基于随机森林算法的草原地上生物量遥感估算方法研究

doi:10.12082/dqxxkx.2021.200605

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (7): 1312-1324.doi: 10.12082/dqxxkx.2021.200605

基于随机森林算法的草原地上生物量遥感估算方法研究

邢晓语¹(), 杨秀春¹^,²^,^*(), 徐斌¹^,², 金云翔¹, 郭剑³^,⁴, 陈昂², 杨东¹, 王平², 朱立博⁵

1.农业部农业信息技术重点实验室中国农业科学院农业资源与农业区划研究所,北京 100081
2.北京林业大学草业与草原学院草地资源与生态研究中心,北京 100083
3.遥感科学国家重点实验室北京师范大学地理科学学部,北京 100875
4.环境遥感与数字城市北京市重点实验室北京师范大学地理科学学部,北京 100875
5.内蒙古呼伦贝尔市畜牧科学研究所, 海拉尔 021008

收稿日期:2020-10-15 修回日期:2021-01-21 出版日期:2021-07-25 发布日期:2021-09-25
通讯作者: * 杨秀春（1975— ）,女,河北迁安人,博士,研究员,主要从事草地资源遥感研究。E-mail: Yangxiuchun@bjfu.edu.cn
作者简介:邢晓语（1996— ）,女,山东淄博人,硕士生,主要从事生态系统服务研究。E-mail: xingxiaoyu51@163.com
基金资助:
国家重点研发计划项目(2017YFC0506504);国家自然科学基金项目(41571105)

Remote Sensing Estimation of Grassland Aboveground Biomass based on Random Forest

XING Xiaoyu¹(), YANG Xiuchun¹^,²^,^*(), XU Bin¹^,², JIN Yunxiang¹, GUO Jian³^,⁴, CHEN Ang², YANG Dong¹, WANG Ping², ZHU Libo⁵

1. Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2. Research Center of Grassland Ecology and Resources, School of Grassland Science, Beijing Forestry University, Beijing 100083, China
3. State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
4. Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
5. Hulunbeier Institute of animal husbandry, Inner Mongolia, Hailar 021008, China

Received:2020-10-15 Revised:2021-01-21 Online:2021-07-25 Published:2021-09-25
Supported by:
National Key Research and Development Program of China(2017YFC0506504);National Natural Science Foundation of China(41571105)

摘要/Abstract

摘要：

草原是我国面积最大的陆地生态系统,生物量是反映生态系统质量和功能的关键指标,准确地掌握草原生物量对草原资源合理利用、生态修复、畜牧业高质量发展都具有重要的意义和作用。本研究以内蒙古锡林郭勒盟为研究区,利用高分一号遥感卫星影像,结合216个野外样本数据,采用随机森林算法（Random Forest,RF）对草原地上生物量（Aboveground Biomass,AGB）遥感估算进行了适用性分析与应用。在运用随机森林算法的过程中,进行了K-折交叉验证、多元共线性诊断、偏效应等一系列分析,完成了随机森林模型的构建,同时,将建模结果与其它模型进行了对比,最终实现了锡林郭勒盟草原AGB的反演估算。结果表明：① 随机森林算法能够较好地规避生物量建模中自变量多元共线性的问题;② 随机森林模型在草原AGB估算中较其它模型具有更好的适用性,模型精度达85%,RMSE为202.13 kg/hm²;③ 应用构建的随机森林算法估算了研究区2017年草原AGB,从结果来看,其空间分布上呈现为自东向西逐渐递减的趋势;从草地类型上看,山地草甸类AGB单产最高,温性草原类总产量最高。研究结果将对草原生态系统监测评估和草原宏观管理具有一定的参考价值。

关键词: 草原地上生物量, 随机森林, 支持向量机, 高分一号, 多元共线性, 偏效应, 机器学习, 回归模型

Abstract:

Grassland is the largest terrestrial ecosystem in China. Biomass is a key indicator of ecosystem quality and ecosystem function. It is of great significance for us to accurately estimate the grassland biomass for the effective and rational use of grassland resources, the restoration of damaged grassland ecosystem, and the high-quality development of animal husbandry. In this study, we took Xilinguole league of Inner Mongolia autonomous region as the research area. We used GF-1 satellite images, ground sample data of 216 sites, and Random Forest (RF) algorithm to estimate Grassland Aboveground Biomass (AGB) and explore the applicability of the algorithm in grassland biomass estimation. Moreover, in order to evaluate the applicability of random forest algorithm in aboveground biomass estimation, we carried out a series of analysis when using the algorithm, such as k-fold cross validation, multicollinearity diagnosis, partial effect and so on. Based this, we completed the construction of the random forest model and compared the modeling results with those from other models. Then, we selected the best model to realize the inversion estimation of grassland aboveground biomass in Xilinguole league. The main conclusions are as follows: (1) In the process of biomass model construction in Xilinguole league, random forest algorithm can avoid multicollinearity problem if there are multiple input variables; (2) The random forest model has better applicability than other models in the estimation of grassland biomass. The accuracy of the random forest model is 85% while the RMSE is 202.13 kg/hm²; (3) Using the random forest model, we estimated the grassland aboveground biomass of the whole study area in 2017. The results indicated that the spatial distribution had a decreasing trend from east to west. When grassland types are concerned, the grassland aboveground biomass yield of mountain meadow was the highest among all grassland types while the total yield of temperate grassland was the highest among all grassland types. The results are not only beneficial to the monitoring and evaluation of grassland ecosystem, but also have a certain reference value for grassland macro management.

Key words: grassland aboveground biomass, random forest, support vector machine, GF-1, multicollinearity, partial effect, machine learning, regression model

邢晓语, 杨秀春, 徐斌, 金云翔, 郭剑, 陈昂, 杨东, 王平, 朱立博. 基于随机森林算法的草原地上生物量遥感估算方法研究[J]. 地球信息科学学报, 2021, 23(7): 1312-1324.DOI:10.12082/dqxxkx.2021.200605

XING Xiaoyu, YANG Xiuchun, XU Bin, JIN Yunxiang, GUO Jian, CHEN Ang, YANG Dong, WANG Ping, ZHU Libo. Remote Sensing Estimation of Grassland Aboveground Biomass based on Random Forest[J]. Journal of Geo-information Science, 2021, 23(7): 1312-1324.DOI:10.12082/dqxxkx.2021.200605

图/表 17

图1

图2

表1

表2

植被指数计算方法

植被指数	计算方法	公式编号	优缺点
NDVI	NDVI= $ρ nir - ρ r ρ nir + ρ r$	（4）	反映地表植被状况以及植被空间分布密度,但易饱和
RVI	RVI= $ρ nir ρ r$	（5）	在植被密集的区域,比NDVI的灵敏度更高
EVI	EVI= $2.5 (ρ nir - ρ r) 1 + ρ nir + 6 ρ r - 7.5 ρ b$	（6）	能够减少残留气溶胶污染、不易饱和
SAVI	SAVI= $(ρ nir - ρ r) (1 + L) ρ nir + ρ r + L$	（7）	修正了NDVI对土壤背景敏感性
OSAVI	OSAVI= $ρ nir - ρ r ρ nir + ρ r + 0.16$	（8）	修正了NDVI对土壤背景敏感性,把参数值进行了固定

表2

图3

图4

表4

图5

图6

表5

表6

表7

图7

表8

表9

图8

图9

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采集时间	传感器标识	景序列号	采集时间	云量覆盖/%	传感器标识	景序列号
2017-07-17	WFV1	3893398	2017-07-17	0	WFV3	3892267
2017-07-17	WFV1	3893464	2017-07-17	0	WFV4	3892289
2017-07-17	WFV2	3893487	2017-08-05	0	WFV1	3961704
2017-07-17	WFV2	3893486	2017-08-30	0	WFV2	4048027
2017-07-17	WFV2	3893485	2017-08-31	0	WFV3	4053670
2017-07-17	WFV3	3892265	2017-08-31	0	WFV4	4053690
2017-07-17	WFV3	3892266

维数	特征值	条件指标	方差比例
维数	特征值	条件指标	常量	NDVI	EVI	OSAVI	RVI	SAVI
1	5.79	1.00	0.00	0.00	0.00	0.00	0.00	0.00
2	0.11	7.14	0.17	0.00	0.01	0.00	0.00	0.00
3	0.06	9.54	0.06	0.02	0.26	0.00	0.02	0.00
4	0.01	17.38	0.01	0.38	0.44	0.00	0.00	0.00
5	0.00	31.52	0.14	0.00	0.14	0.01	0.64	0.05
6	0.00	118.62	0.62	0.59	0.15	0.99	0.34	0.94

模型	特征变量	解释方差百分比/%	模型	特征变量	解释方差百分比/%
1	NDVI、RVI	75.08	14	NDVI、RVI、SAVI	76.43
2	NDVI、EVI	72.66	15	NDVI、OSAVI、SAVI	74.93
3	NDVI、SAVI	75.45	16	NDVI、OSAVI、EVI	75.47
4	NDVI、OSAVI	72.74	17	RVI、OSAVI、SAVI	67.32
5	EVI、OSAVI	67.33	18	RVI、EVI、SAVI	70.72
6	RVI、EVI	70.35	19	RVI、OSAVI、EVI	69.81
7	RVI、SAVI	68.05	20	OSAVI、EVI、SAVI	68.11
8	RVI、OSAVI	65.69	21	NDVI、RVI、OSAVI、EVI	75.57
9	SAVI、EVI	64.04	22	NDVI、RVI、OSAVI、SAVI	75.40
10	SAVI、OSAVI	66.12	23	NDVI、RVI、EVI、SAVI	76.40
11	NDVI、EVI、SAVI	75.26	24	NDVI、OSAVI、EVI、SAVI	75.75
12	NDVI、RVI、OSAVI	74.60	25	RVI、OSAVI、EVI、SAVI	69.70
13	NDVI、RVI、EVI	75.50	26	NDVI、RVI、OSAVI、EVI、SAVI	74.98

自变量x	函数	方程	R²	F
NDVI	线性函数	y=8578.34x-682.73	0.77	535.69
	指数函数	y=220.71e^6.57^x	0.64	281.28
	幂函数	y=13601.63x^1.70	0.69	352.18
RVI	线性函数	y=1053.71x-513.01	0.55	197.42
	指数函数	y=282.33e^0.74^x	0.39	99.96
	幂函数	y=380.49x^1.95	0.50	158.87
EVI	线性函数	y=9308.95x-301.56	0.43	122.90
	指数函数	y=272.34e^7.60^x	0.41	110.52
	幂函数	-	-	-
OSAVI	线性函数	y=9214.90x-321.26	0.65	291.12
	指数函数	y=301.50e^6.86^x	0.51	163.04
	幂函数	y=16726.34x^1.55	0.60	234.53
SAVI	线性函数	y=10055.66x-407.66	0.60	231.82
	指数函数	y=284.69e^7.44^x	0.46	135.05
	幂函数	y=19424.01x^1.61	0.53	180.02

模型	模型或输入变量	训练集			验证集
模型	模型或输入变量	RMSE /(kg/hm²)	REE/%	精度/%	RMSE /(kg/hm²)	REE/%	精度/%
RF	NDVI、RVI、SAVI	202.13	15	85	346.73	30	70
SVM	NDVI、RVI、SAVI	360.38	27	73	413.88	36	64
UCR	y=8578.34NDVI-682.73	434.46	33	67	401.94	35	65
MLR	y=7353.25NDVI+230.05RVI-796.72	259.63	20	80	417.99	36	64

基于随机森林算法的草原地上生物量遥感估算方法研究

Remote Sensing Estimation of Grassland Aboveground Biomass based on Random Forest

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参考文献 42

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草原类型	面积/km²	AGB鲜重
草原类型	面积/km²	单产/(kg/hm²)	总产量/t
低地草甸类	26 014	1756.45	4 569 384
山地草甸类	1577	3029.59	477 963
改良草地	473	1092.77	51 777
沼泽类	334	2165.15	72 456
温性草原化荒漠类	5122	552.77	283 132
温性草原类	108 445	1249.61	13 551 464
温性草甸草原类	24 883	2616.22	6 510 032
温性荒漠类	142	576.49	8191
温性荒漠草原类	29 659	596.31	1 768 611

模型	模型或输入变量	验证集
模型	模型或输入变量	RMSE/(kg/hm²)	REE/%	精度/%
RF	NDVI	389.00	34	66
SVM	NDVI	381.65	33	66
UCR	y=8578.34NDVI-682.73	401.94	35	65

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