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30-m Global Land Cover (GLC) data products permit the detection of land cover changes at the scale of most human land activities, and are therefore used as fundamental information for sustainable development, environmental change studies, and many other societal benefit areas. In the past few years, increasing efforts have been devoted to the accuracy assessment of GlobeLand30 and other finer-resolution GLC data products. However, most of them were conducted either within a limited percentage of map sheets selected from a global scale or in some individual countries (areas), and there are still many areas where the uncertainty of 30-m resolution GLC data products remains to be validated and documented. In order to promote a comprehensive and collaborative validation of 30-m GLC data products, the GEO Global Land Cover Community Activity had organized a project from 2015 to 2017, to examine and explore its major problems, including the lack of international agreed validation guidelines and on-line tools for facilitating collaborative validation activities. With the joint effort of experts and users from 30 GEO member countries or participating organizations, a technical specification for 30-m GLC validation was developed based on the findings and experiences. An on-line validation tool, GLCVal, was developed by integrating land cover validation procedures with the service computing technologies. About 20 countries (regions) have completed the accuracy assessment of GlobeLand30 for their territories with the guidance of the technical specification and the support of GLCVal.

As result of the “Global Land Cover Mapping at Finer Resolution” project led by National Geomatics Center of China (NGCC), one of the first global land cover datasets at 30-meters resolution (GlobeLand30) has been produced for the years 2000 and 2010. The first comprehensive accuracy assessment at a national level of these data (excluding some comparisons in China) has been performed on the Italian area by means of a benchmarking with the more detailed land cover datasets available for some Italian regions. The accuracy evaluation was based on the cell-by-cell comparison between Italian maps and the GlobeLand30 in order to obtain the confusion matrix and its derived statistics (overall accuracy, allocation and quantity disagreements, user and producer accuracy), which help to understand the classification quality. This paper illustrates the adopted methodology and procedures for assessing GlobeLand30 and reports the obtained statistics. The analysis has been performed in eight regions across Italy and shows very good results: the comparison of the datasets according to the first level of Corine Land Cover nomenclature highlights overall accuracy values generally higher than 80%.

Land cover information is vital for research and applications concerning natural resources and environmental modeling. Accuracy assessment is an important dimension in use and production of land cover information. GlobeLand30 is a relatively new global land cover information product with a fine spatial resolution of 30 m and is potentially useful for many applications. This paper describes the methods for and results from the first country-wide and statistically based accuracy assessment of GlobeLand30 2010 land cover dataset over China. For this, a total of 8400 validation sample pixels were collected based on a sampling design featuring two levels of stratification (ten geographical regions, each with nine or eight land-cover classes). Validation sample data with reference class labels were acquired from visual interpretation based on Google Earth high-resolution satellite images. Error matrices for individual regions and entire China were estimated properly based on the sampling design adopted, with the former aggregated to get the latter through suitable weighting. Results were obtained, with agreement at a sample pixel defined both as a match between the map (class) label and either the primary or alternate reference label therein and, more strictly, as a match between the map label and the primary reference label only. Based on the former definition of agreement, the overall accuracy of GlobeLand30 2010 land cover for China was assessed to be 84.2%. User’s accuracy and producer’s accuracy were both greater than 80% for cultivated land, forest, permanent snow and ice, and bareland, with user’s accuracy for water bodies estimated 94.2% (82.1% for wetland, 79.8% for artificial surface) and producer’s accuracy for grassland estimated 89.0%. These indicate that GlobeLand30 2010 depicts land cover circa 2010 in China quite accurately, although estimates of accuracy indicators based on the latter definition of agreement were lower as expected with an estimated national overall accuracy of 81.0%. Regional and class variations in accuracy were revealed and examined in the light of their associations with land cover distributions and patterns. Implications for use and production of GlobeLand30 land cover information were discussed, so were commonality and lack of it between GlobeLand30 and other fine-resolution land cover products.

Land cover plays an important role in the climate and biogeochemistry of the Earth system. It is of great significance to produce and evaluate the global land cover (GLC) data when applying the data to the practice at a specific spatial scale. The objective of this study is to evaluate and validate the consistency of the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) at a provincial scale (Anhui Province, China) based on the Chinese 30 m GLC product (GlobeLand30). A harmonization method is firstly used to reclassify the land cover types between five classification schemes (International Geosphere Biosphere Programme (IGBP) global vegetation classification, University of Maryland (UMD), MODIS-derived Leaf Area Index and Fractional Photosynthetically Active Radiation (LAI/FPAR), MODIS-derived Net Primary Production (NPP), and Plant Functional Type (PFT)) of MCD12Q1 and ten classes of GlobeLand30, based on the knowledge rule (KR) and C4.5 decision tree (DT) classification algorithm. A total of five harmonized land cover types are derived including woodland, grassland, cropland, wetland and artificial surfaces, and four evaluation indicators are selected including the area consistency, spatial consistency, classification accuracy and landscape diversity in the three sub-regions of Wanbei, Wanzhong and Wannan. The results indicate that the consistency of IGBP is the best among the five schemes of MCD12Q1 according to the correlation coefficient (R). The “woodland” LAI/FPAR is the worst, with a spatial similarity (O) of 58.17% due to the misclassification between “woodland” and “others”. The consistency of NPP is the worst among the five schemes as the agreement varied from 1.61% to 56.23% in the three sub-regions. Furthermore, with the biggest difference of diversity indices between LAI/FPAR and GlobeLand30, the consistency of LAI/FPAR is the weakest. This study provides a methodological reference for evaluating the consistency of different GLC products derived from multi-source and multi-resolution remote sensing datasets on various spatial scales.

The period from 2010 to 2020 is a period of rapid economic development in China, with the increasing population and the urbanization level reaching 63.89%. Cultivated land plays an extremely important role in China’s food security. In order to grasp the spatial and temporal distribution and change of cultivated land use in China in time, based on GlobeLand30 data in 2010 and 2020, this study studied the spatial and temporal change characteristics of total cultivated land resources, cultivated land development and utilization degree, dynamic change degree of cultivated land utilization rate, and cultivated land use center and direction by using GIS technology, reclamation index, cultivated land utilization rate dynamic change degree model, cultivated land center shift model and standard deviation ellipse model. The results show that: ①The cultivated land area in China has changed greatly during 2010–2020, with the largest increase in cultivated land area in Inner Mongolia and Xinjiang, while the largest decrease in cultivated land area in Shandong, Jiangsu, Hebei, Guangdong, Henan and Zhejiang; ②The areas with the highest degree of cultivated land development and utilization are mainly concentrated in Shandong, Jiangsu, Henan, Anhui and other provinces in the east, while Qinghai, Tibet and Xinjiang have the lowest degree of cultivated land development and utilization; ③The regions with the highest dynamic change degree index of cultivated land utilization rate are mainly concentrated in Inner Mongolia, Xinjiang, Tibet and Qinghai, while the dynamic change degree index of cultivated land utilization rate in Liaoning, Hebei, Shandong, Jiangsu, Shanghai, Zhejiang and Guangdong shows obvious negative changes; ④The spatial and temporal migration of the center of gravity of cultivated land use in China shows the trend of “moving westward to northward”, and the spatial distribution direction of cultivated land use roughly presents the overall characteristics of northeast-southwest direction. This study can effectively reveal the temporal and spatial variation characteristics of cultivated land use at provincial level in China based on GlobeLand30, and provide a scientific reference for further rational development, utilization and protection of cultivated land resources in China.

Knowing the distributions and changes in global wetlands and their conversion to other land cover types could facilitate our understanding of wetland development, causes of variations, and decision-making for restoration and protection. This study aimed to comprehensively analyze the changes in wetland distributions at global, continental, typical regional, and national scales and the conversions between wetlands and other land cover types in the last 20 years. This study used GlobeLand30 (GL30) data with a 30 m resolution for the years 2000, 2010, and 2020. The main findings of this study are as follows: (1) the area of wetlands continued to increase globally from 2000 to 2020, with a total increase of approximately 4%. Wetland changes from 2010 to 2020 were more significant than those from 2000 to 2010. The regions with significant wetland changes were mainly in the north middle- and high-latitude, and the equatorial middle- and low-latitude, and Oceania and North America were the continents with the highest increase and decrease, respectively; (2) the major conversion of wetlands was mainly natural land cover types, including forest, grassland, water, and tundra, and there were minor conversions due to human activities, including the conversion of wetlands to cropland (~4600 km2) and artificial land (~3400 km2); (3) from 2000 to 2020, the increase in global wetlands was uneven, while the decrease was nearly even at a national scale. Australia had the highest increase due to the conversions from grass, bare land, and water, and Canada had the highest decrease due to the conversion into tundra and forest. The analysis results could more comprehensively characterize the distributions and changes of global wetlands, which may provide basic information and knowledge for related research work and policymaking.

The new type of multi-temporal global land use data with multiple classes is able to provide information on both the different land covers and their temporal changes; furthermore, it is able to contribute to many applications, such as those involving global climate and Earth ecosystem analyses. However, the current accuracy assessment methods have two limitations regarding multi-temporal land cover data that have multiple classes. First, multi-temporal land cover uses data from multiple phases, which is time-consuming and inefficient if evaluated one by one. Secondly, the conversion between different land cover classes increases the complexity of the sample stratification, and the assessments with different types of land cover suffer from inefficient sample stratification. In this paper, we propose a spatiotemporal stratified sampling method for stratifying the multi-temporal GlobeLand30 products for China. The changed and unchanged types of each class of data in the three periods are used to obtain a reasonable stratification. Then, the strata labels are simplified by using binary coding, i.e., a 1 or 0 representing a specified class or a nonspecified class, to improve the efficiency of the stratification. Additionally, the stratified sample size is determined by the combination of proportional allocation and empirical evaluation. The experimental results show that spatiotemporal stratified sampling is beneficial for increasing the sample size of the “change” strata for multi-temporal data and can evaluate not only the accuracy and area of the data in a single data but also the accuracy and area of the data in a multi-period change type and an unchanged type. This work also provides a good reference for the assessment of multi-temporal data with multiple classes.

Forest cover data are fundamental to sustainable forest management and conservation. Available medium-resolution publicly shared forest-related datasets provide primary information on forest distribution. The evaluation of relevant datasets is of great importance to learn about the differences, characterize the accuracy, and provide a reference for rational use. This study presents an evaluation and analysis of the forest-related datasets in China around 2020, including TreeCover and the forest-related layer (latter referred to as the forest datasets) in WorldCover, Esri land cover, FROM-GLC10, GlobeLand30, and GLC_FCS30. These forest datasets, that are obtained by aggregating forest-related lasses based on the classification schemes, are analyzed from spatial consistency and accuracy comparison. The results illustrate that forest datasets with 10m resolution are generally more precise than those with 30m resolution in China. WorldCover shows the highest accuracy, with producer accuracy and user accuracy of 91.4% and 87.09%, respectively. These datasets exhibit high accuracy but great spatial inconsistency. The more consistent the regions are, the more accurate the accuracy is. High consistency (≥5, i.e., classified into forests by five datasets) areas account for 56.49% of areas of forest classified (AFC), while the area of low consistency (≤2) reach 25.51% of AFC. The analysis delves into the datasets, offering a reliable reference for the usage of these datasets.

With the opening up of the Landsat archive, global high resolution land cover maps have begun to appear. However, they often have only a small number of high level land cover classes and they are static products, corresponding to a particular period of time, e.g., the GlobeLand30 (GL30) map for 2010. The OpenStreetMap (OSM), in contrast, consists of a very detailed, dynamically updated, spatial database of mapped features from around the world, but it suffers from incomplete coverage, and layers of overlapping features that are tagged in a variety of ways. However, it clearly has potential for land use and land cover (LULC) mapping. Thus the aim of this paper is to demonstrate how the OSM can be converted into a LULC map and how this OSM-derived LULC map can then be used to first update the GL30 with more recent information and secondly, enhance the information content of the classes. The technique is demonstrated on two study areas where there is availability of OSM data but in locations where authoritative data are lacking, i.e., Kathmandu, Nepal and Dar es Salaam, Tanzania. The GL30 and its updated and enhanced versions are independently validated using a stratified random sample so that the three maps can be compared. The results show that the updated version of GL30 improves in terms of overall accuracy since certain classes were not captured well in the original GL30 (e.g., water in Kathmandu and water/wetlands in Dar es Salaam). In contrast, the enhanced GL30, which contains more detailed urban classes, results in a drop in the overall accuracy, possibly due to the increased number of classes, but the advantages include the appearance of more detailed features, such as the road network, that becomes clearly visible.

The accuracy of training samples used for data classification methods, such as support vector machines (SVMs), has had a considerable positive impact on the results of urban area extractions. To improve the accuracy of urban built-up area extractions, this paper presents a sample-optimized approach for classifying urban area data using a combination of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) for nighttime light data, Landsat images, and GlobeLand30, which is a 30-m global land cover data product. The proposed approach consists of three main components: (1) initial sample generation and data classification into built-up and non-urban built-up areas based on the maximum and minimum intervals of digital numbers from the DMSP-OLS data, respectively; (2) refined sample selection and optimization by the probability threshold of each pixel based on vegetation-cover, using the Landsat-derived normalized differential vegetation index (NDVI) and artificial surfaces extracted from the GlobeLand30 product as the constraints; (3) iterative classification and urban built-up area data extraction using the relationship between these three aspects of data collection together with the training sets. Experiments were conducted for several cities in western China using this proposed approach for the extraction of built-up areas, which were classified using urban construction statistical yearbooks and Landsat images and were compared with data obtained from traditional data collection methods, such as the threshold dichotomy method and the improved neighborhood focal statistics method. An analysis of the empirical results indicated that (1) the sample training process was improved using the proposed method, and the overall accuracy (OA) increased from 89% to 96% for both the optimized and non-optimized sample selection; (2) the proposed method had a relative error of less than 10%, as calculated by an accuracy assessment; (3) the overall and individual class accuracy were higher for artificial surfaces in GlobeLand30; and (4) the average OA obviously improved and the Kappa coefficient in the case of Chengdu increased from 0.54 to 0.80. Therefore, the experimental results demonstrated that our proposed approach is a reliable solution for extracting urban built-up areas with a high degree of accuracy.