Modeling Aboveground Biomass Using MODIS Images and ... - BioOne

Jan., 2017

Journal of Resources and Ecology

Vol. 8 No.1

J. Resour. Ecol. 2017 8(1) 42-49 DOI: 10.5814/j.issn.1674-764x.2017.01.006 www.jorae.cn

Modeling Aboveground Biomass Using MODIS Images and Climatic Data in Grasslands on the Tibetan Plateau FU Gang1, SUN Wei1, LI Shaowei1, ZHANG Jing2, YU Chengqun1, SHEN Zhenxi1,* 1. Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; 2. School of geography, Beijing Normal University, Beijing 100875, China

Abstract: Accurate quantification of aboveground biomass of grasslands in alpine regions plays an important role in accurate quantification of global carbon cycling. The monthly normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), mean air temperature (Ta), ≥5℃ accumulated air temperature (AccT), total precipitation (TP), and the ratio of TP to AccT (TP/AccT) were used to model aboveground biomass (AGB) in grasslands on the Tibetan Plateau. Three stepwise multiple regression methods, including stepwise multiple regression of AGB with NDVI and EVI, stepwise multiple regression of AGB with Ta, AccT, TP and TP/AccT, and stepwise multiple regression of AGB with NDVI, EVI, Ta, AccT, TP and TP/AccT were compared. The mean absolute error (MAE) and root mean squared error (RMSE) values between estimated AGB by the NDVI and measured AGB were 31.05 g m2 and 44.12 g m2, and 95.43 g m2 and 131.58 g m2 in the meadow and steppe, respectively. The MAE and RMSE values between estimated AGB by the AccT and measured AGB were 33.61g m2 and 48.04 g m2 in the steppe, respectively. The MAE and RMSE values between estimated AGB by the vegetation index and climatic data and measured AGB were 28.09 g m2 and 42.71 g m2, and 35.86 g m2 and 47.94 g m2, in the meadow and steppe, respectively. The study finds that a combination of vegetation index and climatic data can improve the accuracy of estimates of AGB that are arrived at using the vegetation index or climatic data. The accuracy of estimates varied depending on the type of grassland.

Key words: enhanced vegetation index; normalized difference vegetation index; air temperature; precipitation; alpine grassland

1

Introduction

Aboveground biomass (AGB), the production of green plant photosynthesis, plays an important role in materials circulation and energy flows of various terrestrial ecosystems (Chu et al., 2013b; Fu et al., 2011b; Yang et al., 2014; Zhou et al., 2013). Aboveground biomass in grassland ecosystems is a food source for livestock (e.g. yak and sheep) and the basis for the development of animal husbandry(Chu et al., 2013b; Fu et al., 2011b; Mi et al., 2010; Zhou et al., 2013). Mean-

while, the AGB in grassland ecosystems also plays an important role in maintaining the regional ecological environment (Wu et al., 2013a, 2013b, 2014b; Yang et al., 2016; Zhou et al., 2013). The Tibetan Plateau is one of the main pasturing areas in China and one of the regions most sensitive to global change (Fu et al., 2012; Ni, 2002; Yao et al., 2000). The meadow and steppe ecosystems on the Tibetan Plateau are two of the most sensitive grassland ecosystems to global change (Ni, 2002; Zhao et al., 2012). Therefore,

Received: 2016-10-24 Accepted: 2016-12-17 Foundation: National Natural Science Foundation of China (31600432), National Key Research Projects of China (2016YFC0502005; 2016YFC0502006), Chinese Academy of Science Western Light Talents Program (Response of livestock carrying capability to climatic change and grazing in the alpine meadow of Northern Tibetan Plateau), Science and Technology Plan Projects of Tibet Autonomous Region (Forage Grass Industry) and National Science and Technology Plan Project of China (2013BAC04B01, 2011BAC09B03, 2007BAC06B01). *Corresponding author: SHEN Zhenxi, E-mail: [email protected]. Citation: FU Gang SUN Wei, LI Shaowei, et al. 2017. Modeling Aboveground Biomass Using MODIS Images and Climatic Data in Grasslands on the Tibetan Plateau. Journal of Resources and Ecology. 8(1): 42-49.

FU Gang, et al.: Modeling Aboveground Biomass using MODIS Images and Climatic Data in Grasslands on the Tibetan Plateau

accurate quantification of AGB is crucial for predicting changes in grass yields and the impact these will have on animal husbandry under global change on the Tibetan Plateau (Fang et al., 2011; Gan et al., 2009; Liu et al., 2015). With the development of remote sensing technology, some earlier studies have used a satellite-based vegetation index (e.g. normalized difference vegetation index, NDVI or enhanced vegetation index, EVI) at various spatial and temporal resolutions to estimate AGB in alpine grasslands on the Tibetan Plateau (Chen et al., 2009; Chu et al., 2013a, 2013b; Du et al., 2011; Fang et al., 2011; Fu et al., 2015b; Gan et al., 2009; Jiang et al., 2015; Liu et al., 2015; Long et al., 2010; Mi et al., 2010; Shen et al., 2008; Yang et al., 2009, 2014, 2016; Zhou et al., 2013). On the other hand, other previous studies found that the variations of AGB in alpine grasslands on the Tibetan Plateau were also affected by climatic factors (e.g. air temperature and precipitation) (Chu et al., 2007; Ding et al., 2007; Fu et al., 2015a, 2015b; Hu et al., 2011; Klein et al., 2007; Li et al., 2011; Peng et al., 2015; Shen et al., 2014; Song et al., 2011; Sun et al., 2013; Wang et al., 2012, 2013; Wu et al., 2014a; Xiong et al., 2014; Yang and Piao, 2006; Yu et al., 2010; Zhong et al., 2010; Zhou et al., 2007), indicating that climatic data may also be used to estimate AGB. However, to our knowledge, no studies of Tibetan Plateau grasslands model AGB using both vegetation indices and climatic data. Therefore, it remains unclear whether the combination of vegetation indices and climatic data produces a more accurate estimate of AGB than those produced with vegetation indices or climatic data alone. In this study, we compiled data from 71 studies related to AGB in alpine meadow or alpine steppe on the Tibetan Plateau. The main objectives were to (1) compare the accuracies of estimated AGB by single measures versus a combination of vegetation index (NDVI or EVI) and climatic data;

Fig.1

2.3

43

and (2) compare the estimation results of AGB between meadow and steppe ecosystems on the Tibetan Plateau.

2 2.1

Materials and methods MODIS vegetation index

Moderate Resolution Imaging Spectroradiometer (MODIS) provides NDVI and EVI product (MOD13A3, Collection 6) with a spatial resolution of 1 km×1 km and a temporal resolution of 1 month. The images of the Tibetan Plateau for the months of June to September during the years 2000 to 2013 were downloaded.

2.2

Aboveground biomass and climatic data

We searched articles published in 2000—2015 using the Web of Science and the China National Knowledge Infrastructure to obtain ground measured AGB on the Tibetan Plateau (Fig.1). We obtained 300 AGB data (6.93—853.24 g m2) for the months of June-September during the years 2000— 2013. The daily precipitation and air temperature data were obtained from the China Meteorological Data Sharing Service System (Shen et al., 2014). 136 meteorological stations for the Tibetan Plateau and surrounding areas were selected. These meteorological data were interpolated into raster data layers with a spatial resolution of 1 km × 1 km using ANUSPLIN 4.2. The daily precipitation and air temperature data were composited into monthly total precipitation (TP) and mean air temperature (Ta). The monthly accumulated temperature (AccT) was the sum of ≥5℃ daily air temperatures. The ratio of TP to AccT (TP/AccT) was treated as a synthesized factor of air temperature and precipitation (Wang et al., 2013; Wu et al., 2014a). The interpolated monthly mean Ta and total precipitation explained 99% and 78% of variations of measured monthly mean Ta and total precipitation, respectively (Fig. 2).

The location of sampling sites and meteorological stations on the Tibetan Plateau

Statistical analysis

Generally, the logarithmic value of AGB (LnAGB) has stronger correlations than AGB with either the vegetation

index or climatic factors (Table 1). Therefore, a natural logarithm transformation was made for AGB prior to any other related analyses. The LnAGB data and related NDVI, EVI, Ta, TP, AccT and TP/AccT were divided into two

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Journal of Resources and Ecology Vol. 8 No. 1, 2017

Fig.2 Relationships between (a) measured and interpolated monthly mean air temperature (Ta), and (b) measured and interpolated monthly total precipitation (TP) across Damxung flux tower and Dazi County meteorological station Table 1 Correlation coefficients of AGB, and LnAGB with NDVI, EVI, Ta, AccT, TP, TP/AccT on the Tibetan Plateau variable

grassland type

NDVI

EVI

Ta

AccT

TP

TP/AccT

meadow

0.53***

0.53***

0.04

0.05

steppe

0.11

0.11

0.46***

0.47***

0.16

0.25*

AGB

0.16*

0.09

meadow+steppe

0.44***

0.45***

0.18**

0.19**

0.13*

0.13*

meadow

0.57***

0.56***

0.07

0.07

0.17*

0.06

steppe

0.25*

0.23*

0.55***

0.56***

0.07

0.32**

meadow+steppe

0.50***

0.49***

0.26***

0.27***

0.09

0.14*

LnAGB

*, ** and *** indicates p < 0.05, p < 0.01 and p < 0.001, respectively. AGB: aboveground biomass; LnAGB: logarithmic value of AGB; NDVI: normalized difference vegetation index; EVI: enhanced vegetation index; Ta: air temperature; AccT: ≥5℃ accumulated temperature; TP: precipitation; TP/AccT: ratio of TP to AccT.

groups. The first group (171 datasets from meadow and 81 datasets from steppe) was used to estimate the parameters of the regression equation between LnAGB and the vegetation index and/or climatic data. The second group (32 datasets from meadow and 16 datasets from steppe) was used as cross validation data. Three stepwise multiple regression methods (i.e., stepwise multiple regression of AGB with NDVI and EVI; stepwise multiple regression of AGB with Ta, AccT, TP and TP/AccT; and stepwise multiple regression of AGB with NDVI, EVI, Ta, AccT, TP and TP/AccT) were developed. The mean absolute error (MAE) and root-meansquared-error (RMSE) were used to assess the predicting accuracies (Fu et al., 2011a; Zhang et al., 2008). The validation model with the lower MAE and RMSE values is the better predictive model (Fu et al., 2011a; Hu et al., 2016). All statistical analyses were performed using SPSS 16.0. n

MAE   ABS ( y predicted  ymeasured ) / n

(1)

and vegetation index are presented in Table 2. The NDVI was included in the regression equations, while the EVI was excluded. The NDVI explained 27%, 6% and 23% of the variations of LnAGB in the meadow, steppe, and meadow+ steppe ecosystems, respectively. The correlation coefficients between LnAGB and NDVI were 0.52, 0.24 and 0.48, respectively, in the meadow, steppe, and meadow+steppe ecosystems. The stepwise multiple regression analyses between LnAGB and climatic data are presented in Table 3. The AccT explained 21% and 4% variations of LnAGB in the steppe, Table 2 Stepwise multiple regression analyses of LnAGB with NDVI, EVI on the Tibetan Plateau grassland type meadow

i 1

RMSE=

3 3.1

1 n  ( y predicted  ymeasured )2 n i 1

steppe

(2)

Results Model building

The stepwise multiple regression analyses between LnAGB

fit paregression rameters coefficient constant

3.58

NDVI

2.41

constant

3.94

NDVI

1.42

meadow+steppe constant NDVI

R2

partial correlation coefficient

p

0.27

0.52