Estimation on seasonal dynamics of alpine grassland aboveground biomass using phenology camera-derived NDVI
Zhe CHEN1, Hao WANG,2,*, Jin-Zhou WANG1, Hui-Jin SHI1, Hui-Ying LIU3, Jin-Sheng HE1,21Institute 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
the National Natural Science Foundation of China(31630009) the National Natural Science Foundation of China(31901168) the National Natural Science Foundation of China(31901145)
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 NDVIRS 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 NDVICam 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 NDVICam, NDVIRS 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. Keywords：phenology camera;near-surface remote sensing;normalized difference vegetation index (NDVI);aboveground biomass;alpine grassland;Qingzang Plateau
PDF (1173KB)元数据多维度评价相关文章导出EndNote|Ris|Bibtex收藏本文 引用本文 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
Fig. 1Seasonal 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.
Fig. 2Diurnal (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.
1.3 植物地上生物量的测定
在2018和2019年分别开展高寒草地地上生物量的野外测定。在生长季(5-9月), 每月对物候相机拍摄范围内的草地进行一至两次的地上生物量监测。每次监测先随机选取4个大样方(5 m × 5 m), 并在每个大样方内设置5个小样方(0.5 m × 0.5 m); 然后, 用RapidSCAN CS-45手持植物冠层光谱仪(Holland Scientific, Nebraska, USA)获取每个小样方内植物的NDVI (NDVIRS), 并收获植物地上绿色部分。植物样品在65 ℃恒温下烘干48 h至恒质量, 随后称质量获得地上生物量。在本研究的两年间, 每次测定15-20个小样方, 共计270个样本数据。
Fig. 3Dynamics 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.
Table 1 表1 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 1Pearson correlation coefficients between normalized difference of vegetation index measured by RapidSCAN and aboveground biomass of alpine grassland in different months of the growing season
Table 2 表2 表22018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 2Fitted 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
RMSEr (%)
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 = 2.0e6.9x
0.65
13.03
24.28
乘幂 Power
y = 615.1x3.3
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 = 0.7e7.7x
0.74
22.25
18.80
乘幂 Power
y = 939.7x5.2
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 = 8.4e4.2x
0.73
38.00
17.64
乘幂 Power
y = 517.6x3.3
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 = 81.9e1.7x
0.16
34.73
12.04
乘幂 Power
y = 402.3x1.2
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 = 48.0e2.5x
0.62
37.92
19.18
乘幂 Power
y = 448.2x1.4
0.63
37.68
19.06
多项式 Quadratic
y = -48.0x2 + 563.9x - 101.8
0.61
37.87
19.15
n, 样本量。加粗项是生长季各月份的最优拟合模型。RMSE, 均方根误差; RMSEr, 相对均方根误差。 n, sample size. The bold parts are the optimal models in different months of the growing season. RMSE, root mean square error; RMSEr, relative root mean square error.
Fig. 4Seasonal 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.
Fig. 5Estimation 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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Remote sensing-based biomass estimation and its spatio-temporal variations in temperate grassland, northern China 2 2014
... 基于经验模型估算法的前人研究多使用生长季单一时期(一般不多于3个月)卫星遥感NDVI和实测地上生物量数据建立关系(金云翔等, 2011; 除多等, 2013a; Main-Knorn et al., 2013; Jin et al., 2014; 张旭琛等, 2015).然而, 已有研究表明, NDVI与地上生物量之间的关系在生长季内可能并不恒定.例如, 在西藏高寒草地, 地上生物量与NDVI在5月呈现为一元线性关系; 在生长季其他月则呈现为指数函数关系, 且函数参数随月份而变化(除多等, 2013b).因此, 仅使用单一时期数据构建经验模型对地上生物量季节动态的估算将会引起较大的不确定性, 而基于生长季不同时期建模的估算可能更为准确. ...
... 此外, 基于卫星NDVI的生物量估算受到大气条件和卫星观测时空分辨率的影响.前人研究发现, 云和气溶胶会降低卫星遥感NDVI估算地上生物量的准确性(葛静等, 2017).卫星影像因其空间分辨率低, 对异质性较强的草地生物量估算能力有限(Piao et al., 2019); 另外, 卫星遥感获取影像的时间间隔较长, 难以实现生物量的实时估算(Jin et al., 2014).近年来, 近地遥感如物候相机和手持式植物冠层光谱仪, 能够连续地获取高时空分辨率的植被光谱信息(Petach et al., 2014; Filippa et al., 2018), 且受大气条件影响较小(Sonnentag et al., 2012; 周宇庭等, 2013), 很大程度上弥补了卫星观测的不足, 为植物地上生物量的准确实时估算提供了新手段.目前, 北美和欧洲已经建立了物候相机观测网, 用于中小尺度的植被生长监测(Wingate et al., 2015; Klosterman et al., 2018).然而, 物候相机在我国还处于起步阶段, 较少被应用于植被生长研究. ...
内蒙古锡林郭勒盟草原产草量动态遥感估算 1 2011
... 基于经验模型估算法的前人研究多使用生长季单一时期(一般不多于3个月)卫星遥感NDVI和实测地上生物量数据建立关系(金云翔等, 2011; 除多等, 2013a; Main-Knorn et al., 2013; Jin et al., 2014; 张旭琛等, 2015).然而, 已有研究表明, NDVI与地上生物量之间的关系在生长季内可能并不恒定.例如, 在西藏高寒草地, 地上生物量与NDVI在5月呈现为一元线性关系; 在生长季其他月则呈现为指数函数关系, 且函数参数随月份而变化(除多等, 2013b).因此, 仅使用单一时期数据构建经验模型对地上生物量季节动态的估算将会引起较大的不确定性, 而基于生长季不同时期建模的估算可能更为准确. ...
内蒙古锡林郭勒盟草原产草量动态遥感估算 1 2011
... 基于经验模型估算法的前人研究多使用生长季单一时期(一般不多于3个月)卫星遥感NDVI和实测地上生物量数据建立关系(金云翔等, 2011; 除多等, 2013a; Main-Knorn et al., 2013; Jin et al., 2014; 张旭琛等, 2015).然而, 已有研究表明, NDVI与地上生物量之间的关系在生长季内可能并不恒定.例如, 在西藏高寒草地, 地上生物量与NDVI在5月呈现为一元线性关系; 在生长季其他月则呈现为指数函数关系, 且函数参数随月份而变化(除多等, 2013b).因此, 仅使用单一时期数据构建经验模型对地上生物量季节动态的估算将会引起较大的不确定性, 而基于生长季不同时期建模的估算可能更为准确. ...
Later springs green-up faster: the relation between onset and completion of green-up in deciduous forests of North America 1 2018
... 此外, 基于卫星NDVI的生物量估算受到大气条件和卫星观测时空分辨率的影响.前人研究发现, 云和气溶胶会降低卫星遥感NDVI估算地上生物量的准确性(葛静等, 2017).卫星影像因其空间分辨率低, 对异质性较强的草地生物量估算能力有限(Piao et al., 2019); 另外, 卫星遥感获取影像的时间间隔较长, 难以实现生物量的实时估算(Jin et al., 2014).近年来, 近地遥感如物候相机和手持式植物冠层光谱仪, 能够连续地获取高时空分辨率的植被光谱信息(Petach et al., 2014; Filippa et al., 2018), 且受大气条件影响较小(Sonnentag et al., 2012; 周宇庭等, 2013), 很大程度上弥补了卫星观测的不足, 为植物地上生物量的准确实时估算提供了新手段.目前, 北美和欧洲已经建立了物候相机观测网, 用于中小尺度的植被生长监测(Wingate et al., 2015; Klosterman et al., 2018).然而, 物候相机在我国还处于起步阶段, 较少被应用于植被生长研究. ...
Multi-factor modeling of above-ground biomass in alpine grassland: a case study in the Three-River Headwaters Region, China 1 2016
... 植物地上生物量(AGB)通常指特定时刻单位面积上植物地上部分的干质量, 对生态系统碳、水和能量的平衡有着重要作用(Scurlock et al., 2002; Piao et al., 2007).同时, 地上生物量影响着草地饲料供给, 决定了草原的畜牧养殖承载力(Yahdjian & Sala, 2006).因此, 准确估算草地地上生物量对于评估草地生产力、优化草地资源管理以及维持草地生态系统稳定性等具有重要意义(Parton et al., 1995; Liang et al., 2016). ...
Monitoring coniferous forest biomass change using a Landsat trajectory-?based approach 1 2013
... 基于经验模型估算法的前人研究多使用生长季单一时期(一般不多于3个月)卫星遥感NDVI和实测地上生物量数据建立关系(金云翔等, 2011; 除多等, 2013a; Main-Knorn et al., 2013; Jin et al., 2014; 张旭琛等, 2015).然而, 已有研究表明, NDVI与地上生物量之间的关系在生长季内可能并不恒定.例如, 在西藏高寒草地, 地上生物量与NDVI在5月呈现为一元线性关系; 在生长季其他月则呈现为指数函数关系, 且函数参数随月份而变化(除多等, 2013b).因此, 仅使用单一时期数据构建经验模型对地上生物量季节动态的估算将会引起较大的不确定性, 而基于生长季不同时期建模的估算可能更为准确. ...
Impact of climate change on grassland production and soil carbon worldwide 1 1995
... 植物地上生物量(AGB)通常指特定时刻单位面积上植物地上部分的干质量, 对生态系统碳、水和能量的平衡有着重要作用(Scurlock et al., 2002; Piao et al., 2007).同时, 地上生物量影响着草地饲料供给, 决定了草原的畜牧养殖承载力(Yahdjian & Sala, 2006).因此, 准确估算草地地上生物量对于评估草地生产力、优化草地资源管理以及维持草地生态系统稳定性等具有重要意义(Parton et al., 1995; Liang et al., 2016). ...
Monitoring vegetation phenology using an infrared-enabled security camera 1 2014
... 此外, 基于卫星NDVI的生物量估算受到大气条件和卫星观测时空分辨率的影响.前人研究发现, 云和气溶胶会降低卫星遥感NDVI估算地上生物量的准确性(葛静等, 2017).卫星影像因其空间分辨率低, 对异质性较强的草地生物量估算能力有限(Piao et al., 2019); 另外, 卫星遥感获取影像的时间间隔较长, 难以实现生物量的实时估算(Jin et al., 2014).近年来, 近地遥感如物候相机和手持式植物冠层光谱仪, 能够连续地获取高时空分辨率的植被光谱信息(Petach et al., 2014; Filippa et al., 2018), 且受大气条件影响较小(Sonnentag et al., 2012; 周宇庭等, 2013), 很大程度上弥补了卫星观测的不足, 为植物地上生物量的准确实时估算提供了新手段.目前, 北美和欧洲已经建立了物候相机观测网, 用于中小尺度的植被生长监测(Wingate et al., 2015; Klosterman et al., 2018).然而, 物候相机在我国还处于起步阶段, 较少被应用于植被生长研究. ...
Changes in biomass carbon stocks in Chinaʼs grasslands between 1982 and 1999 1 2007
... 植物地上生物量(AGB)通常指特定时刻单位面积上植物地上部分的干质量, 对生态系统碳、水和能量的平衡有着重要作用(Scurlock et al., 2002; Piao et al., 2007).同时, 地上生物量影响着草地饲料供给, 决定了草原的畜牧养殖承载力(Yahdjian & Sala, 2006).因此, 准确估算草地地上生物量对于评估草地生产力、优化草地资源管理以及维持草地生态系统稳定性等具有重要意义(Parton et al., 1995; Liang et al., 2016). ...
Plant phenology and global climate change: current progresses and challenges 1 2019
... 此外, 基于卫星NDVI的生物量估算受到大气条件和卫星观测时空分辨率的影响.前人研究发现, 云和气溶胶会降低卫星遥感NDVI估算地上生物量的准确性(葛静等, 2017).卫星影像因其空间分辨率低, 对异质性较强的草地生物量估算能力有限(Piao et al., 2019); 另外, 卫星遥感获取影像的时间间隔较长, 难以实现生物量的实时估算(Jin et al., 2014).近年来, 近地遥感如物候相机和手持式植物冠层光谱仪, 能够连续地获取高时空分辨率的植被光谱信息(Petach et al., 2014; Filippa et al., 2018), 且受大气条件影响较小(Sonnentag et al., 2012; 周宇庭等, 2013), 很大程度上弥补了卫星观测的不足, 为植物地上生物量的准确实时估算提供了新手段.目前, 北美和欧洲已经建立了物候相机观测网, 用于中小尺度的植被生长监测(Wingate et al., 2015; Klosterman et al., 2018).然而, 物候相机在我国还处于起步阶段, 较少被应用于植被生长研究. ...
Estimating net primary productivity from grassland biomass dynamics measurements 1 2002
... 植物地上生物量(AGB)通常指特定时刻单位面积上植物地上部分的干质量, 对生态系统碳、水和能量的平衡有着重要作用(Scurlock et al., 2002; Piao et al., 2007).同时, 地上生物量影响着草地饲料供给, 决定了草原的畜牧养殖承载力(Yahdjian & Sala, 2006).因此, 准确估算草地地上生物量对于评估草地生产力、优化草地资源管理以及维持草地生态系统稳定性等具有重要意义(Parton et al., 1995; Liang et al., 2016). ...
Plant phenological responses to climate change on the Tibetan Plateau: research status and challenges 1 2015
Vegetation structure constrains primary production response to water availability in the Patagonian Steppe 1 2006
... 植物地上生物量(AGB)通常指特定时刻单位面积上植物地上部分的干质量, 对生态系统碳、水和能量的平衡有着重要作用(Scurlock et al., 2002; Piao et al., 2007).同时, 地上生物量影响着草地饲料供给, 决定了草原的畜牧养殖承载力(Yahdjian & Sala, 2006).因此, 准确估算草地地上生物量对于评估草地生产力、优化草地资源管理以及维持草地生态系统稳定性等具有重要意义(Parton et al., 1995; Liang et al., 2016). ...
,2,*, 王金洲1, 石慧瑾1, 刘慧颖3, 贺金生1,2
新窗口打开|下载原图ZIP|生成PPT
新窗口打开|下载原图ZIP|生成PPT
新窗口打开|下载原图ZIP|生成PPT
新窗口打开|下载原图ZIP|生成PPT
新窗口打开|下载原图ZIP|生成PPT
