Preprints
https://doi.org/10.5194/essd-2020-157
https://doi.org/10.5194/essd-2020-157

  20 Oct 2020

20 Oct 2020

Review status: this preprint is currently under review for the journal ESSD.

A 30-meter terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine

Bowen Cao1, Le Yu1,2, Victoria Naipal3, Philippe Ciais4, Wei Li1,2, Yuanyuan Zhao5,6, Wei Wei7, Die Chen7, Zhuang Liu1, and Peng Gong1,2 Bowen Cao et al.
  • 1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
  • 2Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing 100084, China
  • 3Department of Geography, Ludwig-Maximilian University, Munich, Germany
  • 4Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, UMR8212, Gif-sur-Yvette, France
  • 5College of Land Science and Technology, China Agricultural University, Beijing 100083, China
  • 6Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • 7State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

Abstract. The construction of terraces is a key soil conservation practice on agricultural land in China, providing multiple valuable ecosystem services. Accurate spatial information on terraces is needed for both management and research. In this study, the first 30 m resolution terracing map of the entire territory of China is produced by a supervised pixel-based classification using multi-source and multi-temporal data based on the Google Earth Engine (GEE) platform. We extracted time-series spectral features and topographic features from Landsat 8 images and the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) data, classifying cropland area (cultivated land of Globeland30) into terraced and non-terraced type through a random forest classifier. The overall accuracy and kappa coefficient were evaluated by 10875 test samples and achieved values of 94 % and 0.72, respectively. The classification performed best in the Loess Plateau and southwestern China, where terraces are most numerous. Some northeastern, central eastern and southern area had relatively high uncertainty. Typical errors in the mapping results from the sloping cropland (non-terrace cropland with a slope of ≥ 5°), low-slope terraces, and non-crop vegetation. Terraces are widely distributed in China and the total terraced area was estimated to be 53.55 Mha (i.e., 26.43 % of China's cropland area) by pixel counting (PC) method and 58.46 ± 2.99 Mha (i.e., 28.85 % ± 1.48 % of China's cropland area) by error matrix-based model-assisted estimation (EM) method. Elevation and slope were identified as the main features in the terrace/non-terrace classification, and multi-temporal spectral features (such as percentiles of NDVI, TIRS2, BSI) were also essential. Terraces are more challenging to identify than other land use types because of the intra-class feature heterogeneity, inter-class feature similarity and fragmented patches, which should be the focus of future research. Our terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and our terrace map will serve as a landmark for studies on multiple ecosystem services assessments including erosion control, carbon sequestration, and biodiversity conservation. The China terrace map is available to the public at https://doi.org/10.5281/zenodo.3895585 (Cao et al., 2020).

Bowen Cao et al.

 
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Bowen Cao et al.

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A 30-meter terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine Bowen Cao, Le Yu, Victoria Naipal, Philippe Ciais, Wei Li, Yuanyuan Zhao, Wei Wei, Die Chen, Zhuang Liu, and Peng Gong https://doi.org/10.5281/zenodo.3895585

Bowen Cao et al.

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Short summary
In this study, the first 30 m resolution terrace map of China was developed through supervised pixel-based classification using multi-source, multi-temporal data based on the Google Earth Engine platform. The classification performed well with an overall accuracy of 94 %. The terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and the terrace map will be valuable for studies on soil erosion, carbon cycle, and ecosystem services assessments.