. Land cover (LC) determines the energy exchange, water and
carbon cycle between Earth's spheres. Accurate LC information is a
fundamental parameter for the environment and climate studies. Considering
that the LC in China has been altered dramatically with the economic
development in the past few decades, sequential and fine-scale LC monitoring
is in urgent need. However, currently, fine-resolution annual LC dataset
produced by the observational images is generally unavailable for China due
to the lack of sufficient training samples and computational capabilities.
To deal with this issue, we produced the first Landsat-derived annual China
land cover dataset (CLCD) on the Google Earth Engine (GEE) platform, which
contains 30 m annual LC and its dynamics in China from 1990 to 2019. We
first collected the training samples by combining stable samples extracted
from China's land-use/cover datasets (CLUDs) and visually interpreted
samples from satellite time-series data, Google Earth and Google Maps. Using
335 709 Landsat images on the GEE, several temporal metrics were constructed
and fed to the random forest classifier to obtain classification results. We
then proposed a post-processing method incorporating spatial–temporal
filtering and logical reasoning to further improve the spatial–temporal
consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 %
based on 5463 visually interpreted samples. A further assessment based on
5131 third-party test samples showed that the overall accuracy of CLCD
outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC and GlobeLand30. Besides, we intercompared the CLCD with several
Landsat-derived thematic products, which exhibited good consistencies with
the Global Forest Change, the Global Surface Water, and three impervious
surface products. Based on the CLCD, the trends and patterns of China's LC
changes during 1985 and 2019 were revealed, such as expansion of impervious
surface ( + 148.71 %) and water ( + 18.39 %), decrease in cropland
( − 4.85 %) and grassland ( − 3.29 %), and increase in forest ( + 4.34 %). In
general, CLCD reflected the rapid urbanization and a series of ecological
projects (e.g. Gain for Green) in China and revealed the anthropogenic
implications on LC under the condition of climate change, signifying its
potential application in the global change research. The CLCD dataset
introduced in this article is freely available at
 https://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).

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The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019

Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method

Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects.

Land-cover plays an important role in the Earth’s energy balance, the hydrological cycle, and the carbon cycle. Therefore, it is important to evaluate the current global land-cover (GLC) products and to understand the differences between these products so that they can be used effectively in different applications. In this study, three 30-m GLC products, namely GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC30-2015, were evaluated in terms of areal consistency and spatial consistency using the Land Use/Cover Area frame statistical Survey (LUCAS) reference dataset over the European Union (EU). Given the limitations of the traditional confusion matrix used in accuracy assessment, we adjusted the confusion matrices from sample counts by accounting for the class proportions of the map and reported the standard errors of the descriptive accuracy measures in the accuracy assessment. The results revealed the following. (1) The overall accuracy of the GlobeLand30-2010 product was the highest at 88.90 ± 0.68%; this was followed by GLC_FCS30-2015 (84.33 ± 0.80%) and FROM_GLC2015 (65.31 ± 1.0%). (2) The consistency between the GLC_FCS30-2015 and GlobeLand30-2010 is higher than the consistency between other products, with an area correlation coefficient of 0.930 and a proportion of consistent pixels of 52.41%, respectively. (3) Across the area of the EU, the dominant land-cover types such as forest and cropland are the most consistent across the three products, whereas the spatial consistency for bare land, grassland, shrubland, and wetland is relatively low. (4) The proportion of pixels for which the consistency is low accounts for less than 16.17% of pixels, whereas the proportion of pixels for which the consistency is high accounts for about 39.12%. The disagreement between these products primarily occurs in transitional zones with mixed land cover types or in mountain areas. Overall, the GlobeLand30 and GLC-FCS30 products were found to be the most consistent and to have good classification accuracy in the EU, with the disagreement between the three 30-m GLC products mainly occurring in heterogeneous regions.

Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset

Changes of the spatial and temporal characteristics of land-use landscape patterns using multi-temporal Landsat satellite data: A case study of Zhoushan Island, China

. Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m land-cover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the CCI_LC (Climate Change Initiative Global Land Cover) land-cover and MCD43A4 NBAR products (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, a local adaptive random forest model was built for each 5 ∘ × 5 ∘ geographical tile by using the multi-temporal Landsat spectral and texture features and the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC_FCS30-2015 achieved an overall accuracy of 82.5 % and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 % and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 % and kappa coefficient of 0.662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI_LC, MCD12Q1, FROM_GLC, and GlobeLand30) indicated that GLC_FCS30-2015 provides more spatial details than CCI_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM_GLC-2015 and GlobeLand30-2010. They also showed that GLC_FCS30-2015 achieved the best overall accuracy of 82.5 % against FROM_GLC-2015 of 59.1 % and GlobeLand30-2010 of 75.9 %. Therefore, it is concluded that the GLC_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system (containing 16 global LCCS land-cover types as well as 14 detailed and regional land-cover types) with high classification accuracy at 30 m. The GLC_FCS30-2015 global land-cover products produced in this paper are free access at https://doi.org/10.5281/zenodo.3986872 (Liu et al., 2020).

/pdf/glc-fcs30-global-land-cover-product-with-fine-classification-1glmjt0r9k.pdf

GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery

Land-cover mapping is one of the foundations of Earth science As a result of the combined efforts of many scientists, numerous global land-cover (GLC) products with a resolution of 30 m have so far been generated However, the increasing number of fine-resolution GLC datasets is imposing additional workloads as it is necessary to confirm the quality of these datasets and check their suitability for user applications To provide guidelines for users, in this study, the recent developments in currently available 30 m GLC products (including three GLC products and thematic products for four different land-cover types, ie, impervious surface, forest, cropland, and inland water) were first reviewed Despite the great efforts toward improving mapping accuracy that there have been in recent decades, the current 30 m GLC products still suffer from having relatively low accuracies of between 460% and 889% for GlobeLand30-2010, 5771% and 8036% for FROM_GLC-2015, and 6559% and 8433% for GLC_FCS30-2015 The reported accuracies for the global 30 m thematic maps vary from 6786% to 951% for the eight impervious surface products that were reviewed, 5672% to 9736% for the seven forest products, 3273% to 983% for the six cropland products, and 1567% to 997% for the six inland water products The consistency between the current GLC products was then examined The GLC maps showed a good overall agreement in terms of spatial patterns but a limited agreement for some vegetation classes (such as shrub, tree, and grassland) in specific areas such as transition zones Finally, the prospects for fine-resolution GLC mapping were also considered With the rapid development of cloud computing platforms and big data, the Google Earth Engine (GEE) greatly facilitates the production of global fine-resolution land-cover maps by integrating multisource remote sensing datasets with advanced image processing and classification algorithms and powerful computing capability The synergy between the spectral, spatial, and temporal features derived from multisource satellite datasets and stored in cloud computing platforms will definitely improve the classification accuracy and spatiotemporal resolution of fine-resolution GLC products In general, up to now, most land-cover maps have not been able to achieve the maximum (per class or overall) error of 5%–15% required by many applications Therefore, more efforts are needed toward improving the accuracy of these GLC products, especially for classes for which the accuracy has so far been low (such as shrub, wetland, tundra, and grassland) and in terms of the overall quality of the maps

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

Accurately monitoring the spatiotemporal dynamics of impervious surfaces is very important for understanding the process of urbanization. However, the complicated makeup and spectral heterogeneity of impervious surfaces create difficulties for impervious surface monitoring. In this study, we propose an automatic method to capture the spatiotemporal expansion of impervious surfaces using spectral generalization and time series Landsat imagery. First, the multitemporal compositing and relative radiometric normalization methods were used to extract phenological information and ensure spectral consistency between reference imagery and monitored imagery. Second, we automatically derived training samples from the prior MSMT_IS30-2020 impervious surface products and migrated the surface reflectance of impervious surfaces in the reference period of 2020 to other periods (1985–2015). Third, the random forest classification method, trained using the migrated surface reflectance of impervious surfaces and pervious surface training samples at each period, was employed to extract temporally independent impervious surfaces. Further, a temporal consistency check method was applied to ensure the consistency and reliability of the monitoring results. According to qualitative and quantitative validation results, the method achieved an overall accuracy of 90.9% and kappa coefficient of 0.859 in identifying the spatiotemporal expansion of impervious surfaces and performed better in capturing the impervious surface dynamics when compared with other impervious surface datasets. Lastly, our results indicate that a rapid increase of impervious surfaces was observed in the Yangtze River Delta, and the area of impervious surfaces in 2000 and 2020 was 1.86 times and 4.76 times that of 1985, respectively. Therefore, it could be concluded that the proposed method offered a novel perspective for providing timely and accurate impervious surface dynamics.

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Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta

The accuracy assessment of the global leaf area index (LAI) products is an indispensable step before applications. Four popular LAI products, namely MCD15A2H, GLASS, GEOV2, and GLOBMAP, were assessed over croplands in China using field measurements. All of these four products were validated with upscaled reference LAI maps. The validation results revealed uncertainties in these products for crops with RMSE ranging from 0.49 to 1.37 over the validation sites in Beijing, in Zhoukou and Jiaozuo counties in Henan province, Youyi farm in Heilongjiang province, and in Longkang farm in Anhui province. GEOV2 gave the highest accuracy ( R 2 = 0.85, RMSE = 0.49, relative bias = −7.2%) compared to MCD15A2H ( R 2 = 0.54, RMSE = 0.91, and relative bias = −24.4%), GLASS ( R 2 = 0.80, RMSE = 0.73, and relative bias = −23.3%), and GLOBMAP ( R 2 = 0.25, RMSE = 1.37, and relative bias = −55.4%). The LAI products overestimated over the croplands with dark soil-backgrounds in Youyi farm while they underestimated over other validation sites. The seasonal variation of these products was assessed with continuous in situ measurements at Daman Station, Gansu province. All of these four products showed good temporal consistency at Daman Station, while GEOV2 showed the highest accuracy ( R 2 = 0.92, RMSE = 0.41, relative bias = −10.3%). The influence of scaling effect on the products’ accuracy was also investigated, and the scaling differences of these LAI products contributes overestimation at coarse-resolution for croplands with dark soil-backgrounds, while underestimation at coarse-resolution in other validation sties.

/pdf/validation-of-four-coarse-resolution-leaf-area-index-1duqrg7tz4.pdf

Yuan Gao

Papers

GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta

Validation of Four Coarse-Resolution Leaf Area Index Products Over Croplands in China Using Field Measurements