Mapping Highland Barley on the Qinghai–Tibet Combing Landsat OLI Data and Object-Oriented Classification Method
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Data Collection
3.2. Remote Sensing Image Preprocessing
3.3. Sample-Based and Object-Oriented Information Extraction Methods
3.4. Band Optimization
3.5. Highland Barley Cultivation Extent Restriction
3.6. Accuracy Verification
3.7. The Extraction Process of Highland Barley
4. Results
4.1. Highland Barley Extraction Based on Sample Information
4.2. Optimizing the Bands of Highland Barley Extraction in Each Agriculture Area
4.3. The Extraction Results of Highland Barley
4.4. Results of Accuracy Verification
5. Discussion
5.1. Classification and Statistics of Highland Barley Cultivation Area
5.2. The Relationship between Highland Barley and Elevation
5.3. Ways to Improve the Accuracy of Remote Sensing Extraction of Highland Barley Area
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Highland Barley Extraction Area of Counties in Different Divisions
County | Area (ha) | County | Area (ha) | County | Area (ha) |
---|---|---|---|---|---|
Datong | 1379.34 | Ledu | 812.67 | Huzhu | 3496.05 |
Hualong | 4564.58 | Tongren | 1200.35 | Xiahe | 1258.40 |
Jianzha | 380.07 | Xunhua | 389.99 |
County | Area (ha) | County | Area (ha) | County | Area (ha) |
---|---|---|---|---|---|
Aba | 4722.81 | Pulan | 498.01 | Maerkang | 642.17 |
Baqing | 34.34 | Rangtang | 1079.89 | Nima | 31.72 |
Biru | 1566.59 | Ritu | 173.12 | Heishui | 609.99 |
Gaier | 133.97 | Zoige | 762.80 | Jiali | 199.64 |
Geji | 12.83 | Saga | 225.09 | Zhada | 212.88 |
Yushu | 2489.11 | Songpan | 583.56 |
County | Area (ha) | County | Area (ha) | County | Area (ha) |
---|---|---|---|---|---|
Delingha | 1143.29 | Huangzhong | 1181.14 | Haiyan | 938.81 |
Diebu | 1151.98 | Lintan | 1296.00 | Hezuo | 2760.95 |
Dulan | 8187.53 | Luqu | 1046.70 | Huangyuan | 622.89 |
Gangcha | 100.00 | Menyuan | 11,297.36 | Zeku | 1084.57 |
Golmud | 1196.64 | Qilian | 1085.78 | Zhouqu | 1434.17 |
Gonghe | 14,105.70 | Tongde | 5754.35 | Zhuoni | 861.86 |
Guide | 626.45 | Wulan | 753.25 | ||
Guinan | 17,148.20 | Xinghai | 5406.80 |
County | Area (ha) | County | Area (ha) | County | Area (ha) |
---|---|---|---|---|---|
Basu | 1578.10 | Gongbujiangda | 2032.27 | Luolong | 3051.00 |
Batang | 1340.96 | Gongjue | 2706.73 | Muli | 1161.31 |
Bayi | 240.93 | Gongshan | 229.10 | Seda | 721.10 |
Baiyu | 3040.79 | Jiacha | 995.60 | Suo | 1433.10 |
Bianba | 3088.20 | Jiangda | 4119.39 | Weixi | 1205.71 |
Bomi | 1445.40 | Jiulong | 143.24 | Xiangcheng | 878.09 |
Chaya | 2920.20 | Karuo | 4125.24 | Xianggelila | 3660.54 |
Chayu | 892.10 | Kangding | 2119.79 | Xinlong | 2324.57 |
Danba | 257.11 | Lanping | 237.42 | Yajiang | 1544.52 |
Daofu | 2843.57 | Langxia | 594.45 | Mangkang | 2151.20 |
Daocheng | 1835.31 | Leiwuqi | 2838.30 | Milin | 785.30 |
Derong | 720.70 | Litang | 2569.01 | Motuo | 94.50 |
Deqin | 791.55 | Luhuo | 2890.85 | Yulong | 919.35 |
Dingqing | 6123.15 | Lushui | 532.73 | Zuogong | 1976.40 |
County | Area (ha) | County | Area (ha) | County | Area (ha) |
---|---|---|---|---|---|
Angren | 3988.97 | Jilong | 672.89 | Nimu | 1325.03 |
Bailang | 5316.98 | Jiangzi | 6008.92 | Nielamu | 1275.54 |
Banma | 246.60 | Kangma | 2112.89 | Qiongjie | 893.18 |
Chengguan | 266.38 | Lazi | 4372.94 | Qushui | 1699.76 |
Cuomei | 662.16 | Langkazi | 1575.53 | Qusong | 800.33 |
Cuona | 915.95 | Linzhou | 5714.82 | Renbu | 1157.63 |
Dazi | 1357.72 | Longzi | 1458.27 | Sajia | 4146.10 |
Dingjie | 2136.42 | Maqin | 435.15 | Sangri | 361.28 |
Dingri | 6427.01 | Mozhugongka | 2373.86 | Sangzhuzi | 6069.92 |
Duilongdeqing | 2637.92 | Naidong | 941.37 | Xietongmen | 1641.20 |
Gangba | 790.47 | Nanmulin | 4322.75 | Yadong | 194.78 |
Gongga | 2296.08 | Nangqian | 2432.18 | Zhanang | 1868.76 |
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Band | Wavelength Range | Spatial Resolution | General Purpose |
---|---|---|---|
1-COASTAL/AEROSOL | 0.43–0.45 μm | 30 m | Coastal environmental monitoring |
2-Blue | 0.45–0.51 μm | 30 m | Visible light three-band True color is used for feature recognition |
3-Green | 0.53–0.59 μm | 30 m | |
4-Red | 0.64–0.67 μm | 30 m | |
5-NIR | 0.85–0.88 μm | 30 m | Vegetation information extraction |
6-SWIR1 | 1.57–1.65 μm | 30 m | Vegetation drought monitoring Fire monitoring Mineral information extraction |
7-SWIR2 | 2.11–2.29 μm | 30 m | |
8-PAN | 0.50–0.68 μm | 15 m | Feature recognition Data fusion |
9-Cirrus | 1.36–1.38 μm | 30 m | Cirrus detection Data quality evaluation |
Data Name | Temporal Resolution | Spatial Resolution | Data Source |
---|---|---|---|
Landsat 8 OLI images | 2019 | 30 m × 30 m | Institute of Geographic Sciences and Natural Resources Research, CAS http://ids.ceode.ac.cn (accessed on 30 September 2020) [33] |
Digital elevation model | 2010 | 30 m × 30 m | United States Geological Survey https://topotools.cr.usgs.gov (accessed on 15 March 2020) |
Multi-year average precipitation | 1961–2019 | - | China Meteorological Data Network http://data.cma.cn (accessed on 1 January 2020) |
Map of China | 2019 | - | Ministry of Natural Resources of the People’s Republic of China http://bzdt.ch.mnr.gov.cn (accessed on 25 August 2021) |
Qinghai–Tibet Plateau range and boundary line | 2014 | - | Global Change Research Data Publishing & Repository http://www.geodoi.ac.cn (accessed on 1 January 2020) |
Type | Producer’s Accuracy (Pixels) | User’s Accuracy (Pixels) | Producer’s Accuracy (%) | User’s Accuracy (%) | Overall Accuracy (%) | Kappa Coefficient |
---|---|---|---|---|---|---|
Highland barley | 99/109 | 99/107 | 90.83 | 92.52 | 91.74 | 0.83 |
Other crops | 101/109 | 101/111 | 92.66 | 90.99 |
Agricultural Divisions | CGEQ/HFD | ST/FPD | ST/FFD | QT/APD | IMGN/FPD | |
---|---|---|---|---|---|---|
Proportion of Highland barley planting areas | 31.09% | 28.91% | 23.23% | 11.86% | 4.91% | |
Proportion of Highland barley planting areas in different elevation ranges | <2000 m | 0.29% | 0.00% | 1.72% | 0.08% | 2.20% |
2000 to 2500 m | 2.24% | 0.00% | 6.82% | 0.73% | 37.33% | |
2500 to 3000 m | 32.05% | 0.14% | 14.42% | 3.86% | 52.84% | |
3000 to 3500 m | 65.40% | 0.46% | 20.29% | 22.19% | 7.62% | |
3500 to 4000 m | 0.02% | 46.51% | 32.99% | 45.74% | 0.00% | |
4000 to 4500 m | 0.00% | 50.68% | 23.45% | 27.13% | 0.00% | |
4500 to 5000 m | 0.00% | 2.16% | 0.32% | 0.27% | 0.00% | |
≥5000 m | 0.00% | 0.05% | 0.00% | 0.00% | 0.00% | |
Proportion of Highland barley planting area in different slope ranges | <5° | 87.97% | 76.28% | 18.24% | 40.14% | 66.06% |
5 to 10° | 6.69% | 14.53% | 19.70% | 18.94% | 21.95% | |
10 to 15° | 2.38% | 5.21% | 16.19% | 13.44% | 8.10% | |
15 to 20° | 1.08% | 2.08% | 13.35% | 9.66% | 1.99% | |
≥20° | 1.87% | 1.91% | 32.53% | 17.82% | 1.90% |
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Ma, W.; Jia, W.; Su, P.; Feng, X.; Liu, F.; Wang, J. Mapping Highland Barley on the Qinghai–Tibet Combing Landsat OLI Data and Object-Oriented Classification Method. Land 2021, 10, 1022. https://doi.org/10.3390/land10101022
Ma W, Jia W, Su P, Feng X, Liu F, Wang J. Mapping Highland Barley on the Qinghai–Tibet Combing Landsat OLI Data and Object-Oriented Classification Method. Land. 2021; 10(10):1022. https://doi.org/10.3390/land10101022
Chicago/Turabian StyleMa, Weidong, Wei Jia, Peng Su, Xingyun Feng, Fenggui Liu, and Jing’ai Wang. 2021. "Mapping Highland Barley on the Qinghai–Tibet Combing Landsat OLI Data and Object-Oriented Classification Method" Land 10, no. 10: 1022. https://doi.org/10.3390/land10101022