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Fostering deep learning approaches to evaluate the impact of urbanization on vegetation and future prospects

文献类型：期刊论文

作者Zafar, Zeeshan7; Mehmood, Muhammad Sajid4,5,6,7; Shiyan, Zhai6,7; Zubair, Muhammad3; Sajjad, Muhammad1,2; Yaochen, Qin4,5,6,7
刊名ECOLOGICAL INDICATORS
出版日期2023-02-01
卷号146页码:12
关键词Urbanization LSTM-RNN Temporal trends MODIS Enhance vegetation index Pakistan
ISSN号1470-160X
DOI10.1016/j.ecolind.2022.109788
通讯作者Shiyan, Zhai(zsycenu@hotmail.com)
英文摘要Vegetation is an essential component of our global ecosystem and an important indicator of the dynamics and productivity of land cover. Vegetation forecasting research has been accelerated using several deep learning (DL) algorithms through remote sensing (RS) data. In this context, we used artificial intelligence (AI) and the longshort-term memory recurrent neural network (LSTM-RNN) method to explore and forecast future urban-rural vegetation disparities (Delta EVI, where EVI is the enhanced vegetation index) in Pakistan's six megacities using MODIS EVI data. The forecast results revealed that Delta EVI is decreasing in all cities. The Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) were used to evaluate LSTM-RNN. RSME values were recorded as 0.03, 0.07, 0.02, 0.03, 0.05, and 0.06 for Faisalabad, Gujranwala, Rawalpindi, Lahore, Multan, and Sialkot, respectively. MAPE was estimated as 0.12, 0.55, 0.24, 0.18, 0.28, and 0.47 for Faisalabad, Gujranwala, Rawalpindi, Lahore, Multan, and Sialkot, respectively. This situation indicates that LSTM-RNN can be used as a new reliable AI technique for forecasting. The results suggested that the average of forecasted Delta EVI for the next 10 years is -0.23, -0.21, -0.09, -0.13, -0.22, and -0.11 for Faisalabad, Gujranwala, Rawalpindi, Lahore, Multan, and Sialkot, respectively. The findings of this study will help evaluate the impact of urbanization on EVI by leveraging DL techniques along with implementing an urbanization policy for urban development and environmental protection for long-term urban sustainability.
WOS关键词URBAN HEAT-ISLAND ; NEURAL-NETWORK ; SURFACE ; VARIABILITY ; PREDICTION
资助项目Science and Technology Department of Henan Province ; Key Scientific Research Projects of Colleges and Universities in Henan Province ; National Experimental Teaching Demonstrating Center of Henan University ; [222102320397] ; [21A170007] ; [2020HGSYJX004]
WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000900132200002
出版者ELSEVIER
资助机构Science and Technology Department of Henan Province ; Key Scientific Research Projects of Colleges and Universities in Henan Province ; National Experimental Teaching Demonstrating Center of Henan University
源URL[http://ir.igsnrr.ac.cn/handle/311030/188524]  
专题中国科学院地理科学与资源研究所
通讯作者Shiyan, Zhai
作者单位1.Hong Kong Baptist Univ, Dept Geog, Hong Kong, Peoples R China
2.Hong Kong Baptist Univ, Ctr Geocomputat Studies, , SAR, Hong Kong, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
4.Henan Univ, Minist Educ, Kaifeng 475004, Peoples R China
5.Henan Univ, Key Res Inst Yellow River Civilizat & Sustainable, Collaborat Innovat Ctr Yellow River Civilizat Join, Kaifeng 475004, Peoples R China
6.Henan Univ, Key Lab Geospatial Technol Middle & Lower Yellow R, Minist Educ, Kaifeng 475004, Peoples R China
7.Henan Univ, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China
推荐引用方式
GB/T 7714		 Zafar, Zeeshan,Mehmood, Muhammad Sajid,Shiyan, Zhai,et al. Fostering deep learning approaches to evaluate the impact of urbanization on vegetation and future prospects[J]. ECOLOGICAL INDICATORS,2023,146:12.
APA Zafar, Zeeshan,Mehmood, Muhammad Sajid,Shiyan, Zhai,Zubair, Muhammad,Sajjad, Muhammad,&Yaochen, Qin.(2023).Fostering deep learning approaches to evaluate the impact of urbanization on vegetation and future prospects.ECOLOGICAL INDICATORS,146,12.
MLA Zafar, Zeeshan,et al."Fostering deep learning approaches to evaluate the impact of urbanization on vegetation and future prospects".ECOLOGICAL INDICATORS 146(2023):12.

入库方式： OAI收割

来源：地理科学与资源研究所

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