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

Abstract

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.

Keywords

• Urbanization
• LSTM-RNN
• Temporal trends
• MODIS
• Enhance vegetation index
• Pakistan