An Elevation-Based Stratification Model for Simulating Land Use Change
"> Figure 1
<p>Location of Guizhou and Guangxi Karst Mountainous Region, China.</p> "> Figure 2
<p>Overall structure of the elevation-based stratification strategy (SLUCS) model.</p> "> Figure 3
<p>Land use map in 2000 and 2015.</p> "> Figure 4
<p>Conversion between multiple land uses from 2000 to 2015.</p> "> Figure 5
<p>Elevation-based stratifications at the scale parameter of (<b>a</b>) 1000; (<b>b</b>) 3000; (<b>c</b>) 6000; (<b>d</b>) 10,000.</p> "> Figure 6
<p>Parameter determination for (<b>a</b>) area-weighted standard deviation of each stratification (ASD<sub>intra</sub>); (<b>b</b>) mean standard deviation of the stratifications (LV<sub>inter</sub>); (<b>c</b>) optimal segment score (OSS) as a function of scale parameter. Setting in eCognition software: Shape = 0.1, compactness = 0.5.</p> "> Figure 7
<p>Land use structure within stratifications at the optimal segmentation size.</p> "> Figure 8
<p>Comparison of land use in 2015 from (<b>a</b>) referenced visual interpretation; (<b>b</b>) traditional simulation; (<b>c</b>) stratified simulation.</p> "> Figure 9
<p>Projection of land use 2015 under the (<b>a</b>) history trend scenario (historic-condition); (<b>b</b>) socioeconomic priority scenario (planning); (<b>c</b>) Ecological protection scenario (protect).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Land-Use System and Driving Factors
2.3. Overall Model Structure
2.3.1. Elevation-Based Stratification Module
2.3.2. Non-Spatial Land-Use Demand Module
2.3.3. Stratified Suitability Estimation Module
2.3.4. Spatial Allocation of the Land-Use Module
2.4. Accuracy Assessment of Model Simulation
3. Results
3.1. Land-Use Change from 2000 to 2015
3.2. Elevation-Based Stratification
3.3. Accuracy Assessment of SLUCS Model
3.4. Simulation of Land-Use Change in 2030
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Category | Driving Forces | Spatial Scale | Temporal Scale | Data Source |
---|---|---|---|---|
Climatic factors | Mean annual temperature | 0.1° × 0.1° | 1981–2000 | China Meteorological Forcing Dataset, Cold and Arid Regions Science Data Center at Lanzhou |
Mean annual precipitation | 0.1° × 0.1° | 1981–2000 | ||
Soil factors | Soil type | 1 km × 1 km | 1980s | Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences |
Ratio of clay soil | 1 km × 1 km | 1980s | Soil Characteristics Dataset of China from Shangguan et al. [40] | |
Ratio of sandy soil | 1 km × 1 km | |||
Soil organic matter | 1 km × 1 km | 1980s | China Dataset of Soil Properties for Land Surface Modeling [41] | |
Vegetation factors | Vegetation types | 1 km × 1 km | 1980s | Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences |
Normalized Difference Vegetation Index (NDVI) | 250 m × 250 m | 2001–2005 | MOD13Q1 NDVI products-MODIS/Aqua Vegetation Indices (https://ladsweb.nascom.nasa.gov/data/) | |
Topographic factors | Elevation | 90 m × 90 m | 2000 | International Scientific Data Service Platform, Computer Network Information Center, Chinese Academy of Sciences |
Slope | 90 m × 90 m | |||
Aspect | 90 m × 90 m | |||
Land degradation factors | Karst rocky desertification | 100 m × 100 m | 2005 | State Forestry Administration of People’s Republic of China. Bulletin of China’s Karst Rock Desertification |
Socio-economic factors | Population density | 1 km × 1 km | 2000 | Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences |
Gross domestic product density (GDP) | 1 km × 1 km | 2000 | ||
Location factors | Distance to roads | 1 km × 1 km | 2000 | Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences |
Distance to settlements | 1 km × 1 km | 2000 | ||
Distance to rivers | 1 km × 1 km | 2000 |
Land Use Type | Historic-Condition Scenario | Planning Scenario | Protect Scenario | |
---|---|---|---|---|
Change Rate (%) | Change Rate (%) | Change Rate in Natural Reserves (%) | Area Proportion (%) | |
Paddy field | −3.6 | −4.7 1 | 0 | |
Dry land | −2.4 | −3.4 1 | 0 | |
Forest | 0.6 | 2.8 1 | 60.0 3 | |
Grassland | −1.9 | |||
Water | 14.1 | 3.8 2 | ||
Built-up land | 36.9 | 40.4 1 | 0 | |
Bare land | −4.8 |
Land Use Type | Historic-Condition Scenario | Planning Scenario | Protect Scenario |
---|---|---|---|
Paddy field | 0.8 | 0.7 | 0.8 |
Dry land | 0.8 | 0.7 | 0.8 |
Forest | 0.8 | 0.8 | 0.9 |
Grassland | 0.8 | 0.8 | 0.9 |
Water | 0.9 | 0.8 | 0.9 |
Built-up land | 1.0 | 1.0 | 1.0 |
Bare land | 0.5 | 0.5 | 0.6 |
Land Use Type | Area in 2000 (km2) | Area in 2015 (km2) | Change Area (km2) | Change Rate (%) |
---|---|---|---|---|
Paddy field | 22,716 | 21,933 | −783 | −3.45 |
Dry land | 33,970 | 33,185 | −785 | −2.31 |
Forest | 121,071 | 121,800 | 729 | 0.60 |
Grassland | 31,787 | 30,685 | −1102 | −3.47 |
Water | 1986 | 2291 | 305 | 15.36 |
Built-up land | 2580 | 4218 | 1638 | 63.49 |
Bare land | 23 | 21 | −2 | −8.70 |
Driving Factor | Paddy Field | Dry Land | Forest | Grassland | Water | Built-Up Land | Bare Land |
---|---|---|---|---|---|---|---|
Intercept | 2.234 | 2.603 | −2.160 | −0.510 | −3.902 | −9.955 | −4.530 |
Mean annual temperature | 0.106 * | 0.043 * | −0.060 * | −0.055 * | 0.072 * | 0.028 | 0.105 |
Mean annual precipitation | 0.125 * | −0.175 * | −0.031 * | 0.156 * | 0.083 * | 0.007 | −0.414 * |
Soil type | 0.001 | 0.004 | 0.001 | −0.131 * | 0.012 | 0.001 | 0.041 |
Ratio of clay soil | 0.057 * | 0.003 | 0.001 | −0.002 | −0.027 | 0.015 | −0.022 |
Ratio of sandy soil | −0.068 | 0.014 * | −0.002 | 0.002 | −0.032 | 0.005 | −0.010 |
Soil organic matter | 0.043 | 0.010 | −0.002 | −0.024 | −0.015 | 0.073 | 0.026 |
Vegetation types | −0.067 * | −0.045 * | 0.151 * | −0.036 * | −0.010 | −0.058 | −0.111 |
NDVI | −0.039 | −0.044 | 0.072 * | −0.148 * | −0.068 * | −0.017 | −0.039 * |
Elevation | 0.122 * | 0.011 | −0.090 * | 0.070 * | −0.095 * | −0.109 * | 0.138 |
Slope | −0.196 * | −0.116 * | 0.070 * | 0.001 | −0.058 | −0.090 * | −0.058 * |
Aspect | −0.004 | −0.001 | 0.001 | 0.005 * | −0.002 | −0.007 | 0.011 |
Karst rocky desertification | −0.156 * | −0.065 * | 0.025 | 0.088 * | −0.108 | −0.125 * | 0.061 |
Population density | −0.012 | 0.047 * | −2.020 * | −1.050 * | −0.012 | 0.230 * | −2.512 |
GDP | −0.111 * | −0.268 * | −0.154 * | −0.007 | −0.170 * | 0.593 * | −2.782 |
Distance to roads | −0.074 * | −0.038 * | 0.036 * | −0.019 * | 0.008 | −0.117 * | 0.026 |
Distance to settlements | −0.548 * | −0.188 * | 0.139 * | 0.066 * | 0.070 | −0.716 * | −0.425 * |
Distance to rivers | 0.007 | −0.007 | 0.009 | −0.039 * | −0.228 * | −0.013 | −0.244 |
ROC statistic | 0.85 | 0.81 | 0.88 | 0.83 | 0.77 | 0.78 | 0.75 |
Modified Lee and Sallee Metric | Land Use Type | Traditional Simulation | Stratified Simulation |
Paddy field | 0.92 | 0.95 | |
Dry land | 0.92 | 0.94 | |
Forest | 0.96 | 0.97 | |
Grassland | 0.90 | 0.92 | |
Water | 0.61 | 0.73 | |
Built-up land | 0.53 | 0.67 | |
Bare land | 0.91 | 0.91 | |
Kappa coefficient | 0.83 | 0.89 | |
KSimulation | 0.19 | 0.52 |
Land Use Type | Baseline (2015) | Historic-Condition Scenario (2030) | Planning Scenario (2030) | Protect Scenario (2030) | |||
---|---|---|---|---|---|---|---|
Area (km2) | Area (km2) | Change Rate (%) | Area (km2) | Change Rate (%) | Area (km2) | Change Rate (%) | |
Paddy field | 21,933 | 21,146 | −3.6 | 20,898 | −4.7 | 20,219 | −7.8 |
Dry land | 33,185 | 32,374 | −2.4 | 32,070 | −3.4 | 31,037 | −6.5 |
Forest | 121,800 | 122,598 | 0.7 | 125,252 | 2.8 | 128,480 | 5.5 |
Grassland | 30,685 | 29,640 | −3.4 | 27,593 | −10.1 | 26,239 | −14.5 |
Water | 2291 | 2593 | 13.2 | 2377 | 3.8 | 2648 | 15.6 |
Built-up land | 4218 | 5762 | 36.6 | 5923 | 40.4 | 5490 | 30.2 |
Bare land | 21 | 20 | −4.8 | 20 | −4.8 | 20 | −4.8 |
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Xu, E.; Zhang, H.; Yao, L. An Elevation-Based Stratification Model for Simulating Land Use Change. Remote Sens. 2018, 10, 1730. https://doi.org/10.3390/rs10111730
Xu E, Zhang H, Yao L. An Elevation-Based Stratification Model for Simulating Land Use Change. Remote Sensing. 2018; 10(11):1730. https://doi.org/10.3390/rs10111730
Chicago/Turabian StyleXu, Erqi, Hongqi Zhang, and Lina Yao. 2018. "An Elevation-Based Stratification Model for Simulating Land Use Change" Remote Sensing 10, no. 11: 1730. https://doi.org/10.3390/rs10111730