This dataset used 335709 Landsat images from Google Earth Engine to construct China's first annual land cover product (CLCD) derived from Landsat from 1985 to 2022. We collected training samples by combining stable samples extracted from the China Land Use/Cover Dataset (CLUD) with visual interpretation samples from satellite time series data, Google Earth, and Google Maps. Multiple temporal indicators were constructed using all available Landsat data and fed into a random forest classifier to obtain classification results. A post-processing method combining spatiotemporal filtering and logical reasoning was further proposed to improve the spatiotemporal consistency of CLCD.
The projection file of "* _albert. tif" is created using the proj4 string "+proj=aea+lat_1=25+lat_2=47+lat-0=0+lon-0=105+x_0=0+y_0=0+status=WGS84+units=m+no_defs". The CLCD for 2022 has now been released.
Given that USGS no longer maintains Landsat Set 1 data, we are now using Set 2 SR data to update CLCD.
All files in this version have been exported as cloud optimized GeoTIFF for more efficient processing on the cloud. For details, please click https://www.cogeo.org/ 。
Each file comes with an internal overview and color chart built-in to speed up software loading and rendering.
Collect training samples using 335709 Landsat images from Google Earth Engine, combined with stable samples extracted from the China Land Use/Cover Dataset (CLUD) and visual interpretation samples from satellite time series data, Google Earth, and Google Maps.
1. Using Landsat images, construct China's first annual land cover product (CLCD) derived from Landsat from 1985 to 2022;
2. Collect training samples by combining stable samples extracted from the China Land Use/Cover Dataset (CLUD) with visual interpretation samples from satellite time series data, Google Earth, and Google Maps;
3. Construct multiple temporal indicators from all available Landsat data and feed them into a random forest classifier to obtain classification results.
The data quality is good.
This work is licensed under a
Creative
Commons Attribution 4.0 International License.
CSTR:11738.11.NCDC.ZENODO.DB3943.2023
10.5281/zenodo.8176941