Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata–Markov model
- 132 Downloads
The detection and prediction of land use/land cover (LULC) change is crucial for guiding land resource management, planning, and sustainable development. In the view of seasonal rhythm and phenological effect, detection and prediction would benefit greatly from LULC maps of the same seasons for different years. However, due to frequent cloudiness contamination, it is difficult to obtain same-season LULC maps when using existing remote sensing images. This study utilized the spatiotemporal data fusion (STF) method to obtain summer Landsat-scale images in Hefei over the past 30 years. The Cellular Automata–Markov model was applied to simulate and predict future LULC maps. The results demonstrate the following: (1) the STF method can generate the same inter-annual interval summer Landsat-scale data for analyzing LULC change; (2) the fused data can improve the LULC detection and prediction accuracy by shortening the inter-annual interval, and also obtain LULC prediction results for a specific year; (3) the areas of cultivated land, water, and vegetation decreased by 33.14%, 2.03%, and 16.36%, respectively, and the area of construction land increased by 200.46% from 1987 to 2032. The urban expansion rate will reach its peak until 2020, and then slow down. The findings provide valuable information for urban planners to achieve sustainable development goals.
KeywordsLand use and land cover Spatiotemporal data fusion ESTARFM CA–Markov Prediction
We thank the data providers of USGS and the International Scientific & Technical Data Mirror Site, Compute Network Information Center, Chinese Academy of Sciences. We thank Dr. Xiaolin Zhu (The Hong Kong Polytechnic University) for providing access to the ESTARFM IDL code.
This work was supported in part by the Mining environmental restoration and wetland ecological security Collaborative Innovation Center, the National Natural Science Foundation of China under Grant 41501376.
- Alhamdan, M. Z., Oduor, P., Flores, A. I., Kotikot, S. M., Mugo, R., & Ababu, J. (2017). Evaluating land cover changes in Eastern and Southern Africa from 2000 to 2010 using validated Landsat and MODIS data. International Journal of Applied Earth Observation and Geoinformation, 62, 8–26.CrossRefGoogle Scholar
- Dong, T., Liu, J., Qian, B., Zhao, T., Jing, Q., Geng, X., Wang, J., Huffman, T., & Shang, J. (2016). Estimating winter wheat biomass by assimilating leaf area index derived from fusion of landsat-8 and modis data. International Journal of Applied Earth Observation and Geoinformation, 49, 63–74.CrossRefGoogle Scholar
- Emelyanova, I. V., Mcvicar, T. R., Niel, T. G. V., Li, L. T., & Dijk, A. I. J. M. V. (2013). Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: a framework for algorithm selection. Remote Sensing of Environment, 133(12), 193–209.CrossRefGoogle Scholar
- Fu, X., Wang, X., & Yang, Y. J. (2018). Deriving suitability factors for CAMarkov land use simulation model based on local historical data. Journal of Environmental Management, 206(15), 10–19Google Scholar
- Jafari, M. (2016). Dynamic simulation of urban expansion through a ca-markov model case study: hyrcanian region, gilan, iran. European Journal of Remote Sensing, 49(1), 513–529.Google Scholar
- Li, X., Ling, F., Foody, G. M., Ge, Y., Zhang, Y., & Du, Y. (2017). Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps. Remote Sensing of Environment, 196, 293–311.CrossRefGoogle Scholar
- Mizuochi, H., Hiyama, T., Ohta, T., Fujioka, Y., Kambatuku, J. R., & Iijima, M. (2017). Development and evaluation of a lookup-table-based approach to data fusion for seasonal wetlands monitoring: an integrated use of AMSR series, MODIS, and Landsat. Remote Sensing of Environment, 199, 370–388.CrossRefGoogle Scholar
- Monseru, R. A., & Leemansb, R. (1992). Comparing global vegetation maps with the Kappa statistic. Ecological Modelling, 62(4), 275–293.Google Scholar
- Muller, M. R., & Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara region, Ontario, Canada. Landscape Ecology, 9(2), 151–157.Google Scholar
- Shen, H., Huang, L., Zhang, L., Wu, P., & Zeng, C. (2016). Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: a 26-year case study of the city of Wuhan in China. Remote Sensing of Environment, 172, 109–125.CrossRefGoogle Scholar
- Wu, P., Shen, H., Zhang, L., & Göttsche, F. M. (2015). Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature. Remote Sensing of Environment, 156, 169–181.Google Scholar