基于物候相机归一化植被指数估算高寒草地植物地上生物量的季节动态

doi:10.17521/cjpe.2020.0076

植物生态学报 ›› 2021, Vol. 45 ›› Issue (5): 487-495.DOI: 10.17521/cjpe.2020.0076

所属专题：生态遥感及应用；青藏高原植物生态学：遥感生态学

基于物候相机归一化植被指数估算高寒草地植物地上生物量的季节动态

陈哲¹, 汪浩²^,^*(), 王金洲¹, 石慧瑾¹, 刘慧颖³, 贺金生¹^,²

¹北京大学城市与环境学院, 地表过程分析与模拟教育部重点实验室, 北京大学生态中心, 北京 100871
²兰州大学草地农业生态系统国家重点实验室, 兰州 730000
³华东师范大学生态与环境科学学院, 上海 200241

收稿日期:2020-03-19 接受日期:2020-05-28 出版日期:2021-05-20 发布日期:2020-06-12
通讯作者: 汪浩
作者简介:*(wanghao@lzu.edu.cn)
基金资助:
国家自然科学基金(31630009);国家自然科学基金(31901168);国家自然科学基金(31901145)

Estimation on seasonal dynamics of alpine grassland aboveground biomass using phenology camera-derived NDVI

CHEN Zhe¹, WANG Hao²^,^*(), WANG Jin-Zhou¹, SHI Hui-Jin¹, LIU Hui-Ying³, HE Jin-Sheng¹^,²

¹Institute of Ecology, College of Urban and Environmental Sciences, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
²State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou 730000, China
³School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China

Received:2020-03-19 Accepted:2020-05-28 Online:2021-05-20 Published:2020-06-12
Contact: WANG Hao
Supported by:
the National Natural Science Foundation of China(31630009);the National Natural Science Foundation of China(31901168);the National Natural Science Foundation of China(31901145)

摘要/Abstract

摘要：

准确评估地上生物量对优化草地资源管理和理解草地碳、水和能量平衡具有重要意义。该文通过近地遥感归一化植被指数(NDVI)构建最优经验模型, 对青藏高原高寒草地地上生物量进行估算。该文利用2018-2019年5-9月野外实测的地上生物量和植物冠层光谱仪(RapidSCAN)测定的NDVI_RS数据, 构建了生长季不同时期地上生物量的估算模型; 并结合2018年NetCam物候相机测定的NDVI_Cam时间序列数据, 实现地上生物量季节动态的模拟。主要结果: (1) NDVI_Cam、NDVI_RS与地上生物量具有相似的单峰型季节变化格局, 但NDVI峰值出现的时间(7月)较地上生物量(8月)更早; (2)基于NDVI的生物量估算最优经验模型在5、7和9月是幂函数, 在6和8月是二次多项式, 估算精度为0.29-0.77; (3)基于NDVI_Cam时间序列数据, 生长季不同时期建模(R² = 0.91)较单一时期(9月)建模(R² = 0.49)对地上生物量季节动态的估算更为准确。这些结果表明, 近地遥感是估算高寒草地植物地上生物量的有效手段, 开展季节性植物生长调查将有助于准确评估草地资源。

关键词: 物候相机, 近地遥感, 归一化植被指数, 地上生物量, 高寒草地, 青藏高原

Abstract:

Aims Accurate assessment of plant aboveground biomass is important for optimizing grassland resource management and for understanding the balance of carbon, water and energy fluxes in grassland ecosystems. This study constructed the optimal empirical models by near-surface remote sensing normalized difference vegetation index (NDVI) data, and then estimated plant aboveground biomass in an alpine grassland on the Qingzang Plateau.
Methods Using the dataset of both the field-measured aboveground biomass and the NDVI_RS observed by plant canopy spectrometer (RapidSCAN), we constructed the empirical models for estimating aboveground biomass in different phases of the growing season across 2018 and 2019. Using the NDVI_Cam time series observed by phenology camera and the estimated models, we simulated seasonal dynamics of aboveground biomass in 2018.
Important findings (1) The seasonal dynamics of NDVI_Cam, NDVI_RS and aboveground biomass exhibited a similar unimodal pattern; however, the timing of peak NDVI (August) preceded that of peak aboveground biomass (July). (2) The best model for estimating aboveground biomass is the power function in May, July and September, and the quadratic equation in June and August. The estimation accuracy ranged from 0.29 to 0.77. (3) The estimation of aboveground biomass based on the models in different phases of growing season (R² = 0.91) showed a higher accuracy compared to that based on the model at a single time (September)(R² = 0.49). Our results suggest that the near-surface remote sensing is an effective approach for estimating alpine grassland aboveground biomass, and further investigation on the seasonal growth of plants will help accurately evaluate grassland resources.

Key words: phenology camera, near-surface remote sensing, normalized difference vegetation index (NDVI), aboveground biomass, alpine grassland, Qingzang Plateau

陈哲, 汪浩, 王金洲, 石慧瑾, 刘慧颖, 贺金生. 基于物候相机归一化植被指数估算高寒草地植物地上生物量的季节动态. 植物生态学报, 2021, 45(5): 487-495. DOI: 10.17521/cjpe.2020.0076

CHEN Zhe, WANG Hao, WANG Jin-Zhou, SHI Hui-Jin, LIU Hui-Ying, HE Jin-Sheng. Estimation on seasonal dynamics of alpine grassland aboveground biomass using phenology camera-derived NDVI. Chinese Journal of Plant Ecology, 2021, 45(5): 487-495. DOI: 10.17521/cjpe.2020.0076

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2020.0076

https://www.plant-ecology.com/CN/Y2021/V45/I5/487

图/表 7

图1 海北站气温和降水的季节动态以及生长季各时期高寒草地植被生长状况。A, 2018和2019年的月平均气温和月降水量。B-F, 5-9月高寒草地植被生长的物候相机图像。NIR, 近红外图像; RGB, 红绿蓝图像。

Fig. 1 Seasonal dynamics of air temperature and precipitation, and the alpine grassland vegetation growth in different phases of the growing season at Haibei Station. A, Mean monthly air temperature and precipitation in 2018 and 2019. B-F, Pictures of plant growth from May to September as photographed by phenology camera. NIR, near-infrared images; RGB, red-green-blue images.

图2 海北站2018年5-9月物候相机归一化植被指数(NDVICam)的平均日动态(A)和季节动态(B)。A中阴影部分表示10:00-14:00物候相机的NDVI最为稳定。

Fig. 2 Diurnal (A) and seasonal patterns (B) of normalized difference of vegetation index measured by NetCam (NDVICam) from May to September in 2018 at Haibei Station. The shaded part in A indicates that the NDVI of the phenology camera from 10:00-14:00 is the most stable.

图3 海北站2018 (A)和2019年(B)生长季高寒草地手持式植物冠层光谱仪测量的归一化植被指数(NDVI)与地上生物量的变化(平均值±标准误)。

Fig. 3 Dynamics of the normalized difference of vegetation index (NDVI) measured by RapidSCAN and aboveground biomass (mean ± SE) of alpine grassland in the growing seasons of 2018 (A) and 2019 (B) at Haibei Station.

表1 生长季不同月份高寒草地手持式植物冠层光谱仪测量的归一化植被指数与地上生物量之间的相关性

Table 1 Pearson correlation coefficients between normalized difference of vegetation index measured by RapidSCAN and aboveground biomass of alpine grassland in different months of the growing season

月份 Month	样本数 No. of samples	r	p
5	56	0.80	<0.001
6	34	0.88	<0.001
7	64	0.67	<0.001
8	51	0.31	0.03
9	63	0.78	<0.001

表2 2018和2019年生长季高寒草地手持式植物冠层光谱仪测量的归一化植被指数(NDVIRS)(x)与地上生物量(y)之间的经验模型构建

Table 2 Fitted regression equations between normalized difference of vegetation index measured by RapidSCAN (NDVIRS)(x) and aboveground biomass (y) of alpine grassland across the growing seasons of 2018 and 2019

月份 Month	模型 Model	回归方程 Regression equation	R²	RMSE	RMSE_r (%)
5月 May (n = 56)	线性 Linear	y = 397.3x - 130.6	0.67	13.62	25.38
	对数 Logarithm	y = 186.0ln(x) + 198.3	0.65	14.32	26.68
	指数 Exponent	y = 2.0e^6.9^x	0.65	13.03	24.28
	乘幂 Power	y = 615.1x^3.3	0.67	12.45	23.19
	多项式 Quadratic	y = 491.7x² - 83.7x - 14.7	0.65	12.88	24.00
6月 June (n = 34)	线性 Linear	y = 1080.5x - 595.7	0.75	24.99	21.12
	对数 Logarithm	y = 730.4ln(x) + 423.1	0.73	26.82	22.66
	指数 Exponent	y = 0.7e^7.7^x	0.74	22.25	18.80
	乘幂 Power	y = 939.7x^5.2	0.74	21.95	18.55
	多项式 Quadratic	y = 3363.7x² - 3537.5x + 979.2	0.77	20.04	16.94
7月 July (n = 64)	线性 Linear	y = 952.0x - 508.5	0.72	39.94	18.54
	对数 Logarithm	y = 730.2ln(x) + 416.7	0.71	40.35	18.73
	指数 Exponent	y = 8.4e^4.2^x	0.73	38.00	17.64
	乘幂 Power	y = 517.6x^3.3	0.73	37.65	17.48
	多项式 Quadratic	y = 1422.3x² - 1248.1x + 339.51	0.72	38.95	18.08
8月 August (n = 51)	线性 Linear	y = 532.0x - 105.1	0.18	38.72	13.43
	对数 Logarithm	y = 375.4ln(x) + 403.0	0.16	39.01	13.53
	指数 Exponent	y = 81.9e^1.7^x	0.16	34.73	12.04
	乘幂 Power	y = 402.3x^1.2	0.14	35.28	12.23
	多项式 Quadratic	y = 5968.0x² - 8287.3x + 3134.3	0.29	31.70	10.99
9月 September (n = 63)	线性 Linear	y = 507.3x - 85.5	0.61	38.17	19.30
	对数 Logarithm	y = 292.5ln(x) + 371.7	0.61	38.18	19.31
	指数 Exponent	y = 48.0e^2.5^x	0.62	37.92	19.18
	乘幂 Power	y = 448.2x^1.4	0.63	37.68	19.06
	多项式 Quadratic	y = -48.0x² + 563.9x - 101.8	0.61	37.87	19.15

图4 基于物候相机归一化植被指数时间序列数据, 利用生长季不同阶段建模(A)和单一时期(9月)建模(B)分别估算2018年高寒草地生物量的季节动态。

Fig. 4 Seasonal dynamics of alpine grassland biomass estimated by the normalized difference of vegetation index measured by NetCam time series and the models in different phases of growing season (A) and at a single time (September)(B) in 2018.

图5 生长季不同时期建模(A)和单一时期(9月)建模(B)下高寒草地地上生物量的估算精度。实测生物量值是2018年生长季每次野外测定生物量的平均值(n = 10); 估算生物量值通过最优经验拟合模型和物候相机归一化植被指数计算获得。

Fig. 5 Estimation of alpine grassland aboveground biomass using models in different phases of growing season (A) and at a single time (September)(B). The actual biomass is the averaged biomass for each measurement during the growing season of 2018 (n = 10). The estimated biomass is calculated by the optimal model and normalized difference of vegetation index measured by NetCam.

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基于物候相机归一化植被指数估算高寒草地植物地上生物量的季节动态

Estimation on seasonal dynamics of alpine grassland aboveground biomass using phenology camera-derived NDVI

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