A Random Forest-Based CA-Markov Model to Examine the Dynamics of Land Use/Cover Change Aided with Remote Sensing and GIS
<p>Study area.</p> "> Figure 2
<p>Flowchart of the coupled models.</p> "> Figure 3
<p>Intensity analysis of land use for the two periods.</p> "> Figure 4
<p>Performance of RF model for land use in the MRW.</p> "> Figure 5
<p>Variable importance for determining land use change based on RF models. Note: EL, SL, AS, MO, RA, CI, ED, EN, PO, and NL represent elevation, slope, aspect, distance to motorway, distance to high-speed railway, distance to city center, distance to education facilities, distance to entertainment facilities, population density, and VIIRS nighttime lights, respectively.</p> "> Figure 6
<p>Transition suitability images for Scenario Ⅰ.</p> "> Figure 7
<p>Patterns of land use change under Scenario Ⅰ.</p> ">
Abstract
:1. Introduction
2. Material and Methods
2.1. Study Area
2.2. Data Sources
2.3. The RF-CA-Markov Model
2.4. Accuracy Assessment of Land Use/Cover Change
2.5. Scenario Analysis
3. Results
3.1. Patterns of Land Use Change
3.2. Model Training and Validation
3.3. Land Use Change under Different Scenarios
4. Discussion
4.1. LUCC in the MRW
4.2. Driving Factors for Land Use Change
4.3. Effects of Spatial Planning on the Dynamics of Land Use Change
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Year 2010 | Year 2015 | Year 2020 | ||||||
---|---|---|---|---|---|---|---|---|
Acquisition Data | Path/Row | Satellite Senor | Acquisition Data | Path/Row | Satellite Senor | Acquisition Data | Path/Row | Satellite Senor |
31 October 2010 | 119/41 | Landsat 5-TM | 27 September 2015 | 119/41 | Landsat 8-OLI | 10 October 2020 | 119/41 | Landsat 8-OLI |
31 October 2010 | 119/42 | Landsat 5-TM | 27 September 2015 | 119/42 | Landsat 8-OLI | 17 April 2020 | 119/42 | Landsat 8-OLI |
9 December 2010 | 120/41 | Landsat 5-TM | 26 February 2015 | 120/41 | Landsat 8-OLI | 20 February 2020 | 120/41 | Landsat 8-OLI |
9 December 2010 | 120/42 | Landsat 5-TM | 13 May 2015 | 120/42 | Landsat 8-OLI | 20 February 2020 | 120/42 | Landsat 8-OLI |
14 January 2010 | 121/41 | Landsat 5-TM | 11 October 2015 | 121/41 | Landsat 8-OLI | 15 April 2020 | 121/41 | Landsat 8-OLI |
14 January 2010 | 121/42 | Landsat 5-TM | 13 February 2015 | 121/42 | Landsat 8-OLI | 15 April 2020 | 121/42 | Landsat 8-OLI |
Category | Description |
---|---|
Woodland | Any significant clustering of dense vegetation, typically with a closed or dense canopy. |
Grassland | Open areas covered in homogenous grasses with little other vegetation. |
Agriculture | Land used for cultivation, including newly cultivated land, fallow land, swidden land, and rotation plough land. |
Orchard | Areas for planting perennial woody plants and perennial herbs that are used for collecting fruit, leaves, or rhizomes. |
Urban | Human-made structures, roads, railways, large homogenous impervious surfaces. |
Barren | Areas with little vegetation, including exposed rock or soil, desert and sand dunes, dry salt flats/pans, and mines. |
Water | Areas where water is predominant throughout the year. |
Category | Driving Factor | Year |
---|---|---|
Topographic variable | Elevation | |
Slope | ||
Aspect | ||
Proximity variable | Distance to highway | 2015, 2020 |
Distance to high-speed rail | 2015, 2020 | |
Distance to city center (i.e., cities and counties) | 2020 | |
Distance to education facilities (i.e., kindergartens, schools, colleges and universities) | ||
Distance to entertainment facilities (e.g., public garden) | ||
Socioeconomic variable | Population density | 2015, 2020 |
VIIRS nighttime lights | 2015, 2020 |
Observed (Yes) | Observed (No) | |
---|---|---|
Simulated (Yes) | Hits (H) | False Alarms (FA) |
Simulated (No) | Misses (M) | Correct Rejections (CR) |
Period | Origin Category | Woodland | Grassland | Agriculture | Orchard | Urban | Barren | Water | Sum | Loss |
---|---|---|---|---|---|---|---|---|---|---|
2010–2015 | Woodland | 84.13 | 0.11 | 0.36 | 0.12 | 0.2 | 0.01 | 0.01 | 84.94 | 0.81 |
Grassland | 2.51 | 1.59 | 0.02 | 0.06 | 0.1 | 0.01 | 0.03 | 4.32 | 2.73 | |
Agriculture | 0.02 | 0.01 | 3.18 | 0.1 | 0.45 | 3.76 | 0.58 | |||
Orchard | 0.05 | 1.8 | 0.09 | 0.01 | 1.95 | 0.15 | ||||
Urban | 0 | 0 | 0 | 0 | 3.54 | 0 | 0 | 3.54 | 0 | |
Barren | 0.01 | 0.01 | 0.1 | 0.12 | 0.02 | |||||
Water | 0.01 | 1.36 | 1.37 | 0.01 | ||||||
Sum | 86.66 | 1.72 | 3.63 | 2.08 | 4.38 | 0.13 | 1.4 | 100 | ||
Loss | 2.53 | 0.13 | 0.45 | 0.28 | 0.84 | 0.03 | 0.04 | |||
2015–2020 | Woodland | 85.34 | 0.17 | 0.14 | 0.28 | 0.71 | 0.01 | 0.01 | 86.66 | 1.32 |
Grassland | 0.15 | 1.28 | 0.09 | 0.13 | 0.06 | 0.01 | 1.72 | 0.44 | ||
Agriculture | 0.02 | 0.07 | 2.16 | 0.15 | 1.21 | 0.02 | 3.63 | 1.47 | ||
Orchard | 0.03 | 0.03 | 1.81 | 0.2 | 0.01 | 2.08 | 0.27 | |||
Urban | 0 | 0 | 0 | 0 | 4.38 | 0 | 0 | 4.38 | 0 | |
Barren | 0.02 | 0.02 | 0.02 | 0.02 | 0.01 | 0.04 | 0.13 | 0.09 | ||
Water | 0.03 | 0.01 | 0.11 | 0.03 | 1.22 | 1.4 | 0.18 | |||
Sum | 85.56 | 1.58 | 2.44 | 2.5 | 6.6 | 0.05 | 1.27 | 100 | ||
Loss | 0.22 | 0.3 | 0.28 | 0.7 | 2.22 | 0.01 | 0.05 |
Woodland | Grassland | Agriculture | Orchard | Urban | Barren | Water | |
---|---|---|---|---|---|---|---|
Overall accuracy | 98.59% | 99.46% | 98.73% | 99.18% | 98.53% | 99.91% | 99.78% |
Producer’s accuracy | 99.76% | 60.98% | 96.59% | 75.60% | 78.00% | 68.30% | 96.72% |
User’s accuracy | 98.63% | 84.59% | 66.74% | 92.20% | 99.89% | 61.70% | 87.32% |
FOM | 0.99 | 0.64 | 0.65 | 0.75 | 0.78 | 0.6 | 0.85 |
Overall accuracy of map | 0.971 | ||||||
Kappa * | 0.884 |
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Zhang, Z.; Hörmann, G.; Huang, J.; Fohrer, N. A Random Forest-Based CA-Markov Model to Examine the Dynamics of Land Use/Cover Change Aided with Remote Sensing and GIS. Remote Sens. 2023, 15, 2128. https://doi.org/10.3390/rs15082128
Zhang Z, Hörmann G, Huang J, Fohrer N. A Random Forest-Based CA-Markov Model to Examine the Dynamics of Land Use/Cover Change Aided with Remote Sensing and GIS. Remote Sensing. 2023; 15(8):2128. https://doi.org/10.3390/rs15082128
Chicago/Turabian StyleZhang, Zhenyu, Georg Hörmann, Jinliang Huang, and Nicola Fohrer. 2023. "A Random Forest-Based CA-Markov Model to Examine the Dynamics of Land Use/Cover Change Aided with Remote Sensing and GIS" Remote Sensing 15, no. 8: 2128. https://doi.org/10.3390/rs15082128