Abstract

Grassland resources are important land resources. However, grassland degradation has become evident in recent years, which has reduced the function of soil and water conservation and restricted the development of animal husbandry. Timely and accurate monitoring of grassland changes and understanding the degree of degradation are the foundation for the scientific use of grasslands. The grassland degradation index of ground comprehensive evaluation (grassland degradation index, GDIg) is a digital expression of grassland growth that can accurately indicate the degradation of grasslands. In this research, the accuracy of GDIg in evaluating grassland degradation is discussed; the typical areas of grassland degradation in Shangri-La City, i.e., the towns of Jiantang and Xiaozhongdian, are selected as the research area. Through a field survey and spectroscopy combined with Huanjing-1 (HJ-1) satellite image data, grassland degradation was monitored in the study area from 2008 to 2017. The results show that: (1) GDIg based on six indicators, namely, above-ground biomass, cover level, height, biomass of edible herbage, biomass of toxic weeds, and species richness, can effectively indicate grassland degradation, with the accuracy of the degradation grade assessment reaching 98.6%. (2) The correlation between the GDIg and the grey values of 4 wavebands and 7 types of vegetation indexes derived from the HJ-1 is analysed, and the degraded grassland inversion model was built and revised based on HJ-1 data. The grassland degradation evaluation index of remote sensing (GDIrs) model indicates that grassland degradation is proportional to the ratio vegetation index (RVI). (3) The grassland area was 405.40 km2 in the initial monitoring period, accounting for 17.26% of the study area, while at the end of the monitoring period, the area was 338.87 km2, with a loss of 66.53 km2. From 2008 to 2017, the area of non-degraded and slightly degraded grassland in the study area presented a downward trend, with decreases of 59.87 km2 and 49.93 km2, respectively. In contrast, the area of moderately degraded grassland increased by 41.17 km2 from 91.58 km2 in 2008 to 132.74 km2 in 2017, accounting for 39.17% of the grassland. The area of severely degraded grassland was 78.32 km2, accounting for 23.11% of the grassland in 2017. (4) The degraded grasslands in the study area mainly transformed into the degradation-enhanced (deterioration) type. As the transformation rate gradually slows down, the current situation of grassland degradation is not hopeful.

Highlights

  • Since grasslands have the ecological functions of preventing impacts from wind, fixing sand, maintaining water and soil, conserving water, purifying air and maintaining biodiversity, they are one of the most important types of terrestrial ecosystems on Earth

  • With the rate of more than 80%, the grassland degradation information can use the first two principal components to replaces the original six evaluation indicators

  • This study explores the feasibility of using grassland degradation index (GDI) in remote-sensing monitoring of degraded grasslands

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Summary

Introduction

Since grasslands have the ecological functions of preventing impacts from wind, fixing sand, maintaining water and soil, conserving water, purifying air and maintaining biodiversity, they are one of the most important types of terrestrial ecosystems on Earth. Grassland degradation is the deterioration of grassland ecological environment caused by the influence of unfavourable natural factors such as drought, wind and sand erosion, salinity, waterlogging, groundwater level change, or irrational artificial use such as overgrazing, over-excavation, indiscriminate mowing. These negative impacts are represented in terms of low yield of above-ground grassland biomass, declining quality, reduced grassland utilization, and even loss of value in use grassland. Remote-sensing technology has been widely applied in grassland degradation monitoring because of its high efficiency, accuracy and wide monitoring range [5]

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