Cellular Automata Modeling of Land-Use/Land-Cover Dynamics
Questioning the Reliability of Data Sources and Classification Methods
Bibliographic Data
| ID | 7635058 |
|---|---|
| Authors | Yulia Grinblat (0009-0002-4547-7569, Tel Aviv University), Michael Gilichinsky (Ariel University), Itzhak Benenson (0000-0003-0704-0673, Tel Aviv University) |
| Year | 2016 |
| Volume | 106 |
| Issue | 6 |
| Pages | 1299-1320 |
| Publication date | 2016-11-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Annals of the American Association of Geographers (JOURNAL) |
| Journal identifiers | ISSN: 2469-4452 • E-ISSN: 2469-4460 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/24694452.2016.1213154 |
| OpenAlex | W2519499162 |
| Language | EN |
| Citations received | 6 |
| References cited | 66 |
Based on four time intervals within a forty-year period of observation, we construct land-use/land-cover (LULC) maps and estimate the transition probabilities between six LULC states. The maps and transition probability matrices (TPMs) were built based on the high-resolution aerial photos and 30-m multispectral Landsat images for the same years. We considered the TPM constructed from manual classification of the aerial photos as a reference and compared it to the TPM constructed from the Landsat image classified with several methods: mean-shift segmentation followed by random forest classification and three pixel-based methods popular in cellular automata (CA) studies: K-means, iterative self-organizing data analysis techniques (ISODATA), and maximum likelihood. For each classification method, the TPMs were constructed and compared to the TPMs for the aerial photos. We prove that the goodness-of-fit of maps obtained with the three pixel-based methods was insufficient for estimating the LULC TPM. The LULC maps obtained with the object-based classification fit well to those based on the aerial photos, but the estimates of TPM were yet qualitatively different. This article raises doubts regarding the adequacy of Landsat data and standard classification methods for establishing LULC CA model rules and calls for the careful reexamination of the entire land-use CA framework. We appeal for a new view of the CA modeling methodology: It should be based on a long-term series of carefully validated LULC maps that portray different types of land-use dynamics and land planning systems over long and representative periods of population and economic growth
Aerial imagery · Ancillary data · Cellular automaton · Data mining · Geography · Land cover · Land use · Multispectral image · Remote sensing · Computer Science · Ecosystem dynamics and resilience · Land Use and Ecosystem Services · Remote Sensing in Agriculture · Artificial Intelligence
A Cellular Automata Model for Integrated Simulation of Land Use and Transport Interactions
Simulating Urban Expansion from the Perspective of Spatial Anisotropy and Expansion Neighborhood
A novel urban LC change classification using spatial and channel dual mountaineering team conditional generative adversarial network
Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation
Forecasting land-use changes in Mashhad Metropolitan area using Cellular Automata and Markov chain model for 2016-2030
Where will the change come from? Identifying decision-relevant factors in future Land Use and Cover Change (Lucc) scenarios to support planning under uncertainty
The spatiotemporal form of urban growth
Modeling the Spatial Dynamics of Regional Land Use
A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area
Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal
Status of land cover classification accuracy assessment
Dinamica—a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier
Modeling urban land use change by the integration of cellular automaton and Markov model
A survey of image classification methods and techniques for improving classification performance
Using neural networks and GIS to forecast land use changes
The Use of Constrained Cellular Automata for High-Resolution Modelling of Urban Land-Use Dynamics
Calibration of stochastic cellular automata
Random Forests
Corine land cover change detection in Europe (case studies of the Netherlands and Slovakia)
Assessing spatial dynamics of urban growth using an integrated land use model. Application in Santiago Metropolitan Area, 2010–2045
Cellular Automata and Fractal Urban Form
Calibration of Cellular Automata by Using Neural Networks for the Simulation of Complex Urban Systems
| Unique citing works | 6 |
|---|---|
| Citations per year | 1,2 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |