度的影响,本文决定只对其进行投影转换处理,不对空间分辨率进行处理。
2.3.2 分类系统对应关系 各类土地覆被数据中土地覆被类型的划分及相关标准的制定存在一定差异,这使得不同数据间的比较难以进行[4]。Giri等[34]于2005年、Ran等[35]于2010年和Herold等[4]于2008年的相关研究中通过建立分类系统的衔接关系,实现了不同覆被类型的对应。参照已有的分类系统衔接工作[12,36,37],并结合实地考察和各数据产品类型的具体定义,编制了基于类型样点调查的羌塘高原土地覆被分类系统,包括8个一级类型10个二级类型。八套数据的四套分类系统与该分类系统具体的对应关系如表3所示。
Tab. 3 Corresponding relationships of classes in different classification system between eight land cover data sets and ground data

表3 羌塘高原地区不同土地覆被数据与样本点分类系统间的类别对应关系

类型 IGBP (IGBPDIS、MCD12Q1、UMD) FAO LCCS (GLC 2000、GlobCover、CCI-LC) GlobeLand 30
序号 一级 二级
1 草地 草地 草原、草甸 草地
2 高寒草甸 - 草甸 -
3 高寒草原 草地 草原 草地
4 稀疏植被 稀疏植被 - 自然植被与农田镶嵌类型、稀疏植被 -
5 半灌木或矮半灌木荒漠 半灌木或矮半灌木荒漠 稀疏灌木林地 荒漠草地、灌丛、落叶灌丛 灌木林地
6 水体 水体 水体 水体
7 河流 - - -
8 湖泊 - - -
9 沼泽湿地 沼泽湿地 湿地 稀疏草本或木本湿地 湿地
10 居民建设用地 居民建设用地 建设用地 建设用地 人造地表
11 裸地 裸地 裸地 裸地、砾石裸地、松散裸地 裸地
12 雪被和冰川 雪被和冰川 雪被与冰川 永久雪被与冰川 永久雪被与冰川
13 无 - 常绿针叶林、常绿阔叶林、落叶针叶林、落叶阔叶林、混交林、郁闭灌木林地、森林稀疏草原、稀树草原、农田、农田与自然植被镶嵌类型 旱地、灌溉或季节性水淹农田、农田与自然植被镶嵌类型、常绿针叶林 农田、乔木林地

注：表中各数据产品的完整分类代码请参照数据源说明,该表只列出了各数据产品在羌塘高原中的类型。“无”指在本文研究区现实中不存在的土地覆被类型,而各对应的数据产品在本区域中出现的土地覆被类型。

2.3.3 验证方法 本文采用混淆矩阵的方法,以总体精度、制图精度和用户精度为指标衡量土地覆被数据产品的质量,计算公式分别为[38, 39]：
OA = T X × 100 % （1）
P A i = T i X i × 100 % （2）
U A i = T i V i × 100 % （3）
式中：OA为总体精度,是所有被正确识别的像元数T与总样本数X的比值;PAi为某地类i的生产者精度,或者制图精度;Ti为被正确识别为第i类地物的像元数;Xi为实际为第i类地物的样本数;Vi为被分为第i类地物的样本数;UAi为某地类i的用户精度。

3 结果分析

3.1 一致性特征

3.1.1 空间一致性 根据野外调查和相关资料记载[40,41],草地、荒漠和裸地是羌塘高原地区主要的一级土地覆被类型,又以草地类型占比最大。草地主要分布在中部和南部,荒漠主要分布在北部,裸地主要分布在西北部的草地与荒漠过渡带。
羌塘高原主要的3种土地覆被一级类型在不同的数据产品中差异明显（图2）。CCI-LC、GlobeLand 30和GLC 2000数据主要覆被类型是草地,面积比例分别为70.10%、67.69%、62.92%、64.42%,能较好地描述羌塘高原中部和东南部的草地空间分布。MCD12Q1数据中裸地和草地面积比例接近,分别为40.91%和44.96%,但其他三套数据的空间分布与调查结果有明显偏差。
Fig. 2 Distribution of eight data sets at Level I of the classification on the Qiangtang Plateau

图2 七套数据产品在羌塘高原地区一级类型的空间分布特征

注：图中及下文各图中的不一致类别均指研究区现实中不存在的土地覆被类型,而各对应的数据产品在本区域中出现的土地覆被类型。

从七套数据草地二级类型的空间分布中可以发现（图3）,高寒草甸比例非常少,高寒草原空间分布特征和数据精度与一级类型中草地分布一致,CCI-LC、GlobeLand 30、GLC 2000数据相对能较好得识别草地类型。而各数据湖泊类型的空间分布特征基本类似,识别度较高。但与Zhang等[42]2015年目视解译的羌塘高原地区面积大于10 km2的湖泊数据对比,七套土地覆被数据的湖泊面积明显偏小。本文土地覆被数据（1992-2010年）湖泊的面积范围在14712.00~28039.18 km2之间,Zhang等（1991-2013年）获取的湖泊数据面积范围为24502.47~30737.67 km2。
Fig. 3 Distribution of seven data sets at Level II of the classification on the Qiangtang Plateau

图3 七套土地覆被数据产品在羌塘高原地区二级类型的空间分布特征

3.1.2 点位一致性 综合数据一致性和样本点验证的结果来看,一致性程度低,则实际土地覆被类型准确率低;一致性程度高,与实际土地覆被类型之间的关系并不明显（图4a）。总体上,在6851个样本点中,七套数据一致性程度低,主要集中在3和4之间,占54.9%（图4b）。数据间一致性低、数据与样本点间准确度也低（LL）的像元比例最大,占63.86%。即在大部分验证点上,各数据产品对类型判断不一致且判断准确的比例低。数据间一致性在空间上,未呈现明显区域差异特征。具体类型中（图4c）,一致性和准确度高（HH）的类型主要集中在高寒草地和雪被冰川类型上。
Fig. 4 Agreement and accuracy of land cover data sets

图4 土地覆被数据一致性与准确性分布图

注：图b中,纵坐标值为1表示在该样本点上7套数据的分类结果各不相同,值为2表示在该样本点上有2套产品的分类结果是一致的,……,值为7表示有7套产品在该样本点上的分类结果全部一致。

3.2 总体精度

七套数据一级土地覆被类型在羌塘高原地区总体精度均低于56%,可划分为4个等级,由高到低分别为：50%以上,40%~50%,20%~40%以及20%以下。从表4可以看出各数据总体精度情况具体情况,第Ⅰ级：GlobeLand 30（55.09%）和CCI-LC（53.92%）;第Ⅱ级：GLC 2000（49.97%）和CASLU（40.48%）;第Ⅲ级：GlobCover（31.88%）和MCD12Q1（24.61%）;第Ⅳ级：UMD（5.98%）和IGBPDIS（11.76%）。七套数据产品二级类型总体精度为6%~47%,精度等级与一级类型一致。
Table 4 Area proportion and overall accuracy of different land cover types of the seven data products (%)

表4 七套数据产品中不同类型面积比例及总体精度（%）

类型 CCI-LC GLC 2000 GlobCover GlobeLand30 IGBPDIS MCD12Q1 UMD
本文
研究区		 稀疏植被 2.00 0.00 2.51 0.00 0.00 0.00 0.00
湿地 0.07 0.00 0.00 0.31 0.31 0.06 0.00
建设用地 0.00 0.00 0.00 0.00 0.00 0.00 0.00
雪被与冰川 2.25 2.49 1.64 1.19 1.19 1.25 0.00
草地 70.51 67.69 68.84 86.12 40.47 3.53 19.75
高寒荒漠 1.10 16.62 0.00 2.34 2.34 9.49 68.50
水体 4.00 2.77 4.07 4.40 4.40 3.32 4.09
裸地 20.06 9.28 51.31 24.07 24.07 40.91 23.88
一级类型
总体精度		 53.92 49.97 31.88 55.09 11.76 24.61 5.98
二级类型
总体精度		 46.23 39.41 26.23 46.55 10.61 21.46 6.00
等级 Ⅰ Ⅱ Ⅲ Ⅰ Ⅳ Ⅲ Ⅳ
国际报道 总体精度 74.40 68.60 67.50 80.03 66.90 74.80 65.00
参考文献 文献[32] 文献[26] 文献[31] 文献[33] 文献[27] 文献[29, 30] 文献[28]

3.3 不同类型精度

3.3.1 生产者精度 七套数据产品的生产者精度普遍偏低。草地类型中,GLC 2000数据精度为46.19%,CCI-LC数据精度为43.57%,其他数据的制图精度均小于40%。在荒漠类型中,IGBPDIS数据制图精度为84.44%,但其总体精度仅为11.76%。裸地类型识别中,七套数据制图精度均小于50%,最高的仅为39.20%（MCD12Q1）。CCI-LC数据在雪被与冰川类型识别上制图精度为80.01%,且错分概率为7.2%,优于其他数据产品。而各数据在稀疏植被、湿地、水体类型中的制图精度均低于40%（图5a）。
Fig. 5 Producer's accuracy and user's accuracy of seven land cover data sets at Level I of the classification

图5 一级土地覆被类型的生产者精度和用户精度

3.3.2 用户精度 三大主要类型用户精度均低于50%,精度相对较高的类型主要是水体和冰川。在水体类型上,GlobeLand 30准确度为92.10%,漏分概率为64.74%。GlobCover准确度虽然为93.33%,漏分概率为93.95%。可见,GlobeLand 30在水体识别上整体精度更高。在冰川雪被类型上,CCI-LC数据精度更高。GlobeLand 30虽然识别准确率达到97.35%,但其漏分概率（27.89%）,高于CCI-LC数据（图5b）。
3.3.3 面积估算精度 面积估算精度表示生产者精度与用户精度差值,在生产者精度和用户精度均较高的条件下,其值越接近表明该类型面积估算精度越高。经计算,制图精度和用户精度均较高的有GLC 2000、CCI-LC和GlobeLand 30四套数据的草地类型（图5）。其中,面积精度最高的为GlobeLand 30数据。其他类型中,无同时满足制图精度、用户精度和面积精度高的数据（表5）。
Tab. 5 Accuracy of area estimation of different land cover types of the seven data products (%)

表5 七套数据产品中不同类型面积估算精度（%）

数据 稀疏植被 湿地 建设用地 雪被与冰川 草地 高寒荒漠 水体 裸地
CCI-LC 0.00 0.00 0.00 12.70 12.32 7.79 73.25 14.48
GlobalLand 30 0.00 8.75 26.83 25.24 4.41 0.00 57.96 34.43
GLC 2000 0.00 0.00 0.00 36.46 15.00 19.17 40.85 0.68
GlobCover 0.00 0.00 0.00 42.87 1.74 0.00 87.48 21.40
MCD12Q1 0.00 64.17 0.00 55.69 1.18 3.55 81.24 37.59
IGBPDIS 0.00 0.00 0.00 0.00 21.53 73.82 30.03 0.70
UMD 0.00 0.00 0.00 0.00 4.88 42.86 42.54 9.90

3.4 不同类型混淆

利用混淆矩阵,获得数据和样本点在相同位置土地覆被类型对应关系,选取六套总体精度高于20%的数据产品进行统计分析,地类混淆情况如图6所示,各类型与草地和裸地类型混淆严重。草地类型中,CCI-LC数据9.20%混分为裸地,少部分混分为其他类型。GlobeLand 30中,37.83%混分为裸地。GLC 2000数据18.23%混分为荒漠。裸地类型中,GlobeLand 30中24.59%混分为草地。MCD12Q1中,13.11%混分为荒漠。雪被与冰川类型中,CCI-LC中混分为草地和裸地比例分别为10.69%和8.77%。GlobeLand 30雪被与冰川主要混分为裸地（20.35%）,另有少部分混分为其他类型。GLC 2000数据的雪被与冰川有44.98%混分为其他类型。此外,荒漠、稀疏植被、建设用地、湿地、水体主要混分为草地、裸地类型。
Fig. 6 Confusion of Level I of the classification between the six land cover datasets

图6 土地覆被一级类型混淆比例

4 讨论

本文评估的七套数据采用了三套分类系统,即FAO LCCS、IGBP和GlobeLand30。在分析系统对应的过程中,主要问题表现为以下几方面：① UMD分类系统虽然采用了IGBP的分类方案,但UMD土地覆盖数据的分类系统是为SIB模型设计的,SIB的分类方案没有湿地和冰雪类,而是将永久性冰雪类包括在裸地里,而没有湿地类型,这可能会降低UMD数据的精度评价结果。② 羌塘高原的主题范围内基本无林地和耕地类型[23],但七套数据产品中均有林地和耕地类型,尤其IGBPDIS 和UMD数据在羌塘高原的核心区出现了大片林地和耕地类型（图2）,偏离了真实情况。以上问题说明,不仅数据集分类系统的转换过程会影响数据精度[43],分类原则和类型内涵的不同,也是造成本文区的遥感分类解译结果差异因素之一。而出现偏离真实类别的分类系统的原因很大程度是由于缺乏野外实测资料。因此,这些数据的分类系统需要进一步考察修正,以减少分类系统制定过程中的模糊概念。
羌塘高原植被类型和高原环境特征对土地覆被数据分类有重要影响。羌塘高原特殊的植被类型,如草地、裸地、荒漠类型波谱曲线、影像色调、纹理等标识差异小,易造成类型混淆。其次,羌塘高原典型的高原环境对土地覆被提取具有重要的影响。羌塘高原的植被生长季远短于其他低海拔地区[44,45],主要集中在夏季,其他月份难以监测植被类型信息,但土地覆被数据使用的各幅影像的月份可能不一致,如使用非生长季期间的遥感影像数据,则会降低植被类型解译结果。
本文主要不确定性表现为：样本点采集与遥感数据年份存在时间差异。本文样本点数据来自2013-2016年野外调查,选用的七套数据时间跨度约为20年（1992年/1993年-2013年）。但据实地考察发现,羌塘高原大面积属于无人区,主要受自然因素影响,该地区整体上土地覆被类型变化幅度相对较小。其次,样本点空间分布不够均匀。由于环境条件极其恶劣,本文野外获取的样本点数据主要分布在陆面交通条件较好的区域,空间分布不够均匀对精度评价结果会有一定影响,但作为该地区首批基础调查数据之一,是评估该地区土地覆被数据精度的重要参考。

5 结论

在羌塘高原地区,七套数据产品（GLC 2000、IGBPDIS、UMD、MCD12Q1、GlobCover、CCI-LC和GlobeLand 30）中,精度最高的一级类型仅为55.09%左右,未达到可广泛应用的精度要求[1,2]。从具体类型来看,雪被与冰川数据类型制图精度和用户精度整体上高于其他数据,其中最高的是CCI-LC数据,用户精度为92.80%,制图精度为80.10%。GlobeLand 30数据的草地类型面积估算精度最高。
本文评估的七套大尺度土地覆被数据产品在高海拔的羌塘高原寒旱地区精度低,无论从现状分析,还是作为区域土地覆被变化及其生态和环境影响研究的基础数据而言,高原地区土地覆被数据质量都亟待提高。建议如下：① 加强野外实地土地覆被类型的光谱测定、植被样地调查、土壤与土地利用调查及环境要素调查,确定类型解译标志和标准,及梳理类型内涵与定义,调整并完善分类系统,修订和完善遥感解译流程和方法以提升解译能力;② 采用最新的高分辨率影像、包含物候信息的时序数据（SPOT VGT、HJ CCD以及MODIS）,尤其是结合2000年以来MODIS的年内与年际间多期连续的时序数据,并结合野外实际观测分析结果,针对某土地覆被类型在进行深化和创新性研究。
致谢：对野外土地利用覆被调查中给予指导和帮助的中国科学院植物研究所郭柯研究员表示感谢,对参与野外数据采集的赵志龙、谷昌军、郑海朋、王宇坤等表示感谢,对数据分析和论文撰写中给予帮助和提出宝贵建议的王兆锋、聂勇等老师表示感谢。

The authors have declared that no competing interests exist.

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[12]
Kaptué Tchuenté A T, Roujean J, De Jong S M. Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale. International Journal of Applied Earth Observation and Geoinformation, 2011, 13(2): 207-219.Land cover dynamics at the African continental scale is of great importance for global change studies. Actually, four satellite-derived land cover maps of Africa now available, e.g. ECOCLIMAP, GLC2000, MODIS and GLOBCOVER, are based on images acquired in the 2000s. This study aims at stressing the compliances and the discrepancies between these four land cover classifications systems. Each of them used different mapping initiatives and relies on different mapping standards, which supports the present investigation. In order to do a relative comparison of the four maps, a preamble was to reconcile their thematic legends into more aggregated categories after a projection into the same spatial resolution. Results show that the agreement between the four land cover products is between 56 and 69%. While all these land cover datasets show a reasonable agreement in terms of surface types and spatial distribution patterns, mapping of heterogeneous landscapes in the four products is not very successful. Land cover products based on remote sensing imagery can indeed significantly be improved by using smarter algorithms, better timing of image acquisition, improved class definitions. Either will help to improve the accuracy of future land cover maps at the African continental scale. Data producers may use the areas of spatial agreement for training area selection while users might need to verify the information in the areas of disagreement using additional data sources.

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[13]
Schultz M, Tsendbazazr N E, Herold M, et al.Utilizing the Global Land Cover 2000 reference dataset for a comparative accuracy assessment of 1 km global land cover maps. Remote Sensing and Spatial Information Sciences, 2015, 40(7): 503-510.Many investigators use global land cover (GLC) maps for different purposes, such as an input for global climate models. The currentGLC maps used for such purposes are based on different remote sensing data, methodologies and legends. Consequently,comparison of GLC maps is difficult and information about their relative utility is limited. The objective of this study is to analyseand compare the thematic accuracies of GLC maps (i.e., IGBP-DISCover, UMD, MODIS, GLC2000 and SYNMAP) at 1 kmresolutions by (a) re-analysing the GLC2000 reference dataset, (b) applying a generalized GLC legend and (c) comparing theirthematic accuracies at different homogeneity levels. The accuracy assessment was based on the GLC2000 reference dataset with1253 samples that were visually interpreted. The legends of the GLC maps and the reference datasets were harmonized into 11general land cover classes. There results show that the map accuracy estimates vary up to 10-16% depending on the homogeneity ofthe reference point (HRP) for all the GLC maps. An increase of the HRP resulted in higher overall accuracies but reduced accuracyconfidence for the GLC maps due to less number of accountable samples. The overall accuracy of the SYNMAP was the highest atany HRP level followed by the GLC2000. The overall accuracies of the maps also varied by up to 10% depending on the definitionof agreement between the reference and map categories in heterogeneous landscape. A careful consideration of heterogeneouslandscape is therefore recommended for future accuracy assessments of land cover maps.* Corresponding author1. INTRODUCTIONThe consistent and continuous observation of land cover is oneof the most important foundations for understanding the Earth environment and ecosystems (Verburg et al., 2011). Currently,several global land cover datasets (GLC) have been developedand these datasets are evolving towards higher spatial resolution(Gong et al., 2013; Mora et al., 2014) . Most GLC maps weredeveloped by individual groups as one-time efforts and thesubsequent mapping standards reflect the varied interests,requirements and methodologies of the originating programs(Herold et al., 2006). These differences of GLC maps and theeffects of their quality on the model outcome are not alwaysconsidered when selecting a map as an input for specificmodeling applications (Verburg et al., 2011). Uncertainties ofGLC maps can result in considerable differences in modelingoutcomes (Hibbard et al., 2010; Nakaegawa, 2011; Verburg etal., 2011).The accuracies of GLC maps are assessed using independentvalidation datasets and regional maps or cross validated againsttraining datasets. The results of accuracy assessments ofprevious maps indicate that overall area-weighted accuracy isaround 70% for the existing GLC maps (Defourny et al., 2012).However, the use of different approaches in the GLC mapproduction (e.g., classification scheme, data sources andalgorithms) as well as in validation data collection (e.g.,sampling scheme, data source and method of referenceclassification) raise inconsistency issues and make mapcomparisons difficult. Several comparative analyses of landcover maps were conducted at regional levels

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[14]
Bai Y, Feng M, Jiang H, et al.Validation of land cover maps in China using a sampling-based labeling approach. Remote Sensing, 2015, 7(8): 10589-10606.This paper presents a rigorous validation of five widely used global land cover products, i.e., GLCC (Global Land Cover Characterization), UMd (University of Maryland land cover product), GLC2000 (Global Land Cover 2000 project data), MODIS LC (Moderate Resolution Imaging Spectro-radiometer Land Cover product) and GlobCover (GLOBCOVER land cover product), and a national land cover map GLCD-2005 (Geodata Land Cover Dataset for year 2005) against an independent reference data set over China. The land cover reference data sets in three epochs (1990, 2000, and 2005) were collected on a web-based prototype system using a sampling-based labeling approach. Results show that, in China, the highest overall accuracy is observed in GLCD-2005 (72.3%), followed by MODIS LC (68.9%), GLC2000 (65.2%), GlobCover (57.7%) and GLCC (57.2%), while UMd has the lowest accuracy (48.6%); all of the products performed best in representing "Trees" and "Others", well with "Grassland" and "Cropland", but problematic with "Water" and "Urban" across China in general. Moreover, in respect of GLCD-2005, there are significant accuracy differences across seven geographical locations of China, ranging from 46.3% in the Southwest, 77.5% in the South, 79.2% in the Northwest, 80.8% in the North, 81.8% in the Northeast, 82.6% in the Central, to 89.0% in the East. This study indicates that a regionally focused land cover map would in fact be more accurate than extracting the same region from a globally produced map.

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[15]
Lei G, Li A, Bian J, et al.Land cover mapping in Southwestern China using the HC-MMK approach. Remote Sensing, 2016, 8(4): 305.Land cover mapping in mountainous areas is a notoriously challenging task due to the rugged terrain and high spatial heterogeneity of land surfaces as well as the frequent cloud contamination of satellite imagery. Taking Southwestern China (a typical mountainous region) as an example, this paper established a new HC-MMK approach (Hierarchical Classification based on Multi-source and Multi-temporal data and geo-Knowledge), which was especially designed for land cover mapping in mountainous areas. This approach was taken in order to generate a 30 m-resolution land cover product in Southwestern China in 2010 (hereinafter referred to as CLC-SW2010). The multi-temporal native HJ (HuanJing, small satellite constellation for disaster and environmental monitoring) CCD (Charge-Coupled Device) images, Landsat TM (Thematic Mapper) images and topographical data (including elevation, aspect, slope, etc.) were taken as the main input data sources. Hierarchical classification tree construction and a five-step knowledge-based interactive quality control were the major components of this proposed approach. The CLC-SW2010 product contained six primary categories and 38 secondary categories, which covered about 2.33 million km(2) (accounting for about a quarter of the land area of China). The accuracies of primary and secondary categories for CLC-SW2010 reached 95.09% and 87.14%, respectively, which were assessed independently by a third-party group. This product has so far been used to estimate the terrestrial carbon stocks and assess the quality of the ecological environments. The proposed HC-MMK approach could be used not only in mountainous areas, but also for plains, hills and other regions. Meanwhile, this study could also be used as a reference for other land cover mapping projects over large areas or even the entire globe.

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[16]
Stehman S V.Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. International Journal of Remote Sensing, 2014, 35(13): 4923-4939.

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[17]
Stehman S V.Sampling designs for accuracy assessment of land cover. International Journal of Remote Sensing, 2009, 30(20): 5243-5272.The accuracy of a land cover classification is the degree to which the map land cover agrees with the reference land cover classification (i.e. ground condition). The basic sampling designs historically implemented for map accuracy assessment have served well for the error matrix based analyses traditionally used. But contemporary applications of land cover maps place greater demands on accuracy assessment, and sampling designs must be constructed to target objectives such as accuracy of land cover composition and landscape pattern. Sampling designs differ in their suitability to achieve different objectives, and trade-offs among desirable sampling design criteria must be recognized and accommodated when selecting a design. An overview is presented of the sampling designs used in accuracy assessment, and the status of these designs is appraised for meeting current needs. Sampling design features that facilitate multiple-objective accuracy assessments are described.

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[18]
Wickham J, Stehman S V, Gass L, et al.Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD). Remote Sensing of Environment, 2017, 191: 328-341.Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001鈥2006, 2006 2011, and 2001 2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest loss, forest gain, and urban gain had user's accuracies that exceeded 70%. Lower user's accuracies for the other change reporting themes may be attributable to the difficulty in determining the context of grass (e.g., open urban, grassland, agriculture) and between the components of the forest-shrubland-grassland gradient at either the mapping phase, reference label assignment phase, or both. NLCD 2011 user's accuracies for forest loss, forest gain, and urban gain compare favorably with results from other land cover change accuracy assessments.

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[19]
Nie Y, Sheng Y, Liu Q, et al.A regional-scale assessment of Himalayan glacial lake changes using satellite observations from 1990 to 2015. Remote Sensing of Environment, 2017, 189: 1-13.The Himalaya, the world's highest mountain ranges, are home to a large group of glaciers and glacial lakes. Glacial lake outburst floods (GLOFs) in this region have resulted in catastrophic damages and fatalities in the past decades. The recent warming has caused dramatic glacial lake changes and increased potential GLOF risk in the Himalaya. However, our knowledge on the current state and change of glacial lakes in the entire Himalaya is limited. This study maps the current (2015) distribution of glacial lakes across the entire Himalaya and monitors the spatially-explicit evolution of glacial lakes over five time periods from 1990 to 2015 using a total of 348 Landsat images at 3002m resolution. The results show that 4950 glacial lakes in 2015 cover a total area of 455.302±0272.702km 2 , mainly located between 400002m and 570002m above sea level. Himalayan glacial lakes expanded by approximately 14.1% from 1990 to 2015. The changing patterns of supraglacial lakes and proglacial lakes are rather complex, involving both lake disappearance and emergence. Many emergent glacial lakes are found at higher elevations, especially the new proglacial lakes, which have formed as a result of glacier retreat. Spatially heterogeneous changes of Himalayan glacial lakes are observed, with the most significant expansion occurring in the southern slopes of the central Himalaya. Increasing glacier meltwater induced by the Himalayan atmospheric warming is a primary cause for the observed lake expansion. This study provides primary data for future GLOF risk assessments. A total of 118 rapidly expanded glacial lakes are identified as potential vulnerable lakes for the priority of risk assessment.

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[20]
Yao T, Thompson L, Yang W, et al.Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nature Climate Change, 2012, 2(9): 663-667.

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[21]
刘纪远, 徐新良, 邵全琴. 近30年来青海三江源地区草地退化的时空特征. 地理学报, 2008, 63(4): 364-376.在20世纪70年代中后期MSS图像、90年代初期TM图像和2004年TM/ETM图像支持下,通过三期遥感影像的直接对比分析,获得了三江源地区草地退化空间数据集,并在此基础上分析了70年代以来青海三江源地区草地退化的主要时空特征。结果表明:三江源地区草地退化是一个在空间格局上影响范围大,在时间过程上持续时间长的连续变化过程。研究发现,三江源草地退化的格局在70年代中后期已基本形成,70年代中后期至今,草地的退化过程一直在继续发生,总体上不存在90年代至今的草地退化急剧加强现象。草地退化的过程在不同区域和地带有明显不同的表现,如在湿润半湿润地带的草甸类草地上,发生着草地破碎化先导,随后发生覆盖度持续降低,最后形成黑土滩的退化过程;在干旱、半干旱地带的草原类草地上,发生着覆盖度持续降低,最后形成沙地和荒漠化草地的退化过程。三江源地区草地退化具有明显的区域差异,草地退化可以分为7个区,各区草地退化在类型、程度、范围与时间过程方面具有明显不同的特点。

DOI

[Liu Jiyuan, Xu Xinliang, Shao Quanqin.The spatial and temporal characteristics of grassland degradation in the Three-River Headwaters Region in Qinghai province. Acta Geographica Sinca, 2008, 63(4): 364-376.]

[22]
张镱锂, 刘林山, 摆万奇, 等. 黄河源地区草地退化空间特征. 地理学报, 2006, 61(1): 3-14.利用黄河源地区1985年和2000年1:100000土地利用/覆被数据,结合1:250000DEM、道路和居民点数据与野外调查资料,分析草地退化与坡向、海拔及距道路和居民点距离之间的关系,探讨黄河源区15年间土地覆被变化特征与规律。结果表明,退化草地占源区总面积的8.24%,冬春季牧场退化率显著高于夏季牧场;草地退化是黄河源区研究时段内土地利用/覆被变化最主要的特征。草地退化表现为:①阳坡退化率高于阴坡;②受人口密度影响,草地退化率与海拔高度成反比,相关系数为-0.925;③距离居民点越近,退化率越高。尤其当与居民点距离≤12km时,草地退化率与其相关系数高达-0.996;④在距道路4km以内,草地退化率与道路距离成反比,相关系数高达-0.978。1985年以来,源区的草地退化有自然因素的影响,但人类活动的影响仍起主导作用。科学地减少当地居民对草地的过分依赖是解决脆弱的江河源区环境退化的根本。

DOI

[Zhang Yili, Liu Linshan, Bai Wanqi et al. Grassland degradation in the Source Region of the Yellow River. Acta Geographica Sinca, 2006, 61(1): 3-14.]

[23]
祁威. 羌塘高原自然地理特征与寒旱核心区范围探讨. 北京: 中国科学院大学博士学位论文, 2015.青藏高原因强烈隆升，导致大气环流发生变化，形成了高原区域性环流。受此影响，从喀喇昆仑山最干旱的中段北翼的河尾滩、阿克赛钦一带向东延伸至中昆仑山南翼的黑石北湖、羊湖、白戈壁和昂歌库勒，及其以东的干旱区域，远离青藏高原两条水汽输送路径，气候寒冷干旱，成为亚洲的寒旱核心区域。据已有资料，寒旱核心区海拔在4700～5200m间，最暖月均温3～6℃，年降水量约为20～40mm。　　本文在大量野外工作的基础上，基于改则及日土县境内19套土壤温度监测站点及3台自动气象站点1cm、10cm及20cm深度处土壤温度数据，分析了不同深度处土壤温度的季节变化和日变化;利...

[Qi Wei.Analyzing the physical geography characteristics of QiangtangPlateau and identifying the boundary of the cold and dry core region of Qiangtang Plateau. Beijing: Doctoral Dissertation of University of Chinese Academy of Sciences, 2015.]

[24]
张镱锂, 王兆锋, 王秀红, 等. 青藏高原关键区域土地覆被变化及生态建设反思. 自然杂志, 2013, 35(3): 187-192.<p>青藏高原独特而敏感的生态系统，是中国生态安全屏障的重要载体，也是区域经济发展的必要基础。生态建<br />设是生态文明的需求，也是社会经济可持续发展的保障。由于青藏高原内部自然条件差异显著，各区生态功能及区<br />域问题也不尽相同，生态建设亟需因地制宜地提出建设与保护的对策和措施。本文从生态系统的主要功能、脆弱程<br />度、变化趋势及面临风险等特征，辨识出阿里西部、那曲中南部、三江源地区和三江并流区等4 个生态建设的关键<br />区；并在分析各区环境和生态特征与土地覆被变化及其原因的基础上，提出了稳定和提升青藏高原生态安全屏障功<br />能的措施与建议，对于高原生态安全屏障保护与建设具有重要的参考价值。</p>

[Zhang Yili, Wang Zhaofeng, Wang Xiuhong, et al.Land cover changes in the key regions and self reflection on ecological construction of the Tibetan Plateau. Chinese Journal of Nature, 2013, 35(3): 187-192.]

[25]
Guo W Q, Xu J L, Liu S Y, et al.The second glacier inventory dataset of China (version 1.0). Cold and Arid Regions Science Data Center: Lanzhou, China, 2014.

[26]
Bartholomé E, Belward A S.GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 2005, 26(9): 1959-1977.A new global land cover database for the year 2000 (GLC2000) has been produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre. The database contains two levels of land cover information—detailed, regionally optimized land cover legends for each continent and a less thematically detailed global legend that harmonizes regional legends into one consistent product. The land cover maps are all based on daily data from the VEGETATION sensor on‐board SPOT 4, though mapping of some regions involved use of data from other Earth observing sensors to resolve specific issues. Detailed legend definition, image classification and map quality assurance were carried out region by region. The global product was made through aggregation of these. The database is designed to serve users from science programmes, policy makers, environmental convention secretariats, non‐governmental organizations and development‐aid projects. The regional and global data are available free of charge for all non‐commercial applications from http://www.gvm.jrc.it/glc2000.

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[27]
Loveland T R, Reed B C, Brown J F, et al.Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 2000, 21(6-7): 1303-1330.Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission''s Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.

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[28]
Hansen M C, Defries R S, Townshend J R, et al.Global land cover classification at 1 km spatial resolution using a classification tree approach. International journal of remote sensing, 2000, 21(6-7): 1331-1364.This paper on reports the production of a 1 km spatial resolution land cover classification using data for 1992-1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will be included as an at-launch product of the Moderate Resolution Imaging Spectroradiometer (MODIS) to serve as an input for several algorithms requiring knowledge of land cover type. The methodology was derived from a similar effort to create a product at 8 km spatial resolution, where high resolution data sets were interpreted in order to derive a coarse-resolution training data set. A set of 37 294 x 1 km pixels was used within a hierarchical tree structure to classify the AVHRR data into 12 classes. The approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted. Multitemporal AVHRR metrics were used to predict class memberships. Minimum annual red reflectance, peak annual Normalized Difference Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not. Comparisons of the final product with regional digital land cover maps derived from high-resolution remotely sensed data reveal general agreement, except for apparently poor depictions of temperate pastures within areas of agriculture. Distinguishing between forest and non-forest was achieved with agreements ranging from 81 to 92% for these regional subsets. The agreements for all classes varied from an average of 65% when viewing all pixels to an average of 82% when viewing only those 1 km pixels consisting of greater than 90% one class within the high-resolution data sets.

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[29]
Friedl M A, Sulla-Menashe D, Tan B, et al.MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 2010, 114(1): 168-182.Information related to land cover is immensely important to global change science. In the past decade, data sources and methodologies for creating global land cover maps from remote sensing have evolved rapidly. Here we describe the datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4. In addition to using updated input data, the algorithm and ancillary datasets used to produce the product have been refined. Most importantly, the Collection 5 product is generated at 500-m spatial resolution, providing a four-fold increase in spatial resolution relative to the previous version. In addition, many components of the classification algorithm have been changed. The training site database has been revised, land surface temperature is now included as an input feature, and ancillary datasets used in post-processing of ensemble decision tree results have been updated. Further, methods used to correct classifier results for bias imposed by training data properties have been refined, techniques used to fuse ancillary data based on spatially varying prior probabilities have been revised, and a variety of methods have been developed to address limitations of the algorithm for the urban, wetland, and deciduous needleleaf classes. Finally, techniques used to stabilize classification results across years have been developed and implemented to reduce year-to-year variation in land cover labels not associated with land cover change. Results from a cross-validation analysis indicate that the overall accuracy of the product is about 75% correctly classified, but that the range in class-specific accuracies is large. Comparison of Collection 5 maps with Collection 4 results show substantial differences arising from increased spatial resolution and changes in the input data and classification algorithm.

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[30]
Friedl M, Sulla Menashe D.Note to users of MODIS Land Cover (MCD12Q1) Products Report. Washington District of Columbia:NASA, 2011.

[31]
Bontemps S, Defourny P, Bogaert E V, et al.Globcover 2009-Products description and validation Report, Leuve: University of catholique de Louvain, 2011.Excerpt: Introduction] In 2008, the ESA-GlobCover 2005 project delivered to the international community the very first 300- m global land cover map for 2005 as well as bimonthly and annual MERIS (Medium Resolution Imaging Spectrometer Instrument) Fine Resolution (FR) surface reflectance mosaics. The ESA- GlobCover 2005 project, carried out by an international consortium, started in April 2005 and relied on very rich feedback and comments from a large partnership including end-users belonging to international institutions (JRC, FAO, EEA, UNEP, GOFC-GOLD and IGBP) in addition to ESA internal...

[32]
Belgium U.Land Cover CCI Product User Guide Version 2. Leuve: University of catholique de Louvain, 2016.

[33]
Chen J, Chen J, Liao A, et al.Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 103: 7-27.Over 10,000 Landsat-like satellite images are required to cover the entire Earth at 30 m resolution. To derive a GLC map from such a large volume of data necessitates the development of effective, efficient, economic and operational approaches. Automated approaches usually provide higher efficiency and thus more economic solutions, yet existing automated classification has been deemed ineffective because of the low classification accuracy achievable (typically below 65%) at global scale at 30 m resolution. As a result, an approach based on the integration of pixel- and object-based methods with knowledge (POK-based) has been developed. To handle the classification process of 10 land cover types, a split-and-merge strategy was employed, i.e. firstly each class identified in a prioritized sequence and then results are merged together. For the identification of each class, a robust integration of pixel-and object-based classification was developed. To improve the quality of the classification results, a knowledge-based interactive verification procedure was developed with the support of web service technology. The performance of the POK-based approach was tested using eight selected areas with differing landscapes from five different continents. An overall classification accuracy of over 80% was achieved. This indicates that the developed POK-based approach is effective and feasible for operational GLC mapping at 30 m resolution.

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[34]
Giri C, Zhu Z, Reed B.A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote sensing of environment, 2005, 94(1): 123-132.Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced classification algorithms.

DOI

[35]
Ran Y, Li X, Lu L.Evaluation of four remote sensing based land cover products over China. International Journal of Remote Sensing, 2010, 31(2): 391-401.Precise global/regional land cover mapping is foundational data in the studies of land surface processes and modelling.The quantitative assessments of the map quality and classification accuracy for the available land cover maps will help to improve the accuracy of land cover mapping in future.Four land cover data sets over China are compared and evaluated.The data sets include Version 2 global land cover dataset of IGBP,the MODIS Land cover map 2001,global land cover map produced by the University of Maryland,and the land cover map from Global Vegetation Monitoring unit of the European Commission Joint Research Centre(GLC2000).The four maps have different classification systems,which makes the comparison difficult.It is necessary to aggregate these maps by reclassifying them using a unified legend system.A large scale,i.e.1 100 000 land cover map of China was used as the reference data to validate the four maps.The results show that the GLC2000 land cover map has the highest accuracy.But it has obvious rating error locally and its rating accuracy is zero for wetland.The MODIS land cover map ranks second for type area consistency and ranks third for sub fraction overall accuracy as compared with reference data,which may be affect by local rating error.The IGBP land cover map hast good rating accuracy though it has a local rating error and third consistency for type area.The rating accuracy and type area consistency with reference data of UMD land cover map is low wholly.It is believed that the accuracies of all the datasets can not meet the requirement of land surface modelling.Addition,an information fusion strategy is proposed to produce a higher accuracy land cover map of China,whose classification system should be compatible with the well-accepted classification system used in land surface modelling.

DOI

[36]
Giri C, Zhu Z, Reed B.A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sensing of Environment, 2005, 94(1): 123-132.Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced classification algorithms.

DOI

[37]
Ran Y, Li X, Lu L.Evaluation of four remote sensing based land cover products over China. International Journal of Remote Sensing, 2010, 31(2): 391-401.Precise global/regional land cover mapping is foundational data in the studies of land surface processes and modelling.The quantitative assessments of the map quality and classification accuracy for the available land cover maps will help to improve the accuracy of land cover mapping in future.Four land cover data sets over China are compared and evaluated.The data sets include Version 2 global land cover dataset of IGBP,the MODIS Land cover map 2001,global land cover map produced by the University of Maryland,and the land cover map from Global Vegetation Monitoring unit of the European Commission Joint Research Centre(GLC2000).The four maps have different classification systems,which makes the comparison difficult.It is necessary to aggregate these maps by reclassifying them using a unified legend system.A large scale,i.e.1鈭100 000 land cover map of China was used as the reference data to validate the four maps.The results show that the GLC2000 land cover map has the highest accuracy.But it has obvious rating error locally and its rating accuracy is zero for wetland.The MODIS land cover map ranks second for type area consistency and ranks third for sub fraction overall accuracy as compared with reference data,which may be affect by local rating error.The IGBP land cover map hast good rating accuracy though it has a local rating error and third consistency for type area.The rating accuracy and type area consistency with reference data of UMD land cover map is low wholly.It is believed that the accuracies of all the datasets can not meet the requirement of land surface modelling.Addition,an information fusion strategy is proposed to produce a higher accuracy land cover map of China,whose classification system should be compatible with the well-accepted classification system used in land surface modelling.

DOI

[38]
Olofsson P, Foody G M, Herold M, et al.Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 2014, 148: 42-57.61Provides good practice recommendations for accuracy assessment and area estimation61Recommendations are a synthesis of existing methods.61Recommendations satisfy accepted scientific practice.61Recommendations address sampling design, response design and analysis.61An example illustrating recommended workflow is included.

DOI

[39]
黄亚博, 廖顺宝. 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. 地理研究, 2016, 35(8): 1433-1446.以河南省为研究区,对全球首套30 m分辨率土地覆盖产品Globle Land30进行区域尺度精度评价。首先,以中国1∶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Globle Land30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Globle Land30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 Globle Land30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,Globle Land30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,Globle Land30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。

DOI

[Huang Yabo, Liao Shunbao.Regional accuracy assessments of the first global land cover dataset at 30-meter resolution: A case study of Henan province. Geographical Research, 2016, 35(8): 1433-1446.]

[40]
郑度. 喀喇昆仑山—昆仑山地区自然地理. 北京: 科学出版社, 1999.

[Zheng Du, Physical Geography of Karakorum-Kunlun Mountains. Beijing: Science Press, 1999.]

[41]
中国科学院青藏高原综合科学考察队. 西藏植被. 北京: 科学出版社, 1988.

[Changchun Institute of Geography, CAS. Tibet Vegetation. Beijing: Science Press, 1998.]

[42]
Zhang G, Yao T, Piao S, et al.Extensive and drastically different alpine lake changes on Asia's high plateaus during the past four decades. Geophysical Research Letters, 2017, 44(1): 252-260.Asia’s high plateaus are sensitive to climate change and have been experiencing rapid warming over the past few decades. We found 99 new lakes and extensive lake expansion on the Tibetan Plateau during the last four decades, 1970–2013, due to increased precipitation and cryospheric contributions to its water balance. This contrasts with disappearing lakes and drastic shrinkage of lake areas on the adjacent Mongolian Plateau: 208 lakes disappeared, and 75% of the remaining lakes have shrunk. We detected a statistically significant coincidental timing of lake area changes in both plateaus, associated with the climate regime shift that occurred during 1997/1998. This distinct change in 1997/1998 is thought to be driven by large-scale atmospheric circulation changes in response to climate warming. Our findings reveal that these two adjacent plateaus have been changing in opposite directions in response to climate change. These findings shed light on the complex role of the regional climate and water cycles and provide useful information for ecological and water resource planning in these fragile landscapes.

DOI

[43]
Zhang M, Ma M, De Maeyer P, et al.Uncertainties in classification system conversion and an analysis of inconsistencies in Global Land Cover Products. ISPRS International Journal of Geo-Information. 2017, 6(4): 112.In this study, using the common classification systems of IGBP-17, IGBP-9, IPCC-5 and TC (vegetation, wetlands and others only), we studied spatial and areal inconsistencies in the three most recent multi-resource land cover products in a complex mountain-oasis-desert system and quantitatively discussed the uncertainties in classification system conversion. This is the first study to compare these products based on terrain and to quantitatively study the uncertainties in classification system conversion. The inconsistencies and uncertainties decreased from high to low levels of aggregation (IGBP-17 to TC) and from mountain to desert areas, indicating that the inconsistencies are not only influenced by the level of thematic detail and landscape complexity but also related to the conversion uncertainties. The overall areal inconsistency in the comparison of the FROM-GLC and GlobCover 2009 datasets is the smallest among the three pairs, but the smallest overall spatial inconsistency was observed between the FROM-GLC and MODISLC. The GlobCover 2009 had the largest conversion uncertainties due to mosaic land cover definition, with values up to 23.9%, 9.68% and 0.11% in mountainous, oasis and desert areas, respectively. The FROM-GLC had the smallest inconsistency, with values less than 4.58%, 1.89% and 1.2% in corresponding areas. Because the FROM-GLC dataset uses a hierarchical classification scheme with explicit attribution from the second level to the first, this system is suggested for producers of map land cover products in the future.

DOI

[44]
Oteros J, García-Mozo H, Vázquez L, et al.Modelling olive phenological response to weather and topography. Agriculture, Ecosystems & Environment, 2013, 179: 62-68.A detailed analysis was made of the response of olive floral phenology to climate and topography in southern Spain. Field phenological, topographical and meteorological data collected at 12 sampling sites in the province of Cordoba over a 17-year period (1996-2012) were statistically analyzed and used to model local olive phenological behaviour.The study sought to determine: (1) the optimal frequency of phenological sampling during the reproductive period; (2) the major topographical parameters governing local olive reproductive phenology; and (3) the most influential meteorological variables. Findings for the Sign test indicated that weekly sampling yielded accurate results. Correlation and multiple linear regression analysis revealed that altitude and percentage eastward slope were the most influential topographical factors; a positive correlation was detected between delays in phenophases onset and increased altitude and eastward orientation. Correlation and partial least square regression analysis identified air temperature, rainfall, crop evapotranspiration and solar radiation as the major weather factors influencing local olive phenology. (C) 2013 Elsevier B.V. All rights reserved.

DOI

[45]
Zhang G, Zhang Y, Dong J, et al.Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011, Proceedings of the National Academy of Sciences, 2013, 110(11): 4309-4314.As the Earth's third pole, the Tibetan Plateau has experienced a pronounced warming in the past decades. Recent studies reported that the start of the vegetation growing season (SOS) in the Plateau showed an advancing trend from 1982 to the late 1990s and a delay from the late 1990s to 2006. However, the findings regarding the SOS delay in the later period have been questioned, and the reasons causing the delay remain unknown. Here we explored the alpine vegetation SOS in the Plateau from 1982 to 2011 by integrating three long-term time-series datasets of Normalized Difference Vegetation Index (NDVI): Global Inventory Modeling and Mapping Studies (GIMMS, 1982-2006), SPOT VEGETATION (SPOT-VGT, 1998-2011), and Moderate Resolution Imaging Spectroradiometer (MODIS, 2000-2011). We found GIMMS NDVI in 2001-2006 differed substantially from SPOT-VGT and MODIS NDVIs and may have severe data quality issues in most parts of the western Plateau. By merging GIMMS-based SOSs from 1982 to 2000 with SPOT-VGT-based SOSs from 2001 to 2011 we found the alpine vegetation SOS in the Plateau experienced a continuous advancing trend at a rate of similar to 1.04 d.y(-1) from 1982 to 2011, which was consistent with observed warming in springs and winters. The satellite-derived SOSs were proven to be reliable with observed phenology data at 18 sites from 2003 to 2011; however, comparison of their trends was inconclusive due to the limited temporal coverage of the observed data. Longer-term observed data are still needed to validate the phenology trend in the future.

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