Disaggregating population density of the European Union with CORINE land cover

This article describes and compares six disaggregation methods used to produce a dasymetric population density grid of the European Union at a 100 m resolution. Population data were initially available at commune level. The main ancillary information source was CORINE land cover, a land cover map distributed by the European Environment Agency. Information from the Eurostat point survey, land use/cover area frame survey, was also integrated in the parameter estimation of some of the approaches tested. Accurate population data for 1 km cell grids were provided by the Statistical Offices of Austria, Denmark, Finland, the Netherlands, Northern Ireland, Estonia and Sweden. These data provided the basic reference to quantify the accuracy of each method. The best results were obtained with a modified version of the limiting variable method (Eicher, C. and Brewer, C., 2001. Dasymetric mapping and areal interpolation: implementation and evaluation. Cartography and Geographic Information Science, 28, 125–138) that could be implemented, thanks to the national reference grids. For other methods the parameters could be estimated without using the reference grids; among them a method based on logit regression gave the best results. Compared with the traditional choropleth maps that represent a homogeneous density in each commune, the accuracy improvement of the disaggregated maps ranged between 20% and 67% (between 46% and 67% for the best method).     

Assessment of land-use and land-cover changes from 1965 to 2014 in Tam Giang-Cau Hai Lagoon,central Vietnam

This study integrates the use of multi-source and multi-resolution remote sensing, topographic and field-based datasets to quantify land-use and land-cover (LULC) changes along a coastal stretch of Thua Thien Hue Province (central Vietnam). The LULC change analysis involves the Tam Giang-Cau Hai lagoon, the largest lagoon system in Southeast Asia, which is running nearly 70 km along the coast and having about 22,000 ha of water surface.The LULC change analysis was performed by computer-aided visual interpretation for 5 years (1965, 1989, 2000, 2006 and 2014) using satellite imagery from LANDSAT MSS, TM, ETM+ and 8, ASTER and SPOT5. National topographic maps were also used for the 1965 and 2000 years.To adequately represent the LULC features and peculiarities of central Vietnam coastal areas, an adapted CORINE Land Cover nomenclature was used where new 3rd and 4th level classes were adopted. Due to their intrinsic relative high spatial and radiometric resolution, SPOT5 images from 2006 were assumed as a reference for interpretation keys and first delineation. Changes were mapped by editing those vectors representing features which underwent LULC change prior or after 2006. Spatial and temporal changes were analyzed by post-classification approach and validated by ground truth information. High detail object-based classification was finally performed to infer the capability of medium spatial resolution imagery for extracting cadastral scale pond maps. The accuracy of classification was checked by a polygon by polygon comparison with an existing aquaculture facility inventory.Five LULC maps were obtained by applying a legend of 21 classes including two newly defined: “Aquaculture ponds” and “Mangrove forest”. The overall classification accuracy of the LULC map is 85% while the KHAT statistics 0.81 for the year 2006. Accuracy of the object-based aquaculture facilities classification is 84% or better for the SPOT5 imagery and 47.9% for the ASTER imagery. The study provides a synoptic LULC representation for the largest lagoon system of Southeast Asia and delivers quantitative estimates of main changes occurred during the last 50 years. Moreover, it reveals the adaptability of the CORINE Land Cover method outside European environment. Finally, SPOT5 provides good results to map aquaculture features at cadastral scale, even if in some circumstances (e.g. tidal areas), the integration with higher spatial resolution multispectral sensors should be envisaged.     

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。     

A CORINE map of Great Britain by automated means. Techniques for automatic generalization of the Land Cover Map of Great Britain.

Abstract

The European Commission (EC) programme ‘Co–ordination of Information on the Environment’ (CORINE) includes a project to map the land cover of member states. The CORINE map is essentially one which combines land cover and land use, giving 44 separate classes, in vector, displayed at a scale of 1:100000 with a minimum mappable unit of 25 ha. The Institute of Terrestrial Ecology (ITE) has compiled a digital land cover map of Great Britain (LCMGB) from classification of Landsat–TM data, resampled to a 25 m raster, with a minimum mappable unit of 0.125 ha and 25 cover types. This paper describes a pilot study which demonstrates the successful spatial generalisation with contextual interpretation to convert the LCMGB to CORINE specifications using semi–automated techniques within a GIS environment.     

Generating Surface Models of Population Using Dasymetric Mapping*

Aggregated demographic datasets are associated with analytical and cartographic problems due to the arbitrary nature of areal unit partitioning. This article describes a methodology for generating a surface‐based representation of population that mitigates these problems. This methodology uses dasymetric mapping and incorporates areal weighting and empirical sampling techniques to assess the relationship between categorical ancillary data and population distribution. As a demonstration, a 100‐meter‐resolution population surface is generated from U.S. Census block group data for the southeast Pennsylvania region. Remote‐sensing‐derived urban land‐cover data serve as ancillary data in the dasymetric mapping.     

塔里木河干流区土地利用/覆被变化及其生态环境效应

在GIS和遥感技术支持下,利用1973年MSS遥感影像、1983年航空遥感土地利用图、1990年TM、2000年TM和2005年CBERS遥感影像对新疆塔里木河干流区土地利用/覆被变化过程和生态环境效应进行了分析。结果表明,1973~2005年塔里木河干流区土地利用/覆被发生了显著变化,耕地、未利用地和城乡居民及建设用地面积分别增加了25.72×104hm2、17.33×104hm2和0.51×104hm2,林地、草地和湿地面积分别减少了29.10×104hm2、8.63×104hm2和5.84×104hm2。在1973~1983年、1983~1990年、1990~2000年、2000~2005年4个时段中土地利用变化呈"显著变化—缓慢变化—显著变化—急剧变化"的过程。土地利用类型转移的主要方向是林地和草地转化为耕地、耕地转化为城乡居民及建设用地、草地转化为未利用地。土地利用/覆被的变化引起了干流区水环境恶化、土壤质量改变、天然植被严重退化、土地荒漠化和盐渍化加剧、湿地面积减少和生物多样性丧失等一系列生态环境问题。     

High resolution dasymetric model of U.S demographics with application to spatial distribution of racial diversity

Population and demographic data at high spatial resolution is a valuable resource for supporting planning and management decisions as well as an important input to socio-economic academic studies. Dasymetric modeling has been a standard technique to disaggregate census-aggregated units into raster-based data of higher spatial resolution. Although utility of dasymetric mapping has been demonstrated on local and regional scales, few high resolution large-scale models exist due to their high computational cost. In particular, no publicly available high resolution dasymetric model of population distribution over the entire United States is presently available. In this paper we introduce a 3″ (∼90 m) resolution dasymetric model of demographics over the entire conterminous United States. The major innovation is to disaggregate already existing 30″ (∼1 km) and 7.5″ (∼250 m) SEDAC (Socioeconomic Data and Applications Center) Census 2000 grids instead of the original census block-level data. National Land Cover Dataset (NLCD) 2001 is used as ancillary information. This allows for rapid development of a U.S.-wide model for distribution of population and sixteen other demographic variables. The new model is demonstrated to markedly improve spatial accuracy of SEDAC model. To underscore importance of high spatial resolution demographic information other than total population count we demonstrate how maps of several population characteristics can be fused into a “product” map that illustrates complex social issues. Specifically, we introduce a “diversity” categorical map that informs (at nominal 3″ resolution) about spatial distribution of racial diversity, dominant race, and population density simultaneously. Diversity map is compared to a similar map based on census tracts. High resolution raster map allows study of race-diversity phenomenon on smaller scale, and, outside of major metropolitan areas, revels existence of patterns that cannot be deduced from a tract-based map. The new high resolution population and diversity maps can be explored online using our GeoWeb application DataEye available at http://sil.uc.edu/. Both datasets can be also downloaded from the same website.     

Spatial features of land use/land cover change in the United States

With the classification data covering American land-use/land-cover (LUCC) with 30 m resolution from the project of National Land Cover Data (NLCD), we normalized them and made their resolution changed into 1 km ×1 km, created the data of American land-use grade and analyzed the spatial distribution and features of American LUCC as well as the influence of population and altitude on the land-use grade in light of methods of sampling analysis and correlation study. Based on the analysis, we concluded that forestry and grassland, accounting for 71.24% of the whole country, has taken the main part of American land cover, and besides, construction and arable land has occupied 19.22% of the total land, the rest of land cover types, including water area, wetland and underdeveloped land, is 9.54% of the country's total. The developing potential of American land resources is enormous with less destroyed and disturbed ecological environment. Although, in some sense, the population and altitude influence the spatial variation of American land-use grade respectively, the influence of spatial variation of altitude and population density on that of land-use grade is not significanct.     

美国土地利用/土地覆盖变化的空间特征

1IntroductionInrecentyears,studyonglobalchangehasbecomethefocus,onwhichstresshasbeenlaidbytodayhumansociety.Inordertodealwithseriesofenvironmentalproblemssuchasdegradationoflandresources,globalwarming,destructionofozonosphereandsoon,andassurethestabilizationandsustainabledevelopmentofworldeconomy,IGBPhasestablishedthestudyframeforglobalchange.Withavastrangeofstudy,researchonglobalchangehasbroughtforwardagreatdealofimportantscientificquestionsrelatedtofeasibilityforhumanelivingoftheEarth.Inth…     

Comparison of input data with different spatial resolution in landscape pattern analysis – A case study from northern Latvia

A suitable spatial scale needs to be selected in geographical and landscape ecological research, and this requires great consideration as different scales have profound effect on derived landscape spatial patterns. Numerous studies have investigated the effects of different scales on landscape metrics using simulated patterns, but few have been conducted to compare different data sources with variable scale for regional- and landscape-scale assessments. Possibly this has occurred because researchers have been prone to use the best available source, a well-known standard, and easiest to use. This study was conducted to assess the impact of input data resolution on values of landscape pattern metrics in four landscapes at scales 1:10 000, 1:50 000 and 1:100 000. The aim was to determine the applicability of three data sources for thematic models in landscape pattern analyses in the Eastern Baltic region. We found that the utility of CORINE Land Cover data for comprehensive structural assessment in mosaic-type landscapes was very limited, as the level of cartographic generalization excluded many small and linear landscape structure elements with potentially high importance for landscape functioning, such as habitat continuity. We also found that actual area harvested using clearcuts was considerably higher than shown in CORINE data, due to clearcuts size being much smaller than the minimum mapping unit. In the light of this, we suggest using data with spatial resolution corresponding to a cartographic scale of at least 1:50 000, in cases when spatial patches have size up to 25 ha.     

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.     

基于地表覆盖物光谱特征的土地覆被分类系统--以鄱阳湖流域为例

土地覆被主要表现为陆地表面的形态特性和动态变化特征,土地利用是其动态变化的外在驱动力,植被是土地覆被的“主要”而非唯一的组成部分,土地覆被也包含着它的功能特性。地表覆被物的光谱特征及其季节性变化规律是土地覆被的本质特征,也是土地覆被分类的基本依据之一;利用遥感资料所表达的地表覆被物特征值 (如以NDVI为指标的绿度值、始绿期、终绿期、波形、峰值、谷值等) 可以方便地对土地覆被分类标准进行量化处理;对于一些复杂的复合单元,应着重于它的组合 (包括组成成分和结构) 特性及其对地面过程的影响来赋予更确切的内涵。据此,构建了包含7个一级类别和20个二级类别的开放式 (open-end) 的鄱阳湖流域土地覆被分类系统,并且应用SPOT4-VEGETATION 的时序NDVI数据,对各类别的面积比重进行了分析,结果显示鄱阳湖流域的现状覆被主要由常绿覆被和作物-树-草混合覆被以及人工栽培植被 (作物) 等构成。     

海岸带城市土地利用变化分析—以江苏盐城市为例

土地利用/覆被变化（LUCC）是全球变化和区域可持续发展研究的核心问题。海岸带是陆地海洋相互作用和人类活动最敏感的区域,海岸带LUCC的研究对适应环境和自然的挑战具有重要意义。本文以江苏沿海具有代表性的盐城市作为研究区,选取1978-2007年5期Landsat MSS/TM影像作为数据源,在参考国内外遥感分类系统的基础上,结合研究区实际,建立遥感分类系统,使用主成分分析（PCA）遥感分类方法和分类后比较变化检测方法,并在GIS分析技术支持下获取盐城市近30年的土地利用变化数据,分析其结构、数量、速率及格局变化。结果表明：（1）从土地利用结构上看,研究时段内盐城市土地利用变化较大,但利用结构相对稳定;土地利用类型以耕地为主,但近几年城市建设用地大量增加,比重不断攀升;（2）从土地利用变化数量、速率上看,不同时期土地利用类型变化剧烈,一级地类数量增长最大的是建设用地,其中城镇建设用地由1978年的167.01 km2扩张至2007年的725.19 km2,增长至原来的4.34倍,城镇建设用地变化速率在2004-2007年高达15%以上;（3）从空间格局变化看,盐城市中心城区特别是开发区和新辟为市区的盐都区发展速度最快,而滨海港、大丰港的建设和周边国有盐场的改制也显著促进了沿海地区的发展。     

1990—2005年疏勒河流域土地利用/覆盖变化分析

选择疏勒河流域为研究区域,利用1990年TM、2000年ETM和2005年TM影像为信息源,应用地理信息系统和遥感技术,提取三期疏勒河流域土地利用信息,并对土地利用动态进行了分析。结果表明,近15 a来,疏勒河流域土地利用变化缓慢,土地利用格局没有发生显著变化;不同土地利用类型中,耕地面积和建设用地增加较多;林地、草地面积减少;水域在1990—2000年间减少,2000—2005年间增加;未利用地在1990—2000年之间增加,2000—2005年之间减少。     

Satellite image based vegetation classification of a large area using limited ground reference data: a case study in the Usa Basin, north-east European Russia

Predicted global changes can be studied effectively by combining spatially explicit data sets on vegetation and other landscape properties with process models. However, detailed knowledge of the vegetation distribution of remote Arctic areas is relatively scarce. This paper shows how a mesoscale vegetation and land cover classification of a large, remote Arctic area can be conducted at a fine spatial resolution (30 m cell size) using a limited ground reference data set. The study area is the catchment of the River Usa (93 500 km²) in north-eastern European Russia. Vegetation zones in the Usa Basin range from taiga in the south to forest-tundra and tundra in the north, and to alpine in the Ural mountains in the east. Classification was done using a mosaic of spectrally adjusted Landsat TM5 images from five different dates and a semi-supervised method. Ground reference data were collected during the summers of 1998, 1999 and 2000. Accuracy of the 21-class vegetation type/land cover classification produced was tested against test points interpreted from oblique aerial photographs taken from a helicopter (logistic limitations prohibited the collection of representative ground reference data). The main vegetation types (forests, willow dominated stands and meadows, peatlands, tundra heaths, mainly unvegetated areas, and water bodies) were distinguished with relatively high accuracy: 84% of the test points were classified correctly. Spatially detailed land cover data sets like the one described here allow detailed landscape-level analysis and process modelling on many different subjects.     

Broad scale forest cover reconstruction from historical topographic maps

Land cover change is one of the major contributors to global change, but long-term, broad-scale, detailed and spatially explicit assessments of land cover change are largely missing, although the availability of historical maps in digital formats is increasing. The problem often lies in efficiency of analyses of historical maps for large areas. Our goal was to assess different methods to reconstruct land cover and land use from historical maps to identify a time-efficient and reliable method for broad-scale land cover change analysis. We compared two independent forest cover reconstruction methods: first, regular point sampling, and second, wall-to-wall mapping, and tested both methods for the Polish Carpathians (20,000 km²) for the 1860s, 1930s and 1970s. We compared the two methods in terms of their reliability for forest change analysis, relative to sampling error, point location and landscape context including local forest cover, area of the spatial reference unit and forest edge-to-core ratio. Our results showed that the point-based analysis overestimated forest cover in comparison to wall-to-wall mapping by 1–3%, depending on the mapping period. The reasons for the differences were mainly the backdating approach and map generalisation rather than the point grid position or sampling error. When we compared forest cover trajectories over time, we found that the point-based reconstruction captured forest cover dynamics with a comparable accuracy to the wall-to-wall mapping. More broadly, our assessment showed that historical maps can provide valuable data on long-term land cover trends, and that point-based sampling can be an efficient and accurate way to assess forest area and change trends. We suggest that our point-based approach could allow land cover mapping across much of Europe starting in the 1800s. Our findings are important because they suggest that land cover change, a key component of global change, can be assessed over large areas much further back in time than it is commonly done. This would allow to truly understand path dependencies, land use legacies, and historical drivers of land cover change.     

Association of elevation error with surface type,vegetation class and data origin in discrete-returns airborne LiDAR

Airborne LiDAR (light detection and ranging) data are now commonly regarded as the most accurate source of elevation data for medium-scale topographical modelling applications. However, quoted LiDAR elevation error may not necessarily represent the actual errors occurring across all surfaces, potentially impacting the reliability of derived predictions in Geographical Information Systems (GIS). The extent to which LiDAR elevation error varies in association with land cover, vegetation class and LiDAR data source is quantified relative to dual-frequency global positioning system survey data captured in a 400-ha area in Ireland, where four separate classes of LiDAR point data overlap. Quoted elevation errors are found to correspond closely with the minimum requirement recommended by the American Society of Photogrammetry and Remote Sensing for the definition of 95% error in urban areas only. Global elevation errors are found to be up to 5 times the quoted error, and errors within vegetation areas are found to be even larger, with errors in individual vegetation classes reaching up to 15 times the quoted error. Furthermore, a strong skew is noted in vegetated areas within all the LiDAR data sets tested, pushing errors in some cases to more than 25 times the quoted error. The skew observed suggests that an assumption of a normal error distribution is inappropriate in vegetated areas. The physical parameters that were found to affect elevation error most fundamentally were canopy depth, canopy density and granularity. Other factors observed to affect the degree to which actual errors deviate from quoted error included the primary use for which the data were acquired and the processing applied by data suppliers to meet these requirements.     

Land cover changes in the Brazilian Cerrado and Caatinga biomes from 1990 to 2010 based on a systematic remote sensing sampling approach

The main objective of our study was to provide consistent information on land cover changes between the years 1990 and 2010 for the Cerrado and Caatinga Brazilian seasonal biomes. These areas have been overlooked in terms of land cover change assessment if compared with efforts in monitoring the Amazon rain forest. For each of the target years (1990, 2000 and 2010) land cover information was obtained through an object-based classification approach for 243 sample units (10  km × 10  km size), using (E)TM Landsat images systematically located at each full degree confluence of latitude and longitude. The images were automatically pre-processed, segmented and labelled according to the following legend: Tree Cover (TC), Tree Cover Mosaic (TCM), Other Wooded Land (OWL), Other Land Cover (OLC) and Water (W). Our results indicate the Cerrado and Caatinga biomes lost (gross loss) respectively 265,595 km² and 89,656 km² of natural vegetation (TC + OWL) between 1990 and 2010. In the same period, these areas also experienced gain of TC and OWL. By 2010, the percentage of natural vegetation cover remaining in the Cerrado was 47% and in the Caatinga 63%. The annual (net) rate of natural vegetation cover loss in the Cerrado slowed down from −0.79% yr⁻¹ to −0.44% yr⁻¹ from the 1990s to the 2000s, while in the Caatinga for the same periods the rate increased from −0.19% yr⁻¹ to −0.44% yr⁻¹. In summary, these Brazilian biomes experienced both loss and gains of Tree Cover and Other Wooded Land; however a continued net loss of natural vegetation was observed for both biomes between 1990 and 2010. The average annual rate of change in this period was higher in the Cerrado (−0.6% yr⁻¹) than in the Caatinga (−0.3% yr⁻¹).     

Sociopolitical influences on cropland area change in Iraq, 2001–2012

Iraq has experienced significant agricultural land use changes throughout its history, including recent changes initiated by the 2003 US-led military invasion, the end of United Nations' economic sanctions and the onset of overt domestic conflict. Conflict and sociopolitical forces are important drivers of land use/land cover change and often have disproportionate impacts on agricultural systems. However, there has been little research to investigate recent changes in agricultural land use/land cover in Iraq over the recent tumultuous period that has included war and the transition of its political system from a dictatorship to an emerging but beleaguered democracy. Our objectives were to (1) determine if cropland area in Iraq changed significantly between 2001 and 2012, (2) identify regional patterns of cropland area change, and (3) identify sociopolitical forces driving those changes. We used MODIS Land Cover Product yearly data to quantify the amount of land cover dedicated to croplands. We regressed cropland area (ha) on time (2001, 2002, 2003,…) and years with drought influence (yes, no). The results revealed significant changes in cropland area for Iraq as a whole, with cropland area decreasing over 30,000 ha per year. Regionally, there were significant decreases in the Kurdish Autonomous Region and central Iraq, and initial increases in the southern marshlands followed by decreases related to drought.     

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.