Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Dataset and Preprocessing
2.2.1. Remote-Sensing Data
2.2.2. Reference Data
3. Methods
3.1. Land-Use Classes
- Cropland: This class consists of land covered with cropland used mainly for maize, cotton, wheat, pepper, and beet farming.
- Forest: This class consists of land planted with trees for ecological conservation, mainly consisting of evergreen coniferous forests in the northern part of the study area, Populus euphratica trees near the Tarim River in the northern part, and drought-tolerant shrubs such as Tamarix ramosissima Lcdcb and Haloxylon ammodendron.
- Korla pear: This class includes the geographic areas of Kolar pear plantations for economic purposes.
- Grassland: This class mainly includes geographic areas dominated by natural forbs (plants without stems or branches on the ground and lacking a solid structure), mainly encompassing alpine grasslands in the northern part of the study area, with the dominant grass species being Carex stipitiutriculata, Stipa mpurpurea, and Kobresia capillifolia.
- Water: This class mainly includes geographic areas covered by a water body, including lakes such as Bosten Lake, the largest inland freshwater lake in China; rivers such as the Tarim River, the longest inland river in China’s Bayingoleng Mongol Autonomous Region section; and reservoirs.
- Snow and ice: This class includes areas that are permanently covered by snow or glaciers.
- Wetland: This class includes geographic areas dominated by natural herbaceous vegetation that is permanently or periodically inundated by water bodies, which mainly includes the reed wetlands around Bosten Lake in the eastern part of the study area and the grassland wetlands in the northern part of the study area.
- Impervious area and bare soil: This class consists of land covered by buildings, roads, and bare sandy soil. Sandy soils are mainly distributed along the margins of the Taklamakan Desert in the southern part of the study area, and urban buildings are mainly distributed in the central part of the study area.
- Rocky: This class refers to geographical areas covered by rocks, where very few grasses are present.
3.2. Image-Aggregation Scheme
3.2.1. Determination of the Time Window
3.2.2. Temporal Differences in Characteristic Variables
3.3. Classification Algorithm
3.3.1. Classification and Regression Trees
3.3.2. Random Forest
3.3.3. Gradient Tree Boost
3.4. Accuracy Assessment
3.5. Comparison with Existing Land-Use Products
4. Results
4.1. Performance of Experimental Schemes
4.1.1. Performance of Scenarios
4.1.2. Sensitivity of Parameters
4.2. Mapping Results
4.2.1. Visualization of Mapping Results
4.2.2. Results of Area Statistics for Different Classes
4.3. Comparison with Existing Land-Use Products
5. Discussion
5.1. Advantages of Multi-Temporal Image Integration
5.2. Importance of Feature Variables
5.3. Limitations and Prospects
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Land Use Classes | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Cro. | For. | Kor. | Gra. | Wat. | Sno. | Wet. | Imp. | Roc. | Total | |
Field survey | 91 | 9 | 37 | 48 | 39 | 2 | 73 | 195 | 44 | 538 |
Visual interpretation | 92 | 146 | 54 | 95 | 122 | 135 | 78 | 117 | 150 | 989 |
Total | 183 | 155 | 91 | 143 | 161 | 137 | 151 | 312 | 194 | 1527 |
Percentage | 12% | 10% | 6% | 9% | 11% | 9% | 10% | 20% | 13% | 100% |
Training | 125 | 108 | 64 | 98 | 112 | 103 | 98 | 224 | 140 | 1069 |
Validation | 58 | 47 | 27 | 45 | 49 | 34 | 56 | 88 | 54 | 458 |
Schemes | Temporal Datasets Used (☑) for Classification | |||
---|---|---|---|---|
Time1 | Time2 | Time3 | Time4 | |
1 | ☑ | |||
2 | ☑ | |||
3 | ☑ | |||
4 | ☑ | |||
5 | ☑ | ☑ | ||
6 | ☑ | ☑ | ☑ | |
7 | ☑ | ☑ | ☑ | ☑ |
Platform | Band or Index | Wavelength or Formula | Reference |
---|---|---|---|
Sentinel-1 | SD_VV | The standard deviation of VV | [10] |
SD_VH | The standard deviation of VH | [10] | |
Sentinel-2 | SWIR 1 (B11) | Short-wave infrared | [27] |
SWIR 2 (B12) | Short-wave infrared | [27] | |
NDVI | (NIR − Red)/(NIR + Red) | [27] | |
NDWI | (Green − NIR)/(Green + NIR) | [50] | |
TC_Wetness | 0.1509 Blue + 0.1973 Green + 0.3279 Red + 0.3406 NIR − 0.7112 SWIR1 − 0.4572 SWIR2 | [10] | |
BRISI | (ISBAI − BAI)/(ISBAI + BAI),ISBAI = (2SWIR1 − (Red + NIR)/2) /(2SWIR1 + (Red + NIR)/2),BAI = ((Red + SWIR1) − (Blue + Green + SWIR2))/(Red + SWIR1) + (Blue + Green + SWIR2) | [51] | |
NASADEM | Elevation | NASADEM Digital Elevation (30 m) | [47] |
Schemes | CART | RF | GTB | ||||||
---|---|---|---|---|---|---|---|---|---|
OA | Kappa | F1 Score | OA | Kappa | F1 Score | OA | Kappa | F1 Score | |
S1 | 76% | 72% | 86% | 83% | 81% | 91% | 86% | 84% | 93% |
S2 | 81% | 79% | 90% | 87% | 85% | 93% | 89% | 87% | 94% |
S3 | 86% | 84% | 92% | 92% | 91% | 96% | 93% | 92% | 96% |
S4 | 86% | 84% | 93% | 93% | 92% | 96% | 94% | 93% | 97% |
S5 | 82% | 80% | 90% | 89% | 88% | 94% | 91% | 90% | 95% |
S6 | 88% | 86% | 93% | 93% | 92% | 96% | 94% | 93% | 97% |
S7 | 90% | 89% | 95% | 95% | 95% | 98% | 95% | 95% | 98% |
Classification Result of Land-Use Map for S7-GTB | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Cro. | For. | Kor. | Gra. | Wat. | Sno. | Wet. | Imp. | Roc. | Sum | PA | F1 | |
Cro. | 58 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 58 | 100% | 100% |
For. | 0 | 46 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 47 | 98% | 95% |
Kor. | 0 | 0 | 27 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 100% | 95% |
Gra. | 0 | 2 | 0 | 40 | 0 | 0 | 0 | 3 | 0 | 45 | 89% | 92% |
Wat. | 0 | 0 | 0 | 0 | 48 | 0 | 0 | 0 | 1 | 49 | 98% | 98% |
Sno. | 0 | 0 | 0 | 0 | 0 | 34 | 0 | 0 | 0 | 34 | 100% | 97% |
Wet. | 0 | 1 | 1 | 0 | 1 | 0 | 53 | 0 | 0 | 56 | 95% | 97% |
Imp. | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 84 | 1 | 88 | 95% | 93% |
Roc. | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 4 | 47 | 54 | 87% | 91% |
Sum | 58 | 50 | 30 | 42 | 49 | 36 | 53 | 91 | 49 | OA = 95%, Kappa = 95% | ||
UA | 100% | 92% | 90% | 95% | 98% | 94% | 100% | 92% | 96% | F1 score = 98% |
ESA 2020 | ESRI 2021 | Google 2021 | Our Product | |
---|---|---|---|---|
Grassland | 69% | 56% | 73% | 92% |
Water | 81% | 86% | 93% | 98% |
Snow and Ice | 61% | 89% | 97% | 97% |
Wetland | 68% | 84% | 56% | 97% |
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Chen, R.; Yang, H.; Yang, G.; Liu, Y.; Zhang, C.; Long, H.; Xu, H.; Meng, Y.; Feng, H. Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China. Remote Sens. 2023, 15, 3958. https://doi.org/10.3390/rs15163958
Chen R, Yang H, Yang G, Liu Y, Zhang C, Long H, Xu H, Meng Y, Feng H. Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China. Remote Sensing. 2023; 15(16):3958. https://doi.org/10.3390/rs15163958
Chicago/Turabian StyleChen, Riqiang, Hao Yang, Guijun Yang, Yang Liu, Chengjian Zhang, Huiling Long, Haifeng Xu, Yang Meng, and Haikuan Feng. 2023. "Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China" Remote Sensing 15, no. 16: 3958. https://doi.org/10.3390/rs15163958
APA StyleChen, R., Yang, H., Yang, G., Liu, Y., Zhang, C., Long, H., Xu, H., Meng, Y., & Feng, H. (2023). Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China. Remote Sensing, 15(16), 3958. https://doi.org/10.3390/rs15163958