JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (4): 13-22.doi: 10.12302/j.issn.1000-2006.202204047
Special Issue: 第三届中国林草计算机应用大会论文精选(Ⅱ)
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YANG Le1(), HUANG Xiaojun1,*(
), BAO Yuhai1, BAO Gang1, TONG Siqin1, Sudubilig 2
Received:
2022-04-25
Revised:
2023-04-16
Online:
2023-07-30
Published:
2023-07-20
CLC Number:
YANG Le, HUANG Xiaojun, BAO Yuhai, BAO Gang, TONG Siqin, Sudubilig . Effects of UAV flight altitude on the accuracy of monitoring Dendrolimus superans pests by remote sensing[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(4): 13-22.
Table 1
Spectral indexes and their calculation formula"
光谱指数 spectral index | 公式 formula | 参考文献 reference |
---|---|---|
花青素反射指数1 ARI 1 | (1/B2)-(1/B4) | Gitelson等[ |
类胡萝卜素反射指数1 CRI 1 | (1/B2)-(1/B3) | Gitelson等[ |
差值植被指数DVI | B5-B3 | Jordan[ |
增强植被指数EVI | B5/B3 | Pearson等[ |
简单比值指数GMI 1 | B1/B4 | Lichtenthaler[ |
绿光归一化差值植被指数GNDVI | B4/B2 | Gitelson等[ |
绿光归一化差值植被指数2 GNDVI 2 | (B5-B3)/(B5+B3) | Rouse等[ |
利希滕塔莱指数LIC 3 | (B5-B2)/(B5+B2) | Gitelson等[ |
修正型三角植被指数MTVI 2 | (B4-B2)/(B4+B2) | Gitelson等[ |
归一化植被指数NDVI | 2.5(B5-B3)/[1+B5+6B3-7.5B1] | Huete等[ |
比值植被指数RVI | 1.5[1.2(B5-B2)-2.5(B3-B2)]/ (6B5- | Haboudane等[ |
三角形植被指数TVI | 0.5[120(B4-B2)-200(B3-B2)] | Broge等[ |
Table 2
Precision of pest severity monitoring model based on spectral index"
模型 model | 光谱指数 spectral index | 不同航高OA/% overall accuracy for different flight altitude | 不同航高Kappa值 Kappa for different flight altitude | ||||
---|---|---|---|---|---|---|---|
100 m | 150 m | 200 m | 100 m | 150 m | 200 m | ||
支持向量机 SVM | ARI 1 | 50.0 | 56.4 | 51.3 | 0.424 | 0.472 | 0.426 |
CRI 1 | 85.9 | 82.1 | 82.1 | 0.806 | 0.761 | 0.764 | |
DVI | 88.5 | 75.6 | 85.9 | 0.838 | 0.689 | 0.806 | |
EVI | 85.9 | 76.9 | 69.2 | 0.806 | 0.695 | 0.607 | |
GMI 1 | 89.7 | 84.6 | 84.6 | 0.856 | 0.791 | 0.792 | |
GNDVI | 83.3 | 82.1 | 79.5 | 0.780 | 0.764 | 0.734 | |
GNDVI 2 | 91.0 | 84.6 | 80.8 | 0.874 | 0.793 | 0.751 | |
LIC 3 | 89.7 | 79.5 | 85.9 | 0.854 | 0.732 | 0.805 | |
MTVI 2 | 92.3 | 91.9 | 88.5 | 0.889 | 0.874 | 0.840 | |
NDVI | 92.9 | 91.0 | 88.5 | 0.884 | 0.871 | 0.840 | |
RVI | 92.3 | 89.7 | 85.9 | 0.890 | 0.855 | 0.808 | |
TVI | 89.7 | 83.3 | 78.2 | 0.854 | 0.776 | 0.721 | |
随机森林 RF | ARI1 | 47.4 | 35.9 | 39.7 | 0.403 | 0.287 | 0.318 |
CRI 1 | 76.9 | 75.6 | 69.2 | 0.704 | 0.683 | 0.623 | |
DVI | 84.6 | 73.1 | 76.9 | 0.792 | 0.657 | 0.703 | |
EVI | 79.5 | 55.1 | 53.8 | 0.732 | 0.460 | 0.457 | |
GMI 1 | 84.6 | 78.2 | 73.1 | 0.792 | 0.716 | 0.662 | |
GNDVI | 80.8 | 79.5 | 73.1 | 0.749 | 0.730 | 0.666 | |
GNDVI 2 | 88.5 | 79.5 | 76.9 | 0.841 | 0.725 | 0.706 | |
LIC 3 | 87.2 | 73.1 | 84.6 | 0.821 | 0.661 | 0.794 | |
MTVI 2 | 90.6 | 89.7 | 85.9 | 0.867 | 0.854 | 0.809 | |
NDVI | 88.5 | 85.9 | 84.6 | 0.840 | 0.809 | 0.788 | |
RVI | 80.8 | 78.2 | 76.1 | 0.744 | 0.720 | 0.708 | |
TVI | 80.8 | 76.9 | 75.6 | 0.742 | 0.704 | 0.688 |
Table 3
Parameter of pest severity monitoring model based on texture features"
模型 model | 航高/m flight- altitude | 窗口 大小 window size | 均值 mean | 方差 variance | 协同性 homogeneity | 对比度 contrast | 相异性 dissimilarity | 信息熵 entropy | 二阶矩 second moment | 相关性 correlation | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
OA/% | Kappa | OA/% | Kappa | OA/% | Kappa | OA/% | Kappa | OA/% | Kappa | OA/% | Kappa | OA/% | Kappa | OA/% | Kappa | ||||
支持向量机 SVM | 100 | 3×3 | 85.9 | 0.806 | 56.4 | 0.468 | 65.4 | 0.555 | 55.1 | 0.459 | 64.1 | 0.543 | 62.8 | 0.530 | 64.1 | 0.543 | 51.3 | 0.426 | |
5×5 | 83.3 | 0.776 | 60.3 | 0.505 | 66.7 | 0.568 | 56.4 | 0.470 | 64.1 | 0.543 | 65.4 | 0.554 | 64.1 | 0.543 | 43.6 | 0.333 | |||
7×7 | 84.6 | 0.791 | 60.3 | 0.505 | 60.3 | 0.505 | 61.5 | 0.518 | 64.1 | 0.543 | 65.4 | 0.554 | 65.4 | 0.555 | 43.6 | 0.333 | |||
9×9 | 84.6 | 0.791 | 60.3 | 0.505 | 66.7 | 0.568 | 57.7 | 0.477 | 64.1 | 0.543 | 65.4 | 0.554 | 65.4 | 0.555 | 44.9 | 0.353 | |||
150 | 3×3 | 67.9 | 0.594 | 48.7 | 0.402 | 52.6 | 0.437 | 48.7 | 0.396 | 50.0 | 0.407 | 57.7 | 0.482 | 57.7 | 0.480 | 48.7 | 0.395 | ||
5×5 | 69.2 | 0.610 | 53.8 | 0.442 | 50.0 | 0.414 | 50.0 | 0.404 | 51.3 | 0.420 | 56.4 | 0.469 | 59.0 | 0.494 | 50.0 | 0.402 | |||
7×7 | 66.7 | 0.577 | 57.7 | 0.481 | 52.6 | 0.440 | 55.1 | 0.468 | 52.6 | 0.437 | 55.1 | 0.457 | 57.7 | 0.481 | 52.6 | 0.436 | |||
9×9 | 62.8 | 0.536 | 57.7 | 0.491 | 55.1 | 0.466 | 55.1 | 0.455 | 52.6 | 0.438 | 53.8 | 0.444 | 57.7 | 0.481 | 51.3 | 0.414 | |||
200 | 3×3 | 74.4 | 0.675 | 56.4 | 0.481 | 53.8 | 0.441 | 51.3 | 0.415 | 51.3 | 0.415 | 53.8 | 0.452 | 57.7 | 0.490 | 43.6 | 0.334 | ||
5×5 | 73.1 | 0.662 | 57.7 | 0.492 | 56.4 | 0.475 | 53.8 | 0.449 | 55.1 | 0.462 | 55.1 | 0.462 | 59.0 | 0.504 | 43.6 | 0.337 | |||
7×7 | 76.9 | 0.702 | 59.0 | 0.504 | 53.8 | 0.450 | 57.7 | 0.490 | 53.8 | 0.456 | 57.7 | 0.483 | 56.4 | 0.476 | 46.2 | 0.365 | |||
9×9 | 75.6 | 0.687 | 60.3 | 0.519 | 57.7 | 0.490 | 56.4 | 0.466 | 59.0 | 0.504 | 55.1 | 0.453 | 59.0 | 0.504 | 44.9 | 0.353 | |||
随机森林 RF | 100 | 3×3 | 73.1 | 0.651 | 51.3 | 0.443 | 48.7 | 0.399 | 55.1 | 0.478 | 57.7 | 0.501 | 46.2 | 0.384 | 51.3 | 0.438 | 35.9 | 0.280 | |
5×5 | 74.4 | 0.669 | 43.6 | 0.355 | 46.2 | 0.370 | 47.4 | 0.389 | 51.3 | 0.434 | 51.3 | 0.424 | 47.4 | 0.388 | 41.0 | 0.328 | |||
7×7 | 75.6 | 0.685 | 46.2 | 0.384 | 46.2 | 0.384 | 47.4 | 0.388 | 57.7 | 0.503 | 52.6 | 0.450 | 53.8 | 0.460 | 34.6 | 0.263 | |||
9×9 | 73.1 | 0.661 | 46.2 | 0.387 | 52.6 | 0.445 | 51.3 | 0.443 | 51.3 | 0.436 | 47.4 | 0.403 | 53.8 | 0.462 | 39.7 | 0.318 | |||
150 | 3×3 | 56.4 | 0.473 | 43.6 | 0.360 | 44.9 | 0.367 | 44.9 | 0.370 | 42.3 | 0.351 | 51.3 | 0.444 | 35.9 | 0.281 | 37.2 | 0.291 | ||
5×5 | 61.5 | 0.533 | 42.3 | 0.334 | 47.4 | 0.402 | 53.8 | 0.461 | 39.7 | 0.317 | 44.9 | 0.369 | 44.9 | 0.372 | 47.4 | 0.400 | |||
7×7 | 60.3 | 0.520 | 48.7 | 0.410 | 41.0 | 0.329 | 39.7 | 0.319 | 39.7 | 0.319 | 47.4 | 0.396 | 48.7 | 0.410 | 39.7 | 0.324 | |||
9×9 | 56.4 | 0.488 | 48.7 | 0.406 | 46.2 | 0.385 | 39.7 | 0.331 | 42.3 | 0.330 | 47.4 | 0.393 | 42.3 | 0.342 | 42.3 | 0.345 | |||
200 | 3×3 | 69.2 | 0.623 | 51.3 | 0.434 | 38.5 | 0.298 | 39.7 | 0.311 | 44.9 | 0.376 | 46.2 | 0.385 | 37.2 | 0.282 | 41.0 | 0.335 | ||
5×5 | 59.0 | 0.516 | 47.4 | 0.404 | 37.2 | 0.277 | 44.9 | 0.370 | 50.0 | 0.429 | 41.0 | 0.331 | 42.3 | 0.337 | 33.3 | 0.248 | |||
7×7 | 66.7 | 0.602 | 43.6 | 0.356 | 48.7 | 0.414 | 56.4 | 0.493 | 53.8 | 0.464 | 47.4 | 0.400 | 48.7 | 0.415 | 44.9 | 0.377 | |||
9×9 | 72.6 | 0.653 | 38.5 | 0.298 | 56.4 | 0.494 | 47.4 | 0.397 | 55.1 | 0.480 | 50.0 | 0.425 | 60.3 | 0.529 | 38.5 | 0.304 |
Table 4
Precision of pest severity monitoring model based on spectral indexes + texture features"
模型 model | 组合光谱指数 combine spectral signatures | 不同航高OA/% overall accuracy for different flight altitude | 不同航高Kappa值 Kappa for different flight altitude | ||||
---|---|---|---|---|---|---|---|
100 m | 150 m | 200 m | 100 m | 150 m | 200 m | ||
SVM 支持向量机 | SI1+TF(3) | 92.3 | 89.7 | 88.5 | 0.891 | 0.857 | 0.842 |
SI2+TF(5) | 92.3 | 88.5 | 85.9 | 0.891 | 0.841 | 0.810 | |
SI3+TF(7) | 92.3 | 89.7 | 87.2 | 0.891 | 0.857 | 0.825 | |
SI4+TF(9) | 92.3 | 89.7 | 87.2 | 0.891 | 0.857 | 0.825 | |
RF 随机森林 | SI1+TF(3) | 93.6 | 91.0 | 84.6 | 0.908 | 0.874 | 0.794 |
SI2+TF(5) | 93.5 | 92.3 | 83.3 | 0.907 | 0.890 | 0.778 | |
SI3+TF(7) | 91.0 | 91.0 | 87.2 | 0.874 | 0.874 | 0.825 | |
SI4+TF(9) | 92.3 | 91.0 | 87.2 | 0.890 | 0.874 | 0.825 |
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