卫星遥感监测产品在中国森林生态系统的验证和不确定性分析——基于海量无人机激光雷达数据
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刘兵兵, 魏建新, 胡天宇, 杨秋丽, 刘小强, 吴发云, 苏艳军, 郭庆华
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Validation and uncertainty analysis of satellite remote sensing products for monitoring China’s forest ecosystems—Based on massive UAV LiDAR data
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LIU Bing-Bing, WEI Jian-Xin, HU Tian-Yu, YANG Qiu-Li, LIU Xiao-Qiang, WU Fa-Yun, SU Yan-Jun, GUO Qing-Hua
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表3 不同因子对3种卫星遥感监测产品精度的影响分析
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Table 3 Influences of different factors on the accuracy of three satellite remote sensing products
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产品 Product | | 林型 Forest type | 坡度 Slope (°) | 冠层覆盖度 Canopy cover (%) | a | b | c | 0-10 | 10-20 | 20-30 | ≥30 | 0-30 | 30-60 | 60-80 | ≥80 | GLCF TCC | R2 | 0.64 | 0.29 | 0.60 | 0.39 | 0.54 | 0.48 | 0.52 | 0.10 | 0.03 | 0.02 | 0.05 | Bias (%) | 18.13 | 19.96 | 21.37 | 22.81 | 21.72 | 19.54 | 14.49 | -4.78 | 11.55 | 25.39 | 33.35 | RMSE (%) | 28.40 | 34.16 | 29.45 | 31.70 | 31.17 | 31.63 | 28.69 | 17.02 | 25.72 | 32.57 | 36.87 | 像元数量 N | 15 584 | 37 167 | 51 969 | 39 301 | 23 792 | 23 441 | 18 186 | 21 231 | 16 446 | 23 837 | 43 206 | GLASS LAI | R2 | 0.73 | 0.15 | 0.36 | 0.29 | 0.38 | 0.28 | 0.34 | 0.17 | 0.3 | 0.04 | 0.11 | Bias (m2·m-2) | -1.46 | -1.39 | -1.86 | -1.81 | -1.98 | -1.63 | -0.72 | -1.22 | -1.68 | -2.05 | -1.45 | RMSE (m2·m-2) | 1.76 | 2.01 | 2.23 | 2.16 | 2.30 | 2.11 | 1.62 | 1.44 | 2.12 | 2.35 | 2.03 | 像元数量 N | 52 | 196 | 204 | 136 | 87 | 162 | 67 | 24 | 64 | 112 | 252 | GFCH | R2 | 0.35 | 0.40 | 0.34 | 0.45 | 0.37 | 0.31 | 0.37 | 0.09 | 0.14 | 0.27 | 0.25 | Bias (m) | 0.64 | 2.87 | 1.23 | 1.67 | 0.79 | 2.09 | 3.22 | -1.73 | 0.20 | 1.36 | 2.72 | RMSE (m) | 3.91 | 6.29 | 4.22 | 4.10 | 4.40 | 5.84 | 6.72 | 5.16 | 4.83 | 4.40 | 5.61 | 像元数量 N | 16 794 | 85 571 | 74 047 | 58 565 | 34 454 | 41 776 | 41 617 | 9 291 | 19 420 | 32 156 | 115 545 |
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