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Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm
Ecological Indicators 102 (2019) 479–487
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Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm ⁎
T
⁎
Na Zenga,b, Xiaoli Rena, , Honglin Hea,c, , Li Zhanga,c, Dan Zhaod, Rong Gea,b, Pan Lie, Zhongen Niua,b a
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b University of Chinese Academy of Sciences, Beijing 100049, China c University of Chinese Academy of Sciences, College of Resource and Environment, Beijing 100049, China d Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China e Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Aboveground biomass (AGB) The Tibetan Plateau Random Forest (RF) Mean annual temperature (MAT) Mean annual precipitation (MAP)
Effective and accurate monitoring of grassland aboveground biomass (AGB) is necessary for improving our understanding of regional carbon cycle and pastoral agricultural management. In this study, we developed a suitable AGB estimation model for the Tibetan alpine grasslands based on the random forest algorithm, using 256 AGB observation data, remote sensing vegetation indices, meteorological data, and topographical data. We estimated the grassland AGB on the Tibetan Plateau during 2000–2014, analyzed its spatiotemporal changes, and further explored the response of AGB to the variation in climatic factors. The results indicated that (1) the RF model performed well in the AGB estimation, which can explain 86% of the variation of the observation data. (2) The grassland AGB decreased from the southeast to the northwest in this region, with an average value of 77.12 gm−2. (3) In the whole study area, the grassland AGB showed significantly positive correlation with temperature and precipitation. The correlation between grassland AGB and MAP was 0.54 (P < 0.05), much higher than that of MAT (R = 0.38, P < 0.05). (4) The inter-annual variation of AGB on the Tibetan Plateau was significantly and positively correlated with temperature (R2 = 0.45, P < 0.05). This study demonstrated that RF model can help improve our understanding of the spatiotemporal dynamics of the grassland AGB and the effects of climate variation.
1. Introduction Grassland is one of the most widespread vegetation types on the earth, covering approximately 40% of the surface area (Scurlock and Hall, 1998). As an important component of terrestrial ecosystems in China, grassland plays a key role in protecting the ecological environment and grazing economics (Akiyama and Kawamura, 2007). The aboveground biomass (AGB) is an important indicator for characterizing vegetation activity and assessing the carbon storage of ecosystems (Flombaum and Sala, 2007). In grassland ecosystem, the amount of AGB determines grazing capacity, and thus is closely related to the regional development (Zhang et al., 2014a). Therefore, accurate assessment of grassland AGB and its spatiotemporal dynamics is critical for the sustainable use and protection of grassland resource (Anaya et al., 2009; Yu et al., 2010).
Biomass harvesting is a traditional method for grassland AGB estimation and can provide an accurate assessment of AGB in a small area. However, this approach is destructive and time consuming, thus has limitations in both spatial extent and temporal scales (Jobbágy et al., 2002). With the advance in remote sensing technology, satellite data with various spatial and temporal resolutions have become an ideal choice for large-scale grassland monitoring (Claverie et al., 2012). The remote sensing-based methods for grassland AGB estimation can be broadly categorized into two groups: regression models (Xu et al., 2008) and machine learning models (Ali et al., 2015). Most regression models estimate AGB by establishing relationship between recorded biomass and vegetation index (VI) (Kurtz et al., 2010; Huang et al., 2013). The normalized difference vegetation index (NDVI) is the most widely used VI for biomass estimation. Xia et al. (2014) used the MODIS NDVI time series data and a statistical model to estimate the
⁎ Corresponding authors at: Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China (X. Ren and H. He). E-mail addresses: [email protected] (X. Ren), [email protected] (H. He).
https://doi.org/10.1016/j.ecolind.2019.02.023 Received 9 August 2018; Received in revised form 6 December 2018; Accepted 11 February 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Spatial distribution of grassland types and AGB sampling points on the Tibetan Plateau.
grassland AGB respond to the climate variation (Zhang et al., 2016). Employing machine learning algorithm offers an opportunity for more accurately estimating AGB and its pattern on the Tibetan Plateau (Xia et al., 2018). However, the grassland AGB estimation using machine learning algorithm combining vegetation indices and climatic data are few in this region (Yang et al., 2018). The primary objectives of this paper were to (1) construct the AGB estimation model for the Tibetan alpine grasslands based on machine learning method; (2) analyze the magnitude and spatiotemporal dynamics of the grassland AGB as well as the effects of climate variations. Based on the AGB field observation data, we developed a suitable grassland AGB estimation model using the RF algorithm, combined with remote sensing vegetation indices (VIs), meteorological and topographical data. Then, the model was used to derive the spatial and temporal patterns of AGB for the Tibetan Plateau grassland from 2000 to 2014. The correlations of grassland AGB to temperature and precipitation were analyzed to explore the responses of AGB to changes in climate conditions.
global grassland biomass, the results showed that the NDVI-biomass model explained approximately 57% of the observed variation in the calibration data. In addition to the NDVI, other VIs has been used to grassland AGB estimation, such as the difference vegetation index (DVI) (Jin et al., 2014), enhanced vegetation index (EVI) (Yang et al., 2009), soil adjusted vegetation index (SAVI) (Mutanga and Skidmore, 2004), and modified soil adjusted vegetation index (MSAVI) (Qi et al., 1994). Numerous studies have documented the usefulness of regression models in grassland AGB estimation (Gao et al., 2013; Meng et al., 2017). However, the accuracy of regression models, which were mostly based on a single VI, are often limited by the sensitivity of grassland AGB to VIs and the influence of external environmental factors (Ali et al., 2016). In the past two decades, machine learning techniques, such as the artificial neural network (ANN) (Zeng et al., 2017), support vector machines (SVM) (Dusseux et al., 2014), and random forest (RF) (Fassnacht et al., 2014) have been increasingly used for the study of biomass. Compared with the statistical regression models, the methods of machine learning can integrate multifactor, learn high complex nonlinear mappings, and get better simulation results (Powell et al., 2010). Among them, the RF algorithm is a non-parametric ensemble modeling approach, having the capacity to reduce bias and overfitting (Karlson et al., 2015; Ramoelo et al., 2015). Wang et al. (2017b) utilized the RF algorithm to predict grassland AGB biomass in the loess plateau, found that the RF model was more accurate than the SVM. Moreover, some studies have indicated that RF models tend to be more tolerant to outliers and noise (Anaya et al., 2009; Gleason and Im, 2012). The Tibetan Plateau is the largest and highest plateau on the earth, with an area of 257.24 × 104 km2, and the mean altitude is about 4000 m. The grassland is the dominant ecosystem on the Tibetan Plateau, accounting for about 60% of the total area. However, the grassland ecosystem is relatively fragile and highly sensitive to climate change in this region, because of the high altitude and harsh natural conditions (Luo et al., 2002). Over the past 30 years, increasing attention has been given to the grassland AGB on the Tibetan Plateau, due to the increase in climate warming and wetting (Kato et al., 2006; Yang et al., 2008; Yin et al., 2013; Qin et al., 2018). One important question in this region is how the spatial pattern and temporal dynamics of
2. Methodology 2.1. Study area The Tibetan Plateau (26°00′12″ N ∼ 39°46′50″ N, 73°18′52″ E ∼ 104°46′59″ E) is located in the western region of China, with an average elevation of over 4000 m, and an area of approximately 257.24 × 104 km2 (Ding et al., 2013). The study area is in the alpine climate zone, with the mean annual temperature (MAT) at −0.51 °C, and the mean annual precipitation (MAP) at 493.6 mm. According to the ChinaCover database (Zhang et al., 2014b), the area of grassland on the Tibetan Plateau is 151.11 × 104 km2, accounting for 58.74% of the total region. The main grassland types include alpine meadow, alpine steppe, and sparse grassland, which account for about 19.29%, 41.08%, and 39.63% of the total grasslands on the Tibetan Plateau, respectively (Fig. 1).
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NASA’s Earth Observing System Data and Information System (EOSDIS) (http://earthdata.nasa.gov/), which provided NDVI and EVI at a 250 m resolution. For this study, the MOD13A2 data covering the whole study area from 2000 to 2014 during the growing season (May-September) were downloaded. The acquired MODIS NDVI and EVI data were projected and resampled to a 1000 m resolution using the nearest neighbor resampling. The temporal resolution of this product is 16-day, which ensures a better data quality as selected based on the absence of clouds or aerosol. Additionally, to further reduce the effect of clouds and atmospheric contamination, the original time series were smoothed using the double logistic curve fit in the TIMESAT software (Lund University, Lund, Sweden) (Jönsson and Eklundh, 2004). Then, the growing season averaged NDVI and EVI were calculated using the fitted data.
2.2. Data 2.2.1. Field data The grassland AGB field sample data are collected from two different sources: (1) documented literature (Yang et al., 2010) and (2) field measurements. The field survey was conducted in the growing season (May-September) during 2005–2014. The field survey covered the three main grassland types across the plateau. At each sampling site (30 m × 30 m), three independent plots (1 m × 1 m) were selected, with a distance greater than 15 m between each plot. In each plot, all plants were harvested, and oven dried at 85 °C in the lab until the weight remained constant. The dry weights of three plots at each sampling site were averaged to produce the AGB sampling data. Then, based on the standard deviation method (Jiao et al., 2016), the AGB sample data that exceed mean value ± 2 × standard deviation were excluded during data processing. In addition, based on the landcover map, the sample sites distributed at the grassland ecosystems were selected. Finally, a total of 256 sampling points was obtained, including 92 sites from Yang’s literature and 164 sites from our field survey (Fig. 1).
2.3. Random forest model Random forest (RF) is a machine learning algorithm developed by Breiman (2001), which is a series of binary rule-based decisions that dictate how an input relates to its dependent variable. One of the main advantages of RF is that it can accurately describe complex relationship between the independent variables and the dependent variables, which is often occurred when complex ecological systems and environmental variables are introduced (Fu et al., 2017). In this study, seven variables were selected as candidate inputs in the RF modeling process, including remote sensing VIs (NDVI and EVI), meteorological variables (MAT and MAP), and topographical variables (altitude, slope and aspect). To improve the computational efficiency and remove redundant information, the correlation coefficient (R) between the observed AGB and the predictors were calculated, and the variables used to establish AGB model were preferentially chosen when they had higher correlation coefficient. Different input variables were selected to conduct multiple experiments, then the model with the highest accuracy and the least variables was selected for grassland AGB estimation at regional scale. The RF models were implemented using the “random forest” package within the R environment software. For each model, 80% of the sample points were used to model train, and the leaving 20% were used for model validation. The performance of the models was assessed based on the R2, Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC).
2.2.2. Meteorological data The meteorological data including temperature and precipitation with a spatial resolution of 1000 m during 2000–2014 are obtained from the published temperature and precipitation dataset in China scientific data by Wang et al. (2017a). The daily temperature and precipitation observational data of meteorological stations were downloaded from the National Meteorological Information Center (NMIC) of China Meteorological Administration (753 stations) and the Daily Global Historical Climatology Network-Daily (GHCN-D) (345 stations). Then, combined with a digital elevation model (DEM) data, the meteorological data were interpolated to a fitted surface at a spatial resolution of 1 km using the widely used interpolation software (ANUSPLIN). For the regional application in Tibetan Plateau’s grasslands, the spatial data were extracted using the tool (Extract by mask) of ArcGIS (ArcGIS 10.2) software (Environmental Systems Research Institute, Inc., ESRI). Additionally, MAP and MAT were calculated using the daily precipitation and temperature fitted map. 2.2.3. Topographical data The Shuttle Radar Topography Mission (SRTM) is an international mission conducted by NASA and the National Geospatial-Intelligence Agency (https://gdex.cr.usgs.gov/gdex/), providing a DEM product that covers 99.97% of the Earth’s land surface, from 56 °S to 60 °N. In this study, the SRTM elevation data for China at a resolution of 3″ (90 m) was employed. To be consistent with meteorological data, the SRTM DEM was resampled to 1000 m resolution. In addition, slope and aspect data were calculated from the resampled DEM using ArcGIS software.
3. Results 3.1. Random forest model construction and validation The correlation coefficients between the grassland AGB and the environment factors, as well as those between the environment factors, are provided in the Table 1. EVI and NDVI have the highest correlation with AGB (R = 0.68 and 0.64, respectively), suggesting that remote sensing VIs provided a good characterization of the grassland AGB. Among the meteorological variables, the correlation of AGB with MAP was higher than that with MAT (R = 0.57 and 0.21, respectively). In addition, altitude was negatively correlated with AGB (R = −0.20),
2.2.4. Remote sensing data The MODIS VI product (MOD13A2, V006) was obtained from
Table 1 Correlation coefficients between the grassland AGB and the explanatory variables (n = 256). Variable
AGB
EVI
NDVI
MAP
MAT
Altitude
Slope
Aspect
AGB EVI NDVI MAP MAT Altitude Slope Aspect
1 0.68* 0.64* 0.57* 0.21 −0.20 0.17 −0.01
1 0.84* 0.67* 0.35 −0.25 0.22 −0.01
1 0.74* 0.37 −0.27 0.22 0.08
1 0.30 −0.13 0.14 0.17
1 −0.64* 0.20 −0.04
1 0.03 0.02
1 −0.09
1
Note: *Significant correlation, p < 0.05. 481
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Xinjiang were low value areas, with AGB lower than 30 gm−2. Most parts of middle Xizang and western Qinghai had relatively high AGB values, ranging from 30 to 100 gm−2. Eastern Qinghai, Gansu, Sichuan, and Yunnan had the highest AGB, with values larger than 100 gm−2. During 2000 to 2014, the grassland AGB on the Tibetan Plateau exhibited a non-significant increasing trend, with an average rate of 0.19 gm−2a−1 (R2 = 0.20, P = 0.09). (Fig. 4). There was a relatively large interannual fluctuation of grassland AGB from 2006 to 2009, which was consistent with the findings of Mao et al. (2014) and Zeng et al. (2017). The highest grassland AGB occurred in 2006, which was 79.57 gm−2, and the lowest AGB was 72.12 gm−2 in 2000.
Table 2 The performance of AGB estimation models for the grasslands on the Tibetan Plateau. Model
Variables
R2
RMSE
ΔAIC
No.1 No.2 No.3 No.4 No.5 No.6 No.7 No.8 No.9 No.10 No.11 No.12
EVI, NDVI, MAP EVI, NDVI, MAP, MAT EVI, NDVI, MAP, altitude EVI, NDVI, MAP, slope EVI, NDVI, MAP, aspect EVI, NDVI, MAP, MAT, altitude EVI, NDVI, MAP, MAT, slope EVI, NDVI, MAP, MAT, aspect EVI, NDVI, MAP, MAT, altitude, slope EVI, NDVI, MAP, MAT, altitude, aspect EVI, MAT, MAP, altitude NDVI, MAT, MAP, altitude
0.69 0.78 0.75 0.70 0.69 0.86 0.82 0.80 0.85 0.84 0.76 0.73
63.52 54.11 58.34 62.61 63.72 43.60 47.68 51.63 46.57 46.40 56.71 60.16
188.67 108.57 147.11 183.28 192.27 0 45.80 86.55 35.74 33.87 132.60 162.84
3.3. Temporal correlation of AGB to MAT and MAP To understand the responses of grassland AGB to climate change, we analyzed changes in interannual MAT and MAP and their relationships with AGB during 2000–2014. During the 15-year study period, the MAT on the Tibetan Plateau exhibited an increasing trend (Fig. 4). There was large interannual fluctuation in temperature from 2006 to 2009. Coupled with interannual variations of temperature, AGB showed a similar changing trend. As shown in Fig. 5, the grassland AGB on the Tibetan Plateau significantly increased with the increase in MAT (R2 = 0.45, P < 0.05). For every 1 °C increased, the AGB increased by 3.07 gm−2. However, the inter-annual variation of AGB did not show any significant correlation with the MAP (R2 = 0.01, P = 0.70). Fig. 6 shows the temporal correlation of AGB with MAT and MAP for each pixel on the Tibetan Plateau during 2000–2014. In general, the temporal correlation of AGB with MAT is higher that with MAP. Specifically, in the most area of the eastern Tibetan plateau and parts of northern Xizang, accounting for about 68% of the total grassland area, the grassland AGB was positively correlated with MAT (Fig. 6a). The grassland AGB and MAP showed a positive relationship over 52% of the grassland area, which located in the most parts of middle Xizang and western Qinghai. For the three different grassland types, the correlation between AGB and MAT in alpine meadow, alpine steppe and sparse grassland were all higher than that of MAP.
ΔAIC: the model’s AIC minus minimal AIC of all the models.
which indicated that the grassland AGB in the area decreased with the increase in altitude. Meanwhile, the correlations also existed among the environmental variables. There is a strongly and significantly positive correlation between EVI and NDVI (R = 0.84). MAP showed significantly positive correlation with EVI and NDVI (R = 0.67 and 0.74, respectively), and MAT decreases with the increases of altitude (R = 0.64). Based on the correlation analysis between grassland AGB and the explanatory variables at site-level, a series of experiments are designed and conducted, and the results are listed in Table 2. The simulated AGB exhibited strong correlations with observed grassland AGB across these twelve models, with R2 ranging from 0.69 to 0.86, RMSE ranging from 43.60 to 63.72 gm−2. When EVI, NDVI, MAP, MAT and altitude were used as input variables, the model No.6 has the highest R2 (0.86) and the lowest RMSE (43.60 gm−2) and AIC (Table 2, Fig. 2). Thus, the No.6 model was selected to simulate the grassland AGB on the Tibetan Plateau. 3.2. Spatial distribution and inter-annual dynamics of grassland AGB
3.4. Effects of temperature and precipitation on the spatial pattern of grassland AGB
The estimated AGB was 77.12 gm−2 on average during 2000 to 2014 on the Tibetan Plateau. For the different grassland types, alpine meadow has the largest AGB (154.72 gm−2), followed by alpine steppe (76.43 gm−2), and the lowest AGB was found in sparse grassland (41.65 gm−2). The spatial distribution of AGB exhibited obvious heterogeneity on the Tibetan Plateau, decreasing from the southeast to the northwest (Fig. 3), which is similar to the spatial pattern of MAT and MAP. More specifically, parts of northwestern Xizang and southern
Table 3 shows the climate conditions of the three different grassland types. Alpine meadow was mainly located in the eastern and southeastern regions of the Tibetan Plateau (Fig. 1), which had relatively high MAP and MAP (587.99 mm, 0.37 °C). Alpine steppe was widely distributed in the central and northern parts of the Tibetan Plateau, where the MAP and MAT were relatively lower (510.80 mm, −0.59 °C). Sparse grassland was mainly found in the northern and western Tibetan Plateau, with the lowest MAP and MAT (415.44 mm, −1.48 °C). All these lead to the higher AGB in alpine meadow and lower AGB in alpine steppe and sparse grassland. Moreover, the spatial correlations of AGB with MAT and MAP in different grassland types were calculated. Across the entire area, the grassland AGB showed significantly positive correlation with temperature and precipitation. The correlation between grassland AGB and MAP was 0.54 (P < 0.05), much higher than that of MAT (R = 0.38, P < 0.05) (Table 3). Furthermore, the correlation between AGB and MAP in alpine meadow, alpine steppe and sparse grassland were all higher than that of MAT. Therefore, we speculated that precipitation had a greater impact on the AGB spatial distribution than temperature on the Tibetan Plateau. To further analyze the extent to which AGB responses to climate factors along the gradient of temperature and precipitation, we calculated the average AGB with each MAT (1 °C) and MAP (100 mm) levels. As shown in the Figs. 7, 4 °C is a turning point, below which AGB significantly increases with MAT (R2 = 0.94, P < 0.05); however, above this point, AGB exhibits a decreasing trend with temperature
Fig. 2. The relationship between the estimated AGB based on the No.6 RF model and the observed AGB. 482
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Fig. 3. Spatial distribution of grassland AGB modeled by the RF model on the Tibetan Plateau.
4. Discussion 4.1. Comparison between estimated grassland AGB and previous studies In our study, the estimated grassland AGB was 77.12 gm−2 on the Tibetan Plateau during 2000–2014, which falls in the variation range of the previous studies (40 ∼ 120 gm−2) (Table 4). Xia et al. (2018) estimated the AGB of the Tibetan Plateau grassland to be 78.40 gm−2, which was the closest to the value obtained in our study. Several previous studies have indicated that the biomass of terrestrial ecosystems on the Tibetan Plateau has increased over the past 15 years (Mao et al., 2014; Zhang et al., 2016). This is consistent with the results of our study, which indicated the temporal dynamics of AGB in the study area presents an increasing trend. The differences in the estimated grassland AGB reported among these studies may be due to two factors. The first is the different data used to estimate grassland AGB, including the field sample data, remote sensing data, and land cover data. Chen et al. (2014) indicated that the performance of the data-driven methods is highly restricted by the quantity of training sample data. Meanwhile, the spatial distribution of sample points also has a great influence on the estimation of grassland AGB. For example, the AGB sampling data used by Jiao et al were mostly distributed in the eastern part of the Tibetan Plateau, where the grassland AGB was relatively high. Thus, their statistical result (120.73 gm−2) was much higher than those of other studies. The representativeness of field samples is important for accurate estimation of grassland AGB, which is also revealed by other studies (Ma et al., 2010; Gao et al., 2013). In addition, the time mismatch between field observations and VI also might induce uncertainties in the AGB estimation. Secondly, the different approaches may also introduce uncertainty in the AGB estimation. Jia et al. (2016) quantify the spatial uncertainty of regional grassland biomass, and found that the model forms could bring 13% uncertainty to the grassland biomass estimation. Yang et al. (2018) reported that the model performance of ANN and linear regression models were much different, even when the data employed were the same. Because of its simple structure and convenient operation, statistical regression models have been widely used in the biomass estimation. However, due to its difficultly in expressing complex
Fig. 4. Inter-annual variations in grassland AGB (a), MAT (b) and MAP (c) on the Tibetan Plateau during 2000–2014.
(R2 = 0.64, P = 0.10). Meanwhile, when MAP is less than 400 mm, the grassland AGB was almost constant (R2 = 0.07, P = 0.73); when MAP is between 400 mm and 800 mm, AGB significantly increases with MAP (R2 = 0.95, P < 0.05); when MAP is above 800 mm, the AGB of grassland changed slightly (R2 = 0.23, P = 0.32).
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Fig. 5. Correlation between AGB and MAT and MAP during 2000–2014.
4.3. Effects of climatic variables on the temporal variation of grassland AGB
nonlinear relationships, the model simulation accuracy is limited. The present study demonstrated that random forest can integrate multifactors to develop multivariate models, and get better performance in grassland AGB estimation.
The regional climate has been widely reported as the primary driver of temporal variation of grassland AGB (Jobbagy and Sala, 2000; Bai et al., 2004). However, the importance of temperature and precipitation on grassland AGB temporal variation is still controversial. Ma et al. (2010) found that the year to year biomass variation was well coupled with inter-annual variation of January-July precipitation in northern China. However, the study by Zhang et al. (2016) indicated that the temporal variation of grassland AGB on the Tibetan Plateau was strongly correlated with the variation in temperature. In our study, a positive and significant correlation only exists between the grassland AGB and MAT (R2 = 0.45, P < 0.05), suggesting that increased temperature may be associated with the temporal variation of AGB. Some studies suggested that the warmer climate might result in increased AGB due to the enhanced photosynthetic rate and the longer growing season (White et al., 1999; Piao et al., 2003). First, the increase in temperature may increase the activity of photosynthetic enzymes and accelerates the biochemical reaction of photosynthesis. Second, the extension of the growing season and early spring phenology, may enhance the carbon sequestration in vegetation (Piao et al., 2008). Piao et al. (2007) reported that the growing season of alpine meadow in China was extended by approximately 0.9 days per year between 1982 and 1999 due to the increase in temperature. In the Tibetan alpine regions, because of the unique geographical location and altitude, besides precipitation, runoff from mountain glacial meltwater is also an important source of available water to vegetation in the area (Piao
4.2. Effects of climatic variables on the spatial pattern of grassland AGB The spatial patterns of grassland AGB are usually affected by multiple factors, such as climate condition, soil texture and soil moisture content (Churkina and Running, 1998); among them, the climate is often thought to play an important role (Knapp and Smith, 2001; Shi, 2006; Bai et al., 2008). In this study, when MAT is less than 4 °C, AGB is significantly correlated with both MAT and MAP; when MAT is above than 4 °C, AGB exhibits a decreasing trend with MAT, and the spatial distribution of AGB is mainly restricted by precipitation. In the whole region, precipitation had a greater impact on the AGB spatial distribution than temperature. Many studies have illustrated that grassland AGB is more sensitive to precipitation than temperature in this region (Shi, 2006; Yang et al., 2009, 2010; Jiao et al., 2016). For example, Jiang et al. (2015) found that the MAP and MAT explained 56.6% and 10.9% of grassland AGB spatial variation, respectively. However, when the altitude is greater than 5000 m, MAT can explain 25.00% of the spatial variation in the AGB, much higher than that of MAP (12.96%), which may be induced by the lower temperature in the higher altitude area. This is probably because that low temperature may inhibit biogeochemical cycles and reduce soil nitrogen availability for plant growth (Yang et al., 2010).
Fig. 6. The temporal correlation with AGB of the MAT and MAP during 2000–2014 at pixel scale. 484
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Table 3 The averaged AGB, MAT, MAP and the correlation of AGB with MAT and MAP for each grassland type. Grassland type
Mean
Correlation with AGB
AGB (gm Alpine meadow Alpine steppe Sparse grassland Total
154.72 76.43 41.65 77.12
−2
)
Temperature (°C)
Precipitation (mm)
0.37 −0.59 −1.48 −0.76
587.99 510.80 415.44 487.95
Temperature *
0.40 0.45* 0.29 0.38*
Precipitation 0.46* 0.59* 0.48* 0.54*
Note: *p < 0.05.
correlation analysis of single variable. Furthermore, this paper mainly focuses on the AGB of Tibetan alpine grasslands, which is closely related to the grass yield and grazing economy. Below-ground biomass (BGB) also accounts for a great proportion in grassland biomass, while the estimation of BGB was not involved in this paper due to the lack of observational data.
et al., 2010). The increase of temperature may also stimulate the growth of grassland though increasing mountain glacial meltwater.
4.4. Implications and limitations Compared with the traditional statistical models, the RF model has obvious advantages in describing complex nonlinear relationship, which is often occurred between biophysical parameter and complex environmental variables (Mutanga et al., 2012). The results of the present study demonstrated that RF model is a useful and effective method for AGB estimation. The AGB values predicted by RF model were very close to the observed values (R2 = 0.84; RMSE = 84.74 gm−2). Moreover, the overall accuracy of the RF model in our study were higher than other studies conducted in the same area based on different models, with the range from 0.40 to 0.63 (Yang et al., 2009; Liu et al., 2017). Although our study provides the comprehensive assessment of grassland AGB on the Tibetan Plateau, some limitations still exist. First, the sampling sites were few on the western Tibetan Plateau due to the poor accessibility; it may limit the accuracy of AGB estimation on this region. Second, the RF models contain a stochastic element that results in a different biomass model in each iteration when they are applied to the same data; therefore, repeated training is required to obtain an optimal RF model and achieve reasonable results. Third, the effects of temperature and precipitation on the spatial pattern and temporal variation of grassland AGB are analyzed by the correlation analysis of single variable. However, the interaction between temperature and precipitation may affect the accuracy of the results. We further analyzed the effects of temperature and precipitation on grassland AGB using partial correlation analysis. The results showed that precipitation is still more important in shaping spatial patterns of grassland AGB on the Tibetan Plateau (R = 0.45, P < 0.05), and the temporal variation of AGB still had higher correlation with the variation in MAT (R = 0.69, P < 0.05). The results of partial correlation are consistent with that of
5. Conclusions The magnitude and spatiotemporal of grassland ABG are crucial estimated for grazing economic. In this study, RF algorithm was used to combine field observation data, remote sensing vegetation indices, meteorological data, and topographical data, for estimating grassland AGB on the Tibetan Plateau from 2000 to 2014. Through the experiment comparison, five variables were selected as the input variables of the RF model to estimate AGB of grassland, including remote sensing VIs (NDVI and EVI), meteorological variables (MAT and MAP), and topographical variables (altitude). The modeled AGB (2000–2014) showed an obvious spatial heterogeneity and non-significant increasing trend, decreasing from the southeast to the northwest, with a mean value of 77.12 gm−2 and an average increasing rate of 0.19 gm−2a−1. The spatial distribution of grassland AGB is sensitive to precipitation, while the temporal dynamics of AGB were significant correlated with that in temperature. This study demonstrated that the RF is an effective method in the grassland AGB estimation on the Tibetan Plateau. Acknowledgments This study was supported by National Key Research and development program of China (2016YFC0500204), National Basic Work of Science and Technology (2015FY110700), National Key Research and development program of China (2015CB954102), National Natural Science Foundation of China (41571424, 31700417, 41601478), Science and Technology Service Network Initiative of Chinese Academy
Fig. 7. Averaged grassland AGB along the MAT (1 °C) and MAP (100 mm) gradients. 485
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Table 4 Comparison of grassland AGB on the Tibetan Plateau estimated by different studies. AGB (gm−2)
Area (×104 km2)
Study period
Approach
Model input Data
References
68.8 58.11 59.3 74.11 43.33 63.69 120.73 78.40
112.8 113.6 — 129.5 122.8 120 — 132
2001–2004 1980–1990 2001–2004 1982–2006 2005 1981–2010 1980–2014 —
linear regression site grassland survey site grassland survey exponential regression exponential regression exponential regression literature data analysis RF
EVI — — NDVI NDVI NDVI — annual precipitation, summer NDVI, summer relative humidity
Yang et al. (2009) Ni (2004) Yang et al. (2010) Ma et al. (2010) Xu et al. (2007) Yu (2013) Jiao et al. (2016) Xia et al. (2018)
Note: AGB = Carbon density/0.45.
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partitioning across China's grasslands. Glob. Ecol. Biogeogr. 19, 268–277. Yang, S., Feng, Q., Liang, T., Liu, B., Zhang, W., Xie, H., 2018. Modeling grassland aboveground biomass based on artificial neural network and remote sensing in the ThreeRiver Headwaters Region. Remote Sens. Environ. 204, 448–455. Yin, Y., Wu, S., Zhao, D., Zhen, D., Pan, T., 2013. Modeled effects of climate change on actual evapotranspiration in different eco-geographical regions in the Tibetan Plateau. J. Geogr. Sci. 23, 195–207. Yu, H., 2013. Dynamice of Grassland Growth and Its Response to Climate Change on Tibetan Plateau. Lanzhou University, Lanzhou. China in Chinese. Yu, L., Zhou, L., Liu, W., Zhou, H.K., 2010. Using remote sensing and GIS technologies to estimate grass yield and livestock carrying capacity of Alpine Grasslands in Golog Prefecture, China. Pedosphere 20, 342–351. Zeng, N., Ren, X., He, H., Zhang, L., Li, P., Li, Z., Zhang, L., 2017. Aboveground biomass of grassland in the three-river headwaters region based on neural network. Res. Environ. Sci. 30, 59–66. Zhang, L., Wu, B.F., Li, X.S., Xing, Q., 2014b. Classification system of China land cover budget. Acta Ecol. Sin. 34, 7158–7166. Zhang, J., Zhang, L., Liu, W., Qi, Y., Wo, X., 2014a. Livestock-carrying capacity and overgrazing status of alpine grassland in the Three-River Headwaters region, China. J. Geogr. Sci. 24, 303–312. Zhang, L., Zhou, G., Ji, Y., Bai, Y.F., 2016. Spatiotemporal dynamic simulation of grassland carbon storage in China. Sci. China 59, 1946–1958.
Wang, Y., Wu, G., Deng, L., Tang, Z., Wang, K., Sun, W., Shangguan, Z., 2017b. Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm. Sci. Rep. 7, 6940. White, M.A., Running, S.W., Thornton, P.E., 1999. The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest. Int. J. Biometeorol. 42, 139–145. Xia, J., Liu, S., Liang, S., Chen, Y., Xu, W., Yuan, W., 2014. Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006. Remote Sens. 6, 1783–1802. Xia, J., Ma, M., Liang, T., Wu, C., Yang, Y., Zhang, L., Zhang, Y., Yuan, W., 2018. Estimates of grassland biomass and turnover time on the Tibetan Plateau. Environ. Res. Lett. 13 (1), 014020. Xu, B., Yang, X., Wei, W., Qin, Z., Liu, H., Miao, J., 2007. Remote sensing monitoring upon the grass production in China. Acta Ecol. Sin. 27, 405–413. Xu, B., Yang, X., Tao, W., Qin, Z., Liu, H., Miao, J., Bi, Y., 2008. MODIS-based remote sensing monitoring of grass production in China. Int. J. Remote Sens. 29, 5313–5327. Yang, Y., Fang, J., Tang, Y., Ji, C., Zheng, C., He, J., Zhu, B., 2008. Storage, patterns and controls of soil organic carbon in the Tibetan grasslands. Glob. Change Biol. 14, 1592–1599. Yang, Y.H., Fang, J.Y., Pan, Y.D., Ji, C.J., 2009. Aboveground biomass in Tibetan grasslands. J. Arid Environ. 73, 91–95. Yang, Y., Fang, J., Ma, W., Guo, D., Mohammat, A., 2010. Large-scale pattern of biomass
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm ⁎
T
⁎
Na Zenga,b, Xiaoli Rena, , Honglin Hea,c, , Li Zhanga,c, Dan Zhaod, Rong Gea,b, Pan Lie, Zhongen Niua,b a
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b University of Chinese Academy of Sciences, Beijing 100049, China c University of Chinese Academy of Sciences, College of Resource and Environment, Beijing 100049, China d Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China e Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Aboveground biomass (AGB) The Tibetan Plateau Random Forest (RF) Mean annual temperature (MAT) Mean annual precipitation (MAP)
Effective and accurate monitoring of grassland aboveground biomass (AGB) is necessary for improving our understanding of regional carbon cycle and pastoral agricultural management. In this study, we developed a suitable AGB estimation model for the Tibetan alpine grasslands based on the random forest algorithm, using 256 AGB observation data, remote sensing vegetation indices, meteorological data, and topographical data. We estimated the grassland AGB on the Tibetan Plateau during 2000–2014, analyzed its spatiotemporal changes, and further explored the response of AGB to the variation in climatic factors. The results indicated that (1) the RF model performed well in the AGB estimation, which can explain 86% of the variation of the observation data. (2) The grassland AGB decreased from the southeast to the northwest in this region, with an average value of 77.12 gm−2. (3) In the whole study area, the grassland AGB showed significantly positive correlation with temperature and precipitation. The correlation between grassland AGB and MAP was 0.54 (P < 0.05), much higher than that of MAT (R = 0.38, P < 0.05). (4) The inter-annual variation of AGB on the Tibetan Plateau was significantly and positively correlated with temperature (R2 = 0.45, P < 0.05). This study demonstrated that RF model can help improve our understanding of the spatiotemporal dynamics of the grassland AGB and the effects of climate variation.
1. Introduction Grassland is one of the most widespread vegetation types on the earth, covering approximately 40% of the surface area (Scurlock and Hall, 1998). As an important component of terrestrial ecosystems in China, grassland plays a key role in protecting the ecological environment and grazing economics (Akiyama and Kawamura, 2007). The aboveground biomass (AGB) is an important indicator for characterizing vegetation activity and assessing the carbon storage of ecosystems (Flombaum and Sala, 2007). In grassland ecosystem, the amount of AGB determines grazing capacity, and thus is closely related to the regional development (Zhang et al., 2014a). Therefore, accurate assessment of grassland AGB and its spatiotemporal dynamics is critical for the sustainable use and protection of grassland resource (Anaya et al., 2009; Yu et al., 2010).
Biomass harvesting is a traditional method for grassland AGB estimation and can provide an accurate assessment of AGB in a small area. However, this approach is destructive and time consuming, thus has limitations in both spatial extent and temporal scales (Jobbágy et al., 2002). With the advance in remote sensing technology, satellite data with various spatial and temporal resolutions have become an ideal choice for large-scale grassland monitoring (Claverie et al., 2012). The remote sensing-based methods for grassland AGB estimation can be broadly categorized into two groups: regression models (Xu et al., 2008) and machine learning models (Ali et al., 2015). Most regression models estimate AGB by establishing relationship between recorded biomass and vegetation index (VI) (Kurtz et al., 2010; Huang et al., 2013). The normalized difference vegetation index (NDVI) is the most widely used VI for biomass estimation. Xia et al. (2014) used the MODIS NDVI time series data and a statistical model to estimate the
⁎ Corresponding authors at: Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China (X. Ren and H. He). E-mail addresses: [email protected] (X. Ren), [email protected] (H. He).
https://doi.org/10.1016/j.ecolind.2019.02.023 Received 9 August 2018; Received in revised form 6 December 2018; Accepted 11 February 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Spatial distribution of grassland types and AGB sampling points on the Tibetan Plateau.
grassland AGB respond to the climate variation (Zhang et al., 2016). Employing machine learning algorithm offers an opportunity for more accurately estimating AGB and its pattern on the Tibetan Plateau (Xia et al., 2018). However, the grassland AGB estimation using machine learning algorithm combining vegetation indices and climatic data are few in this region (Yang et al., 2018). The primary objectives of this paper were to (1) construct the AGB estimation model for the Tibetan alpine grasslands based on machine learning method; (2) analyze the magnitude and spatiotemporal dynamics of the grassland AGB as well as the effects of climate variations. Based on the AGB field observation data, we developed a suitable grassland AGB estimation model using the RF algorithm, combined with remote sensing vegetation indices (VIs), meteorological and topographical data. Then, the model was used to derive the spatial and temporal patterns of AGB for the Tibetan Plateau grassland from 2000 to 2014. The correlations of grassland AGB to temperature and precipitation were analyzed to explore the responses of AGB to changes in climate conditions.
global grassland biomass, the results showed that the NDVI-biomass model explained approximately 57% of the observed variation in the calibration data. In addition to the NDVI, other VIs has been used to grassland AGB estimation, such as the difference vegetation index (DVI) (Jin et al., 2014), enhanced vegetation index (EVI) (Yang et al., 2009), soil adjusted vegetation index (SAVI) (Mutanga and Skidmore, 2004), and modified soil adjusted vegetation index (MSAVI) (Qi et al., 1994). Numerous studies have documented the usefulness of regression models in grassland AGB estimation (Gao et al., 2013; Meng et al., 2017). However, the accuracy of regression models, which were mostly based on a single VI, are often limited by the sensitivity of grassland AGB to VIs and the influence of external environmental factors (Ali et al., 2016). In the past two decades, machine learning techniques, such as the artificial neural network (ANN) (Zeng et al., 2017), support vector machines (SVM) (Dusseux et al., 2014), and random forest (RF) (Fassnacht et al., 2014) have been increasingly used for the study of biomass. Compared with the statistical regression models, the methods of machine learning can integrate multifactor, learn high complex nonlinear mappings, and get better simulation results (Powell et al., 2010). Among them, the RF algorithm is a non-parametric ensemble modeling approach, having the capacity to reduce bias and overfitting (Karlson et al., 2015; Ramoelo et al., 2015). Wang et al. (2017b) utilized the RF algorithm to predict grassland AGB biomass in the loess plateau, found that the RF model was more accurate than the SVM. Moreover, some studies have indicated that RF models tend to be more tolerant to outliers and noise (Anaya et al., 2009; Gleason and Im, 2012). The Tibetan Plateau is the largest and highest plateau on the earth, with an area of 257.24 × 104 km2, and the mean altitude is about 4000 m. The grassland is the dominant ecosystem on the Tibetan Plateau, accounting for about 60% of the total area. However, the grassland ecosystem is relatively fragile and highly sensitive to climate change in this region, because of the high altitude and harsh natural conditions (Luo et al., 2002). Over the past 30 years, increasing attention has been given to the grassland AGB on the Tibetan Plateau, due to the increase in climate warming and wetting (Kato et al., 2006; Yang et al., 2008; Yin et al., 2013; Qin et al., 2018). One important question in this region is how the spatial pattern and temporal dynamics of
2. Methodology 2.1. Study area The Tibetan Plateau (26°00′12″ N ∼ 39°46′50″ N, 73°18′52″ E ∼ 104°46′59″ E) is located in the western region of China, with an average elevation of over 4000 m, and an area of approximately 257.24 × 104 km2 (Ding et al., 2013). The study area is in the alpine climate zone, with the mean annual temperature (MAT) at −0.51 °C, and the mean annual precipitation (MAP) at 493.6 mm. According to the ChinaCover database (Zhang et al., 2014b), the area of grassland on the Tibetan Plateau is 151.11 × 104 km2, accounting for 58.74% of the total region. The main grassland types include alpine meadow, alpine steppe, and sparse grassland, which account for about 19.29%, 41.08%, and 39.63% of the total grasslands on the Tibetan Plateau, respectively (Fig. 1).
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NASA’s Earth Observing System Data and Information System (EOSDIS) (http://earthdata.nasa.gov/), which provided NDVI and EVI at a 250 m resolution. For this study, the MOD13A2 data covering the whole study area from 2000 to 2014 during the growing season (May-September) were downloaded. The acquired MODIS NDVI and EVI data were projected and resampled to a 1000 m resolution using the nearest neighbor resampling. The temporal resolution of this product is 16-day, which ensures a better data quality as selected based on the absence of clouds or aerosol. Additionally, to further reduce the effect of clouds and atmospheric contamination, the original time series were smoothed using the double logistic curve fit in the TIMESAT software (Lund University, Lund, Sweden) (Jönsson and Eklundh, 2004). Then, the growing season averaged NDVI and EVI were calculated using the fitted data.
2.2. Data 2.2.1. Field data The grassland AGB field sample data are collected from two different sources: (1) documented literature (Yang et al., 2010) and (2) field measurements. The field survey was conducted in the growing season (May-September) during 2005–2014. The field survey covered the three main grassland types across the plateau. At each sampling site (30 m × 30 m), three independent plots (1 m × 1 m) were selected, with a distance greater than 15 m between each plot. In each plot, all plants were harvested, and oven dried at 85 °C in the lab until the weight remained constant. The dry weights of three plots at each sampling site were averaged to produce the AGB sampling data. Then, based on the standard deviation method (Jiao et al., 2016), the AGB sample data that exceed mean value ± 2 × standard deviation were excluded during data processing. In addition, based on the landcover map, the sample sites distributed at the grassland ecosystems were selected. Finally, a total of 256 sampling points was obtained, including 92 sites from Yang’s literature and 164 sites from our field survey (Fig. 1).
2.3. Random forest model Random forest (RF) is a machine learning algorithm developed by Breiman (2001), which is a series of binary rule-based decisions that dictate how an input relates to its dependent variable. One of the main advantages of RF is that it can accurately describe complex relationship between the independent variables and the dependent variables, which is often occurred when complex ecological systems and environmental variables are introduced (Fu et al., 2017). In this study, seven variables were selected as candidate inputs in the RF modeling process, including remote sensing VIs (NDVI and EVI), meteorological variables (MAT and MAP), and topographical variables (altitude, slope and aspect). To improve the computational efficiency and remove redundant information, the correlation coefficient (R) between the observed AGB and the predictors were calculated, and the variables used to establish AGB model were preferentially chosen when they had higher correlation coefficient. Different input variables were selected to conduct multiple experiments, then the model with the highest accuracy and the least variables was selected for grassland AGB estimation at regional scale. The RF models were implemented using the “random forest” package within the R environment software. For each model, 80% of the sample points were used to model train, and the leaving 20% were used for model validation. The performance of the models was assessed based on the R2, Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC).
2.2.2. Meteorological data The meteorological data including temperature and precipitation with a spatial resolution of 1000 m during 2000–2014 are obtained from the published temperature and precipitation dataset in China scientific data by Wang et al. (2017a). The daily temperature and precipitation observational data of meteorological stations were downloaded from the National Meteorological Information Center (NMIC) of China Meteorological Administration (753 stations) and the Daily Global Historical Climatology Network-Daily (GHCN-D) (345 stations). Then, combined with a digital elevation model (DEM) data, the meteorological data were interpolated to a fitted surface at a spatial resolution of 1 km using the widely used interpolation software (ANUSPLIN). For the regional application in Tibetan Plateau’s grasslands, the spatial data were extracted using the tool (Extract by mask) of ArcGIS (ArcGIS 10.2) software (Environmental Systems Research Institute, Inc., ESRI). Additionally, MAP and MAT were calculated using the daily precipitation and temperature fitted map. 2.2.3. Topographical data The Shuttle Radar Topography Mission (SRTM) is an international mission conducted by NASA and the National Geospatial-Intelligence Agency (https://gdex.cr.usgs.gov/gdex/), providing a DEM product that covers 99.97% of the Earth’s land surface, from 56 °S to 60 °N. In this study, the SRTM elevation data for China at a resolution of 3″ (90 m) was employed. To be consistent with meteorological data, the SRTM DEM was resampled to 1000 m resolution. In addition, slope and aspect data were calculated from the resampled DEM using ArcGIS software.
3. Results 3.1. Random forest model construction and validation The correlation coefficients between the grassland AGB and the environment factors, as well as those between the environment factors, are provided in the Table 1. EVI and NDVI have the highest correlation with AGB (R = 0.68 and 0.64, respectively), suggesting that remote sensing VIs provided a good characterization of the grassland AGB. Among the meteorological variables, the correlation of AGB with MAP was higher than that with MAT (R = 0.57 and 0.21, respectively). In addition, altitude was negatively correlated with AGB (R = −0.20),
2.2.4. Remote sensing data The MODIS VI product (MOD13A2, V006) was obtained from
Table 1 Correlation coefficients between the grassland AGB and the explanatory variables (n = 256). Variable
AGB
EVI
NDVI
MAP
MAT
Altitude
Slope
Aspect
AGB EVI NDVI MAP MAT Altitude Slope Aspect
1 0.68* 0.64* 0.57* 0.21 −0.20 0.17 −0.01
1 0.84* 0.67* 0.35 −0.25 0.22 −0.01
1 0.74* 0.37 −0.27 0.22 0.08
1 0.30 −0.13 0.14 0.17
1 −0.64* 0.20 −0.04
1 0.03 0.02
1 −0.09
1
Note: *Significant correlation, p < 0.05. 481
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Xinjiang were low value areas, with AGB lower than 30 gm−2. Most parts of middle Xizang and western Qinghai had relatively high AGB values, ranging from 30 to 100 gm−2. Eastern Qinghai, Gansu, Sichuan, and Yunnan had the highest AGB, with values larger than 100 gm−2. During 2000 to 2014, the grassland AGB on the Tibetan Plateau exhibited a non-significant increasing trend, with an average rate of 0.19 gm−2a−1 (R2 = 0.20, P = 0.09). (Fig. 4). There was a relatively large interannual fluctuation of grassland AGB from 2006 to 2009, which was consistent with the findings of Mao et al. (2014) and Zeng et al. (2017). The highest grassland AGB occurred in 2006, which was 79.57 gm−2, and the lowest AGB was 72.12 gm−2 in 2000.
Table 2 The performance of AGB estimation models for the grasslands on the Tibetan Plateau. Model
Variables
R2
RMSE
ΔAIC
No.1 No.2 No.3 No.4 No.5 No.6 No.7 No.8 No.9 No.10 No.11 No.12
EVI, NDVI, MAP EVI, NDVI, MAP, MAT EVI, NDVI, MAP, altitude EVI, NDVI, MAP, slope EVI, NDVI, MAP, aspect EVI, NDVI, MAP, MAT, altitude EVI, NDVI, MAP, MAT, slope EVI, NDVI, MAP, MAT, aspect EVI, NDVI, MAP, MAT, altitude, slope EVI, NDVI, MAP, MAT, altitude, aspect EVI, MAT, MAP, altitude NDVI, MAT, MAP, altitude
0.69 0.78 0.75 0.70 0.69 0.86 0.82 0.80 0.85 0.84 0.76 0.73
63.52 54.11 58.34 62.61 63.72 43.60 47.68 51.63 46.57 46.40 56.71 60.16
188.67 108.57 147.11 183.28 192.27 0 45.80 86.55 35.74 33.87 132.60 162.84
3.3. Temporal correlation of AGB to MAT and MAP To understand the responses of grassland AGB to climate change, we analyzed changes in interannual MAT and MAP and their relationships with AGB during 2000–2014. During the 15-year study period, the MAT on the Tibetan Plateau exhibited an increasing trend (Fig. 4). There was large interannual fluctuation in temperature from 2006 to 2009. Coupled with interannual variations of temperature, AGB showed a similar changing trend. As shown in Fig. 5, the grassland AGB on the Tibetan Plateau significantly increased with the increase in MAT (R2 = 0.45, P < 0.05). For every 1 °C increased, the AGB increased by 3.07 gm−2. However, the inter-annual variation of AGB did not show any significant correlation with the MAP (R2 = 0.01, P = 0.70). Fig. 6 shows the temporal correlation of AGB with MAT and MAP for each pixel on the Tibetan Plateau during 2000–2014. In general, the temporal correlation of AGB with MAT is higher that with MAP. Specifically, in the most area of the eastern Tibetan plateau and parts of northern Xizang, accounting for about 68% of the total grassland area, the grassland AGB was positively correlated with MAT (Fig. 6a). The grassland AGB and MAP showed a positive relationship over 52% of the grassland area, which located in the most parts of middle Xizang and western Qinghai. For the three different grassland types, the correlation between AGB and MAT in alpine meadow, alpine steppe and sparse grassland were all higher than that of MAP.
ΔAIC: the model’s AIC minus minimal AIC of all the models.
which indicated that the grassland AGB in the area decreased with the increase in altitude. Meanwhile, the correlations also existed among the environmental variables. There is a strongly and significantly positive correlation between EVI and NDVI (R = 0.84). MAP showed significantly positive correlation with EVI and NDVI (R = 0.67 and 0.74, respectively), and MAT decreases with the increases of altitude (R = 0.64). Based on the correlation analysis between grassland AGB and the explanatory variables at site-level, a series of experiments are designed and conducted, and the results are listed in Table 2. The simulated AGB exhibited strong correlations with observed grassland AGB across these twelve models, with R2 ranging from 0.69 to 0.86, RMSE ranging from 43.60 to 63.72 gm−2. When EVI, NDVI, MAP, MAT and altitude were used as input variables, the model No.6 has the highest R2 (0.86) and the lowest RMSE (43.60 gm−2) and AIC (Table 2, Fig. 2). Thus, the No.6 model was selected to simulate the grassland AGB on the Tibetan Plateau. 3.2. Spatial distribution and inter-annual dynamics of grassland AGB
3.4. Effects of temperature and precipitation on the spatial pattern of grassland AGB
The estimated AGB was 77.12 gm−2 on average during 2000 to 2014 on the Tibetan Plateau. For the different grassland types, alpine meadow has the largest AGB (154.72 gm−2), followed by alpine steppe (76.43 gm−2), and the lowest AGB was found in sparse grassland (41.65 gm−2). The spatial distribution of AGB exhibited obvious heterogeneity on the Tibetan Plateau, decreasing from the southeast to the northwest (Fig. 3), which is similar to the spatial pattern of MAT and MAP. More specifically, parts of northwestern Xizang and southern
Table 3 shows the climate conditions of the three different grassland types. Alpine meadow was mainly located in the eastern and southeastern regions of the Tibetan Plateau (Fig. 1), which had relatively high MAP and MAP (587.99 mm, 0.37 °C). Alpine steppe was widely distributed in the central and northern parts of the Tibetan Plateau, where the MAP and MAT were relatively lower (510.80 mm, −0.59 °C). Sparse grassland was mainly found in the northern and western Tibetan Plateau, with the lowest MAP and MAT (415.44 mm, −1.48 °C). All these lead to the higher AGB in alpine meadow and lower AGB in alpine steppe and sparse grassland. Moreover, the spatial correlations of AGB with MAT and MAP in different grassland types were calculated. Across the entire area, the grassland AGB showed significantly positive correlation with temperature and precipitation. The correlation between grassland AGB and MAP was 0.54 (P < 0.05), much higher than that of MAT (R = 0.38, P < 0.05) (Table 3). Furthermore, the correlation between AGB and MAP in alpine meadow, alpine steppe and sparse grassland were all higher than that of MAT. Therefore, we speculated that precipitation had a greater impact on the AGB spatial distribution than temperature on the Tibetan Plateau. To further analyze the extent to which AGB responses to climate factors along the gradient of temperature and precipitation, we calculated the average AGB with each MAT (1 °C) and MAP (100 mm) levels. As shown in the Figs. 7, 4 °C is a turning point, below which AGB significantly increases with MAT (R2 = 0.94, P < 0.05); however, above this point, AGB exhibits a decreasing trend with temperature
Fig. 2. The relationship between the estimated AGB based on the No.6 RF model and the observed AGB. 482
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Fig. 3. Spatial distribution of grassland AGB modeled by the RF model on the Tibetan Plateau.
4. Discussion 4.1. Comparison between estimated grassland AGB and previous studies In our study, the estimated grassland AGB was 77.12 gm−2 on the Tibetan Plateau during 2000–2014, which falls in the variation range of the previous studies (40 ∼ 120 gm−2) (Table 4). Xia et al. (2018) estimated the AGB of the Tibetan Plateau grassland to be 78.40 gm−2, which was the closest to the value obtained in our study. Several previous studies have indicated that the biomass of terrestrial ecosystems on the Tibetan Plateau has increased over the past 15 years (Mao et al., 2014; Zhang et al., 2016). This is consistent with the results of our study, which indicated the temporal dynamics of AGB in the study area presents an increasing trend. The differences in the estimated grassland AGB reported among these studies may be due to two factors. The first is the different data used to estimate grassland AGB, including the field sample data, remote sensing data, and land cover data. Chen et al. (2014) indicated that the performance of the data-driven methods is highly restricted by the quantity of training sample data. Meanwhile, the spatial distribution of sample points also has a great influence on the estimation of grassland AGB. For example, the AGB sampling data used by Jiao et al were mostly distributed in the eastern part of the Tibetan Plateau, where the grassland AGB was relatively high. Thus, their statistical result (120.73 gm−2) was much higher than those of other studies. The representativeness of field samples is important for accurate estimation of grassland AGB, which is also revealed by other studies (Ma et al., 2010; Gao et al., 2013). In addition, the time mismatch between field observations and VI also might induce uncertainties in the AGB estimation. Secondly, the different approaches may also introduce uncertainty in the AGB estimation. Jia et al. (2016) quantify the spatial uncertainty of regional grassland biomass, and found that the model forms could bring 13% uncertainty to the grassland biomass estimation. Yang et al. (2018) reported that the model performance of ANN and linear regression models were much different, even when the data employed were the same. Because of its simple structure and convenient operation, statistical regression models have been widely used in the biomass estimation. However, due to its difficultly in expressing complex
Fig. 4. Inter-annual variations in grassland AGB (a), MAT (b) and MAP (c) on the Tibetan Plateau during 2000–2014.
(R2 = 0.64, P = 0.10). Meanwhile, when MAP is less than 400 mm, the grassland AGB was almost constant (R2 = 0.07, P = 0.73); when MAP is between 400 mm and 800 mm, AGB significantly increases with MAP (R2 = 0.95, P < 0.05); when MAP is above 800 mm, the AGB of grassland changed slightly (R2 = 0.23, P = 0.32).
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Fig. 5. Correlation between AGB and MAT and MAP during 2000–2014.
4.3. Effects of climatic variables on the temporal variation of grassland AGB
nonlinear relationships, the model simulation accuracy is limited. The present study demonstrated that random forest can integrate multifactors to develop multivariate models, and get better performance in grassland AGB estimation.
The regional climate has been widely reported as the primary driver of temporal variation of grassland AGB (Jobbagy and Sala, 2000; Bai et al., 2004). However, the importance of temperature and precipitation on grassland AGB temporal variation is still controversial. Ma et al. (2010) found that the year to year biomass variation was well coupled with inter-annual variation of January-July precipitation in northern China. However, the study by Zhang et al. (2016) indicated that the temporal variation of grassland AGB on the Tibetan Plateau was strongly correlated with the variation in temperature. In our study, a positive and significant correlation only exists between the grassland AGB and MAT (R2 = 0.45, P < 0.05), suggesting that increased temperature may be associated with the temporal variation of AGB. Some studies suggested that the warmer climate might result in increased AGB due to the enhanced photosynthetic rate and the longer growing season (White et al., 1999; Piao et al., 2003). First, the increase in temperature may increase the activity of photosynthetic enzymes and accelerates the biochemical reaction of photosynthesis. Second, the extension of the growing season and early spring phenology, may enhance the carbon sequestration in vegetation (Piao et al., 2008). Piao et al. (2007) reported that the growing season of alpine meadow in China was extended by approximately 0.9 days per year between 1982 and 1999 due to the increase in temperature. In the Tibetan alpine regions, because of the unique geographical location and altitude, besides precipitation, runoff from mountain glacial meltwater is also an important source of available water to vegetation in the area (Piao
4.2. Effects of climatic variables on the spatial pattern of grassland AGB The spatial patterns of grassland AGB are usually affected by multiple factors, such as climate condition, soil texture and soil moisture content (Churkina and Running, 1998); among them, the climate is often thought to play an important role (Knapp and Smith, 2001; Shi, 2006; Bai et al., 2008). In this study, when MAT is less than 4 °C, AGB is significantly correlated with both MAT and MAP; when MAT is above than 4 °C, AGB exhibits a decreasing trend with MAT, and the spatial distribution of AGB is mainly restricted by precipitation. In the whole region, precipitation had a greater impact on the AGB spatial distribution than temperature. Many studies have illustrated that grassland AGB is more sensitive to precipitation than temperature in this region (Shi, 2006; Yang et al., 2009, 2010; Jiao et al., 2016). For example, Jiang et al. (2015) found that the MAP and MAT explained 56.6% and 10.9% of grassland AGB spatial variation, respectively. However, when the altitude is greater than 5000 m, MAT can explain 25.00% of the spatial variation in the AGB, much higher than that of MAP (12.96%), which may be induced by the lower temperature in the higher altitude area. This is probably because that low temperature may inhibit biogeochemical cycles and reduce soil nitrogen availability for plant growth (Yang et al., 2010).
Fig. 6. The temporal correlation with AGB of the MAT and MAP during 2000–2014 at pixel scale. 484
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Table 3 The averaged AGB, MAT, MAP and the correlation of AGB with MAT and MAP for each grassland type. Grassland type
Mean
Correlation with AGB
AGB (gm Alpine meadow Alpine steppe Sparse grassland Total
154.72 76.43 41.65 77.12
−2
)
Temperature (°C)
Precipitation (mm)
0.37 −0.59 −1.48 −0.76
587.99 510.80 415.44 487.95
Temperature *
0.40 0.45* 0.29 0.38*
Precipitation 0.46* 0.59* 0.48* 0.54*
Note: *p < 0.05.
correlation analysis of single variable. Furthermore, this paper mainly focuses on the AGB of Tibetan alpine grasslands, which is closely related to the grass yield and grazing economy. Below-ground biomass (BGB) also accounts for a great proportion in grassland biomass, while the estimation of BGB was not involved in this paper due to the lack of observational data.
et al., 2010). The increase of temperature may also stimulate the growth of grassland though increasing mountain glacial meltwater.
4.4. Implications and limitations Compared with the traditional statistical models, the RF model has obvious advantages in describing complex nonlinear relationship, which is often occurred between biophysical parameter and complex environmental variables (Mutanga et al., 2012). The results of the present study demonstrated that RF model is a useful and effective method for AGB estimation. The AGB values predicted by RF model were very close to the observed values (R2 = 0.84; RMSE = 84.74 gm−2). Moreover, the overall accuracy of the RF model in our study were higher than other studies conducted in the same area based on different models, with the range from 0.40 to 0.63 (Yang et al., 2009; Liu et al., 2017). Although our study provides the comprehensive assessment of grassland AGB on the Tibetan Plateau, some limitations still exist. First, the sampling sites were few on the western Tibetan Plateau due to the poor accessibility; it may limit the accuracy of AGB estimation on this region. Second, the RF models contain a stochastic element that results in a different biomass model in each iteration when they are applied to the same data; therefore, repeated training is required to obtain an optimal RF model and achieve reasonable results. Third, the effects of temperature and precipitation on the spatial pattern and temporal variation of grassland AGB are analyzed by the correlation analysis of single variable. However, the interaction between temperature and precipitation may affect the accuracy of the results. We further analyzed the effects of temperature and precipitation on grassland AGB using partial correlation analysis. The results showed that precipitation is still more important in shaping spatial patterns of grassland AGB on the Tibetan Plateau (R = 0.45, P < 0.05), and the temporal variation of AGB still had higher correlation with the variation in MAT (R = 0.69, P < 0.05). The results of partial correlation are consistent with that of
5. Conclusions The magnitude and spatiotemporal of grassland ABG are crucial estimated for grazing economic. In this study, RF algorithm was used to combine field observation data, remote sensing vegetation indices, meteorological data, and topographical data, for estimating grassland AGB on the Tibetan Plateau from 2000 to 2014. Through the experiment comparison, five variables were selected as the input variables of the RF model to estimate AGB of grassland, including remote sensing VIs (NDVI and EVI), meteorological variables (MAT and MAP), and topographical variables (altitude). The modeled AGB (2000–2014) showed an obvious spatial heterogeneity and non-significant increasing trend, decreasing from the southeast to the northwest, with a mean value of 77.12 gm−2 and an average increasing rate of 0.19 gm−2a−1. The spatial distribution of grassland AGB is sensitive to precipitation, while the temporal dynamics of AGB were significant correlated with that in temperature. This study demonstrated that the RF is an effective method in the grassland AGB estimation on the Tibetan Plateau. Acknowledgments This study was supported by National Key Research and development program of China (2016YFC0500204), National Basic Work of Science and Technology (2015FY110700), National Key Research and development program of China (2015CB954102), National Natural Science Foundation of China (41571424, 31700417, 41601478), Science and Technology Service Network Initiative of Chinese Academy
Fig. 7. Averaged grassland AGB along the MAT (1 °C) and MAP (100 mm) gradients. 485
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Table 4 Comparison of grassland AGB on the Tibetan Plateau estimated by different studies. AGB (gm−2)
Area (×104 km2)
Study period
Approach
Model input Data
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68.8 58.11 59.3 74.11 43.33 63.69 120.73 78.40
112.8 113.6 — 129.5 122.8 120 — 132
2001–2004 1980–1990 2001–2004 1982–2006 2005 1981–2010 1980–2014 —
linear regression site grassland survey site grassland survey exponential regression exponential regression exponential regression literature data analysis RF
EVI — — NDVI NDVI NDVI — annual precipitation, summer NDVI, summer relative humidity
Yang et al. (2009) Ni (2004) Yang et al. (2010) Ma et al. (2010) Xu et al. (2007) Yu (2013) Jiao et al. (2016) Xia et al. (2018)
Note: AGB = Carbon density/0.45.
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