Integrating Remote Sensing and a Markov-FLUS Model to Simulate Future Land Use Changes in Hokkaido, Japan
<p>The elevation map of Hokkaido (Source: Shuttle Radar Topography Mission (SRTM)).</p> "> Figure 2
<p>The population change in Hokkaido from 1980 to 2040 based on government statistic (Source: <a href="http://www.pref.hokkaido.lg.jp" target="_blank">www.pref.hokkaido.lg.jp</a> (accessed on: 15 March 2021)) and population projection by NIPSSR.</p> "> Figure 3
<p>Land use change factors: (<b>a</b>) DEM, (<b>b</b>) slope, (<b>c</b>) DTROAD, (<b>d</b>) DTRAIL, (<b>e</b>) TOC, (<b>f</b>) TBD, (<b>g</b>) PD, (<b>h</b>) Protected area.</p> "> Figure 3 Cont.
<p>Land use change factors: (<b>a</b>) DEM, (<b>b</b>) slope, (<b>c</b>) DTROAD, (<b>d</b>) DTRAIL, (<b>e</b>) TOC, (<b>f</b>) TBD, (<b>g</b>) PD, (<b>h</b>) Protected area.</p> "> Figure 3 Cont.
<p>Land use change factors: (<b>a</b>) DEM, (<b>b</b>) slope, (<b>c</b>) DTROAD, (<b>d</b>) DTRAIL, (<b>e</b>) TOC, (<b>f</b>) TBD, (<b>g</b>) PD, (<b>h</b>) Protected area.</p> "> Figure 4
<p>Flow chart of methodology.</p> "> Figure 5
<p>Land use change trend in Hokkaido for 2000, 2010 and 2019.</p> "> Figure 6
<p>Land use distributions in Hokkaido for (<b>a</b>) 2000, (<b>b</b>) 2010 and (<b>c</b>) 2019.</p> "> Figure 7
<p>Examples of land use spatial change: north of the Tokachi subprefectural Bureau in 2000 (<b>a</b>) and 2019 (<b>b</b>); west of Nemuro subprefectural Bureau in 2000 (<b>c</b>) and 2019 (<b>d</b>); cultivated land in Hiyama subprefectural Bureau in 2000 (<b>e</b>) and 2019 (<b>f</b>).</p> "> Figure 8
<p>Land use simulation in different scenarios in 2040: (<b>a</b>) ND, (<b>b</b>) CP, and (<b>c</b>) FP.</p> "> Figure 9
<p>Change of area from 2000 to 2040: (<b>a</b>) cultivated land, (<b>b</b>) forest, (<b>c</b>) waterbody, (<b>d</b>) construction, (<b>e</b>) grassland, (<b>f</b>) others.</p> "> Figure 9 Cont.
<p>Change of area from 2000 to 2040: (<b>a</b>) cultivated land, (<b>b</b>) forest, (<b>c</b>) waterbody, (<b>d</b>) construction, (<b>e</b>) grassland, (<b>f</b>) others.</p> "> Figure 10
<p>Four exampled cultivated regions (1)–(4) for comparing ND and CP scenarios in 2040: (<b>a</b>–<b>d</b>) are in ND scenario, (<b>e</b>–<b>h</b>) are in CP scenario.</p> "> Figure 11
<p>Areas of cultivated land in ND, CP, and FP scenarios in 2040.</p> "> Figure 12
<p>Areas of forest in ND, CP, and FP scenarios in 2040.</p> "> Figure 13
<p>Two exampled forestry regions (M) and (N) for comparing ND and FP scenarios: (<b>a</b>,<b>b</b>) are in ND scenario, (<b>c</b>,<b>d</b>) are in FP scenario.</p> "> Figure 13 Cont.
<p>Two exampled forestry regions (M) and (N) for comparing ND and FP scenarios: (<b>a</b>,<b>b</b>) are in ND scenario, (<b>c</b>,<b>d</b>) are in FP scenario.</p> ">
Abstract
:1. Introduction
2. Study Area and Data
3. Methodology
3.1. Land Use Classification
3.2. Land Use Simulation in 2040
3.2.1. Logistic Regression Analysis for Qualitative Analysis of Driving Factors
3.2.2. Autocorrelation Factor
3.2.3. Coupled Markov-FLUS Model
4. Results
4.1. Land Use Classification Results
4.2. Land Use Simulation Results
4.2.1. Qualitative Analysis for Driving Factors
4.2.2. Accuracy Assessment for Land Use Simulation
4.2.3. Scenario-Based Future Land Use Prediction
5. Discussion
5.1. Past-to-Future Land Use Pattern (2000–2040)
5.2. Comparison of Scenario-Based Land Use Situations
5.3. Recommendations and Suggestions
5.4. Limitations and Future Works
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Satellite | Sensor | Spatial Resolution (m) | Targeted Year | Origin |
---|---|---|---|---|
Landsat-5 | TM | 30 | 2010 | USGS |
Landsat-7 | ETM+ | 30 | 2000 | USGS |
Landsat-8 | OLI | 30 | 2019 | USGS |
Factor | Units | Source |
---|---|---|
DEM | m | USGS |
Slope | degree | USGS |
DTROAD | Euclidean distance (m) | Open Street Map |
DTRAIL | Euclidean distance (m) | Open Street Map |
TOC | % Weight | Harmonized World Soil Database |
TBD | kg/dm3 | Harmonized World Soil Database |
PD | /km2 | WorldPop |
Protected area | National parks | GSI |
Land Use Types | Description |
---|---|
Cultivated land | Arable land that is intended to plow and sow and raise crops for farming. |
Forest | Land dominated by trees, including deciduous forest and evergreen forest |
Waterbody | Rivers, lakes, ponds, wetlands. |
Construction | Balconies, terraces (with or without roof), mezzanine floors and other detachable habitable areas. |
Grassland | Grass, herb, and temporary meadows, including natural and artificial grass. |
Others | Land covered by snow for long time and bare land in the top of mountains |
Land Use Type | Training (Points) | Testing (Polygons) |
---|---|---|
Cultivated land | 198 | 202 |
Forest | 580 | 254 |
Construction | 117 | 180 |
Waterbody | 23 | 70 |
Grassland | 702 | 314 |
Others | 75 | 51 |
Total | 1695 | 1071 |
Year | Cultivated Land | Forest | Waterbody | Construction | Grassland | Others |
---|---|---|---|---|---|---|
2000 | 8701.3 | 56,808.4 | 1374.1 | 1460.9 | 14,401.9 | 177.6 |
2010 | 7383.7 | 56,546.5 | 1268.1 | 1447.9 | 16,056.9 | 221.1 |
2019 | 8059.2 | 55,157.4 | 1419.4 | 1571.9 | 16,581.4 | 134.9 |
2000/2010 | Cultivated Land | Forest | Waterbody | Construction | Grassland | Others | Total |
---|---|---|---|---|---|---|---|
Cultivated land | 5446.8 | 466.5 | 118.8 | 328.3 | 1021.2 | 2.1 | 7383.7 |
Forest | 528.0 | 53,239.2 | 91.7 | 15.3 | 2649.7 | 22.6 | 56,546.5 |
Waterbody | 87.1 | 112.5 | 876.1 | 20.7 | 170.7 | 1.1 | 1268.1 |
Construction | 318.8 | 29.5 | 26.6 | 974.8 | 97.1 | 1.1 | 1447.9 |
Grassland | 2315.0 | 2893.2 | 257.2 | 118.9 | 10,401.8 | 70.9 | 16,056.9 |
Others | 5.8 | 67.5 | 3.7 | 2.9 | 61.4 | 79.8 | 221.1 |
Total | 8701.3 | 56,808.4 | 1374.1 | 1460.9 | 14,401.9 | 177.6 | 82,924.2 |
2010/2019 | Cultivated Land | Forest | Waterbody | Construction | Grassland | Others | Total |
---|---|---|---|---|---|---|---|
Cultivated land | 5155.0 | 497.3 | 88.0 | 317.1 | 1999.4 | 2.4 | 8059.2 |
Forest | 330.8 | 52,054.3 | 93.9 | 15.0 | 2588.1 | 75.3 | 55,157.4 |
Waterbody | 188.7 | 135.6 | 856.7 | 26.6 | 208.2 | 3.7 | 1419.4 |
Construction | 394.4 | 39.3 | 32.0 | 987.6 | 115.6 | 3.0 | 1571.9 |
Grassland | 1313.1 | 3787.3 | 196.4 | 100.4 | 11,118.9 | 65.3 | 16,581.4 |
Others | 1.7 | 32.7 | 1.1 | 1.2 | 26.8 | 71.3 | 134.9 |
Total | 7383.7 | 56,546.5 | 1268.1 | 1447.9 | 16,056.9 | 221.1 | 82,924.2 |
2000 | 2010 | 2019 | ||||
---|---|---|---|---|---|---|
UA (%) | PA (%) | UA (%) | PA (%) | UA (%) | PA (%) | |
Cultivated land | 96 | 81 | 96 | 72 | 96 | 73 |
Forest | 91 | 98 | 92 | 98 | 90 | 96 |
Waterbody | 95 | 99 | 97 | 99 | 91 | 99 |
Construction | 87 | 94 | 84 | 94 | 82 | 94 |
Grassland | 83 | 87 | 74 | 89 | 77 | 86 |
Others | 67 | 64 | 59 | 61 | 58 | 69 |
OA (%) | 90 | 88 | 88 | |||
Kappa | 0.87 | 0.84 | 0.84 |
Regression Coefficients | Cultivated Land | Forest | Waterbody | Construction | Grassland | Others |
---|---|---|---|---|---|---|
DEM | 0.000316 | −0.000376 | 0.000871 | −0.000457 | 0.00047 | − |
Slope | −0.008638 | 0.012464 | −0.011263 | 0.004122 | −0.005547 | 0.008596 |
DTROAD | −0.000531 | 0.000353 | −0.000129 | −0.001422 | 0.000177 | 0.000855 |
DTRAIL | −0.000008 | 0.000002 | −0.000024 | −0.000051 | −0.000088 | −0.000378 |
TOC | 0.001158 | −0.001131 | 0.000082 | 0.001035 | 0.00022 | − |
TBD | 0.005384 | −0.010975 | − | 0.002957 | − | − |
PD | −0.000064 | 0.00003 | − | − | 0.00018 | −0.000586 |
Constant | 0.161217 | 1.662243 | 1.736424 | 0.266319 | 0.21557 | 1.795446 |
ROC | 0.881 | 0.798 | 0.999 | 0.986 | 0.976 | 0.951 |
Cultivated Land | Forest | Waterbody | Construction | Grassland | Others | |
---|---|---|---|---|---|---|
Cultivated land | 1 | 1 | 1 | 1 | 1 | 1 |
Forest | 1 | 1 | 1 | 1 | 1 | 1 |
Waterbody | 0 | 0 | 1 | 0 | 0 | 0 |
Construction | 0 | 0 | 0 | 1 | 0 | 0 |
Grassland | 1 | 1 | 1 | 1 | 1 | 1 |
Others | 1 | 1 | 1 | 1 | 1 | 1 |
Year | Cultivated Land | Forest | Waterbody | Construction | Grassland | Others |
---|---|---|---|---|---|---|
2030 | 277,954 | 2,515,995 | 51,968 | 59,843 | 768,193 | 11,567 |
2040 | 267,204 | 2,521,686 | 50,919 | 57,678 | 776,127 | 11,906 |
Scenario/Land Use | Cultivated Land | Forest | Waterbody | Construction | Grassland | Others |
---|---|---|---|---|---|---|
Natural Development | 6012.1 | 56,225.8 | 1442.3 | 1653.3 | 17,463.7 | 126.9 |
Cultivated land Protection | 8013.9 | 54,918.5 | 1433.1 | 1590.8 | 16,841.0 | 126.9 |
Forest Protection | 6011.9 | 56,738.2 | 1439.1 | 1650.5 | 16,958.5 | 126.0 |
2019/2040 (ND) | Cultivated Land | Forest | Waterbody | Construction | Grassland | Others | Total |
---|---|---|---|---|---|---|---|
Cultivated land | 5569.3 | 151.2 | 0.0 | 0.0 | 291.6 | 0.0 | 6012.1 |
Forest | 1312.3 | 53,300.5 | 0.0 | 0.0 | 1606.8 | 6.3 | 56,225.8 |
Waterbody | 17.1 | 2.8 | 1419.4 | 0.0 | 3.1 | 0.0 | 1442.3 |
Construction | 62.0 | 3.5 | 0.0 | 1571.9 | 16.0 | 0.0 | 1653.3 |
Grassland | 1098.6 | 1699.3 | 0.0 | 0.0 | 14,663.9 | 1.9 | 17,463.7 |
Others | 0.0 | 0.3 | 0.0 | 0.0 | 0.0 | 126.6 | 126.9 |
Total | 8059.2 | 55,157.4 | 1419.4 | 1571.9 | 16,581.4 | 134.9 | 82,924.2 |
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Chen, Z.; Huang, M.; Zhu, D.; Altan, O. Integrating Remote Sensing and a Markov-FLUS Model to Simulate Future Land Use Changes in Hokkaido, Japan. Remote Sens. 2021, 13, 2621. https://doi.org/10.3390/rs13132621
Chen Z, Huang M, Zhu D, Altan O. Integrating Remote Sensing and a Markov-FLUS Model to Simulate Future Land Use Changes in Hokkaido, Japan. Remote Sensing. 2021; 13(13):2621. https://doi.org/10.3390/rs13132621
Chicago/Turabian StyleChen, Zhanzhuo, Min Huang, Daoye Zhu, and Orhan Altan. 2021. "Integrating Remote Sensing and a Markov-FLUS Model to Simulate Future Land Use Changes in Hokkaido, Japan" Remote Sensing 13, no. 13: 2621. https://doi.org/10.3390/rs13132621