Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata
Open Access
- 13 April 2021
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 13 (8), 1499
- https://doi.org/10.3390/rs13081499
Abstract
Rapid urban expansion has seriously threatened ecological security and the natural environment on a global scale, thus, the simulation of dynamic urban expansion is a hot topic in current research. Existing urban expansion simulation models focus on the mining of spatial neighborhood features among driving factors, however, they ignore the over-fitting, gradient explosion, and vanishing problems caused by the long-term dependence of time series data, which results in limited model accuracy. In this study, we proposed a new dynamic urban expansion simulation model. Considering the long-time dependence issue, long short term memory (LSTM) was employed to automatically extract the transformation rules through memory units and provide the optimal attribute features for cellular automata (CA). This study selected Lanzhou, which is a semi-arid region in Northwest China, as an example to confirm the validity of the model performance using data from 2000 to 2020. The results revealed that the overall accuracy of the model was 91.01%, which was higher than that of the traditional artificial neural network (ANN)-CA and recurrent neural network (RNN)-CA models. The LSTM-CA framework resolved existing problems with the traditional algorithm, while it significantly reduced complexity and improved simulation accuracy. In addition, we predicted urban expansion to 2030 based on natural expansion (NE) and ecological constraint (EC) scenarios, and found that EC was an effective control strategy. This study provides a certain theoretical basis and reference value toward the realization of new urbanization and ecologically sound civil construction, in the context of territorial spatial planning and healthy/sustainable urban development.Funding Information
- National Key R&D Program of China (2018YFC1903700, lzujbky-2020-71)
This publication has 53 references indexed in Scilit:
- Land use/cover predictions incorporating ecological security for the Yangtze River Delta region, ChinaEcological Indicators, 2020
- Identifying key landscape pattern indices influencing the ecological security of inland river basin: The middle and lower reaches of Shule River Basin as an exampleScience of The Total Environment, 2019
- Spatially Explicit Mapping of Soil Conservation Service in Monetary Units Due to Land Use/Cover Change for the Three Gorges Reservoir Area, ChinaRemote Sensing, 2019
- Greening in Rural Areas Increases the Surface Urban Heat Island IntensityGeophysical Research Letters, 2019
- Estimates of shifts in ecosystem service values due to changes in key factors in the Manas River basin, northwest ChinaScience of The Total Environment, 2018
- Climate change will constrain the rapid urban expansion in drylands: A scenario analysis with the zoned Land Use Scenario Dynamics-urban modelScience of The Total Environment, 2018
- Simulating urban growth boundaries using a patch-based cellular automaton with economic and ecological constraintsInternational Journal of Geographical Information Science, 2018
- Influence of urban expansion on the urban heat island effect in ShanghaiInternational Journal of Geographical Information Science, 2016
- Understanding the dynamic of greenspace in the urbanized area of Beijing based on high resolution satellite imagesUrban Forestry & Urban Greening, 2015
- Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980sJournal of Geographical Sciences, 2014