TY - JOUR
T1 - Fostering deep learning approaches to evaluate the impact of urbanization on vegetation and future prospects
AU - Zafar, Zeeshan
AU - Sajid Mehmood, Muhammad
AU - Shiyan, Zhai
AU - Zubair, Muhammad
AU - Sajjad, Muhammad
AU - Yaochen, Qin
N1 - Funding Information:
This research was funded by the Science and Technology Department of Henan Province (222102320397); Key Scientific Research Projects of Colleges and Universities in Henan Province (grant number 21A170007); The National Experimental Teaching Demonstrating Center of Henan University (grant number 2020HGSYJX004).
Publisher Copyright:
© 2022 The Authors.
PY - 2023/2
Y1 - 2023/2
N2 - 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 long-short-term memory recurrent neural network (LSTM-RNN) method to explore and forecast future urban–rural vegetation disparities (ΔEVI, where EVI is the enhanced vegetation index) in Pakistan's six megacities using MODIS EVI data. The forecast results revealed that ΔEVI 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 ΔEVI 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.
AB - 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 long-short-term memory recurrent neural network (LSTM-RNN) method to explore and forecast future urban–rural vegetation disparities (ΔEVI, where EVI is the enhanced vegetation index) in Pakistan's six megacities using MODIS EVI data. The forecast results revealed that ΔEVI 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 ΔEVI 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.
KW - Enhance vegetation index
KW - LSTM-RNN
KW - MODIS
KW - Pakistan
KW - Temporal trends
KW - Urbanization
UR - http://www.scopus.com/inward/record.url?scp=85144007803&partnerID=8YFLogxK
U2 - 10.1016/j.ecolind.2022.109788
DO - 10.1016/j.ecolind.2022.109788
M3 - Journal article
AN - SCOPUS:85144007803
SN - 1470-160X
VL - 146
JO - Ecological Indicators
JF - Ecological Indicators
M1 - 109788
ER -
