Spatially-explicit modelling of grassland classes – an improved method of integrating a climate-based classification model with interpolated climate surfaces
Xiaoni Liu A B C F , Hongxia Wang A , Jing Guo D , Jingqiong Wei A , Zhengchao Ren A , Jinglan Zhang C , Degang Zhang A B , Dongrong Pan E and Fengping Wang AA Key Laboratory of Grassland Ecology System (Gansu Agricultural University), Ministry of Education, Lanzhou 730070, China.
B College of Grassland Science, Gansu Agricultural University, Lanzhou 730070, China.
C Science and Engineering Faculty, Queensland University of Technology, Brisbane, Qld 4001, Australia.
D Lanzhou University of Finance and Economics, Lanzhou 730020, China.
E Central Station for Popularising Grassland Techniques of Gansu Province, Lanzhou 730020, China.
F Corresponding author. Email: liuxn@gsau.edu.cn
The Rangeland Journal 36(2) 175-183 https://doi.org/10.1071/RJ13103
Submitted: 9 October 2013 Accepted: 11 February 2014 Published: 3 April 2014
Abstract
Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterised by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the inverse distance-weighted approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were ‘cool temperate-arid temperate zonal semi-desert’, ‘cool temperate-humid forest steppe and deciduous broad-leaved forest’, ‘temperate-extra-arid temperate zonal desert’, and ‘frigid per-humid rain tundra and alpine meadow’. The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies’ decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities, which will help to prevent overgrazing and land degradation.
Additional keywords: AMMRR, CSCS, GIS, grassland classification, interpolation method, spatial climate variables.
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