Chinese Science Bulletin

, Volume 57, Issue 11, pp 1298–1310 | Cite as

Changes in global potential vegetation distributions from 1911 to 2000 as simulated by the Comprehensive Sequential Classification System approach

  • TianGang Liang
  • QiSheng Feng
  • JianJun Cao
  • HongJie Xie
  • HuiLong Lin
  • Jun Zhao
  • JiZhou Ren
Open Access
Article Agronomy

Abstract

Vegetation classification models play an important role in studying the response of the terrestrial ecosystem to global climate change. In this paper, we study changes in global Potential Natural Vegetation (PNV) distributions using the Comprehensive Sequential Classification System (CSCS) approach, a technique that combines geographic information systems. Results indicate that on a global scale there are good agreements among maps produced by the CSCS method and the globally well-accepted Holdridge Life Zone (HLZ) and BIOME4 PNV models. The potential vegetation simulated by the CSCS approach has 6 major latitudinal zones in the northern hemisphere and 2 in the southern hemisphere. In mountainous areas it has obvious altitudinal distribution characteristics due to topographic effects. The distribution extent for different PNV classes at various periods has different characteristics. It had a decreasing trend for the tundra and alpine steppe, desert, sub-tropical forest and tropical forest categories, and an increasing trend for the temperate forest and grassland vegetation categories. The simulation of global CSCS-based PNV classes helps to understand climate-vegetation relationships and reveals the dynamics of potential vegetation distributions induced by global changes. Compared with existing statistical and equilibrium models, the CSCS approach provides similar mapping results for global PNV and has the advantage of improved simulation of grassland classes.

Keywords

potential natural vegetation spatial distribution biogeography model CSCS approach 

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Copyright information

© The Author(s) 2011

Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • TianGang Liang
    • 1
  • QiSheng Feng
    • 1
  • JianJun Cao
    • 2
    • 3
  • HongJie Xie
    • 4
  • HuiLong Lin
    • 1
  • Jun Zhao
    • 5
  • JiZhou Ren
    • 1
  1. 1.State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
  2. 2.State Key Laboratory for Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina
  3. 3.Basic Geographic Information Center of Gansu ProvinceLanzhouChina
  4. 4.Department of Geological Sciences, Laboratory for Remote Sensing and GeoinformaticsUniversity of Texas at San AntonioSan AntonioUSA
  5. 5.College of Geography and Environmental ScienceNorthwest Normal UniversityLanzhouChina

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