新疆农业科学 ›› 2024, Vol. 61 ›› Issue (10): 2527-2536.DOI: 10.6048/j.issn.1001-4330.2024.10.020
• 植物保护·土壤肥料·节水灌溉·农业装备工程与机械化·草业 • 上一篇 下一篇
侯正清1(), 颜安2(
), 谢开云2, 袁以琳1, 夏雯秋3, 肖淑婷1, 张振飞1, 孙哲1
收稿日期:
2024-03-28
出版日期:
2024-10-20
发布日期:
2024-11-07
通信作者:
颜安(1983-),男,四川资阳人,教授,博士,硕士生/博士生导师,研究方向为数字农业与生态环境遥感监测,(E-mail)yanan@xjau.edu.cn作者简介:
侯正清(1999-),女,新疆昭苏人,硕士研究生,研究方向为农业信息化,(E-mail)287511284@qq.com
基金资助:
HOU Zhengqing1(), YAN An2(
), XIE Kaiyun2, YUAN Yilin1, XIA Wenqiu3, XIAO Shuting1, ZHANG Zhenfei1, SUN Zhe1
Received:
2024-03-28
Published:
2024-10-20
Online:
2024-11-07
Correspondence author:
YAN An (1983-), male, from Ziyang,Sichuan,professor, Ph.D., Master/Doctoral's supervisor, research direction: digital agriculture and ecological environment remote sensing monitoring,(E-mail)yanan@xjau.edu.cnSupported by:
摘要:
【目的】 研究无人机多特征构建天山假狼毒地上生物量(AGB)估算模型的能力,为草地退化程度的分级提供参考依据。【方法】 天山假狼毒(Diarthron tianschanicum)作为退化指示植物之一,其生长状况可反映草地退化程度。通过可见光高空间分辨率遥感影像提取光谱特征、纹理特征和天山假狼毒覆盖度,将三者分别作为输入量建立一元线性模型,三类特征融合构建多元逐步回归与人工神经网络模型,分析多特征融合估算天山假狼毒AGB的效果。【结果】 (1)盛花期为天山假狼毒最佳覆盖度提取窗口期,利用RF算法构建的天山假狼毒提取模型效果较为理想,总体精度在81%以上。(2)光谱特征、纹理特征、覆盖度均与天山假狼毒AGB具有相关性,其中纹理特征cor_G相关性最高,达0.784。(3)对比单一植被指数、纹理特征、覆盖度及任意两种特征组合作为输入量,多特征融合估算天山假狼毒AGB的精度最高,R2、RMSE为0.870、15.383,并利用人工神经网络模型对此结论进行验证。【结论】 融合光谱特征、纹理指数、覆盖度能有效提高天山假狼毒AGB估算精度。
中图分类号:
侯正清, 颜安, 谢开云, 袁以琳, 夏雯秋, 肖淑婷, 张振飞, 孙哲. 基于多特征融合的无人机天山假狼毒地上生物量估算[J]. 新疆农业科学, 2024, 61(10): 2527-2536.
HOU Zhengqing, YAN An, XIE Kaiyun, YUAN Yilin, XIA Wenqiu, XIAO Shuting, ZHANG Zhenfei, SUN Zhe. Estimation of above ground biomass of drone Diarthron tianschanicum based on multi feature fusion[J]. Xinjiang Agricultural Sciences, 2024, 61(10): 2527-2536.
物种 Species | 高度 Height(cm) | 盖度 Coverage(%) | 密度 Density/plant(m2) | 重要值 Important value |
---|---|---|---|---|
天山假狼毒 Stelleropsis tianschanica | 30.188±5.522 | 27.999±14.224 | 3.125±0.976 | 0.205 |
紫苞鸢尾 Iris ruthenica | 18.086±2.854 | 28.046±10.126 | 16.556±5.577 | 0.159 |
无芒雀麦 Smooth bromegrass | 25.233±14.769 | 15.569±8.164 | 48.183±52.220 | 0.159 |
夏至草 Horehound | 13.511±3.483 | 15.008±8.780 | 70.348±54.406 | 0.157 |
百里香 Thyme | 11.387±1.697 | 10.889±5.271 | 15.048±13.048 | 0.157 |
车前 Snokeweed | 14.237±13.342 | 13.567±4.375 | 15.500±8.635 | 0.141 |
表1 研究区群落主要植物数量特征
Tab.1 Quantitative characteristics of main plants in the study area community
物种 Species | 高度 Height(cm) | 盖度 Coverage(%) | 密度 Density/plant(m2) | 重要值 Important value |
---|---|---|---|---|
天山假狼毒 Stelleropsis tianschanica | 30.188±5.522 | 27.999±14.224 | 3.125±0.976 | 0.205 |
紫苞鸢尾 Iris ruthenica | 18.086±2.854 | 28.046±10.126 | 16.556±5.577 | 0.159 |
无芒雀麦 Smooth bromegrass | 25.233±14.769 | 15.569±8.164 | 48.183±52.220 | 0.159 |
夏至草 Horehound | 13.511±3.483 | 15.008±8.780 | 70.348±54.406 | 0.157 |
百里香 Thyme | 11.387±1.697 | 10.889±5.271 | 15.048±13.048 | 0.157 |
车前 Snokeweed | 14.237±13.342 | 13.567±4.375 | 15.500±8.635 | 0.141 |
植被指数 Vegetation Indexes | 计算公式 Formula |
---|---|
归一化绿红差异指数 Normalized green-red difference index,NGRDI | (G-R)/(G+R)[ |
归一化绿蓝差异指数 Normalized green and blue disparity index,NGBDI | (G-B)/(G+B)[ |
绿蓝比值指数Green-blue ratio index,GBRI | G/B[ |
红光标准化值Normalized redness intensity,NRI | R/(R+B+G)[ |
绿光标准化值Normalized greenness intensity,NGI | G/(R+B+G)[ |
蓝光标准化值Normalized blueness intensity,NBI | B/(R+B+G)[ |
红绿比值指数Red-green ratio index,RGRI | R/G[ |
过红指数Excess red index,ExR | 1.4R-G[ |
表2 可见光植被指数及计算公式
Tab.2 Visible light vegetation index and calculation formula
植被指数 Vegetation Indexes | 计算公式 Formula |
---|---|
归一化绿红差异指数 Normalized green-red difference index,NGRDI | (G-R)/(G+R)[ |
归一化绿蓝差异指数 Normalized green and blue disparity index,NGBDI | (G-B)/(G+B)[ |
绿蓝比值指数Green-blue ratio index,GBRI | G/B[ |
红光标准化值Normalized redness intensity,NRI | R/(R+B+G)[ |
绿光标准化值Normalized greenness intensity,NGI | G/(R+B+G)[ |
蓝光标准化值Normalized blueness intensity,NBI | B/(R+B+G)[ |
红绿比值指数Red-green ratio index,RGRI | R/G[ |
过红指数Excess red index,ExR | 1.4R-G[ |
纹理特征 Texture feature | 公式 Formula |
---|---|
均值 Mean | |
方差 Variance | |
同质性 Homogeneity | |
对比度 Contrast | |
差异性 Dissimilarity | |
熵 Entropy | |
二阶距 Second Moment | |
相关性 Correlation |
表3 纹理特征及其计算公式
Tab.3 Texture features and its formulas
纹理特征 Texture feature | 公式 Formula |
---|---|
均值 Mean | |
方差 Variance | |
同质性 Homogeneity | |
对比度 Contrast | |
差异性 Dissimilarity | |
熵 Entropy | |
二阶距 Second Moment | |
相关性 Correlation |
模型输入 Model input | 模型公式 Model formula | 决定系数 R2 | 均方根 误差 RMSE (g/m2) |
---|---|---|---|
ExR | Y=26.173-2.028X | 0.544 | 29.315 |
NRI | Y=-188.86+919.344X | 0.400 | 33.623 |
NBI | Y=628.877-1 971.515X | 0.381 | 34.166 |
NGRDI | Y=-65.082+582.556X | 0.318 | 35.860 |
NGBDI | Y=--166.357+1 080.686X | 0.467 | 35.741 |
NGI | Y=-843.908+2 060.782X | 0.308 | 36.122 |
GBRI | Y=-24.727+59.308X | 0.137 | 40.334 |
RGRI | Y=485.473-664.829X | 0.492 | 30.934 |
mean_B | Y=280.073-8.103 624X | 0.345 | 35.144 |
hom_B | Y=229.254-947.174X | 0.220 | 38.294 |
cor_B | Y=145.009-217.139X | 0.193 | 38.999 |
mean_G | Y=-52.137+3.867X | 0.511 | 30.360 |
hom_G | Y=93.821-0.692X | 0.186 | 39.164 |
cor_G | Y=-68.339+369.227X | 0.606 | 27.237 |
hom_R | Y=-225.691-914.770 713X | 0.195 | 38.940 |
cor_R | Y=-62.148+355.199 683X | 0.576 | 28.251 |
FVC | Y=-15.636+161.079X | 0.501 | 30.664 |
表4 天山假狼毒AGB的一元线性估算模型与精度评价
Tab.4 Univariate linear estimation models and accuracy evaluation for AGB of Diarthron tianschanicum
模型输入 Model input | 模型公式 Model formula | 决定系数 R2 | 均方根 误差 RMSE (g/m2) |
---|---|---|---|
ExR | Y=26.173-2.028X | 0.544 | 29.315 |
NRI | Y=-188.86+919.344X | 0.400 | 33.623 |
NBI | Y=628.877-1 971.515X | 0.381 | 34.166 |
NGRDI | Y=-65.082+582.556X | 0.318 | 35.860 |
NGBDI | Y=--166.357+1 080.686X | 0.467 | 35.741 |
NGI | Y=-843.908+2 060.782X | 0.308 | 36.122 |
GBRI | Y=-24.727+59.308X | 0.137 | 40.334 |
RGRI | Y=485.473-664.829X | 0.492 | 30.934 |
mean_B | Y=280.073-8.103 624X | 0.345 | 35.144 |
hom_B | Y=229.254-947.174X | 0.220 | 38.294 |
cor_B | Y=145.009-217.139X | 0.193 | 38.999 |
mean_G | Y=-52.137+3.867X | 0.511 | 30.360 |
hom_G | Y=93.821-0.692X | 0.186 | 39.164 |
cor_G | Y=-68.339+369.227X | 0.606 | 27.237 |
hom_R | Y=-225.691-914.770 713X | 0.195 | 38.940 |
cor_R | Y=-62.148+355.199 683X | 0.576 | 28.251 |
FVC | Y=-15.636+161.079X | 0.501 | 30.664 |
模型输入量 Model input | 模型公式 Model formula | 因子 Factor | 决定 系数 R2 | 均方根误差 RMSE (g/m2) |
---|---|---|---|---|
VIs+FVC | Y=-0.888-1.907exr+93.623FVC | exr,FVC | 0.712 | 23.281 |
TIs+FVC | Y=-160.891+7.335mean_G-9.285mean_B+1.439hom_G+994.878hom_B+60.704FVC | mean_G,mean_B,hom_G,hom_B,FVC | 0.830 | 17.398 |
VIs+TIs | Y=-400.961+6.644mean_G+1 487.090hom_R-7.485mean_B+1.138hom_G+728.831ngbdi | mean_G,hom_R,mean_B,hom_G,ngbdi | 0.850 | 16.739 |
VIS+TIS+FVC | Y=778.539-1.141exr+53.348FVC+1445.883hom_B+641.677nri+2.519mean_G-2152.134nbi | Exr,FVC,hom_B,nri,mean_G,nbi,ngi | 0.870 | 15.383 |
表5 天山假狼毒AGB的多元线性逐步回归估算模型与精度评价
Tab.5 Multivariate linear stepwise regression estimation models and accuracy evaluation for AGB of Diarthron tianschanicum
模型输入量 Model input | 模型公式 Model formula | 因子 Factor | 决定 系数 R2 | 均方根误差 RMSE (g/m2) |
---|---|---|---|---|
VIs+FVC | Y=-0.888-1.907exr+93.623FVC | exr,FVC | 0.712 | 23.281 |
TIs+FVC | Y=-160.891+7.335mean_G-9.285mean_B+1.439hom_G+994.878hom_B+60.704FVC | mean_G,mean_B,hom_G,hom_B,FVC | 0.830 | 17.398 |
VIs+TIs | Y=-400.961+6.644mean_G+1 487.090hom_R-7.485mean_B+1.138hom_G+728.831ngbdi | mean_G,hom_R,mean_B,hom_G,ngbdi | 0.850 | 16.739 |
VIS+TIS+FVC | Y=778.539-1.141exr+53.348FVC+1445.883hom_B+641.677nri+2.519mean_G-2152.134nbi | Exr,FVC,hom_B,nri,mean_G,nbi,ngi | 0.870 | 15.383 |
模型输入 Model input | 决定系数 R2 | 均方根误差 RMSE (g/m2) |
---|---|---|
VIs | 0.648 | 24.060 |
TIs | 0.680 | 18.249 |
FVC | 0.562 | 17.261 |
VIs+TIs | 0.785 | 20.345 |
VIs+FVC | 0.726 | 23.987 |
TIs+FVC | 0.787 | 22.511 |
VIs+TIs+FVC | 0.806 | 22.685 |
表6 AGB的人工神经网络预测模型与精度评价
Tab.6 Artificial neural network forecast model and accuracy evaluation of AGB
模型输入 Model input | 决定系数 R2 | 均方根误差 RMSE (g/m2) |
---|---|---|
VIs | 0.648 | 24.060 |
TIs | 0.680 | 18.249 |
FVC | 0.562 | 17.261 |
VIs+TIs | 0.785 | 20.345 |
VIs+FVC | 0.726 | 23.987 |
TIs+FVC | 0.787 | 22.511 |
VIs+TIs+FVC | 0.806 | 22.685 |
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