草业学报 ›› 2022, Vol. 31 ›› Issue (4): 177-188.DOI: 10.11686/cyxb2021072
• 研究论文 • 上一篇
秦格霞1(), 吴静1(), 李纯斌1, 吉珍霞1, 邱政超2, 李颖1
收稿日期:
2021-02-25
修回日期:
2021-03-29
出版日期:
2022-04-20
发布日期:
2022-01-25
通讯作者:
吴静
作者简介:
Corresponding author. E-mail: wujing@gsau.edu.cn基金资助:
Ge-xia QIN1(), Jing WU1(), Chun-bin LI1, Zhen-xia JI1, Zheng-chao QIU2, Ying LI1
Received:
2021-02-25
Revised:
2021-03-29
Online:
2022-04-20
Published:
2022-01-25
Contact:
Jing WU
摘要:
使用机器学习算法快速、准确、大范围监测草地地上生物量(AGB)是目前研究热点,但不同机器学习算法因训练样本、超参数设置不同而存在较大差异。基于实测草地AGB和同期遥感数据、气象数据、地形数据,选择与草地AGB相关性较强的13个因子作为深度神经网络 (DNN)、随机森林算法(RF)、梯度提升回归树(GBRT)、支持向量机(SVR)、人工神经网络(ANN)和高斯过程回归(GPR)算法的输入变量,建立草地AGB预测模型并从模型预测精度、稳定性、样本敏感性等方面综合评价6种模型应用潜力,分析2020年天祝藏族自治县生长季(4-9月)内草地AGB时空变化特征及其对气候的响应。结果表明:1)DNN估算草地AGB的综合性能最佳,但稳定性较差,对样本敏感性较高;GPR综合性能次于DNN,稳定性和精度均较好;GBRT、RF模拟精度较高,稳定性差;SVR和ANN精度相对其他模型较差,SVR稳定性较高,ANN稳定性较差。2)天祝藏族自治县草地AGB集中在50~250 g·m-2,不同月份草地AGB空间异质性较大,整体表现为从西北向东南呈下降趋势。3)山地草甸、高寒草甸和温性草原中的AGB变化与气温表现出较为明显的正相关关系。降水量对高寒草甸、温性草原和山地草甸的影响不明显,但对温性荒漠草原类的影响较大,AGB随降水量减少呈现减少态势。以上研究结果可为监测草地生物量的方法选择和参数设置提供一定技术支持和参考依据。
秦格霞, 吴静, 李纯斌, 吉珍霞, 邱政超, 李颖. 基于机器学习算法的天祝藏族自治县草地地上生物量反演[J]. 草业学报, 2022, 31(4): 177-188.
Ge-xia QIN, Jing WU, Chun-bin LI, Zhen-xia JI, Zheng-chao QIU, Ying LI. Inversion of grassland aboveground biomass in Tianzhu Zangzu Autonomous County based on a machine learning algorithm[J]. Acta Prataculturae Sinica, 2022, 31(4): 177-188.
图3 实测草地AGB数据分布绿色代表高寒草甸;金黄色代表温性草原;蓝色代表山地草甸;紫色代表温性荒漠草原。Green represents alpine meadow; golden yellow represents warm steppe; blue represents slope meadow; purple represents temperate desert steppe.
Fig.3 Map of the measured data
图4 AGB与待选自变量相关系数*: P<0.05; **: P<0.01; 绿色、黄色、粉色、蓝色分别代表极显著正相关、极显著负相关、显著正相关、显著负相关The green, yellow, pink and blue denotes the significant positive effects, significant negative effects, positive effects, negative effects.
Fig.4 Correlation coefficients between the AGB and the explanatory variables
机器学习模型 Machine learning models | 决定系数R2 | 均方根误差 RMSE | 平均绝对误差 MAE | |||
---|---|---|---|---|---|---|
范围Range | 平均Mean | 范围Range | 平均Mean | 范围Range | 平均Mean | |
DNN | 0.80~0.88 | 0.85 | 25.43~32.30 | 32.30 | 4.23~6.14 | 5.63 |
GBRT | 0.74~0.88 | 0.84 | 21.51~49.63 | 32.31 | 4.55~7.29 | 5.95 |
RF | 0.75~0.88 | 0.84 | 30.41~43.48 | 37.21 | 4.55~6.63 | 5.58 |
SVR | 0.77~0.84 | 0.80 | 39.55~49.24 | 46.01 | 6.33~7.92 | 7.46 |
ANN | 0.74~0.87 | 0.81 | 38.04~54.19 | 45.99 | 4.95~9.36 | 6.54 |
GPR | 0.76~0.88 | 0.83 | 32.60~51.03 | 39.72 | 4.94~7.14 | 6.02 |
表1 重复30次的R2、RMSE、MAE的统计
Table 1 Statistical table of R2, RMSE and MAErepeated 30 times
机器学习模型 Machine learning models | 决定系数R2 | 均方根误差 RMSE | 平均绝对误差 MAE | |||
---|---|---|---|---|---|---|
范围Range | 平均Mean | 范围Range | 平均Mean | 范围Range | 平均Mean | |
DNN | 0.80~0.88 | 0.85 | 25.43~32.30 | 32.30 | 4.23~6.14 | 5.63 |
GBRT | 0.74~0.88 | 0.84 | 21.51~49.63 | 32.31 | 4.55~7.29 | 5.95 |
RF | 0.75~0.88 | 0.84 | 30.41~43.48 | 37.21 | 4.55~6.63 | 5.58 |
SVR | 0.77~0.84 | 0.80 | 39.55~49.24 | 46.01 | 6.33~7.92 | 7.46 |
ANN | 0.74~0.87 | 0.81 | 38.04~54.19 | 45.99 | 4.95~9.36 | 6.54 |
GPR | 0.76~0.88 | 0.83 | 32.60~51.03 | 39.72 | 4.94~7.14 | 6.02 |
图9 各种草地类型AGB对气温和降水的响应机制Ⅰ: 天祝藏族自治县4种草地类型平均值Average of grassland types in Tianzhu Zangzu Autonomous County; Ⅱ: 高寒草甸Alpine meadow; Ⅲ: 山地草甸Slope meadow; Ⅳ: 温性草原Warm steppe; Ⅴ: 温性荒漠草原Temperate desert steppe; PRE: 降水Precipitation; TEM: 气温Temperature.
Fig.9 Response mechanism of AGB in different grassland types to temperature and precipitation
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