東華大學圖書館 |

Monitoring and Prediction of Wetland Dynamics in Dongting Lake Area, China.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Monitoring and Prediction of Wetland Dynamics in Dongting Lake Area, China./
作者:	Wang, Minzi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	164 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-09, Section: A.
Contained By:	Dissertations Abstracts International80-09A.
標題:	Geography. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10977394
ISBN:	9780438914193

Monitoring and Prediction of Wetland Dynamics in Dongting Lake Area, China.
Wang, Minzi.

Monitoring and Prediction of Wetland Dynamics in Dongting Lake Area, China. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 164 p.

Source: Dissertations Abstracts International, Volume: 80-09, Section: A.

Thesis (Ph.D.)--Southern Illinois University at Carbondale, 2018.

This item must not be added to any third party search indexes.

Wetland, which contains about 20-30% of global soil carbon pool (Lal, 2008), is one of the world's most important environmental resources for long-term carbon storage, and plays a vital role in global carbon cycling, especially in mitigating carbon concentration in the atmosphere. However, it is also the ecosystem that has been most seriously abused and suffering from continuous degradation and loss across the world. During the past few centuries, about 50% of the world's wetland has been lost due to increasing anthropogenic disturbances and global warming (Mitsch & Gosselink, 2007; Gibbs, 2000; Dugan, 1993; Zedler and Kercher, 2005). One typical example is the wetland in Dongting Lake area of China, which was once China's largest freshwater wetland and now has become the second one. During the past few decades, the Lake has experienced many significant changes causing the rapid degradation, shrinkage and fragmentation of its wetland. Therefore, monitoring the changes of the Lake wetland in spatial distribution and temporal trend and predicting its potential dynamics under climate change and human induced disturbances are becoming increasingly important for linking policy decision-making with regulatory actions and subsequent land-use activities. The overall objective of this project is to monitor the wetland changes in the Lake area and predict its dynamics in the future using proposed land use and land cover (LULC) classification, change detection and modelling approaches. To start with, this study examined the spatiotemporal dynamics of the Lake wetland patterns during the past half century through analyzing remotely sensed images acquired on six time points, including 1978, 1984, 1994, 2001, 2004, 2009, and 2013. A hybrid knowledge-based classification method which combines supervised and expert classification systems was first applied to conduct image classifications with special attention to the classification accuracy of the wetland categories including water, paddy field, reed and marsh categories. After that, a post-classification based change detection technique was carried out to monitor the dynamics of the Lake wetland. The error matrices and Kappa coefficients were than used to assess the classification accuracy. The classification results demonstrated that the proposed hybrid classification approach could discriminate the wetland categories from others with the high accuracy of 96.9%, 93.7%, 82.6%, and 82.4% for water, paddy field, reed, and marsh categories, respectively. The LULC analysis based on the classification showed that wetland area (reed and marsh) in the Lake area has decreased with a dramatic decrease trend after the Three Gorges Dam being fully operated in 2003. To predict future wetland changes and allocate the changes effectively, an integrated model incorporating the logistic, the Markov, and the Conversion of Land use and its Effects (CLUE-S) models has been developed and utilized to 1) produce the LULC probability surface maps; 2) to simulate the LULC change demand in 2013 and 2025 of which the demand for 2013 was then used for validating the results of this integrated model by comparing with the actual LULC maps of the same year; 3) to allocate the simulated changes of 2013 and 2025 based on the obtained LULC probability surface maps and some user-defined rules including land use conversion rules and conversion elasticity. The results from the model validation indicated that the integrated model performed very well with an overall modelling accuracy and Kappa statistic of 80.2% and 74.9%, respectively. The results also suggested that the wetland area is likely to undergo further decrease of another 256.3 km2 by 2025. In summary, this study focused on the development of a unique and integrated approach for the LULC image classification, change detection and prediction of the wetland area-Dongting Lake region in which the landscape was complex and experiencing fast and dramatic changes due to the construction of the TGD. The approach can be easily extended to other wetland associated studies. By providing the information of the long-term wetland dynamics and simulation of its future changes in the Lake area, this research will also enhance our understanding of wetland resources, their dynamics and relationships with human activity induced disturbances and thus promote our ability to make informed use and wise restoration regulations of wetlands.

ISBN: 9780438914193Subjects--Topical Terms:

524010
Geography.
Subjects--Index Terms:

Dongting Lake

Monitoring and Prediction of Wetland Dynamics in Dongting Lake Area, China.
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300 $a 164 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-09, Section: A.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Wang, Guangxing.
502 $a Thesis (Ph.D.)--Southern Illinois University at Carbondale, 2018.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Wetland, which contains about 20-30% of global soil carbon pool (Lal, 2008), is one of the world's most important environmental resources for long-term carbon storage, and plays a vital role in global carbon cycling, especially in mitigating carbon concentration in the atmosphere. However, it is also the ecosystem that has been most seriously abused and suffering from continuous degradation and loss across the world. During the past few centuries, about 50% of the world's wetland has been lost due to increasing anthropogenic disturbances and global warming (Mitsch & Gosselink, 2007; Gibbs, 2000; Dugan, 1993; Zedler and Kercher, 2005). One typical example is the wetland in Dongting Lake area of China, which was once China's largest freshwater wetland and now has become the second one. During the past few decades, the Lake has experienced many significant changes causing the rapid degradation, shrinkage and fragmentation of its wetland. Therefore, monitoring the changes of the Lake wetland in spatial distribution and temporal trend and predicting its potential dynamics under climate change and human induced disturbances are becoming increasingly important for linking policy decision-making with regulatory actions and subsequent land-use activities. The overall objective of this project is to monitor the wetland changes in the Lake area and predict its dynamics in the future using proposed land use and land cover (LULC) classification, change detection and modelling approaches. To start with, this study examined the spatiotemporal dynamics of the Lake wetland patterns during the past half century through analyzing remotely sensed images acquired on six time points, including 1978, 1984, 1994, 2001, 2004, 2009, and 2013. A hybrid knowledge-based classification method which combines supervised and expert classification systems was first applied to conduct image classifications with special attention to the classification accuracy of the wetland categories including water, paddy field, reed and marsh categories. After that, a post-classification based change detection technique was carried out to monitor the dynamics of the Lake wetland. The error matrices and Kappa coefficients were than used to assess the classification accuracy. The classification results demonstrated that the proposed hybrid classification approach could discriminate the wetland categories from others with the high accuracy of 96.9%, 93.7%, 82.6%, and 82.4% for water, paddy field, reed, and marsh categories, respectively. The LULC analysis based on the classification showed that wetland area (reed and marsh) in the Lake area has decreased with a dramatic decrease trend after the Three Gorges Dam being fully operated in 2003. To predict future wetland changes and allocate the changes effectively, an integrated model incorporating the logistic, the Markov, and the Conversion of Land use and its Effects (CLUE-S) models has been developed and utilized to 1) produce the LULC probability surface maps; 2) to simulate the LULC change demand in 2013 and 2025 of which the demand for 2013 was then used for validating the results of this integrated model by comparing with the actual LULC maps of the same year; 3) to allocate the simulated changes of 2013 and 2025 based on the obtained LULC probability surface maps and some user-defined rules including land use conversion rules and conversion elasticity. The results from the model validation indicated that the integrated model performed very well with an overall modelling accuracy and Kappa statistic of 80.2% and 74.9%, respectively. The results also suggested that the wetland area is likely to undergo further decrease of another 256.3 km2 by 2025. In summary, this study focused on the development of a unique and integrated approach for the LULC image classification, change detection and prediction of the wetland area-Dongting Lake region in which the landscape was complex and experiencing fast and dramatic changes due to the construction of the TGD. The approach can be easily extended to other wetland associated studies. By providing the information of the long-term wetland dynamics and simulation of its future changes in the Lake area, this research will also enhance our understanding of wetland resources, their dynamics and relationships with human activity induced disturbances and thus promote our ability to make informed use and wise restoration regulations of wetlands.
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650 4 $a Geography. $3 524010
650 4 $a Geographic information science. $3 3432445
650 4 $a Remote sensing. $3 535394
653 $a Dongting Lake
653 $a Hybrid classification
653 $a Integrated model
653 $a Wetland dynamics
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710 2 $a Southern Illinois University at Carbondale. $b Environmental Resources & Policy. $3 1282377
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793 $a English
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