Estimation of Grassland Carrying Capacity by Applying High Spatiotemporal Remote Sensing Techniques in Zhenglan Banner, Inner Mongolia, China

Pengyao Qin,Ting Gao,Zengyuan Li,Zhihai Gao,Ziyu Yan,Yifu Li,Bin Sun

doi:10.3390/su13063123

Abstract

Overgrazing directly leads to grassland degradation, which is a serious constraint to the sustainable development of animal husbandry. In drylands, grassland biomass is highly heterogeneous in space and time. It is difficult to achieve sustainable utilization of grassland resources by focusing only on the average annual carrying capacity assessment obtained from grass yield. Here, we proposed a novel approach for assessing grassland carrying capacity, taking Zhenglan Banner (County) in Inner Mongolia as the study area. First, monthly grass yield at 30 m spatial resolution was estimated, derived from Carnegie–Ames–Stanford Approach (CASA) model and spatial and temporal adaptive reflectance fusion model (STARFM). Then, based on the degree of sand mobility and degradation condition of typical steppe, the utilization patterns for sandy land and typical steppe in different grazing seasons were developed separately to obtain available grass yield. Finally, the carrying capacity at the Gacha (Village)-scale was estimated and the current livestock carrying status was evaluated to facilitate the grassland refined management. In Zhenglan Banner, the carrying capacity was 237.46 thousand cattle-units in summer. The grassland resources are being overgrazed, with an overload rate of 19.32%. At Gacha-scale, the maximum reasonable stock density was ranged from 0.06 cattle-unit/ha to 0.42 cattle-unit/ha. Fifty-one Gachas exhibited livestock overload. This study is expected to provide technical support and scientific reference data for ecological conservation and grassland management in the study area, as well as in dryland pastoral areas of northern China.

Highlights

The Zhenglan Banner is located in the south of Xilin Gol League, Inner Mongolia, China, geographically between 41◦ 560 –43◦ 110 N and 115◦ 000 –116◦ 420 E and covering a total area of about 10,000 km2 (Figure 1) [81]
net primary production (NPP)-based grass yield estimation model in this study constructed by fusing high spatialtemporal resolution remotely sensed data and optimized Carnegie–Ames–Stanford Approach (CASA) model could achieve more detailed spatial delineation and capture intra-annual dynamic
The high spatial resolution of grass yield estimation results contribute to improve the accuracy of livestock carrying capacity assessment at the

Summary

Introduction

Grasslands are one of the most important types of terrestrial ecosystems on Earth because of their ecological functions, such as wind protection, sand fixation, soil and water conservation, air purification, and biodiversity maintenance. Grasslands have an important economic value as the basis for animal husbandry and the production of mutton, beef, dairy, and other products [2,3]. In China, natural grasslands are the largest terrestrial ecosystem, accounting for 41.7% of the country’s total land area and play an important role in livestock production and ecological restoration [4,5,6]. Animal husbandry based on the natural grasslands has been operating in China for almost a thousand years. Over the past century, little attention has been paid to the coordination of ecological protection and production functions in the grasslands [9].

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