基于多期数据集的中亚五国土地利用/覆盖变化分析

针对目前中亚地区土地利用变化和分布格局方面的信息相对匮乏,现有资料较为陈旧且零散,无法满足中亚生态与环境变化研究需求的现状,利用全球的UMD, DISCover,GLC2000,GlobCover2005和GlobCover2009的5期土地覆被遥感数据集,提取中亚地区长时间序列土地覆被信息。并针对上述4个土地覆被分类系统无法进行直接对比和变化分析的问题,分别将其综合为4类土地覆被类型：耕地、自然植被、水体和其他,以分析近30 a中亚土地利用/土地覆被变化趋势。中亚土地利用类型多样,草地、裸地、农田、灌丛占绝对优势。自前苏联解体以后,20世纪90年代初至2000年期间,耕地面积大幅度减少,至2010年尽管有所恢复,但仍无法达到20世纪90年代初水平。而自然植被表现出了相反的趋势,这说明在此时间段内,由于社会政治制度的变化和市场经济的建立,耕地发生了较大规模的弃耕,弃耕地通常转换为草地、灌丛等自然植被。近10 a由于社会经济条件的变化,前苏联解体后所弃耕的土地又被收复和重新开发为耕地。90年代初至2000年期间,水体呈现先减少后增加的趋势。利用全球基于多期不同信息源获得的中亚土地覆被数据,尽管分类体系不统一,但均可较好地表征当时地表覆被状况。这在一定程度上弥补了中亚地区土地覆被数据不足的现状。通过对耕地、自然植被、水体及其他土地覆被类型进行大类合并,可基本体现中亚土地覆被的宏观特征和变化趋势。     

七套土地覆被数据在羌塘高原的精度评价

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 at30 m resolution(Globe Land 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Globe Land 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。     

多源遥感土地覆被产品在欧洲地区的一致性分析

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

基于随机森林与特征选择的藏东南土地覆被分类方法及精度评价

由于云污染、实地验证点的匮乏,以及地形地貌的复杂、破碎化,多云山区土地覆被的准确分类较难实现。以藏东南这一典型的多云山区及生态过渡区为研究区,基于Google Earth Engine(GEE)平台和野外实测数据,结合多光谱数据、雷达数据、高程数据、辅助数据,提取光谱特征、纹理特征、地形特征等信息,利用递归特征消除法对特征进行优化,并采用随机森林算法构建分类模型,以期有效利用多源遥感数据提高土地覆被分类精度。结果表明：(1)并非特征越多分类精度越高,特征选择后数量由58个减至38个,分类精度（总体精度93.96%,Kappa系数0.92）较未优化前（总体精度93.11%,Kappa系数0.92）略有提升。(2)地形特征及雷达特征对藏东南土地覆被分类具有重要作用,地形特征对多数土地覆被类型的分类精度具有影响,而雷达数据对裸地、建设用地、灌丛影响较大,分类过程中如不考虑地形及雷达特征,总体精度分别降至88.98%,92.48%。纹理特征以及时序特征仅对提高具有明显纹理以及时序变化的土地覆被类型的精度有帮助。结合随机森林和特征优化算法,能够在保证土地覆被分类精度的同时,高效整合多源数据信息,...     

全球土地覆盖GlobCover2009数据中的中国区域湿地数据精度评价

目前,湿地方面的全球性数据产品寥寥无几。GlobCover2009作为免费的分辨率最高的全球土地覆盖数据产品,成为研究全球性湿地的重要参考数据源之一。目前为止,还没有学者对该数据产品中湿地类型的精度做出具体评价。以目视解译的中国2008年湿地遥感制图数据为参考,从湿地面积、类型和空间一致性在不同区域的分布等方面,对GlobCover2009数据产品在中国区域内湿地相关类型产品的分类精度进行了评价。评价结果显示,GlobCover2009数据产品中,中国的湿地类型总体分类精度不高,湿地面积一致性为46%,总体精度为32%,Kappa系数为0.13;其中,沼泽湿地的制图精度(0.05%)远低于水体制图精度(53.34%),水体的用户精度达到90.18%,沼泽湿地的用户精度仅为11.76%。对于沼泽湿地分布较广泛的中国东北地区和西北地区,该数据产品的分类精度也很低,分别仅为17%和13%;造成此现象的原因,除GlobCover2009数据产品没有专门对沼泽湿地进行定义和分类外,二者使用的数据源在空间分辨率及时间上的差异也是主要原因。     

基于阈值分割的黑龙江省森林类型遥感识别

全球变化背景下,准确获取森林覆盖是监测森林资源动态、实现林业可持续发展的重要基础。为将省级尺度森林资源清查面积资料空间化,以黑龙江省为例,利用1999-2003年该省森林资源清查面积数据,结合2000年500 m分辨率的MODIS数据,构建了基于阈值分割的森林类型遥感识别方法。该方法利用不同地表覆被类型归一化植被指数时间序列的季节分异特征,以森林资源清查面积为标准,设定森林类型的划分阈值,识别了黑龙江省森林类型的空间分布。最后,基于分层随机抽样和精度评价方法,表明森林类型识别结果与地面参考数据具有较高的一致性,总体分类精度为78.1%;特别是季节特征明显的落叶林,精度可达80%以上。本文所构建的方法可将森林清查统计数据进行准确的空间定位,同时结合多期森林资源连续清查资料和遥感信息,可为识别并量化区域生态系统生物量和碳库变化等提供科技支撑。     

基于谐波分析和线性混合模型的河北平原区土地覆被分类研究

土地覆被分类是研究土地利用/覆被变化的基础数据和关键环节。以16d合成MODIS-EVI时间序列数据为主要数据源,采用谐波分析方法分析不同土地覆被类型的季节性变化规律和物候特征差异,引入谐波特征值构建线性混合模型,提取不同端元的丰度值。从土地覆被类型较齐全、谐波特征具有代表性的石家庄地区高空间分辨率影像上选择训练样本,确定MODIS纯净像元和混合像元的划分阈值,对河北平原区进行土地覆被分类制图。结果表明,与河北省县级土地调查统计数据对比,一年两熟耕地、一年一熟耕地、园地及有林地、自然陆地表面的总量精度分别为90.19%、86.17%、85.96%和77.82%,平均总量精度为85.03%;与石家庄地区9个县(市)一年两熟耕地和一年一熟耕地基于TM的分类结果对比,平均面积相对误差分别为10.25%、13.98%。受粗空间分辨率和合成周期、水热条件以及种植模式破碎化限制,混合像元主要集中在河北平原中东部地区,一年两熟耕地、一年一熟耕地、园地及有林地混合面积比重较大。     

喜马拉雅中部柯西河流域土地覆被现状研究（英文）

位于喜马拉雅中部的柯西河流域(Koshi River Basin,简称KRB),是恒河支流也是南亚极为重要的跨境流域。流域内海拔落差巨大、生境复杂、生态系统类型完整、土地覆被类型多样且区域差异明显,是全球气候变化的敏感区之一。本研究基于Landsat TM、野外考察及植被图等多源数据,运用3S技术,编制了高精度的柯西河流域土地覆被数据,分析了流域土地覆被现状特征。研究表明:(1)2010年KRB土地覆被从流域源头至下游由雪被和水体(冰川)、裸地、稀疏植被、草地、湿地、灌丛、森林、农田、水体(河流和湖泊)、建设用地等9类组成。其中,以草地、森林、裸地和农田为主,分别占流域面积的25.83%、21.19%、19.31%和15.09%。而对气候变化敏感的冰川面积仅占5.72%。(2)KRB南、北坡土地覆被类型组成与结构迥异。北坡以草地、裸地和冰川分布为主,南坡以森林、农田和裸地为主;草地在北坡的分布面积远高于南坡,二者比例是6.67:1,而森林面积的97.13%分布在南坡,这些森林大多分布在河谷中部和南部平原地区,且与农田交错分布。(3)与环境相适应,流域主要覆被类型的垂直分布也具有明显的地带性特征。土地覆被由低到高,依次为农田、森林、灌丛和农田混合型、草地、稀疏植被、裸地和水体(冰川)的分布。研究结果为土地利用和覆被变化研究、为高山地区尤其是跨境流域的生态系统保护与管理、土地资源利用和可持续发展提供科学依据。     

基于MODIS影像的土地覆被分类研究——以京津冀地区为例

在全球变化研究中,如何快速、准确获取土地覆被信息对该项研究有着至关重要的作用.随着遥感科学的不断发展和应用领域的深入,研究者可以利用遥感影像进行土地覆被分类研究,并且具有准确、快速、自动化等优点.本文利用MODIS数据具有的多光谱、多时相特点,以京津冀地区为例,选取2013 年全年16-day 的MOD13Q1/EVI时间序列数据、2013 年5 月份一期的MOD09Q1(1、2 波段)和MOD09A1(3-7 波段)产品,并运用时间序列谐波分析法对全年MOD13Q1/EVI 时间序列数据进行去云、去噪的平滑重建处理,使其数据更能反映物候周期性变化规律.选择谐波分析后的全年MOD13Q1/EVI 时间序列数据、MODIS数据的1-7 波段地表反射率和NDWI(归一化差异水体指数)、MNDWI(改进归一化差异水体指数)和NDSI(土壤亮度指数),构建了3 种特征变量组合方案的CART决策树,分别进行京津冀地区的土地覆被分类研究.结果表明:方案一(全年EVI 的23 个时相)、方案二(方案一+MOD09 的1-7 波段地表反射率)和方案三(方案二+MNDWI+NDSI+NDWI)的总体分类精度分别达到86.70%、89.98%、91.34%,Kappa系数分别为84.94%、88.66%、90.20%.研究表明,仅利用MODIS遥感影像自身多种分类特征和决策树方法对宏观土地覆被分类就可达到较高精度,显示了本文分类方法在实践中的可行性及MODIS数据在区域尺度土地覆被分类研究方面的优势与潜力.     

基于决策树和MODIS植被指数时间序列的中亚土地覆盖分类

利用MODIS植被指数时间序列对中亚土地覆盖类型分类进行了研究。MODIS数据时间分辨率高,时间序列数据可以表征植被生理活动的动态变化。从时间序列数据中提取植被物候信息,可以实现对不同土地覆盖类型的定量描述。MODIS数据质量信息波段,记录了研究区遥感数据质量,提供植被指数可用性、气溶胶处理、云、冰雪可能性、合成方法等信息,为植被指数时间序列噪声的去除提供一种新的方法。决策树分类结构清晰、不基于正态统计分布假设、效率高、分类精度高。分类结果与统计数据比较,两者一致性较好,精度验证总体精度95.76％,kappa系数0.9516。     

七套土地覆被数据在羌塘高原的精度评价（英文）

We analyzed the spatial local accuracy of land cover(LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System(IGBPDIS), Global Land cover mapping at 30 m resolution(GlobeLand30), MODIS Land Cover Type product(MCD12 Q1), Climate Change Initiative Land Cover(CCI-LC), Global Land Cover 2000(GLC2000), University of Maryland(UMD), and GlobCover 2009(GlobCover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression(GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12 Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy(OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy(HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.     

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

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

     

Large-scale land cover mapping with the integration of multi-source information based on the Dempster–Shafer theory

Land cover type is a crucial parameter that is required for various land surface models that simulate water and carbon cycles, ecosystem dynamics, and climate change. Many land use/land cover maps used in recent years have been derived from field investigations and remote-sensing observations. However, no land cover map that is derived from a single source (such as satellite observation) properly meets the needs of land surface simulation in China. This article presents a decision-fuse method to produce a higher-accuracy land cover map by combining multi-source local data based on the Dempster–Shafer (D–S) evidence theory. A practical evidence generation scheme was used to integrate multi-source land cover classification information. The basic probability values of the input data were obtained from literature reviews and expert knowledge. A Multi-source Integrated Chinese Land Cover (MICLCover) map was generated by combining multi-source land cover/land use classification maps including a 1:1,000,000 vegetation map, a 1:100,000 land use map for the year 2000, a 1:1,000,000 swamp-wetland map, a glacier map, and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001). The merit of this new map is that it uses a common classification system (the International Geosphere-Biosphere Programme (IGBP) land cover classification system), and it has a unified 1 km resolution. The accuracy of the new map was validated by a hybrid procedure. The validation results show great improvement in accuracy for the MICLCover map. The local-scale visual comparison validations for three regions show that the MICLCover map provides more spatial details on land cover at the local scale compared with other popular land cover products. The improvement in accuracy is true for all classes but particularly for cropland, urban, glacier, wetland, and water body classes. Validation by comparison with the China Forestry Scientific Data Center (CFSDC)–Forest Inventory Data (FID) data shows that overall forest accuracies in five provinces increased to between 42.19% and 88.65% for our MICLCover map, while those of the MODIS2001 map increased between 27.77% and 77.89%. The validation all over China shows that the overall accuracy of the MICLCover map is 71%, which is higher than the accuracies of other land cover maps. This map therefore can be used as an important input for land surface models of China. It has the potential to improve the modeling accuracy of land surface processes as well as to support other aspects of scientific land surface investigations in China.     

基于MODIS数据的北京西北部地区土地覆盖分类研究

本文主要基于MODIS 16天合成的NDVI时间序列数据、8天合成 LST数据、1∶5万DEM数据以及其他辅助数据相结合,进行北京西北部地区土地覆盖分类的研究。首先选取适合于MODIS数据分类的土地覆盖分类系统,然后用PCA方法对NDVI时间序列数据进行信息增强与压缩处理,以排除各种干扰因素,提高分类精度。最后结合LST数据、DEM数据及降雨温度数据,利用?齂-均值非监督分类法,进行研究区的土地覆盖分类,经过分类后处理,得到北京西北部地区的土地覆盖分类图。分类结果表明,使用250m分辨率MODIS数据,结合本文所用方法,能够实现较大区域的土地覆盖分类,并且能达到较高的分类精度。     

Land use/land cover (LULC) classification with MODIS time series data and validation in the Amur River Basin

There is a need for improved and up-to-date land use/land cover (LULC) data sets over an intensively changing area in the Amur River Basin (ARB) in support of science and policy applications focused on understanding of the role and response of the LULC to environmental change issues. The main goal of this study was to map LULC in the ARB using MODIS 250-m Normalized Difference Vegetation Index (NDVI), Land Surface Vegetation Index (LSWI), and reflectance time series data for 2001 and 2007. Another goal was to test the consistency of the classification results using relatively coarse resolution MODIS imagery data in order to develop a methodology for rapid production of an up-to-date LULC data set. The results on MODIS land cover were evaluated using existing land use/cover data as derived from Landsat TM data. It was found that the MODIS 250-m NDVI data sets featured sufficient spatial, spectral and temporal resolution to detect unique multi-temporal signatures for the region’s major land cover types. It turned out that MODIS 250 NDVI time series data have high potential for large-basin land use/land cover monitoring and information updating for purposes of environmental basin research and management.     

首套全球30 m分辨率土地覆被产品区域尺度精度评价——以河南省为例

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

Spatial-temporal variability of snow cover over the Amur River Basin inferred from MODIS daily snow products in recent decades

MODIS snow products MOD10A1\MYD10A1 provided us a unique chance to investigate snow cover as well as its spatial-temporal variability in response to global changes from regional and global perspectives. By means of MODIS snow products MOD10A1\MYD10A1 derived from an extensive area of the Amur River Basin, mainly located in the Northeast part of China, some part in far east area of the former USSR and a minor part in Republic of Mongolia, the reproduced snow datasets after removal of cloud effects covering the whole watershed of the Amur River Basin were generated by using 6 different cloud-effect-removing algorithms. The accuracy of the reproduced snow products was evaluated with the time series of snow depth data observed from 2002 to 2010 within the Chinese part of the basin, and the results suggested that the accuracies for the reproduced monthly mean snow depth datasets derived from 6 different cloud-effect-removing algorithms varied from 82% to 96%, the snow classification accuracies (the harmonic mean of Recall and Precision) was higher than 80%, close to the accuracy of the original snow product under clear sky conditions when snow cover was stably accumulated. By using the reproduced snow product dataset with the best validated cloud-effect-removing algorithm newly proposed, spatial-temporal variability of snow coverage fraction (SCF), the date when snow cover started to accumulate (SCS) as well as the date when being melted off (SCM) in the Amur River Basin from 2002 to 2016 were investigated. The results indicated that the SCF characterized the significant spatial heterogeneity tended to be higher towards East and North but lower toward West and South over the Amur River Basin. The inter-annual variations of SCF showed an insignificant increase in general with slight fluctuations in majority part of the basin. Both SCS and SCM tended to be slightly linear varied and the inter-annual differences were obvious. In addition, a clear decreasing trend in snow cover is observed in the region. Trend analysis (at 10% significance level) showed that 71% of areas between 2,000 and 2,380 m a.s.l. experienced a reduction in duration and coverage of annual snow cover. Moreover, a severe snow cover reduction during recent years with sharp fluctuations was investigated. Overall spatial-temporal variability of Both SCS and SCM tended to coincide with that of SCF over the basin in general.