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Automatic alpine treeline extraction using high-resolution forest cover imagery

  • role： First author第一作者
  • Affiliation：

    School of Environmental Science and Engineering, South University of Science and Technology, Shenzhen 518055, China

  • Email：12131072@mail.sustech.edu.cn
  • Introduction：江鑫，研究方向为定量遥感、深度学习与自然资源要素分类和提取。E-mail：12131072@mail.sustech.edu.cn
JIANG Xin1,  
  • Affiliation：

    School of Environmental Science and Engineering, South University of Science and Technology, Shenzhen 518055, China

    School of Earth and Environment, University of Leeds, Leeds, LS29JT, UK

HE Xinyue12,  
  • Affiliation：

    School of Environmental Science and Engineering, South University of Science and Technology, Shenzhen 518055, China

WANG Dashan1,  
  • Affiliation：

    School of Environmental Science and Engineering, South University of Science and Technology, Shenzhen 518055, China

ZOU Junyu1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Environmental Science and Engineering, South University of Science and Technology, Shenzhen 518055, China

  • Email：zengzz@sustech.edu.cn
  • Introduction：曾振中，研究方向为生态水文、陆气相互作用、全球环境变化和地球系统模拟等。E-mail：zengzz@sustech.edu.cn
ZENG Zhenzhong1*

Resümee

Alpine treeline is not only an important source in calibrating global climate change but also a fundamental element in scientifically managing forest resources. Furthermore, the location, area size, and change patterns of forest lines are also used as essential information in monitoring and modeling the environment. The alpine forest line of the Qinling Mountains is located in the ecological staggered zone at high altitude, with an obvious distribution of altitudinal spectrum, which is an important north-south geographical dividing line in China. Therefore, a novel approach for the rapid and accurate identification of alpine treeline in the Qinling Mountains must be developed.We propose a remote-sensor-based algorithm for extracting alpine treelines in the Qinling Mountains by combining the high-resolution global forest cover data in 2000 with a digital elevation model and mountain distribution data. Specifically, tree cover is first extracted from the forest cover data. Next, the highest point of the study area is determined from the elevation data. Finally, the 8-connected domain search algorithm is employed to find the boundary between forest and non-forest covers to determine the alpine treeline. The algorithm is validated by high-resolution Google Earth images, GPS ground-based data, and the NDVI dataset. Further, we systematically investigate the relationship between the alpine treeline distribution and geographical factors (elevation, slope, and aspect) in the study area using the elevation data.The distribution of treelines in this paper is consistent with the actual treelines distribution in Google Earth images, further demonstrating the performance of the proposed algorithm. The elevation of treelines in the Qinling Mountains ranges from 2400 m to 3800 m. The treelines are concentrated in steep slope areas ranging from 15° to 55°. The distribution of alpine treeline elevation shows significant slope differences, with the treeline on the south slope being higher than those on the north slope, and the treelines on the east slope being higher than those on the west slope.The treelines obtained by our algorithm match the actual treelines in the Google Earth images of the study area well, showing an outstanding performance in the integrity and boundary connectivity of treelines. Given the capability of remote sensing technology to observe the Earth in a large scale and the high data quality and accessibility of satellite image data, the proposed algorithm for extracting alpine treelines can be further applied to global treeline mapping to provide technical support for global mountain ecosystem monitoring, conservation, and restoration.

Schlüsselwort

alpine treeline;remote sensing;automatic extraction;spatial distribution;global

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