Multi-scale spatiotemporal wetland loss and its critical influencing factors in China determined using innovative grid-based GWR
Wetlands serve as a critical habitat for many plants and animals. However, with urban expansion and other processes, the wetlands in China are lost at an alarming rate in recent decades. This study quantifies the pace of wetland loss in the past two decades and investigates the hidden mechanisms of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23002868 |
| _version_ | 1797851999523307520 |
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| author | Aohua Tian Tingting Xu Jay Gao Chang Liu Letao Han |
| author_facet | Aohua Tian Tingting Xu Jay Gao Chang Liu Letao Han |
| author_sort | Aohua Tian |
| collection | DOAJ |
| description | Wetlands serve as a critical habitat for many plants and animals. However, with urban expansion and other processes, the wetlands in China are lost at an alarming rate in recent decades. This study quantifies the pace of wetland loss in the past two decades and investigates the hidden mechanisms of wetland loss via a proposed innovative grid-based geographically weighted regression computing unified device architecture (Grid-GWR-CUDA) method. Also assessed in this study is the relative importance of four natural factors (precipitation, digital elevation model (DEM), slope, evaporation), and four human related factors (distance to city, distance to roads, population, and gross domestic product (GDP)) in wetland loss at both the national and regional levels. The importance of some critical factors is compared across different regions. The results indicated that GDP, population size and DEM are the three most significant factors impacting on the rate of wetland loss at the national level. They are still the most influential factors for most regions at the sub-national level while precipitation is an important factor only in the eastern regions. Therefore, the main causes of wetland loss in China are attributed to human socioeconomic activities. Compared with the traditional geographically weighted regression (GWR) method, the proposed Grid-GWR-CUDA method is innovative enough to handle large data volumes efficiently while retaining all data samples at a fine (e.g., 30 m) spatial resolution because the computational efficiency is significantly improved via graphics processing unit (GPU) acceleration. It improves the accuracy of regression by several folds over traditional ordinary least square and GWR. It has the potential to predict future wetland loss if supplied with the latest land cover data. |
| first_indexed | 2024-04-09T19:26:58Z |
| format | Article |
| id | doaj.art-ad02d09207294ddc8e21fd0f7f69df57 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-09T19:26:58Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-ad02d09207294ddc8e21fd0f7f69df572023-04-05T08:06:10ZengElsevierEcological Indicators1470-160X2023-05-01149110144Multi-scale spatiotemporal wetland loss and its critical influencing factors in China determined using innovative grid-based GWRAohua Tian0Tingting Xu1Jay Gao2Chang Liu3Letao Han4Chongqing University of Posts and Telecommunications, School of Software Engineering, ChinaChongqing University of Posts and Telecommunications, School of Software Engineering, China; The University of Auckland, School of Environment Science, New Zealand; Corresponding author.The University of Auckland, School of Environment Science, New ZealandChongqing University of Posts and Telecommunications, School of Software Engineering, ChinaChongqing University of Posts and Telecommunications, School of Software Engineering, ChinaWetlands serve as a critical habitat for many plants and animals. However, with urban expansion and other processes, the wetlands in China are lost at an alarming rate in recent decades. This study quantifies the pace of wetland loss in the past two decades and investigates the hidden mechanisms of wetland loss via a proposed innovative grid-based geographically weighted regression computing unified device architecture (Grid-GWR-CUDA) method. Also assessed in this study is the relative importance of four natural factors (precipitation, digital elevation model (DEM), slope, evaporation), and four human related factors (distance to city, distance to roads, population, and gross domestic product (GDP)) in wetland loss at both the national and regional levels. The importance of some critical factors is compared across different regions. The results indicated that GDP, population size and DEM are the three most significant factors impacting on the rate of wetland loss at the national level. They are still the most influential factors for most regions at the sub-national level while precipitation is an important factor only in the eastern regions. Therefore, the main causes of wetland loss in China are attributed to human socioeconomic activities. Compared with the traditional geographically weighted regression (GWR) method, the proposed Grid-GWR-CUDA method is innovative enough to handle large data volumes efficiently while retaining all data samples at a fine (e.g., 30 m) spatial resolution because the computational efficiency is significantly improved via graphics processing unit (GPU) acceleration. It improves the accuracy of regression by several folds over traditional ordinary least square and GWR. It has the potential to predict future wetland loss if supplied with the latest land cover data.http://www.sciencedirect.com/science/article/pii/S1470160X23002868Geographically weighted regressionDrivers of wetland lossMulti-scale analysisSpatiotemporal changeParallel computationChina |
| spellingShingle | Aohua Tian Tingting Xu Jay Gao Chang Liu Letao Han Multi-scale spatiotemporal wetland loss and its critical influencing factors in China determined using innovative grid-based GWR Ecological Indicators Geographically weighted regression Drivers of wetland loss Multi-scale analysis Spatiotemporal change Parallel computation China |
| title | Multi-scale spatiotemporal wetland loss and its critical influencing factors in China determined using innovative grid-based GWR |
| title_full | Multi-scale spatiotemporal wetland loss and its critical influencing factors in China determined using innovative grid-based GWR |
| title_fullStr | Multi-scale spatiotemporal wetland loss and its critical influencing factors in China determined using innovative grid-based GWR |
| title_full_unstemmed | Multi-scale spatiotemporal wetland loss and its critical influencing factors in China determined using innovative grid-based GWR |
| title_short | Multi-scale spatiotemporal wetland loss and its critical influencing factors in China determined using innovative grid-based GWR |
| title_sort | multi scale spatiotemporal wetland loss and its critical influencing factors in china determined using innovative grid based gwr |
| topic | Geographically weighted regression Drivers of wetland loss Multi-scale analysis Spatiotemporal change Parallel computation China |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23002868 |
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