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

Zhou, Xiao; Liu, Liangyun; Chen, Xidong; Gao, Yuan; Xie, Sujuan; Murai, Jun

doi:10.5194/essd-13-2753-2021

Cited by 589 publications

(320 citation statements)

References 78 publications

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“…Based on the quality flag, we selected 3000 "sure" and "quite sure" Geo-Wiki samples located in China. The other was the Global Land Cover Validation Sample Set (GLCVSS) (Zhao et al, 2014), which followed a random sampling strategy to ensure even distribution of test samples at a global scale. The classification system of the GLCVSS was the same with FROM_GLC (Table S1).…”

Section: Third-party Validation Samplesmentioning

confidence: 99%

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

Yang

Huang

2021

Earth Syst. Sci. Data

1,389

251

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Abstract. 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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Section: Third-party Validation Samplesmentioning

confidence: 99%

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

Yang

Huang

2021

Earth Syst. Sci. Data

1,389

251

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“…The product selected for the land cover transformation probability statistics in 2015 and 2020 was GLC_FCS30. GLC_FCS30 was produced by the Aerospace Information Research Institute, Chinese Academy of Sciences [39], by combining time series from Landsat imagery and high-quality training data from the Global Spatial Temporal Spectra Library on the GEE computing platform. GLC_FCS30-2015 was validated using three different validation systems (containing different land cover details) using 44,043 validation samples.…”

Section: And 2020 Glc_fcs30 Productsmentioning

confidence: 99%

Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes

et al. 2021

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To address problems in remote sensing image change detection, this study proposes a method for identifying spurious changes based on an eco-geographical zoning knowledge base and crowdsourced data mining. After preliminary change detection using the super pixel cosegmentation method, eco-geographical zoning is introduced, and the rules of spurious change are collected based on the knowledge of expert interpreters, and from statistics on existing land cover products according to each eco-geographical zone. Uncertain changed patches with a high possibility of spurious change according to the eco-geographical zoning rule were published in the form of a map service on an online platform, and then crowd tagging information on spurious changed patches was collected. The Hyperlink-Induced Topic Search (HITS) algorithm was used to calculate the spurious change degree of changed patches. We selected the northern part of Laos as the experimental area and the Chinese GF-1 Wide Field View (WFV) images for change detection to verify the effectiveness of the method. The results show that the accuracy of change detection improves by 23% after removing the spurious changes. Spurious changes caused by clouds, river water turbidity, spectral differences in cultivated land before and after harvest, and changes in shrubs, grassland, and forest density, can be removed using an eco-geographical zoning knowledge base and crowdsourced data mining methods.

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“…One of their most important applications is monitoring dynamic changes in land use cover type using several LUCC products obtained from different periods. Zhang et al [39] used multitemporal Landsat images, high-quality training data, and a machine learning algorithm to produce the LUCC products in the years 2015 and 2020 with a 30m resolution over the globe land. The LUCC datasets include 22 land cover types with a higher accuracy of 84.33%, such as impervious land, bare land, forest, grassland, shrub land, wetland, and water areas.…”

Section: Globe Land Use Cover Change Datasetmentioning

confidence: 99%

“…The LUCC datasets include 22 land cover types with a higher accuracy of 84.33%, such as impervious land, bare land, forest, grassland, shrub land, wetland, and water areas. Further detailed information is documented by Zhang et al [39] and the LUCC datasets can be downloaded from https://zenodo.org/record/3986872 (accessed on 10 September 2021) for LUCC-2015 and https://zenodo.org/record/4280923 (accessed on 10 September 2021) for LUCC-2020.…”

Section: Globe Land Use Cover Change Datasetmentioning

confidence: 99%

Fusing Retrievals of High Resolution Aerosol Optical Depth from Landsat-8 and Sentinel-2 Observations over Urban Areas

Lin

Xing

et al. 2021

Remote Sensing

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Recent studies have shown that the high-resolution satellite Landsat-8 has the capability to retrieve aerosol optical depth (AOD) over urban areas at a 30 m spatial resolution. However, its long revisiting time and narrow swath limit the coverage and frequency of the high resolution AOD observations. With the increasing number of Earth observation satellites launched in recent years, combining the observations of multiple satellites can provide higher temporal-spatial coverage. In this study, a fusing retrieval algorithm is developed to retrieve high-resolution (30 m) aerosols over urban areas from Landsat-8 and Sentinel-2 A/B satellite measurements. The new fusing algorithm was tested and evaluated over Beijing city and its surrounding area in China. The validation results show that the retrieved AODs show a high level of agreement with the local urban ground-based Aerosol Robotic Network (AERONET) AOD measurements, with an overall high coefficient of determination (R2) of 0.905 and small root mean square error (RMSE) of 0.119. Compared with the operational AOD products processed by the Landsat-8 Surface Reflectance Code (LaSRC-AOD), Sentinel Radiative Transfer Atmospheric Correction code (SEN2COR-AOD), and MODIS Collection 6 AOD (MOD04) products, the AOD retrieved from the new fusing algorithm based on the Landsat-8 and Sentinel-2 A/B observations exhibits an overall higher accuracy and better performance in spatial continuity over the complex urban area. Moreover, the temporal resolution of the high spatial resolution AOD observations was greatly improved (from 16/10/10 days to about two to four days over globe land in theory under cloud-free conditions) and the daily spatial coverage was increased by two to three times compared to the coverage gained using a single sensor.

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

Cited by 589 publications

References 78 publications

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

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

Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes

Fusing Retrievals of High Resolution Aerosol Optical Depth from Landsat-8 and Sentinel-2 Observations over Urban Areas

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