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Research On Ecosystem Health Diagnosis And Evolution Under The Artificial Intervention In Karst Desertification Area

Posted on:2017-05-05Degree:MasterType:Thesis
Country:ChinaCandidate:S Z ChenFull Text:PDF
GTID:2271330503978620Subject:Physical geography
Abstract/Summary:PDF Full Text Request
Karst rocky desertification is the main ecological environment problem of ecological protection, construction and sustainable development in Guizhou mountain area and even in Southwest China. The health diagnosis of ecosystem health in Karst rocky desertification area and the response of human intervention is the foundation of the ecological construction and the comprehensive management of rocky desertification. It has certain theoretical value and practical guiding significance for ecological restoration and reconstruction in southwest Karst region.In view of the main problems of ecological environment in Karst rocky desertification area, the vulnerability of ecological environment and its influencing factors in rocky desertification area were analyzed in detail. Using remote sensing and geographic information system technology, combined with field survey data, extraction of thematic information, ecosystem health diagnosis database were established. The ecosystem health diagnosis index system of Karst rocky desertification was constructed in the three aspects,which is ecological environment support system, the resources and environment support system and the social and economic support system. Then,The typical diagnosis units were selected, and the weight of the index weights were calculated by genetic analytic hierarchy process and entropy weight method, and the quantitative diagnosis and analysis of rocky desertification ecosystem health was made by the ecosystem health diagnosis model. Therefore, main research results are:(1) Ecosystem health in the rocky desertification has been improved. which the comprehensive control of rocky desertification have made certain achievement, excellent health zone does not exist, the demonstration area is still dominated by unhealthy and middle health levels. During the 14 years, Healthy regions showed a rising trend, rising from 3.75% in 2000 to 8.78% in 2014; middle health area show inverted "V" shaped trend; unhealth and ill areas showed a shallow "V" shaped trend. Due to the input of rocky desertification comprehensive treatment project in 2000 to 2010, the ecosystem health was improved, and the local human disturbance(such as quarry) was strong, forcing ecosystem health of the original fragile rocky desertification were decline in 2010 to 2014.(2) Ecosystem health space showed regional differences, The ecological system of peak cluster depressions and peak cluster valley where have better water and heat conditions is health. Ecosystem health of regional where imple the rocky desertification comprehensive treatment continued to improve. Ecosystem health in strong human disturbance area is changeable and even worse.(3) Spatially coupled the human intervention based on the pattern and ecosystem health evolution pattern in karst rocky desertification area, we can find the improvement ratio of the ecosystem health where have imple the rocky desertification comprehensive treatment project was significantly higher than the non implementation area. The influence of different rocky desertification comprehensive treatment project of the ecosystem health is different, Afforestation> terracing > orchard > water bowling > shelter forest > artificial grass.(4) We used the Markov model to simulate the healthy development trend of ecological system in 2020 to 2050 in the Huajiang demonstration area. In the next 30 years, it’s still not a healthy level. The area of healthy level will show a slow and steady increase trend. The area of the middle health level will increase steadily, which is predicted to increase by 2.8% in 2050; The area of unhealthy levels will continue to decline from 34.78% in 2010 to 26.6% in 2050; The area of the ill level will be reduced by 2.44%.
Keywords/Search Tags:Karst rocky desertification, Ecosystem health, Human intervention, diagnosis, Markov prediction
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