Scientia Silvae Sinicae ›› 2020, Vol. 56 ›› Issue (5): 80-88.doi: 10.11707/j.1001-7488.20200509
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Li Zhong,Yunzhi Chen*,Xiaoqin Wang
Received:
2017-12-07
Online:
2020-05-25
Published:
2020-06-13
Contact:
Yunzhi Chen
CLC Number:
Li Zhong,Yunzhi Chen,Xiaoqin Wang. Forest Disturbance Monitoring Based on Time Series of Landsat Data[J]. Scientia Silvae Sinicae, 2020, 56(5): 80-88.
Table 1
Description of remote sensing images"
获取日期 Data acquired | 卫星 Satellite | 影像质量 Image quality |
2000-09-15 | Landsat5 | 1%云覆盖1% cloud cover |
2001-10-20 | Landsat5 | 高High |
2002-11-08 | Landsat5 | 高High |
2003-10-26 | Landsat5 | 高High |
2004-10-28 | Landsat5 | 高High |
2005-10-31 | Landsat5 | 1%云覆盖1% cloud cover |
2006-11-03 | Landsat5 | 高High |
2007-10-05 | Landsat5 | 1%云覆盖1% cloud cover |
2008-11-24 | Landsat5 | 9%云覆盖9% cloud cover |
2009-10-10 | Landsat5 | 4%云覆盖4% cloud cover |
2010-10-29 | Landsat5 | 4%云覆盖4% cloud cover |
2011-09-14 | Landsat5 | 7%云覆盖7% cloud cover |
2013-10-05 | Landsat8 | 高High |
2014-10-08 | Landsat8 | 高High |
2016-09-27 | Landsat8 | 高High |
Table 2
Parameters used in LandTrendr"
过程Step | 参数Parameter | 参数值Value |
分割Seg- mentation | 最少所需影像Min-image | 6 |
Kernelsize | 3 | |
最大分段数Max-segments | 5 | |
噪声值Despike | 0.9 | |
恢复率Recovery_threshold | 0.5 | |
最优模型比例Optimal model ratio | 0.75 | |
滤波Filter | 1年植被覆盖损失阈值Pct_veg_loss1 | 10 |
10年植被覆盖损失Pct_veg_loss10 | 3 | |
干扰前覆盖阈值Pre_dist_cover | 20 | |
植被生长比例阈值Pct_veg_gain | 5 | |
制图Mapping | 最小制图单位Min-mapping | 21 pixel |
Table 3
Confusion matrix"
森林 Forest | 非森林 Non-forest | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2013 | 2014 | 2016 | 总计 Total(%) | 用户精度 User precision(%) | |
森林Forest | 69.224 | 1.294 | 0.022 | 0.067 | 0.089 | 0.133 | 0.089 | 0.245 | 0.133 | 0.022 | 0.022 | 0.044 | 0.067 | 0.022 | 71.492 | 96.86 | |||
非森林Non-forest | 0.802 | 21.478 | 22.280 | 96.40 | |||||||||||||||
2000 | 0.029 | 0.002 | 0.251 | 0.001 | 0.283 | 88.62 | |||||||||||||
2001 | 0.001 | 0.019 | 0.001 | 0.021 | 90.00 | ||||||||||||||
2002 | 0.012 | 0.206 | 0.218 | 94.34 | |||||||||||||||
2003 | 0.020 | 0.002 | 0.001 | 0.334 | 0.004 | 0.361 | 92.40 | ||||||||||||
2004 | 0.161 | 0.004 | 0.895 | 0.003 | 1.063 | 84.18 | |||||||||||||
2005 | 0.012 | 0.002 | 0.164 | 0.005 | 0.184 | 89.18 | |||||||||||||
2006 | 0.030 | 0.002 | 0.380 | 0.006 | 0.419 | 90.82 | |||||||||||||
2007 | 0.014 | 0.003 | 0.002 | 0.414 | 0.034 | 0.467 | 88.68 | ||||||||||||
2008 | 0.010 | 0.968 | 0.067 | 1.046 | 92.59 | ||||||||||||||
2009 | 0.124 | 0.001 | 0.887 | 0.012 | 1.024 | 86.60 | |||||||||||||
2010 | 0.003 | 0.003 | 0.394 | 0.001 | 0.452 | 87.10 | |||||||||||||
2011 | 0.040 | 0.001 | 0.292 | 0.006 | 0.001 | 0.342 | 85.54 | ||||||||||||
2013 | 0.004 | 0.001 | 0.190 | 0.001 | 0.197 | 96.52 | |||||||||||||
2014 | 0.004 | 0.112 | 0.003 | 0.119 | 93.68 | ||||||||||||||
2016 | 0.001 | 0.032 | 0.032 | 97.87 | |||||||||||||||
总计 Total(%) | 70.565 | 22.795 | 0.273 | 0.020 | 0.208 | 0.400 | 0.988 | 0.167 | 0.521 | 0.509 | 0.127 | 1.087 | 0.428 | 0.314 | 0.241 | 0.181 | 0.057 | 100.000 | 总体精度 Overall precision:96.26 |
生产者精度Producer precision(%) | 98.13 | 94.22 | 91.96 | 93.10 | 99.01 | 83.36 | 90.62 | 98.35 | 72.99 | 81.38 | 77.66 | 81.57 | 92.13 | 93.01 | 78.92 | 61.74 | 55.42 | Kappa:0.92 |
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