A novel and direct ecological risk assessment index for environmental degradation based on response curve approach and remotely sensed data. (March 2019)

• Record Type: Journal Article
• Authors: Tan, Jianbo; Li, Ainong; Lei, Guangbin; Bian, Jinhu; Zhang, Zhenjian
• Is Part Of: Ecological indicators. Volume 98(2019)
• Page Start: 783
• Page End: 793
• Publication Date: 2019-03
• Subjects: Environmental degradation -- Ecological risk assessment index -- Response curve of ecosystems to environment -- Remotely sensed data -- Xishuangbanna; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2018.11.038
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: We developed a systematic and direct methodology for ecological risk assessments. We proposed a method to acquire the response curves of ecosystem to its environment through Satellite data. We identified the risk of temperature changes to natural forests in Xishuangbanna. Abstract: Ecosystems are at risk of environmental degradation by the intensification of human activities and climate change over the world. This manuscript aims to present a systematic and direct methodology for abiotic risk assessment. In this study, a novel ecological risk assessment index called "performance relative severity index" (PRS) was developed to directly identify the risk of ecosystem collapse due to abiotic stress. The index is acquired by calculating the degree of deviation from performance of ecosystems to optimum performance that caused by the degradation of environment. First, the key abiotic variables distinguishing ecosystems should be identified. Second, the response curve indicating the relationship between ecosystem performance and key abiotic variables should be constructed. Due to the lack of the information on the response curve for some regions, the minimum and maximum points of optimum and tolerance range, defining the response curve of ecosystem to abiotic variables, should be acquired. The minimum and maximum optimal points were obtained through the relationships between the index derived from remotely sensed data and abiotic variables, and the minimum and maximum tolerance points were acquired by the relationships between the occurrence of ecosystem and its environment. Based on the response curve of ecosystem to abiotic variable, the performance of ecosystem to current abiotic variables were available. Finally, the PRS was acquired through calculating the ratio of the change in the performance of ecosystems caused by environmental degradation to the change that would cause an ecosystem to collapse. The proposed methodology was applied to calculate the PRS of the natural forest regarding temperature in Xishuangbanna, a biodiversity hotspot located in Southwestern China. The results show that the suitable bioclimatic zone for ecosystems in Xishuangbanna would be shifted to higher altitude region under global warming. Considering ecosystems are always lag behind the effects of climate changes, these conversions indicate potential heat-stress to the natural forests of lowland in Xishuangbanna due to significantly rising temperatures in the future.