A novel alpine land cover classification strategy based on a deep convolutional neural network and multi-source remote sensing data in Google Earth Engine

ABSTRACT

Alpine land cover (ALC) is facing many challenges with climatic change, biodiversity reduction and other cascading ecosystem damage triggered by natural and anthropogenic interference. Although several global land cover products and thematic maps are already available, their mapping accuracy of alpine and montane regions remains unsatisfactory due to the data acquisition, methodology, and workflow design constraints. Therefore, in this paper, a deep convolutional neural network (DCNN) in Google Earth Engine (GEE) was developed to map the ALC types of the Yarlung Zangbo river basin (YZRB) in the Tibetan plateau using multi-source remote sensing data. The DCNN algorithm was offline trained using automatically generating samples and online deployed in the GEE for a large-scale ALC mapping. Moreover, a set of fine land cover classification system (containing 14 ALC types) was also established in accordance with the natural situation of the YZRB. The overall accuracy and kappa were 86.24% and 0.8156, which were higher than traditional classification algorithms. The spatial distribution of ALC types was analyzed in different gradient zones, and a clear altitudinal characteristic was noticed. The terrain of the YZRB from upper-stream to down-stream with an elevation dramatically decreases, and corresponding to vertical zonal changes from glacier and permanent snow/ice, barren gravel land, alpine desert steppe, alpine steppe, alpine meadow, shrubs, to tree cover. The product can provide valuable land cover information to support alpine ecosystem conservation.

KEYWORDS:

• Alpine land cover mapping
• deep convolutional neural network
• multi-source remote sensing data
• Google Earth Engine
• Yarlung Zangbo river basin

Acknowledgments

This research was funded by the National Natural Science Foundation of China (No. U22A20567), the Hubei Provincial Key Research and Development Program (No. 2020BCA074), the International Science and Technology Cooperation Project of Hubei Province (No. 2022EHB018), and the Science and Technology Partnership Program, Ministry of Science and Technology of China (No. KY201802007).

Disclosure statement

The authors declare that they have no known commercial, personal and competing financial interest that have a conflict of interest with respect to this paper.

Data availability statement

The DeepGEE-ALC data sets can be downloaded from http://data.tpdc.ac.cn/zh-hans/data. The codes are available from the corresponding author, L.W., upon reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15481603.2023.2233756.