基于无人机多光谱影像植被指数与纹理特征的冬小麦地上部生物量估算

doi:10.3969/j.issn.1004-1524.20221748

浙江农业学报 ›› 2023, Vol. 35 ›› Issue (12): 2966-2976.DOI: 10.3969/j.issn.1004-1524.20221748

基于无人机多光谱影像植被指数与纹理特征的冬小麦地上部生物量估算

朱永基¹^,²(), 陶新宇¹^,², 陈小芳¹^,², 苏祥祥¹^,², 刘吉凯¹^,², 李新伟¹^,²^,^*()

1.安徽科技学院资源与环境学院,安徽凤阳 233100
2.安徽省农业废弃物肥料化利用与耕地质量提升工程研究中心,安徽凤阳 233100

收稿日期:2022-12-05 出版日期:2023-12-25 发布日期:2023-12-27
作者简介:朱永基(1999—),男,安徽宿州人,硕士研究生,主要从事农业遥感、作物表型监测研究。E-mail:2024177548@qq.com
通讯作者: *李新伟,E-mail:lixw@ahstu.edu.cn
基金资助:
安徽省科技攻关重大专项(201903a06020001);国家重点研发计划(2018YFD0300901-2);新疆生产建设兵团绿洲生态重点实验室开放课题发展基金项目计划(201903);安徽省高等学校科学研究项目(2022AH051623);安徽省高等学校科学研究项目(2023AH051855)

Estimation of above-ground biomass of winter wheat based on vegetation indexes and texture features of multispectral images captured by unmanned aerial vehicle

ZHU Yongji¹^,²(), TAO Xinyu¹^,², CHEN Xiaofang¹^,², SU Xiangxiang¹^,², LIU Jikai¹^,², LI Xinwei¹^,²^,^*()

1. College of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, Anhui, China
2. Agricultural Waste Fertilizer Utilization and Cultivated Land Quality Improvement Engineering Research Center, Anhui Province, Fengyang 233100, Anhui, China

Received:2022-12-05 Online:2023-12-25 Published:2023-12-27

摘要/Abstract

摘要：

为了实现对冬小麦生物量的高效无损监测,于2020—2021年间设置田间试验,利用大疆精灵4多光谱版(P4M)无人机获取冬小麦6个关键生育期的多光谱影像,对冬小麦的地上部生物量(above-ground biomass,AGB)与多光谱影像的植被指数和纹理特征进行相关性分析,筛选特征变量,并分别采用线性回归、偏最小二乘回归(partial least squares regression,PLSR)、随机森林(random forest,RF)3种方法构建基于不同特征组合的AGB估算模型。结果显示:植被指数与冬小麦AGB的相关性要高于纹理特征。将植被指数与纹理特征融合使用,在不同生育期不同算法下,均可有效地降低光谱特征的饱和现象,提升模型估算冬小麦生物量的精度。基于筛选的特征运用线性回归估算AGB时,孕穗期和成熟期的精度较好;而运用PLSR与RF估算生物量的最佳时期则是抽穗期。综上,植被指数耦合纹理特征可以有效地提高冬小麦生物量估算的效果,基于消费级无人机可在中小尺度上快速准确估算冬小麦生物量。

关键词: 无人机, 多光谱, 冬小麦, 生物量, 纹理特征, 植被指数

Abstract:

In order to achieve efficient non-destructive monitoring of winter wheat biomass, a field experiment was carried out from 2020 to 2021. Multispectral images were collected at 6 key growth stages by DJI Phantom 4 Multispectral (P4M) unmanned aerial vehicle (UAV). The correlations within the above-ground biomass (AGB) of winter wheat, vegeatation indexes and texture features of the multispectral images were analyzed, the characteristic variables were screened, and the linear regression, partial least squares regression (PLSR), random forest (RF) methods were used to construct biomass estimation models with different combinations of characteristic variables. It was shown that the correlation between vegetation index and winter wheat AGB was higher than that between texture feature and winter wheat AGB. The combination of vegetation index and texture feature could effectively reduce the spectral saturation at growth stages, and improve the estimation accuracy. For the AGB estimation based on linear regression with the screened characteristic variable(s), the performace at booting and mature stages was good; while for the the AGB estimation based on PLSR and RF, the performance at heading stage was good. In general, texture features coupled with vegetation indexes could effectively improve the estimation accuracy of winter wheat AGB. Estimation of winter wheat AGB via consumer-grade UAV was feasible at small and medium scales.

Key words: unmanned aerial vehicle, multispectral, winter wheat, biomass, texture features, vegetation index

中图分类号:

S126

朱永基, 陶新宇, 陈小芳, 苏祥祥, 刘吉凯, 李新伟. 基于无人机多光谱影像植被指数与纹理特征的冬小麦地上部生物量估算[J]. 浙江农业学报, 2023, 35(12): 2966-2976.

ZHU Yongji, TAO Xinyu, CHEN Xiaofang, SU Xiangxiang, LIU Jikai, LI Xinwei. Estimation of above-ground biomass of winter wheat based on vegetation indexes and texture features of multispectral images captured by unmanned aerial vehicle[J]. Acta Agriculturae Zhejiangensis, 2023, 35(12): 2966-2976.

图/表 7

参考文献 36

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指标 Index	各时期与生物量的相关系数Coefficients of correlation with biomass at certain growth stage
指标 Index	拔节期 Jointing stage	孕穗期 Booting stage	抽穗期 Heading stage	开花期 Flowering stage	灌浆期 Filling stage	成熟期 Mature stage
Mean	-0.84^***	0.71^***	0.70^***	-0.66^***	-0.61^***	-0.55^***
Var	0.77^***	0.51^**	0.47^***	0.57^***	0.65^***	0.57^***
Ent	0.66^***	0.51^**	0.34^*	0.43^*	0.55^***	0.63^***
NDVI	0.82^***	0.64^***	0.69^***	0.69^***	0.75^***	0.84^***
DVI	0.83^***	0.72^***	0.72^***	0.70^***	0.78^***	0.82^***
RVI	0.80^***	0.76^***	0.77^***	0.78^***	0.80^***	0.79^***
GNDVI	0.82^***	0.67^***	0.71^***	0.72^***	0.77^***	0.85^***
NDRE	0.82^***	0.70^***	0.75^***	0.76^***	0.80^***	0.56^***
RECI	0.81^***	0.70^***	0.76^***	0.77^***	0.81^***	0.58^***
TCARI	0.80^***	0.77^***	0.75^***	0.76^***	0.80^***	0.76^***

指标 Index	各时期与生物量的相关系数Coefficients of correlation with biomass at certain growth stage
指标 Index	拔节期 Jointing stage	孕穗期 Booting stage	抽穗期 Heading stage	开花期 Flowering stage	灌浆期 Filling stage	成熟期 Mature stage
Mean	-0.84^***	0.71^***	0.70^***	-0.66^***	-0.61^***	-0.55^***
Var	0.77^***	0.51^**	0.47^***	0.57^***	0.65^***	0.57^***
Ent	0.66^***	0.51^**	0.34^*	0.43^*	0.55^***	0.63^***
NDVI	0.82^***	0.64^***	0.69^***	0.69^***	0.75^***	0.84^***
DVI	0.83^***	0.72^***	0.72^***	0.70^***	0.78^***	0.82^***
RVI	0.80^***	0.76^***	0.77^***	0.78^***	0.80^***	0.79^***
GNDVI	0.82^***	0.67^***	0.71^***	0.72^***	0.77^***	0.85^***
NDRE	0.82^***	0.70^***	0.75^***	0.76^***	0.80^***	0.56^***
RECI	0.81^***	0.70^***	0.76^***	0.77^***	0.81^***	0.58^***
TCARI	0.80^***	0.77^***	0.75^***	0.76^***	0.80^***	0.76^***

生育时期 Growth stage	回归方程 Regression equation	植被指数 Vegetation index	R²	RMSE/(kg·hm^-2)	MAE/(kg·hm^-2)
拔节期Jointing stage	Y=5903.1X-264.18	DVI	0.72	291.69	227.02
孕穗期Booting stage	Y=151.98X+2630.5	TCARI	0.56	1 483.98	1 255.93
抽穗期Heading stage	Y=288.81X+2876.2	RVI	0.66	1 625.48	1 285.38
开花期Flowering stage	Y=531.35X+3407.3	RVI	0.59	2 790.83	2 165.32
灌浆期Filling stage	Y=0.7919X+2569.8	RECI	0.78	2 228.92	1 783.60
成熟期Mature stage	Y=53795X-14256	GNDVI	0.72	3 186.10	2 550.66

生育时期 Growth stage	回归方程 Regression equation	植被指数 Vegetation index	R²	RMSE/(kg·hm^-2)	MAE/(kg·hm^-2)
拔节期Jointing stage	Y=5903.1X-264.18	DVI	0.72	291.69	227.02
孕穗期Booting stage	Y=151.98X+2630.5	TCARI	0.56	1 483.98	1 255.93
抽穗期Heading stage	Y=288.81X+2876.2	RVI	0.66	1 625.48	1 285.38
开花期Flowering stage	Y=531.35X+3407.3	RVI	0.59	2 790.83	2 165.32
灌浆期Filling stage	Y=0.7919X+2569.8	RECI	0.78	2 228.92	1 783.60
成熟期Mature stage	Y=53795X-14256	GNDVI	0.72	3 186.10	2 550.66

生育期 Growth stage	回归方程 Regression equation	R²	RMSE/(kg·hm^-2)	MAE/(kg·hm^-2)
拔节期Jointing stage	Y=-88.129×X_Mean+1 538.180×X_DVI+4 396.559	0.72	290.24	225.70
孕穗期Booting stage	Y=-179.779×X_Mean+230.607×X_TCARI+6 275.049	0.57	1 470.04	1 246.08
抽穗期Heading stage	Y=-402.281×X_Mean+523.738×X_RVI+11 134.649	0.69	1 537.85	1 179.70
开花期Flowering stage	Y=621.788×X_Mean+889.089×X_RVI-21 373.639	0.63	2 659.75	2 285.44
灌浆期Filling stage	Y=653.858×X_Var+10 446.017×X_RECI+1 338.706	0.79	2 109.77	1 683.14
成熟期Mature stage	Y=14 332.459×X_Ent+47 702.454×X_GNDVI-38 563.559	0.74	3111.02	2 596.56

基于无人机多光谱影像植被指数与纹理特征的冬小麦地上部生物量估算

Estimation of above-ground biomass of winter wheat based on vegetation indexes and texture features of multispectral images captured by unmanned aerial vehicle

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生育期 Growth stage	植被指数Vegetation indexes						植被指数+纹理特征Vegetation indexes+texture features
	建模集Modeling set			验证集Validation set			建模集Modeling set			验证集Validation set
	R²	RMSE/ (kg· hm^-2)	MAE/ (kg· hm^-2)	R²	RMSE/ (kg· hm^-2)	MAE/ (kg· hm^-2)	R²	RMSE/ (kg· hm^-2)	MAE/ (kg· hm^-2)	R²	RMSE/ (kg· hm^-2)	MAE/ (kg· hm^-2)
拔节期	0.93	151.48	112.68	0.46	486.82	363.89	0.98	77.61	63.17	0.56	453.26	368.62
Jointing stage
孕穗期	0.64	1 334.05	1 179.94	0.60	1 372.66	1 068.87	0.64	1 340.06	1 144.86	0.62	1 343.03	1 033.66
Booting stage
抽穗期	0.69	1 543.34	1 172.91	0.66	2 864.57	2 255.11	0.78	1 289.56	984.51	0.82	2 428.92	1 847.91
Heading stage
开花期	0.62	2 677.76	2 200.77	0.51	2 632.76	2 349.72	0.75	2 179.16	1 841.78	0.57	2 592.52	1 815.60
Flowering stage
灌浆期	0.76	2 263.58	1 792.08	0.51	4 007.59	3 437.22	0.89	1 543.50	1 416.67	0.53	3 751.17	3 144.52
Filling stage
成熟期	0.82	2 582.25	2 024.81	0.69	3 196.87	2 788.14	0.84	2 422.84	1 966.21	0.73	2 949.30	2 479.46
Mature stage

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