Assessing the degradation of grassland ecosystems based on the advanced local net production scaling method—The case of Inner Mongolia, China

Abstract

The gradual degradation of grasslands on a global scale goes hand‐in‐hand with significant challenges for agriculture and animal husbandry development. Numerous relevant policies and projects have been implemented to protect and restore Chinese ecosystems, but it is still unclear to what degree grassland ecosystems can be recovered. In view of this, we constructed an advanced local net production scaling (ALNS) method by replacing the classification method by self‐organizing feature maps (SOFM) and tailoring the ideal state evaluation method in the LNS method. The ALNS method is used to analyze differences within grassland ecosystems, explore the ideal state of grassland ecosystems, and define degradation as the degree to which the actual state deviates from the ideal state, representing the degree to which grassland ecosystems can be recovered. It thereby quantifies and assesses the overall degradation of such systems in Inner Mongolia. Based on the results, more than 98.5% of the total grassland area failed to reach the ideal state, with the highest levels in the northeast with DN (degraded net primary productivity) values exceeding 200 gc/(m2·yr), followed by the midlands with DN values from 50 to 200 gc/(m2·yr) and the southwest with DN values between 0 and 150 gc/(m2·yr). The ALNS method can efficiently assess grassland ecosystem degradation and can be used to indicate the deviation degrees from ideal states, facilitating the development of protection and restoration programs for grassland ecosystems.

Baseline and status of desertification in Central Asia

“…The LNS method requires that most pixels within an LCC have a uniform productive potential so that DS is determined relative to it. Therefore, the magnitude of desertification is depicted by the gap between actual and potential productivity (Lang et al, 2020;Prince et al, 2009), and the DS is expressed as:…”

“…Although the percentile-based one can overcome the bias of anomalies, the choice of percentile is a subjective preference (Lang et al, 2020;Wessels et al, 2008). Therefore, establishing a robust framework to determine the NPP p is especially important for the widespread application of LNS.…”

“…Therefore, establishing a robust framework to determine the NPP p is especially important for the widespread application of LNS. Besides, the LNS method has been successfully applied in land degradation/desertification assessments in Zimbabwe, Australia, China, and other countries (Jackson & Prince, 2016;Lang et al, 2020;Prince et al, 2009;Wessels et al, 2008).…”

Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

“…Previous studies on grassland degradation assessment are mainly based on remote sensing technology and parameter assessment systems ( Gao et al, 2006 ; Feng et al, 2009 ; Lang et al, 2021 ). When using this method, indexes for grassland degradation are always uncertain, and most index parameters are set according to the subjective experience of researchers, resulting in the results being unreliable.…”

Patterns and Controls of the Latent and Sensible Heat Fluxes in the Brazilian Pampa Biome

“…The areas have been used inappropriately, through an excessive load of animals, which has caused environmental impacts due to vegetation cover loss, invasion of exotic species, and soil erosion [8,9]. Studies have shown that pasture ecosystem degradation not only alters its structure and ecosystem productivity but also affects surface-atmosphere interactions, changing heat and water transport [10][11][12][13].…”