基于植被指数融合天山假狼毒地上生物量的估测

doi:10.6048/j.issn.1001-4330.2024.11.020

摘要/Abstract

摘要：

【目的】研究多源数据估算天山假狼毒地上生物量(AGB)的能力。【方法】采用搭载可见光和多光谱传感器的无人机平台采集盛花期与结实期信息,获取可见光植被指数、多光谱植被指数及两者相融合的植被指数,分别以多元线性回归(MLR)、逐步线性回归(SMLR)、随机森林回归(RF)建立单一植被指数与融合植被指数的AGB估测模型,采用决定系数(R²)、调整后决定系数( $R_{a d j}^{2}$ )和均方根误差(RMSE)评价估算模型。【结果】(1)近红外和红边波段组合的植被指数对天山假狼毒AGB较为敏感,可以较好的估算天山假狼毒的AGB。(2)在不同生育期中,盛花期估算效果最佳;基于融合植被指数的多元线性逐步回归估测模型中拟合效果最佳,模型的R²、 $R_{a d j}^{2}$ 、RMSE为0.837、0.831和7.357。(3)与基于单一类型的植被指数估测模型相比,基于融合植被指数建立的估测模型拟合精度最佳、稳定性更好。【结论】融合植被指数可有效增加光谱信息,提高模型预测精度。

关键词: 无人机; 天山假狼毒地上生物量; 植被指数; 可见光; 多光谱

Abstract:

【Objective】 In order to explore the ability of multi-source data to estimate aboveground biomass (AGB) of D.tianschanicum.【Methods】 A drone platform equipped with visible light and multispectral sensors was used to collect information on blooming and heading stages and obtain visible light vegetation index, multispectral vegetation index, and a combination of the two vegetation indices.Multiple linear regression (MLR), stepwise linear regression (SMLR) Random Forest Regression (RF) were applied to establish an AGB estimation model for single vegetation index and fused vegetation index by using the determination coefficient (R²), and to valuate the estimation model with the adjusted coefficient of determination ( $R_{a d j}^{2}$ ) and root mean square error (RMSE).【Results】 The vegetation index in the combination of near-infrared and red edge bands was more sensitive to the AGB of D. tianschanicum, so selecting;The peak flowering period had the best estimation effect among different growth stages, and the fitting effect was the best in the multiple linear stepwise regression estimation model based on the fusion vegetation index. The model's R², $R_{a d j}^{2}$ and RMSE were 0.837, 0.831, and 7.357;Compared with vegetation index estimation models based on a single type, estimation models based on fused vegetation indices hadthe best fitting accuracy and better stability. 【Conclusion】 The fusion of vegetation index can effectively increase spectral information and improve model prediction accuracy.

Key words: drones; Diarthron tianschanicum aboveground biomass; vegetation index; visible light; multispectral

中图分类号:

S812

侯正清, 颜安, 谢开云, 袁以琳, 夏雯秋, 肖淑婷, 张振飞, 孙哲. 基于植被指数融合天山假狼毒地上生物量的估测[J]. 新疆农业科学, 2024, 61(11): 2787-2796.

HOU Zhengqing, YAN An, XIE Kaiyun, YUAN Yilin, XIA Wenqiu, XIAO Shuting, ZHANG Zhenfei, SUN Zhe. Estimation of aboveground biomass of Diarthron tianschanicum based on vegetation index fusion[J]. Xinjiang Agricultural Sciences, 2024, 61(11): 2787-2796.

图/表 10

表1 可见光与多光谱植被指数及计算公式

Tab.1 Visible light and multispectral vegetation index and calculation formula

植被指数 Vegetation Indexes	计算公式 Formula	参考文献 References
红绿比值指数(RGRI)	r/g	[13]
红蓝比值指数RBRI	r/b	[13]
红绿植被指数(GRVI)	(g-r)/(g+r)	[13]
绿叶指数(GLA)	(2g-r-b)/(2*g+r+b)	[13]
改进型绿红植被指数(MGRVI)	(g²-r²)/(g²+r²)	[13]
地平面影像指数(GLI)	(2g-r-b)/(2g+r+b)	[13]
过绿指数减过红指数(EXGR)	2g-r-b-(1.4r-g)	[13]
彩色植被指数(CIVE)	0.441r-0.881g+0.385 6b+18.787 45	[13]
过红指数(ExR)	1.4r-g	[13]
大气阻抗植被指数VARI	(g-r)/(g+r-b)	[13]
重归一化植被指数(GRDVI)	(m₅-m₂)/(m₅+m₂)1/2	[13]
改进的非线性植被指数(MNLI)	(1.5 $m_{5}^{2}$ -1.5m₂)/( $m_{5}^{2}$ +m₁+0.5)	[13]
非线性植被指数(NLI)	( $m_{5}^{2}$ -m₁)/( $m_{5}^{2}$ +m₁)	[13]
归一化植被指数(NDVI)	(m₅-m₁)/(m₅+m₁)	[13]
绿色归一化植被指数(GNDVI)	(m₅-m₂)/(m₅+m₂)	[13]
非线性植被指数1(2NLI)	( $m_{5}^{2}$ -m₂)/( $m_{5}^{2}$ +m₂)	[13]
改进的非线性植被指数(GMNLI)	1.5( $m_{5}^{2}$ -m₂)/( $m_{5}^{2}$ +m₂+0.5)	[13]
绿色叶绿素指数(GCI)	(m₅/m₂)-1	[13]
红边叶绿素指数(RECI)	(m₅/m₄)-1	[13]
改进简单比值植被指数(MSR)	(m₅/m₁-1)/[(m₅/m₁)¹^/²+1]	[13]

表1 可见光与多光谱植被指数及计算公式

Tab.1 Visible light and multispectral vegetation index and calculation formula

植被指数 Vegetation Indexes	计算公式 Formula	参考文献 References
红绿比值指数(RGRI)	r/g	[13]
红蓝比值指数RBRI	r/b	[13]
红绿植被指数(GRVI)	(g-r)/(g+r)	[13]
绿叶指数(GLA)	(2g-r-b)/(2*g+r+b)	[13]
改进型绿红植被指数(MGRVI)	(g²-r²)/(g²+r²)	[13]
地平面影像指数(GLI)	(2g-r-b)/(2g+r+b)	[13]
过绿指数减过红指数(EXGR)	2g-r-b-(1.4r-g)	[13]
彩色植被指数(CIVE)	0.441r-0.881g+0.385 6b+18.787 45	[13]
过红指数(ExR)	1.4r-g	[13]
大气阻抗植被指数VARI	(g-r)/(g+r-b)	[13]
重归一化植被指数(GRDVI)	(m₅-m₂)/(m₅+m₂)1/2	[13]
改进的非线性植被指数(MNLI)	(1.5 $m_{5}^{2}$ -1.5m₂)/( $m_{5}^{2}$ +m₁+0.5)	[13]
非线性植被指数(NLI)	( $m_{5}^{2}$ -m₁)/( $m_{5}^{2}$ +m₁)	[13]
归一化植被指数(NDVI)	(m₅-m₁)/(m₅+m₁)	[13]
绿色归一化植被指数(GNDVI)	(m₅-m₂)/(m₅+m₂)	[13]
非线性植被指数1(2NLI)	( $m_{5}^{2}$ -m₂)/( $m_{5}^{2}$ +m₂)	[13]
改进的非线性植被指数(GMNLI)	1.5( $m_{5}^{2}$ -m₂)/( $m_{5}^{2}$ +m₂+0.5)	[13]
绿色叶绿素指数(GCI)	(m₅/m₂)-1	[13]
红边叶绿素指数(RECI)	(m₅/m₄)-1	[13]
改进简单比值植被指数(MSR)	(m₅/m₁-1)/[(m₅/m₁)¹^/²+1]	[13]

表2 可见光植被指数与天山假狼毒AGB相关性

Tab.2 Correlation between visible light vegetation index and Diarthron tianschanicum AGB

植被指数 Vegetation indexes	盛花期 Blooming stage	结实期 Fruiting stage
RGRI	0.633^**	-0.360^**
VARI	-0.599^**	0.345^**
EXR	0.482^**	-0.360^*
GRVI	0.381^**	0.362^**
GLA	0.374^**	0.333^**
MGRVI	-0.360^*	0.361^**
GLI	0.318^*	0.371^**
EXGR	0.384^**	0.368^**
CIVE	0.313^*	0.371^*
RBRI	0.321^*	-0.086

表3 多光谱植被指数与天山假狼毒AGB相关性

Tab.3 Correlation between multispectral vegetationindex and Diarthron tianschanicum AGB

植被指数 Vegetation indexes	盛花期 Blooming stage	结实期 Fruiting stage
GRDVI	0.660^**	0.483^**
MNLI	0.550^**	-0.352^**
NLI	0.471^**	-0.342^**
NDVI	0.458^**	0.438^**
GNDVI	0.457^**	0.463^**
2NLI	0.453^**	-0.431^**
GMNLI	0.446^**	-0.434^**
GCI	-0.439^**	0.560^**
MSR	-0.424^**	0.405^**
RECI	-0.407^**	0.473^**

表4 融合植被指数与天山假狼毒AGB相关性

Tab.4 Correlation between integrated vegetation index and Diarthron tianschanicum AGB

植被指数 Vegetation index	盛花期 Blooming stage	结实期 Fruiting stage
RGRI×GRDVI	0.768^**	0.415^**
RGRI×MNLI	0.538^**	-0.513^**
RGRI×NLI	0.534^**	-0.513^**
VARI×GRDVI	0.517^**	0.582^**
VARI×MNLI	0.535^**	0.370^**
VARI×NLI	0.535^**	0.348^**
EXR×GRDVI	0.520^**	-0.454^**
EXR×MNLI	0.537^**	-0.452^**
EXR×NLI	0.533^**	-0.452^**

表5 基于可见光植被指数估测天山假狼毒AGB模型拟合精度

Tab.5 Estimating the fitting accuracy of Diarthron tianschanicum AGB model based on visible light vegetation index

生育期 Growth stage	方法 Method	自变量 Independent variable	R²	${R^{2}}_{a d j}$	RMSE
盛花期 Blooming stage	SMLR	VARI、EXR、MGRVI	0.697	0.678	10.591
	MLR	RBRI、VARI、EXR、GLI、RGRI、MGRVI、 GRVI、GLA、EXGR、CIVE	0.711	0.645	10.587
	RF		0.720	0.685	8.263
结实期 Fruiting stage	SMLR	GLI	0.278	0.138	13.123
	MLR	RBRI、VARI、EXR、GLI、RGRI、MGRVI、 GRVI、GLA、EXGR、CIVE	0.344	0.3	12.852
	RF		0.483	0.465	9.776

表5 基于可见光植被指数估测天山假狼毒AGB模型拟合精度

Tab.5 Estimating the fitting accuracy of Diarthron tianschanicum AGB model based on visible light vegetation index

生育期 Growth stage	方法 Method	自变量 Independent variable	R²	${R^{2}}_{a d j}$	RMSE
盛花期 Blooming stage	SMLR	VARI、EXR、MGRVI	0.697	0.678	10.591
	MLR	RBRI、VARI、EXR、GLI、RGRI、MGRVI、 GRVI、GLA、EXGR、CIVE	0.711	0.645	10.587
	RF		0.720	0.685	8.263
结实期 Fruiting stage	SMLR	GLI	0.278	0.138	13.123
	MLR	RBRI、VARI、EXR、GLI、RGRI、MGRVI、 GRVI、GLA、EXGR、CIVE	0.344	0.3	12.852
	RF		0.483	0.465	9.776

表6 基于多光谱植被指数估测天山假狼毒AGB模型拟合精度

Tab.6 Estimating the fitting accuracy of Diarthron tianschanicum AGB model based on multispectral vegetation index

生育期 Growth stage	方法 Method	自变量 Independent variable	R²	${R^{2}}_{a d j}$	RMSE
盛花期 Blooming stage	SMLR	MNLI、GRDVI、NDVI	0.763	0.747	8.988
	MLR	GMNLI、GRDVI、NDVI、RECI、MNLI、MSR、 GCI、NLI、GNDVI、2NLI	0.804	0.753	8.876
	RF		0.794	0.779	7.318
结实期 Fruiting stage	SMLR	MSR	0.349	0.355	10.877
	MLR	GMNLI、GRDVI、NDVI、RECI、MNLI、MSR、 GCI、NLI、GNDVI、2NLI	0.620	0.613	8.301
	RF		0.681	0.562	9.693

表6 基于多光谱植被指数估测天山假狼毒AGB模型拟合精度

Tab.6 Estimating the fitting accuracy of Diarthron tianschanicum AGB model based on multispectral vegetation index

生育期 Growth stage	方法 Method	自变量 Independent variable	R²	${R^{2}}_{a d j}$	RMSE
盛花期 Blooming stage	SMLR	MNLI、GRDVI、NDVI	0.763	0.747	8.988
	MLR	GMNLI、GRDVI、NDVI、RECI、MNLI、MSR、 GCI、NLI、GNDVI、2NLI	0.804	0.753	8.876
	RF		0.794	0.779	7.318
结实期 Fruiting stage	SMLR	MSR	0.349	0.355	10.877
	MLR	GMNLI、GRDVI、NDVI、RECI、MNLI、MSR、 GCI、NLI、GNDVI、2NLI	0.620	0.613	8.301
	RF		0.681	0.562	9.693

表7 基于融合植被指数估测天山假狼毒AGB模型拟合精度

Tab.7 Estimating the fitting accuracy of Diarthron tianschanicum AGB model based on fusion vegetation index

生育期 Growth stage	方法 Method	自变量 Independent variable	R²	${R^{2}}_{a d j}$	RMSE
盛花期 Blooming stage	SMLR	RGRI×GRDVI、VARI×GRDVI	0.837	0.831	7.357
	MLR	VARI×GRDVI、VARI×MNLI、RGRI×MNLI、 RGRI×NLI、EXR×GRDVI、EXR×MNLI、EXR×NLI、 VARI×NLI、RGRI×GRDVI	0.844	0.809	7.821
	RF		0.831	0.820	8.054
结实期 Fruiting stage	SMLR	VARI×GRDVI、VARI×NLI	0.603	0.586	9.057
	SMLR	VARI×GRDVI、VARI×NLI	0.627	0.573	9.194
	MLR	VARI×GRDVI、VARI×MNLI、RGRI×MNLI、 RGRI×NLI、EXR×GRDVI、EXR×MNLI、EXR×NLI、 VARI×NLI、RGRI×GRDVI	0.627	0.573	9.194
	MLR		0.719	0.627	7.249
	RF		0.719	0.627	7.249

表7 基于融合植被指数估测天山假狼毒AGB模型拟合精度

Tab.7 Estimating the fitting accuracy of Diarthron tianschanicum AGB model based on fusion vegetation index

生育期 Growth stage	方法 Method	自变量 Independent variable	R²	${R^{2}}_{a d j}$	RMSE
盛花期 Blooming stage	SMLR	RGRI×GRDVI、VARI×GRDVI	0.837	0.831	7.357
	MLR	VARI×GRDVI、VARI×MNLI、RGRI×MNLI、 RGRI×NLI、EXR×GRDVI、EXR×MNLI、EXR×NLI、 VARI×NLI、RGRI×GRDVI	0.844	0.809	7.821
	RF		0.831	0.820	8.054
结实期 Fruiting stage	SMLR	VARI×GRDVI、VARI×NLI	0.603	0.586	9.057
	SMLR	VARI×GRDVI、VARI×NLI	0.627	0.573	9.194
	MLR	VARI×GRDVI、VARI×MNLI、RGRI×MNLI、 RGRI×NLI、EXR×GRDVI、EXR×MNLI、EXR×NLI、 VARI×NLI、RGRI×GRDVI	0.627	0.573	9.194
	MLR		0.719	0.627	7.249
	RF		0.719	0.627	7.249

图1 基于可见光植被指数构建的天山假狼毒各生育期估测模型精度验证

Fig.1 Accuracy verification of estimating models for different growth stages of Diarthron tianschanicum based on visible light vegetation index

图2 基多光谱植被指数构建的天山假狼毒各生育期估测模型精度验证

Fig.2 Validation of the precision of the estimating model for different growth stages of Diarthron tianschanicum based on multispectral vegetation index construction

图3 基融合植被指数构建的天山假狼毒各生育期估测模型精度验证

Fig.3 Accuracy verification of estimating models for different growth stages of Diarthron tianschanicum based on fusion vegetation index construction

参考文献 32

[1]	李宏, 陈卫民, 陈翔, 等. 新疆伊犁草原毒害草种类及其发生与危害[J]. 草业科学, 2010, 27(11): 171-173.
	LI Hong, CHEN Weimin, CHEN Xiang, et al. Poisonous weed species and their harmfulness in the grassland of Yili regions, Xinjiang Uygur Autonomous Region of China[J]. Pratacultural Science, 2010, 27(11): 171-173.
[2]	张鲜花, 安沙舟, 王显瑞, 等. 白喉乌头种群空间分布格局初步研究[J]. 草地学报, 2012, 20(3): 428-433. DOI
	ZHANG Xianhua, AN Shazhou, WANG Xianrui, et al. Spatial distribution patterns of Aconitum leucostomum[J]. Acta Agrestia Sinica, 2012, 20(3): 428-433. DOI
[3]	牛琼梅, 单贵莲, 罗钦, 等. 毒害草入侵扩散对滇西北亚高山草甸土壤微生物多样性的影响[J]. 草地学报, 2023, 31(7): 1996-2004. DOI
	NIU Qiongmei, SHAN Guilian, LUO Qin, et al. Effect of invasion and diffusion of poisonous weeds on soil microbial diversity in subalpine meadow in northwest Yunnan[J]. Acta Agrestia Sinica, 2023, 31(7): 1996-2004. DOI
[4]	王宏生, 宋梅玲, 王玉琴, 等. 不同恢复措施对黄帚橐吾及毒害草型退化草地群落的影响[J]. 中国草地学报, 2022, 44(4): 32-39.
	WANG Hongsheng, SONG Meiling, WANG Yuqin, et al. Effects of different restoration methods on Ligularia virgaurea and toxic weed dominated degraded grassland community[J]. Chinese Journal of Grassland, 2022, 44(4): 32-39.
[5]	吕绍伦, 赵阳, 卢吉瑞, 等. 伊犁州毒草害空间分布特征及防治[J]. 农村经济与科技, 2022, 33(3): 24-27.
	LYU Shaolun, ZHAO Yang, LU Jirui, et al. Spatial distribution characteristics and control of poisonous weeds in Yili Prefecture[J]. Rural Economy and Science-Technology, 2022, 33(3): 24-27.
[6]	杨晨, 沙日扣, 苏日拉格, 等. 中国天然草原毒害草种类分布、毒性及防控与利用研究进展[J]. 家畜生态学报, 2022, 43(11): 88-96. DOI
	YANG Chen, SHA Rikou, SU Rilage, et al. Research progress on distribution, toxicity, control and utilization of poisonous grass species in natural grassland of China[J]. Journal of Domestic Animal Ecology, 2022, 43(11): 88-96.
[7]	岳方正, 李璇, 杨鼎, 等. 我国主要草原有害生物防治指标制定与分析[J]. 草业科学, 2022, 39(9): 1773-1781.
	YUE Fangzheng, LI Xuan, YANG Ding, et al. Establishment and analysis of key pest control indicators in grasslands in China[J]. Pratacultural Science, 2022, 39(9): 1773-1781.
[8]	范宏, 刘素红, 陈吉军, 等. 基于深度学习的白喉乌头与牧草高精度分类研究[J]. 江苏农业科学, 2021, 49(12): 173-180.
	FAN Hong, Liu Suhong, Chen Jijun, et al. Study on high-precision classification of Aconitum leucostomum Worosch and pasture based on deep learning[J]. Jiangsu Agricultural Sciences, 2021, 49(12): 173-180.
[9]	梁俊欢. 基于无人机影像和深度学习方法的白喉乌头识别研究[D]. 乌鲁木齐: 新疆农业大学, 2022.
	LIANG Junhuan. Recognition of Aconitum Leucostomum Based on UVA Image and Deep Learning Method[D]. Urumqi: Xinjiang Agricultural University, 2022.
[10]	王晗, 向友珍, 李汪洋, 等. 基于无人机多光谱遥感的冬油菜地上部生物量估算[J]. 农业机械学报, 2023, 54(8): 218-229.
	WANG Han, XIANG Youzhen, LI Wangyang, et al. Estimation of winter rapeseed above-ground biomass based on uav multi-spectral remote sensing[J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(8): 218-229.
[11]	刘建刚, 赵春江, 杨贵军, 等. 无人机遥感解析田间作物表型信息研究进展[J]. 农业工程学报, 2016, 32(24): 98-106.
	LIU Jiangang, ZHAO Chunjiang, YANG Guijun, et al. Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(24): 98-106.
[12]	Che Y P, Wang Q, Xie Z W, et al. Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography[J]. Annals of Botany, 2020, 126(4): 765-773. DOI PMID
[13]	孙永祺, 陈梦媛, 黄倩, 等. 低空无人机遥感在油料作物表型分析中的应用[J]. 浙江大学学报(农业与生命科学版), 2023, 49(4): 472-483.
	SUN Yongqi, CHEN Mengyuan, HUANG Qian, et al. Application of low-altitude unmanned aerial vehicle remote sensing in the phenotypic analysis of oil crops[J]. Journal of Zhejiang University (Agriculture and Life Sciences), 2023, 49(4): 472-483.
[14]	向友珍, 安嘉琪, 赵笑, 等. 基于无人机多光谱遥感的大豆生长参数和产量估算[J]. 农业机械学报, 2023, 54(8): 230-239.
	XIANG Youzhen, AN Jiaqi, ZHAO Xiao, et al. Soybean growth parameters and yield estimation based on UAV multispectral remote sensing[J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(8): 230-239.
[15]	Osco L P, Junior J M, Ramos A P M, et al. Leaf nitrogen concentration and plant height prediction for maize using UAV-based multispectral imagery and machine learning techniques[J]. Remote Sensing, 2020, 12(19): 3237.
[16]	Lu J S, Cheng D L, Geng C M, et al. Combining plant height, canopy coverage and vegetation index from UAV-based RGB images to estimate leaf nitrogen concentration of summer maize[J]. Biosystems Engineering, 2021, 202: 42-54.
[17]	Zhou X B, Kono Y, Win A, et al. Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches[J]. Plant Production Science, 2021, 24(2): 137-151.
[18]	秦彩杰, 李勇. 作物病虫害自动识别技术研究——基于视频监控和无人机平台[J]. 农机化研究, 2021, 43(10): 42-45, 50.
	QIN Caijie, LI Yong. Research on automatic identification technology of crop diseases and insect pests—based on video monitoring and UAV platform[J]. Journal of Agricultural Mechanization Research, 2021, 43(10): 42-45, 50.
[19]	杨国峰, 何勇, 冯旭萍, 等. 无人机遥感监测作物病虫害胁迫方法与最新研究进展[J]. 智慧农业, 2022, 4(1): 1-16.
	YANG Guofeng, HE Yong, FENG Xuping, et al. Methods and new research progress of remote sensing monitoring of crop disease and pest stress using unmanned aerial vehicle[J]. Smart Agriculture, 2022, 4(1): 1-16. DOI
[20]	Lan Y B, Huang K H, Yang C, et al. Real-time identification of rice weeds by UAV low-altitude remote sensing based on improved semantic segmentation model[J]. Remote Sensing, 2021, 13(21): 4370.
[21]	Zhu H, Jiang Y, Zhang C, et al. Research on mosaic method of UAV low-altitude remote sensing image based on SIFT and SURF[J]. Journal of Physics: Conference Series, 2022, 2203(1): 012027.
[22]	Tetila E C, Machado B B, Astolfi G, et al. Detection and classification of soybean pests using deep learning with UAV images[J]. Computers and Electronics in Agriculture, 2020, 179: 105836.
[23]	张正健, 李爱农, 边金虎, 等. 基于无人机影像可见光植被指数的若尔盖草地地上生物量估算研究[J]. 遥感技术与应用, 2016, 31(1): 51-62.
	ZHANG Zhengjian, LI Ainong, BIAN Jinhu, et al. Estimating aboveground biomass of grassland in zoige by visible vegetation index derived from unmanned aerial vehicle image[J]. Remote Sensing Technology and Application, 2016, 31(1): 51-62.
[24]	聂松伟. 基于无人机多光谱遥感的冬小麦长势指标监测[D]. 乌鲁木齐: 新疆农业大学, 2022.
	NIE Songwei. Monitoring of Winter Wheat Growth Index Based on UAV Multi-spectral Remote Sensing[D]. Urumqi: Xinjiang Agricultural University, 2022.
[25]	吴培强, 任广波, 张程飞, 等. 无人机多光谱和LiDAR的红树林精细识别与生物量估算[J]. 遥感学报, 2022, 26(6): 1169-1181.
	WU Peiqiang, REN Guangbo, ZHANG Chengfei, et al. Fine identification and biomass estimation of mangroves based on UAV multispectral and LiDAR[J]. National Remote Sensing Bulletin, 2022, 26(6): 1169-1181.
[26]	杭艳红, 苏欢, 于滋洋, 等. 结合无人机光谱与纹理特征和覆盖度的水稻叶面积指数估算[J]. 农业工程学报, 2021, 37(9): 64-71.
	HANG Yanhong, SU Huan, YU Ziyang, et al. Estimation of rice leaf area index combining UAV spectrum, texture features and vegetation coverage[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(9): 64-71.
[27]	牛庆林, 冯海宽, 周新国, 等. 冬小麦SPAD值无人机可见光和多光谱植被指数结合估算[J]. 农业机械学报, 2021, 52(8): 183-194.
	NIU Qinglin, FENG Haikuan, ZHOU Xinguo, et al. Combining UAV visible light and multispectral vegetation indices for estimating SPAD value of winter wheat[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(8): 183-194.
[28]	李长春, 牛庆林, 杨贵军, 等. 基于无人机数码影像的大豆育种材料叶面积指数估测[J]. 农业机械学报, 2017, 48(8): 147-158.
	LI Changchun, NIU Qinglin, YANG Guijun, et al. Estimation of leaf area index of soybean breeding materials based on UAV digital images[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(8): 147-158.
[29]	孙奇, 关琳琳, 焦全军, 等. 基于植被指数融合的冬小麦生物量反演研究[J]. 遥感技术与应用, 2021, 36(2): 391-399. DOI
	SUN Qi, GUAN Linlin, JIAO Quanjun, et al. Research on retrieving biomass of winter wheat based on fusing vegetation index[J]. Remote Sensing Technology and Application, 2021, 36(2): 391-399.
[30]	刘玉杰, 封志明, 邓福英. 基于草地退化指示植物大狼毒的植被指数研究[J]. 草地学报, 2014, 22(3): 455-460. DOI
	LIU Yujie, FENG Zhiming, DENG Fuying. Vegetation indices of Euphorbia jolkinii As an indicator plant of grassland degradation[J]. Acta Agrestia Sinica, 2014, 22(3): 455-460. DOI
[31]	干珠扎布, 段敏杰, 高清竹, 等. 藏北高寒退化草地天山假狼毒群落地面光谱特征及其生物量估测[C]. 国际能源与环境会议(ICEE), 2011, 303-306.
	Ganzhuzhabu, DUAN Minjie, GAO Qingzhu, et al. Spectral reflectance and biomass estimation of Diarthron tianschanicum in northern Tibet[C]. International Conference on Energy and Environment (ICEE), 2011, 303-306.
[32]	凯楠, 刘咏梅, 李京忠, 等. 青海瑞香狼毒叶绿素含量高光谱预测模型[J]. 生态学杂志, 2017, 36(4): 1150-1157.
	KAI Nan, LIU Yongmei, LI Jingzhong, et al. Hyperspectal predicting model of chlorophyll content of Stellera chamaejasme in Qinghai Province[J]. Chinese Journal of Ecology, 2017, 36(4): 1150-1157.

编辑推荐 0

Metrics

阅读次数

全文

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	1	0	0	6

来源	本网站	其他网站

次数	5	2
比例	71%	29%

摘要

最新录用	在线预览	正式出版

0	0	67

来源	本网站	其他网站

次数	31	36
比例	46%	54%