Abstract
Land use and land cover change (LUCC) modeling has continuously been a major research theme in the field of land system science, which interprets the causes and consequences of land use dynamics. In particular, models that can obtain long-term land use data with high precision are of great value in research on global environmental change and climate impact, as land use data are important model input parameters for evaluating the effect of human activity on nature. However, the accuracy of existing reconstruction and prediction models is inadequate. In this context, this study proposes an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model (CLRPM), which meets the demand for fine-scale LUCC reconstruction and prediction. This model applies the deep learning method, which far exceeds the performance of traditional machine learning methods, and uses CNN to extract spatial features and provide greater proximity information. Taking Baicheng city in Northeast China as an example, we verify that CLRPM achieved high-precision annual LUCC reconstruction and prediction, with an overall accuracy rate 9.38% higher than that of the existing models. Additionally, the error rate was reduced by 49.5%. Moreover, this model can perform multilevel LUCC classification category reconstructions and predictions. This study casts light on LUCC models within the high-precision and fine-grained LUCC categories, which will aid LUCC analyses and help decision-makers better understand complex land-use systems and develop better land management strategies.
Highlights
Land use and land cover change (LUCC) is an area of study that examines the relationship between human economic activities and ecology and helps explain these interactions [1,2,3]
(2005, 2010, 2015, 2020), indicating that the overall accuracy is ranked in the following order: ANN < K-Nearest Neighbor (KNN) < Random Forest (RF) < convolutional neural network (CNN)
Zhenlai County, China was selected in that study, and the performance of the proposed DLURM was validated by comparing the DLURM to the HLURM and cellular automata (CA)–Markov models. (HLURM is an excellent integrated model that has been published, is widely recognized and exemplifies the capabilities of the current mature models.) The experimental results showed that DLURM had a significantly better overall accuracy in terms of reconstruction, reaching 92.87%
Summary
Land use and land cover change (LUCC) is an area of study that examines the relationship between human economic activities and ecology and helps explain these interactions [1,2,3]. With the successive implementation of the Land-Use and Land-Cover Change Science/Research Plan, Global 4.0/). The improvement and supplementation of long-term series LUCC data with high-resolution information is essential to promote the systematic and comprehensive study of LUCC processes and their effects [13]. With the rapid development of remote sensing technology, the number of high-resolution, wide-coverage satellite images has increased exponentially, providing broad prospects for using intelligent recognition technology to generate spatiotemporal land use (LU) data [14]. Obtaining high-quality interannual images covering large areas is challenging, and some historical data and documents are no longer available
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