Effects of UAV flight altitude on the accuracy of monitoring Dendrolimus superans pests by remote sensing

doi:10.12302/j.issn.1000-2006.202204047

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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Effects of UAV flight altitude on the accuracy of monitoring Dendrolimus superans pests by remote sensing

YANG Le¹(), HUANG Xiaojun¹^,^*(), BAO Yuhai¹, BAO Gang¹, TONG Siqin¹, Sudubilig ²

1. Inner Mongolia Key Laboratory of Remote Sensing & Geography Information System, Inner Mongolia Key Laboratory of Disaster and Ecological Security on the Mongolia Plateau, College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
2. College of Environment, Northeast Normal University, Changchun 130024, China

Received:2022-04-25 Revised:2023-04-16 Online:2023-07-30 Published:2023-07-20

Abstract

Abstract:

【Objective】This study aims to explore the influence of unmanned aerial vehicles(UAV) flight altitude mechanism on the accuracy of monitoring larch caterpillar (Dendrolimus superans) insect pests, and provide an important reference for ground UAV remote sensing monitoring of forest pests.【Method】 The areas known for frequent occurrences of D. superans in Da Hinggan Mountains were selected and multispectral remote sensing images collected by UAV at different flight altitudes were used as the basic data. This study obtained the canopy spectral indexes and texture features of 386 healthy, mild, and severely damaged trees by D. superans. Analysis of variances and continuous projection algorithms were used to extract the spectral features sensitive to pest severity. The pest severity monitoring model was constructed using random forest and support vector machine algorithms, and expounded the influence of flight altitude on monitoring accuracy.【Result】(1) The accuracy of overall (mild + severe) monitoring of the spectral indexes and texture features decreased with an increase in flight altitudes. However, the accuracy of mild and severe monitoring of trees damaged by D. superans exhibited different trends. (2) The pest monitoring accuracy of the combination of spectral indices (MTVI 2, GNDVI 2, DVI, GMI 1 and GNDVI) + texture feature (MEA 3) was the best, and the overall accuracy and Kappa coefficient were 92.3% and 0.891, respectively. However, the overall and accuracy of mild monitoring decreased with an increase in flight altitudes, where the decline rate was 0.04%/m and 0.03%/m, respectively, and the accuracy of severe monitoring increased (the rise rate was 0.03%/m). 【Conclusion】 The flight altitudes significantly impacted the accuracy of UAV ground pest monitoring. There was a difference in the rate and trend between the accuracies of mild and severe monitoring. The rate of change in the accuracy of mild monitoring with flight altitude was faster than that of the accuracy of the severe monitoring. Thus, an early identification of pests using a high-precision UAV remote sensing, adaptable to various flight altitudes, is needed to monitor pest severity and improve the expected effects.

Key words: Dendrolimus superans, insect pest, unmanned aerial vehicles(UAV), flight altitude, multi-spectral remote sensing monitoring

CLC Number:

S763.305

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.

Figures/Tables 6

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^[18]
类胡萝卜素反射指数1 CRI 1	(1/B₂)-(1/B₃)	Gitelson等^[19]
差值植被指数DVI	B₅-B₃	Jordan^[20]
增强植被指数EVI	B₅/B₃	Pearson等^[21]
简单比值指数GMI 1	B₁/B₄	Lichtenthaler^[22]
绿光归一化差值植被指数GNDVI	B₄/B₂	Gitelson等^[23]
绿光归一化差值植被指数2 GNDVI 2	(B₅-B₃)/(B₅+B₃)	Rouse等^[24]
利希滕塔莱指数LIC 3	(B₅-B₂)/(B₅+B₂)	Gitelson等^[25]
修正型三角植被指数MTVI 2	(B₄-B₂)/(B₄+B₂)	Gitelson等^[25]
归一化植被指数NDVI	2.5(B₅-B₃)/[1+B₅+6B₃-7.5B₁]	Huete等^[26]
比值植被指数RVI	1.5[1.2(B₅-B₂)-2.5(B₃-B₂)]/ $[(2 B 5 + 1) 2$ - (6B₅- $B 3$ -0.5)	Haboudane等^[27]
三角形植被指数TVI	0.5[120(B₄-B₂)-200(B₃-B₂)]	Broge等^[28]

Table 1

Fig.1

Table 2

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
模型 model	航高/m flight- altitude	窗口大小 window size	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 3

Table 4

Fig.2

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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
模型 model	光谱指数 spectral index	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

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
模型 model	组合光谱指数 combine spectral signatures	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

Effects of UAV flight altitude on the accuracy of monitoring Dendrolimus superans pests by remote sensing

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[1]	LI Yun-xian. Study on the Accurate Control Techniques of Pest in Plantation Forest [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2006, 30(06): 132-134.
[2]	WANG Yan~1,LI Jun~2,GE Jian-ming~3,FENG Chen~1,MA Feng-lin~1. Effects of Microclimate in Woodland and Its Impacts on Four Pests [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2004, 28(06): 115-117.
[3]	Xue Xianqing Cui Li(Department of Forestry) (Library)Qi Yan. THE ADVANCE FOR SCIENCE AND TECHNOLOGY ON FORECAST OF FOREST INSECT PESTS IN CHINA [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 1992, 16(04): 80-86.