Creation of New Global Land Cover Map with Map Integration

We present here a new approach to the development of a global land cover map. We combined three existing global land cover maps (MOD12, GLC2000, and UMD) based on the principle that the majority view prevails and validated the resulting map by using information collected as part of the Degree Confluence Project (DCP). We used field survey information gathered by DCP volunteers from 4211 worldwide locations to validate the new land cover map, as well as the three existing land cover maps that were combined to create it. Agreement between the DCP-derived information and the land cover maps was 61.3% for our new land cover map, 60.3% for MOD12, 58.9% for GLC2000, and 55.2% for UMD. Although some of the improvements we achieved were not statistically significant, this project has shown that an improved land cover map can be developed and well-validated globally using our method.

Integrated Use of Existing Global Land Cover Datasets for Producing a New Global Land Cover Dataset with a Higher Accuracy: A Case Study in Eurasia

It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.

Development of Global Cropland Agreement Level Analysis by Integrating Pixel Similarity of Recent Global Land Cover Datasets

Global cropland monitoring is important when considering tactical strategies for achieving food sustainability. Different global land cover (GLC) datasets providing cropland information have already been published and they are used in many applications. The different data input methods, classification techniques, class definitions and production years among the different GLC datasets make them all independently useful sources of information. This study attempted to produce a cropland agreement level (CAL) analysis based on the integration of several cropland datasets to more accurately estimate cropland area distribution. Estimating cropland area and how it has changed on a national level was done by converting the level of cropland agreement into percentages with an existing cropland fraction map. A pre-analysis showed that the four GLC datasets used in the 2005 and 2010 groups had similar year input data acquisitions. Therefore, we placed these four datasets (GlobCover, MODIS LC, GLCNMO and ESACCI LC) into 2005 and 2010 year-groups and selected them to process dataset integration through a CRISP approach. The results of this process proposed four agreement levels for this CAL analysis, and the model correlation was converted into percentage values. The cropland estimate results from the CAL analysis were observed along with FAO data statistics and showed the highest accuracy, with a 0.70 and 0.71 regression value for 2005 and 2010 respectively. In the cropland area change analysis, this CAL change analysis had the highest level of accuracy when describing the total size of cropland area change from 2005 and 2010 when compared to other individual original GLC datasets.

A global land cover map produced through integrating multi-source datasets

In the past decades,global land cover datasets have been produced but also been criticized for their low accuracies,which have been affecting the applications of these datasets.Producing a new global dataset requires a tremendous amount of efforts;however,it is also possible to improve the accuracy of global land cover mapping by fusing the existing datasets.A decision-fuse method was developed based on fuzzy logic to quantify the consistencies and uncertainties of the existing datasets and then aggregated to provide the most certain estimation.The method was applied to produce a 1-km global land cover map(SYNLCover)by integrating five global land cover datasets and three global datasets of tree cover and croplands.Efforts were carried out to assess the quality:1)inter-comparison of the datasets revealed that the SYNLCover dataset had higher consistency than these input global land cover datasets,suggesting that the data fusion method reduced the disagreement among the input datasets;2)quality assessment using the human-interpreted reference dataset reported the highest accuracy in the fused SYNLCover dataset,which had an overall accuracy of 71.1%,in contrast to the overall accuracy between 48.6%and 68.9%for the other global land cover datasets.

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

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

Accuracies of Global Land Cover Maps Checked against Fluxnet Sites

Global land cover data products are key sources of information in understanding the complex interactions between human activities and global change. They play a critical role in improving performances of ecosystem, hydrological and atmospheric models. Three freely available global land cover products developed in the United States are popularly used by the scientific community. These include two global maps developed separately by the United States Geological Survey （USGS） and the University of Maryland （UMD） with NOAA Advanced Very High Resolution Radiometer （ AVHRR ） data, and one developed by Boston University with the EOS Moderate Resolution Imaging Spectroradiometer （ MODIS） data. They are compared with known land cover types at 250 available Fluxnet sites around the world. The overall accuracies are 37%, 36% and 42%, respectively for the USGS, UMD and Boston global land cover maps, Some future global land cover mapping strategies are suggested.

Identifying and Quantifying Pixel-Level Uncertainty among Major Satellite Derived Global Land Cover Products

Accurate global land cover(GLC), as a key input for scientific communities, is important for a wide variety of applications. In order to understand the current suitability and limitation of GLC products, the discrepancy and pixellevel uncertainty in major GLC products in three epochs are assessed in this study by using an integrated uncertainty index(IUI) that combines the thematic uncertainty and local classification accuracy uncertainty. The results show that the overall spatial agreements(Ao values) between GLC products are lower than 58%, and the total areas of forests are very consistent in major GLC products, but significant differences are found in different forest classes.The misclassification among different forest classes and mosaic types can account for about 20% of the total disagreements. The mean IUI almost reaches 0.5, and high uncertainty mostly occurs in transition zones and heterogeneous areas across the world. Further efforts are needed to make in the land cover classifications in areas with high uncertainty. Designing a classification scheme for climate models, with explicit definitions of land cover classes in the threshold of common attributes, is urgently needed. Information of the pixel-level uncertainty in major GLC products not only give important implications for the specific application, but also provide a quite important basis for land cover fusion.

Integrating global land cover datasets for deriving user-specific maps

Global scale land cover(LC)mapping has interested many researchers over the last two decades as it is an input data source for various applications.Current global land cover(GLC)maps often do not meet the accuracy and thematic requirements of specific users.This study aimed to create an improved GLC map by integrating available GLC maps and reference datasets.We also address the thematic requirements of multiple users by demonstrating a concept of producing GLC maps with user-specific legends.We used a regression kriging method to integrate Globcover-2009,LC-CCI-2010,MODIS-2010 and Globeland30 maps and several publicly available GLC reference datasets.Overall correspondence of the integrated GLC map with reference LC was 80%based on 10-fold crossvalidation using 24,681 sample sites.This is globally 10%and regionally 6–13%higher than the input map correspondences.Based on LC class presence probability maps,expected LC proportion maps at coarser resolution were created and used for characterizing mosaic classes for land system modelling and biodiversity assessments.Since more reference datasets are becoming freely accessible,GLC mapping can be further improved by using the pool of all available reference datasets.LC proportion information allow tuning LC products to specific user needs.

Comparison of precipitation and evapotranspiration of five different land-cover types in the high mountainous region

Many rivers originate in high mountainous regions. However, the effects of climate warming on the runoff and water balance in these regions remain unclear due to the lack of observational data from harsh environments, and the variable influences of climate change on alpine land-cover types with different water balances. Using observations and simulations from Coup Model, water-balance values collected at five alpine land-cover types(steppe, shrub meadow, moist meadow, swamp meadow, and moraine) in a small alpine watershed, the Qilian Mountains in Northwest China, from October 2008 to September 2014, were compared. Measured evapotranspiration, multilayer soil temperatures and water contents, and frozen-depth data were used to validate Coup Model outputs. The results show that elevation is the primary influence on precipitation, evapotranspiration, and runoff coefficients in alpine regions. Land-cover types at higher elevations receive more precipitation and have a larger runoff coefficient. Notably, climate warming not only increases evapotranspiration but also particularly increases the evapotranspiration/precipitation ratio due to an upward shift in the optimum elevation of plant species. These factors lead to decrease runoff coefficients in alpine basins.

Impact of land use/cover changes on carbon storage in a river valley in arid areas of Northwest China

Soil carbon pools could become a CO_2 source or sink, depending on the directions of land use/cover changes. A slight change of soil carbon will inevitably affect the atmospheric CO_2 concentration and consequently the climate. Based on the data from 127 soil sample sites, 48 vegetation survey plots, and Landsat TM images, we analyzed the land use/cover changes, estimated soil organic carbon（SOC） storage and vegetation carbon storage of grassland, and discussed the impact of grassland changes on carbon storage during 2000 to 2013 in the Ili River Valley of Northwest China. The results indicate that the areal extents of forestland, shrubland, moderate-coverage grassland（MCG）, and the waterbody（including glaciers） decreased while the areal extents of high-coverage grassland（HCG）,low-coverage grassland（LCG）, residential and industrial land, and cultivated land increased. The grassland SOC density in 0–100 cm depth varied with the coverage in a descending order of HCG〉MCG〉LCG.The regional grassland SOC storage in the depth of 0–100 cm in 2013 increased by 0.25×1011 kg compared with that in 2000. The regional vegetation carbon storage（S_（rvc）） of grassland was 5.27×10~9 kg in2013 and decreased by 15.7% compared to that in 2000. The vegetation carbon reserves of the under-ground parts of vegetation（S_（ruvb）） in 2013 was 0.68×10~9 kg and increased by approximately 19.01%compared to that in 2000. This research can improve our understanding about the impact of land use/cover changes on the carbon storage in arid areas of Northwest China.

LAND USE AND LAND COVER CHANGE IN CHINA

This paper deals with land use and land cover change in China in the last 500 years. It aims at sensitizing related fields in giving due consideration on integration of environmental change and social economic development process and at proposing a framework for discussion towards further study. Based on historical review,several important changes were summarized as: natural vegetation were replaced by secondary or cultivated Plants, land cover changes differ from time to time and from place to place:and cropping pattern change is a sensitive indicator of land cover change in China. In the history of Cnina, many factors contributed to land use and land cover change either in positive or negative Ways. Four major driving forces were concluded, namely, climatic change, social transformation and turbulence, technology improvement, and international trade competition. In the last Part of tne Paper, a proposal on 'land use/land cover cnange' is framed, so as to calling comments on improving tne study and collaborations from varied authorities and researcn institutions.

基于GlobeLand30的全球城乡建设用地空间分布与变化统计分析

城乡建设用地分布与变化是人类活动的直观标志和生态足迹,在环境变化研究、地理国(世)情监测和可持续发展研究等方面发挥着重要作用。以往人们对一些城市、区域或国家的城乡建设用地分布与变化进行过较为深入系统的研究,但在全球尺度上,这方面研究尚为空白。本文是利用我国自主研制的世界上首套30m空间分辨率全球地表覆盖数据集GlobeLand30的人造地表数据层,首次开展了全球城乡建设用地的空间分布及变化的统计分析。它采用用地面积、构成占比和增量占比等主要指标,统计全球范围内城乡建设用地的空间分布及2000年至2010年10年间的变化,重点分析了2010年全球、各大洲及主要国家的城乡建设用地分布现状与地域差异,2000年至2010年全球、主要国家的建设用地变化以及其主要土地来源。研究结果表明,2010年全球城乡建设用地总面积为118.75×104km2,占全球陆表面积的0.88%;2000年至2010年全球城乡建设用地面积增加了5.74×104 km2,变化率为5.08%,其中,中国和美国新增城乡建设用地约占全球的一半;新增城乡建设用地占用最多的是耕地,占总量的50.26%。这些为研究全球陆表人类活动的空间分布特征与变化趋势提供了翔实的信息和知识。

基于地质大数据技术对云南土壤重金属地质高背景区的划定

为系统研究云南省土壤重金属地质高背景区的分布范围及超标元素,以全省1∶20万水系沉积物重金属元素含量数据和区域地质图为基础,采用GIS空间分析功能,并利用昆明、玉溪、昭通等地区的土壤重金属数据进行验证,确定云南省土壤重金属含量值超农用地筛选值的地质单元61个,占全省国土面积的21.09%,其中位于地质高背景区的耕地面积约284.41万公顷,占全省国土面积的7.22%;影响土壤重金属超标的岩性主要为碳酸盐岩、基性—超基性火山岩、中基性侵入岩、含煤碎屑岩和含基性组分碎屑岩;地质高背景区超标重金属元素主要为Cu、Cr、Ni、Cd,而As主要于碳酸盐岩地层中存在超标风险,Pb、Zn仅于个别地层中存在超标风险,Hg基本无超标风险。

GlobeLand30湿地细化分类研究

基于30 m地表覆盖数据产品完成湿地精细化分类,能够更好地满足当前较高分辨率及较详尽全球湿地数据的应用需求。本文在深入分析湿地分类体系与细化方法的基础上,提出以湿地细化类别的定义、多元知识的分层分类、亚类数据精细化提取为主线的总体研究思路,制定了基于先验知识的对象系统筛选、基于森林数据的同位像元提取、基于最佳阈值的极大似然掩膜的主体分类方法,并应用于数据生产实践获得8个亚类信息。该方法克服了常规手段普遍存在的周期长、效率低等弊端,实现了全球较高分辨率湿地亚类数据的快速精确制图,总体分类精度达82.6%,对地理世情及其他地表覆盖研究具有借鉴意义。

High-resolution land cover classification:cost-effective approach for extraction of reliable training data from existing land cover datasets

There has been a significant increase in the availability of global high-resolution land cover(HRLC)datasets due to growing demand and favorable technological advancements.However,this has brought forth the challenge of collecting reference data with a high level of detail for global extents.While photo-interpretation is considered optimal for collecting quality training data for global HRLC mapping,some producers of existing HRLCs use less trustworthy sources,such as existing land cover at a lower resolution,to reduce costs.This work proposes a methodology to extract the most accurate parts of existing HRLCs in response to the challenge of providing reliable reference data at a low cost.The methodology combines existing HRLCs by intersection,and the output represents a Map Of Land Cover Agreement(MOLCA)that can be utilized for selecting training samples.MOLCA’s effectiveness was demonstrated through HRLC map production in Africa,in which it generated 48,000 samples.The best classification test had an overall accuracy of 78%.This level of accuracy is comparable to or better than the accuracy of existing HRLCs obtained from more expensive sources of training data,such as photo-interpretation,highlighting the cost-effectiveness and reliability potential of the developed methodology in supporting global HRLC production.

Predicting of Land Surface Temperature Distribution in Freetown City, Sierra Leone by Using Polynomial Curve Fitting Model

Global warming has attracted much concern about the worldwide organization, civil society groups, researchers, and so forth because the worldwide surface temperature has been expanding. This investigation intends to assess and compare the ability of a combination of land cover indices to predict the future distribution of land surface temperatures in Freetown using the Polynomial model analysis. Landsat satellite images of 1988, 1998, 2000, 2010, and 2018 of the Freetown Metropolitan zone were utilized for analysis. The investigation had adopted two land covers indices, Modification of normalized difference water index and Urban Index (UI) (e.g., MNDWI and UI) and applied a multi regression equation for forecasting the future LST. The stimulation results propose that the development will be accompanied by surface temperature increases, especially in Freetown’s western urban area. The temperature prevailing in the west of the metropolitan area may increase in the city somewhere in the range from 1988 to 2018. Additionally, the results of the LST prediction show that the model is perfect. Our discoveries can be represented as a helpful device for policymakers and community awareness by giving a scientific basis for sustainable urban planning and management.

Recognition of climatic effects of land use/land cover change under global warming

Greenhouse gas emissions and land use/land cover change(LUCC)are two human activities notably affecting climate change.Will temperature and precipitation increase significantly during global warming resulting in more pronounced LUCC climatic effects?Considering the interannual forcing of these two factors,the NCAR Community Atmosphere Model(CAM4.0)was used in this study to investigate the importance of climatological background to LUCC impacts.Experiments based on the difference in the background climate,the greenhouse gas concentrations in 1850 and in the present age indicate contrary changes in climate sensitivity through estimations of the radiative forcing associated with LUCC,which are 0.54°C/(W/m2)and 0.26°C/(W/m2),respectively.Therefore,the background climate appears to play an important role in the regional impact of LUCC,especially at higher latitudes.In addition,global warming predominantly influences snow-albedo feedback in the mid-latitudes,thus determining the impact of LUCC,whereas the regional difference in precipitation caused by global warming is responsible for the differing climate response to LUCC in the tropics and subtropics.

四类全球土地覆盖数据在中国区域的精度评价

该研究以中国耕地类别为研究对象,选择2000年中国土地利用数据(NLCD-2000)为参考数据,利用比较分析法,从面积数量精度和空间位置精度两方面对目前4类全球土地覆盖数据(UMD、IGBP-DISCover、MODIS和GLC2000)产品进行了精度验证,并分析研究了4类数据精度的异同性。结果表明,4类全球数据对中国耕地数量特征和空间位置特征的估测具有明显的区域差异性。MODIS数据集和GLC2000数据集对中国耕地制图的总体精度要高于UMD数据集和IGBP-DISCover数据集。4类数据制图精度高的区域主要分布在中国的农业主产区,而误差大的区域主要分布在中国山区或耕地比例低的区域。低空间分辨率的信息源、基于像元的分类方法,以及中国复杂地形特征是4类全球土地覆盖数据精度差异的主要原因。

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。

中国土地利用/覆被变化研究的现状与问题

土地利用 /覆被变化是全球变化的重要组成部分和原因 ,因而是全球变化领域中重要的研究方向之一。本文在评述了中国土地利用 /覆被变化研究的现状、取得的进展及存在问题的基础上 ,提出应把土地利用 /覆被变化研究的理论体系、不同空间尺度的土地利用 /覆被变化的动态模型、土地利用 /覆被变化与全球环境变化和可持续发展之间的关系作为重点加以研究 ,以提高中国土地利用 /覆被变化的研究水平。