全球土地覆被数据集中哈萨克斯坦草地分布的异同及其成因

doi:10.12082/dqxxkx.2019.180518

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (3): 372-383.doi: 10.12082/dqxxkx.2019.180518

• 地理空间分析综合应用 • 上一篇下一篇

全球土地覆被数据集中哈萨克斯坦草地分布的异同及其成因

赖晨曦^1,²(), 闫慧敏^2,^3,^*(), 杜文鹏^2,³, 胡云锋^2,³

1. 长安大学地球科学与资源学院,西安 710054
2. 中国科学院地理科学与资源研究所,北京 100101
3. 中国科学院大学,北京 100049

收稿日期:2018-10-13 修回日期:2018-12-13 出版日期:2019-03-15 发布日期:2019-03-15
作者简介:
作者简介：赖晨曦（1994-）,男,江西宜春人,硕士,主要从事生态环境遥感研究。E-mail: laicxjxycja@126.com
基金资助:
中国科学院战略性先导科技专项（A类）资助（XDA20010202）;国家重点研发项目（2016YFC 05037000）

The Variations and Causes of Grassland Distribution in Kazakhstan from the Global Land Cover Datasets

Chenxi LAI^1,²(), Huimin YAN^2,^3,^*(), Wenpeng DU^2,³, Yunfeng HU^2,³

1. School of Earth Science and Resources, Chang'an University, Xi'an 710054, China
2. Institute of Geographic Sciences and Natural Research, Chinese Academy of Sciences, Beijing 100101, China
3. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2018-10-13 Revised:2018-12-13 Online:2019-03-15 Published:2019-03-15
Contact: Huimin YAN
Supported by:
The Strategic Priority Research Program of the Chinese Academy of Sciences, No.XDA20010202;National Key Research and Development Program of China, No.2016YFC0503700.

摘要/Abstract

摘要：

受社会制度变迁和气候变化的影响,哈萨克斯坦是中亚地区生态退化和草畜矛盾问题最为突出的国家。近百年来,放牧方式的改变、农业开垦的占用、加之暖干化的气候变化影响,使得哈萨克斯坦各类草地生态系统变化的时空格局具有鲜明的特点。因此,研究哈萨克斯坦草地退化的过程与机制对认识中亚地区草地生态系统对气候变化和人类活动的响应尤为重要,也是对绿色丝路建设过程中区域生态可持续发展的科学支撑。土地覆被数据是生态变化研究的基础数据,但目前广泛使用的各套全球数据集间往往存在很大的差异,这会导致对生态变化成因的认知以及对未来变化的模型模拟产生更大的不确定性。本研究从对草地类型识别的定义、空间分布的一致性和空间分布差异的原因3方面对比5类全球土地覆被数据（UMD 1992-1993、MCD12Q1 2001、GLC 2000、CCI-LC 2000、Glob Cover 2005）中哈萨克斯坦草地分布的异同,以期为哈萨克斯坦的相关研究中土地覆被数据集的选择提供依据。研究结果表明：① 分类系统中对草地类型的界定、遥感数据源、辅助分类数据、分类方法、验证数据和方法的不同是5套数据草地资源分布差异的主要原因,其中MCD12Q1数据与其他4套数据的草地分布面积相差最大;② 5套数据中草地分布都重叠（完全一致）或四套数据重叠（高度一致）的区域仅占39.66%,主要位于哈萨克斯坦典型草原带和部分半荒漠草原带;围绕典型草地分布区,空间一致性由内向外逐渐降低。5套数据完全不一致区域占26.78%,主要位于荒漠草原带;③ CCI-LC2000数据与其他几类数据的重叠区域最高,有76%的草地与5套数据的完全一致以及高度一致区重叠;在分布不一致区域中,极易造成混淆的土地覆被类型主要为旱作耕地、灌溉耕地、耕地与自然植被镶嵌体、裸地以及灌丛。

关键词: 全球土地覆被数据, 草地资源, 空间一致性, 类型混淆, 哈萨克斯坦

Abstract:

Affected by social institutional transformation and climate change, Kazakhstan is the most significant country with ecological degradation and grass-livestock contradiction in Central Asia. Over the past century, the distinct characteristics of various grassland ecosystems have changed due to agricultural reclamation, changes in grazing patterns, and climate change in Kazakhstan. Therefore, it is important to study the process and mechanisms of grassland degradation in Kazakhstan in order to understand the responses of grassland ecosystems to climate change and human activities in Central Asia. These findings may also support regional ecological sustainable development in the construction of green silk roads. Ecological change research is based on the land cover statistics. However, there are significant differences between the current widely-used global data sets, leading to uncertainty in the understanding of ecological variation and the simulation of future change. This study compared the similarities and differences of grassland distribution using five types of global land cover data (UMD 1992-1993, MCD12Q1 2001, GLC 2000, CCI-LC 2000, Glob Cover 2005). Grassland type identification, consistency of spatial distribution and the cause of spatial distribution variation were used to provide the basis for selection of land cover datasets in Kazakhstan. Results showed that: ① the primary cause of differences in grassland definitions were differing remote sensing data sources, ancillary data, classification methods, verification methods, and data within the five data sets. The MCD12Q1 data had the largest difference in grassland distribution area; ② the area of grassland distribution overlaps within the five data sets (complete consistency) or within the four data sets (high consistency) accounted for only 39.66% of the total, which were mainly located in the typical grassland and part of the semi-desert grassland. The spatial consistency gradually decreased from the inside to the outside around the typical grassland distribution zone. An inconsistent zone within the five data sets accounted for 26.78%, mainly located in the desert grassland; ③ CCI-LC2000 data had the highest areas of overlap compared to other types of data. There were 76% of the grassland overlapped with areas of complete consistency or high consistency in the five data sets. In the inconsistent areas, the most easily confused land cover types were mainly rainfed cropland, irrigated cropland, mosaic cropland and natural vegetation, bare areas and shrub land.

Key words: global land cover data, grassland resources, spatial consistency, type confusion, Kazakhstan

赖晨曦, 闫慧敏, 杜文鹏, 胡云锋. 全球土地覆被数据集中哈萨克斯坦草地分布的异同及其成因[J]. 地球信息科学学报, 2019, 21(3): 372-383.DOI:10.12082/dqxxkx.2019.180518

Chenxi LAI, Huimin YAN, Wenpeng DU, Yunfeng HU. The Variations and Causes of Grassland Distribution in Kazakhstan from the Global Land Cover Datasets[J]. Journal of Geo-information Science, 2019, 21(3): 372-383.DOI:10.12082/dqxxkx.2019.180518

图/表 9

图1

表1

5套土地覆被数据集基本信息

数据	空间分辨率/m	分类体系	数据源时段	数据源传感器	分类方法	输入数据和辅助数据	数据精度/%	数据精度验证方法
UMD^①	1000	IGBP 14类	1992-04 1993-03	AVHRR	将全球作为整体进行分类;采用监督分类树分类（水体与城镇掩膜去除）,分类树输出的叶子节点中所有像元赋值为占比最大的类别	水体数据：MODIS传感器制作的水体掩膜;城镇数据：IGBP DIS Cover数据集的城镇数据;1992-04至1993-03中AVHRR的5个波段和NDVI组成的41个规则矩阵;辅助选取样本数据主要是 Landsat MSS影像解译得到	65.00	与其它区域数字数据集比较法进行验证（如美国的MRLC和欧洲的CEC等）
MCD12Q1^②	500	IGBP 17类	2001	MODIS	将全球作为整体进行分类;采用集成监督分类算法,其基本算法是决策树,使用boosting估计集成分类,对集成决策树的结果进行后处理	将整年Terra和Aqua数据的1~7波段的光谱和时间信息作为输入数据,并补充EVI数据,训练样本数据包括地球陆地上的1860个站点数据;辅助选取样本数据主要是 Landsat TM影像	78.30	基于90%的随机训练样本数据,采用交叉验证方法来验证精度
GLC^③	1000	LCCS 22类	1999- 2000	SPOT4 Vegetation	将全球分为19个区进行单独分类;采用非监督分类的ISODATA算法进行分类	中亚地区,将每月合成的NDVI数据作为输入;采用的样本数据主要是实地调查、Landsat影像解译、SPOT/ Vegetation和AVHRR数据的物候信息、现有土地利用或植被地图以及专家知识	68.60	结合已有相关数据进行对比验证精度及修正;采用置信度-统计抽样法
CCI-LC^④	300	LCCS 22类	1998- 2002	MERIS和SPOT Vegetation	将全球分为22个气候区进行分类;首先基于2003-2012年MERIS FR和RR数据,利用添加机器学习法改进的Glob Cover 非监督分类链和结合监督分类技术,生成为期10年的2003-2012年全球土地覆被图,然后基于SPOT VGT时间序列的回溯和更新技术得出2010、2005和2000年地图	基于空间分辨率300 m的MERIS FR影像以及MERIS RR数据（用于补偿某些地区缺乏MERIS FR）;逐日的空间分辨率为1000 m的SPOT VEGETATION数据,并将1998-2002年用于扩大时间覆盖范围	74.10	验证数据（欧洲外）由18位专家参与收集并进行精度验证,欧洲部分采用Glob Cover 2009的验证数据进行验证;采用抽样标记法验证
Glob Cover^⑤	300	LCCS 22类	2004-12 2006-06	ENVISA/MIRIS	将全球分为22个气候区进行分类;水体进行掩膜后,监督分类提取湿地和城镇,非监督分类的多维聚类分类其它类型,并自动赋值为LCCS分类系统	2004-12至2006-06的300m空间分辨率的13个光谱波段;其中水体数据由ENVIASAT卫星携带的MERIS传感器自带的水/路边界进行掩膜得到,并结合SRTM得到的水体数据进行改善,城镇则通过单独的监督分类进行提取	73.00	验证数据由16位专家参与收集,建立数据库进行验证;采用统计抽样进行专家判断

表1

表2

图2

表3

图3

表4

图4

表5

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数据集	类型	定义描述	面积/万km²
UMD1992-1993	草地	草地覆盖率>10%	129.04
MCD12Q1 2001	草地	草地覆盖率>10%	209.41
GLC2000	草地	草地覆盖率>15%	16.73
GLC2000	稀疏草本或灌丛	稀疏草地或稀疏灌木覆盖率<15%	89.65
CCI-LC2000	草地	草地覆盖率>15%	107.21
CCI-LC2000	稀疏植被	树/灌丛/草地的覆盖率<15%	0.66
Glob Cover2005	草地	草本覆盖率>15%	7.00
Glob Cover2005	稀疏植被	乔木植被/灌丛/草地<15%	89.59

类型	面积/（万km²）	百分比/%
完全一致（C5）	43.59	19.30
高度一致（C4）	45.98	20.36
基本一致（C3）	35.35	15.66
低度一致（C2）	40.43	17.90
完全不一致（C1）	60.48	26.78

CCI-LC2000/ UMD1992-1993						CCI-LC2000/ MCD12Q1 2001
CCI-LC2000 不一致类型	面积/万km²	百分比/%	UMD1992-1993 不一致类型	面积/万km²	百分比/%	CCI-LC2000 不一致类型	面积/万km²	百分比/%	MCD12Q1 2001 不一致类型	面积/万km²	百分比/%
旱作耕地	8.61	14.68	多树草原	1.11	1.89	旱作耕地	12.15	10.51	耕地	6.34	5.48
灌溉耕地	4.03	6.87	灌丛	7.03	11.99	灌溉耕地	6.71	5.80	其他类型	0.87	0.75
裸地	24.68	42.08	耕地	10.14	17.29	自然植被和耕地镶嵌体	1.36	1.18
其他类型	2.53	4.31	其他类型	0.52	0.89	裸地	85.39	73.85
						其他类型	2.81	2.43
CCI-LC2000/ GLC2000						CCI-LC2000/ Glob Cover 2005
CCI-LC2000 不一致类型	面积/万km²	百分比/%	GLC2000 不一致类型	面积/万km²	百分比/%	CCI-LC2000 不一致类型	面积/万km²	百分比/%	GlobCover2005 不一致类型	面积/万km²	百分比/%
旱作耕地	4.71	8.54	耕地	10.05	18.22	旱作耕地	4.44	5.26	旱作耕地	5.37	6.36
灌溉耕地	4.39	7.96	耕地、草地和灌丛镶嵌体	3.46	6.27	灌溉耕地	2.00	2.37	耕地和自然植被镶嵌体	30.05	35.61
裸地	15.17	27.50	裸地	14.43	26.16	裸地	20.13	23.87	裸地	19.29	22.86
其他类型	2.32	4.20	其他类型	0.64	1.15	其他类型	1.30	1.54	其他类型	1.80	2.13

全球土地覆被数据集中哈萨克斯坦草地分布的异同及其成因

The Variations and Causes of Grassland Distribution in Kazakhstan from the Global Land Cover Datasets

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