A novel cellular automata model integrated with deep learning for dynamic spatiotemporal land use change simulation Abstract   Land use change (LUC) exhibits obvious spatiotemporal dependency. Previous cellular automata (CA)based methods usually treated the LUC dynamics as Markov processes and proposed a series of CAMarkov models, which however, were intrinsically unable to capture the longterm temporal dependency. Meanwhile, such models used only numerical proportion of neighboring land use (LU) types to represent neighborhood effects of LUC, which inevitably neglected the complicated spatial heterogeneity and thus caused inaccurate simulation results. To address these problems, this paper presents a novel CA model integrated with deep learning (DL) techniques to model spatiotemporal LUC dynamics. Our DLCA model firstly uses a convolutional neural network to capture latent spatial features for complete representation of neighborhood effects. A recurrent neural network then extracts historical information of LUC from timeseries land use maps. A random forest is appended as binary change predictor to avoid the imbalanced sample problem during model training.  Land use data collected from 2000 to 2014 of the Dongguan City, China were used to verify our proposed DLCA model. The input data from 2000 to 2009 were used for model training, the 2010 data for model validation, and the data collected from 2011 to 2014 were used for model evaluation. In addition, four traditional CA models of multilayer perceptron (MLP)CA, support vector machine (SVM)CA, logistic regression (LR)CA and random forest (RF)CA were also developed for accuracy comparisons. The simulation results demonstrate that the proposed DLCA model accurately captures longterm spatiotemporal dependency for more accurate LUC prediction results. The DLCA model raised prediction accuracy by 9.3%xe2x80x9311.67% in 2011xe2x80x932014 in contrast to traditional CA models.

Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests Analysis of urban land use dynamics is essential for assessing ecosystem functionalities and climate change impacts. The focus of this study is on monitoring the characteristics of urban expansion in HangJiaHu and evaluating its influences on forests by applying 30m multispectral Landsat data and a machine learning algorithm. Firstly, remote sensed images were preprocessed with radiation calibration, atmospheric correction and topographic correction. Then, the C5.0 decision tree was used to establish classification trees and then applied to make land use maps. Finally, spatiotemporal changes were analyzed through dynamic degree and land use transfer matrix. In addition, average land use transfer probability matrix (ATPM) was utilized for the prediction of land use area in the next 20 years. The results show that: (1) C5.0 decision tree performed with precise accuracy in land use classification, with an average total accuracy and kappa coefficient of more than 90.04% and 0.87. (2) During the last 20 years, land use in HangJiaHu has changed extensively. Urban area expanded from 5.84% in 1995 to 21.32% in 2015, which has brought about enormous impacts on cultivated land, with 198,854 hectares becoming urban, followed by forests with 19,823 hectares. (3) Land use area prediction based on the ATPM revealed that urbanization will continue to expand at the expense of cultivated land, but the impact on the forests will be greater than the past two decades. Rationality of urban land structure distribution is important for economic and social development. Therefore, remotely sensed technology combined with machine learning algorithms is of great significance to the dynamic detection of resources in the process of urbanization.