Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.</p

Urban expansion in Auckland, New Zealand: a GIS simulation via an intelligent self-adapting multiscale agent-based model

Abstract: When modelling urban expansion dynamics, cellular automata models focus mostly on the physical environments and cell neighbours, but ignore the ‘human’ aspect of the allocation of urban expansion cells. This limitation is overcome here using an intelligent self-adapting multiscale agent-based model. To simulate the urban expansion of Auckland, New Zealand, a total of 15 urban expansion drivers/constraints were considered over two periods (2000–2005, 2005–2010). The modelling takes into consideration both a macro-scale agent (government) and micro-scale agents (residents of three income levels), and their multi-level interactions. In order to achieve reliable simulation results, ABM was coupled with an artificial neural network to reveal the learning process and heterogeneity of the multi-sub-residential agents. The ANN-ABM accurately simulated the urban expansion of Auckland at both the global and local scales, with kappa simulation value at 0.48 and 0.55, respectively. The validated simulation result shows that the intelligent and self-adapting ANN-ABM approach is more accurate than an ABM with a general type of agent model (kappa simulation = 0.42) at the global scale, and more accurate than an ANN-based CA model (kappa simulation = 0.47) at the local scale. Simulation inaccuracy stems mostly from the outdated master land use plan.</p

Enhancing Landsat image based aboveground biomass estimation of black locust with scale bias-corrected LiDAR AGB map and stratified sampling

There is a growing interest in leveraging LiDAR-generated forest Aboveground Biomass (LG-AGB) data as a reference to retrieve AGB from satellite observations. However, the biases arising from the upscaling process and the impact of the sampling strategy on model accuracy still need to be resolved. In this study, we first corrected the bias arising from upscaling the LG-AGB map to match the spatial resolution of Landsat observations. Subsequently, the stratified random sampling method was used to select training samples from the corrected LG-AGB map (cLG-AGB) for the Random Forest (RF) regression model. The RF model features were extracted from the Landsat observations and auxiliary data. The impact of strata numbers on model accuracy was explored during the sampling process. Finally, independent validation was conducted using in situ measurements. The results indicated that: (1) about 68% of the biases can be corrected in the up-scale transformation; (2) compared to no stratification, a three-strata model achieved a 6.5% improvement in AGB estimation accuracy while requiring a 37.8% reduction in sample size; (3) the black locust forest had a low saturation point at 60.52 ± 4.46 Mg/ha AGB and 72.4% AGB values were underestimated and the remaining were overestimated. In summary, our study provides a framework to harmonize near-surface LiDAR and satellite data for AGB estimation in plantation forest ecosystems with small patch sizes and fragmented distribution.</p