- Email: [email protected]
Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China
Ecological Indicators 57 (2015) 486–496
Contents lists available at ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China Wang Li a,b,∗ , Zheng Niu a , Ni Huang a , Cheng Wang c , Shuai Gao a,∗ , Chaoyang Wu a a b c
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China University of Chinese Academy of Sciences, Beijing 100049, China Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
a r t i c l e
i n f o
Article history: Received 25 November 2014 Received in revised form 11 April 2015 Accepted 15 April 2015 Keywords: Airborne LiDAR Maize biomass Leaf area index Canopy height
a b s t r a c t Crop biomass is an important ecological indicator of growth, light use efficiency, and carbon stocks in agroecosystems. Light detection and ranging (LiDAR) or laser scanning has been widely used to estimate forest structural parameters and biomass. However, LiDAR is rarely used to estimate crop parameters because the short, dense canopies of crops limit the accuracy of the results. The objective of this study is to explore the potential of airborne LiDAR data in estimating biomass components of maize, namely aboveground biomass (AGB) and belowground biomass (BGB). Five biomass-related factors were measured during the entire growing season of maize. The field-measured canopy height and leaf area index (LAI) were identified as the factors that most directly affect biomass components through Pearson’s correlation analysis and structural equation modeling (SEM). Field-based estimation models were proposed to estimate maize biomass components during the tasseling stage. Subsequently, the maize height and LAI over the entire study area were derived from LiDAR data and were used as input for the estimation models to map the spatial pattern of the biomass components. The results showed that the LiDAR-estimated biomass was comparable to the field-measured biomass, with root mean squared errors (RMSE) of 288.51 g/m2 (AGB), and 75.81 g/m2 (BGB). In conclusion, airborne LiDAR has great potential for estimating canopy height, LAI, and biomass components of maize during the peak growing season. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Crop biomass, which is defined as the dried weight of crop plant matter within a unit area, is an important ecological indicator of growth, light use efficiency, and carbon stocks in agro-ecosystems. Crop biomass also acts as an energy source that can be converted into various bio-fuel indicators, such as CO2 efflux, to provide indirect insight into energy exchanges at the local, regional and continental scales. A rapid, economical and quantitative estimation of crop biomass is vital for crop production and accessibility risk management, global markets, policy-making and decision-making (Becker-Reshef et al., 2010). Generally, crop biomass can be categorized into aboveground biomass (AGB) and belowground biomass (BGB), according to the distinct growing places of different plant
∗ Corresponding authors at: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, P.O. Box 9718, No. 20 Datun Road, Olympic Science & Technology Park of CAS, Beijing 100101, China. Tel.: +86 010 64889215; fax: +86 010 64889215. E-mail addresses: [email protected] (W. Li), [email protected] (S. Gao). http://dx.doi.org/10.1016/j.ecolind.2015.04.016 1470-160X/© 2015 Elsevier Ltd. All rights reserved.
components. A close relationship exists between these two biomass components through the allocation of photosynthetic resources during the growing season. Maize AGB is prone to expansion via the support of a large root system (Zong and Shanggunag, 2013), which is directly reflected in the physical appearance of the aboveground community structure and function (Wardle et al., 2004). Moreover, root biomass or belowground biomass, productivity, and mortality of maize plants are highly dynamic over space and time in a terrestrial ecosystem (Zong and Shanggunag, 2013). Because of this behavior, plant roots are easily influenced by biotic and abiotic stresses than aboveground organs (Brassard et al., 2009; Wardle et al., 2004). However, belowground biomass is not well-studied because of the limited accessibility to roots compared with the aboveground component (Franco et al., 2011). Large-scale spatial and temporal measurements of crop biomass are laborious and expensive; thus, these measurements are challenging for ecologists and agronomists. Characterized by its capability of collecting information at regional and global scales, remote sensing is the only reasonable method that can facilitate temporal estimation of biomass over large areas. Many studies have attempted to estimate crop biomass according to the close
W. Li et al. / Ecological Indicators 57 (2015) 486–496
487
Fig. 1. Location of the study site in (a) Gansu Province, Northwest China (upper left), and distributions of the field plots overlaid by a digital orthophoto map (DOM); (b) the surface model with normalized heights.
relationship between field-measured biomass and remotely sensed spectral values of the crop canopy (Liu et al., 2010). Vegetation structure is the dominant factor responsible for remotely sensed spectral reflectance of the canopy in visible and near-infrared bands (Gao et al., 2013). Forest biomass is closely related to tree structures, such as tree height and diameter at breast height according to the relative growth equation (Li et al., 2014). A similar relationship can be found between plant heights and crop biomass (Gao et al., 2013). Therefore, maize AGB is hypothesized to be highly structure-related. However, remotely sensed spectral values tend to be saturated in locations covered by dense vegetation with a high leaf area index (LAI) and AGB. In recent decades, light detection and ranging (LiDAR) or the laser scanning technique, which has a high penetration capability, is likely to overcome this problem by collecting layered canopy features. Airborne LiDAR can be a powerful tool for estimating fractional vegetation cover (FVC) (Korhonen et al., 2011), LAI (Richardson et al., 2009; Solberg, 2010; Zhao et al., 2009; Zheng and Moskal, 2009), and AGB (Li et al., 2014; Popescu et al., 2011) using LiDAR metrics in forest areas. The precision of LiDAR metrics is affected by sensor factors such as footprint size, echo-detection algorithm, wavelength, and incidence angle. Some of these factors may vary with the flight altitude and the topography of scanned targets (Höfle and Pfeifer, 2007). However, the feasibility of LiDAR metrics for estimating crop parameters still requires further investigation. Compared with forests, crops are more affected by the saturation problem because crop canopies are denser and the penetration depth of remote sensing detecting beam is limited. A high-density LiDAR point cloud is required to detect dense crop canopies and to increase the penetration probability. A higher point density will increase the cost of an airborne flight, which is currently more expensive than mobile laser scanners mounted on agricultural vehicles or unmanned aerial systems (UASs). Although terrestrial LiDAR can achieve a high point density and high detection accuracy at a lower cost for vegetation monitoring, such as individual maize plants (Höfle, 2014), its spatial detection range is small compared with airborne LiDAR. Thus, airborne LiDAR will likely be a promising technique to extend crop biomass estimations from the plot level to a larger spatial extent. Additionally, airborne LiDAR is often mounted along with many other sensors, such as microwave radiometers and hyperspectral camera (Li et al., 2009),
which facilitates the combined use of multi-source remote sensing in scientific and agro-ecological studies over large areas. Therefore, in this study, we aimed to further explore the feasibility of airborne LiDAR for estimating biomass components of maize. We first tried to extend the estimation to BGB, aside from AGB by referring to forest studies. The factors that directly affect maize biomass components were identified and used to develop field-based estimation models, which were subsequently applied to map spatial patterns of maize biomass components over the entire study site. 2. Materials 2.1. Study site The study site is located in Zhangye City, Gansu Province, China (Fig. 1). The site is a core experimental Heihe Watershed Allied Telemetry Experimental Research (HiWATER) area for flux observations, and is also part of an artificial oasis–riparian ecosystem–wetland–desert system with an average elevation of 1400 m above sea level (Li et al., 2009, 2013). The primary topography is irrigated, heterogeneous, seed maize that is interspersed with spring wheat, vegetables, orchards, and residential areas (Li et al., 2013). Most of the maize is sown in late April, and flowering occurs near mid-August, followed by harvesting between late September and early October. The mean annual temperature is 6 ◦ C with the coldest temperatures in January and the warmest in July. The mean annual precipitation is approximately to 100–250 mm, but the potential evaporation is as high as 1200–1800 mm per year. Therefore, agriculture mainly involves irrigation and agricultural development is typically influenced by drought. 2.2. Airborne LiDAR Small footprint airborne LiDAR data were acquired for the study site on July 19, 2012, aiming to measure vegetation structure parameters and derive aerodynamic roughness (Xiao and Wen, 2013a). The Leica ALS70 system was configured to emit laser pulses in the near-infrared band (1064 nm) at a scan angle of ±18◦ , a beam divergence of 0.15 mrad, and an average footprint size of 22.5 cm. The geographical coordinates (easting, northing and elevation)
488
W. Li et al. / Ecological Indicators 57 (2015) 486–496
Table 1 Acquisition date of the field measurements. Date growth stage
Seedling
Jointing
Tasseling
Filling
Milking
First period Second period
2012/05/25 2012/05/31
2012/06/23 2012/06/28
2012/07/13 2012/07/23
2012/08/03 2012/08/12
2012/08/28 –
were recorded by a dual-frequency differential global positioning system (GPS) with an inertial measurement unit. The vertical placement accuracy of the instrument was 5–30 cm. Repetitive flights with a 60% side-overlap were conducted at a nominal height of 1500 m above the ground, leading to an average pulse density of 6.7 point/m2 . All of the points were geo-referenced to the Universal Transverse Mercator (UTM) Zone 47N/WGS-84 projection. Raw LiDAR data were converted to LAS binary format files by recording the geographical coordinates, intensity, and number of returns. The raw LiDAR data were first filtered by removing outliers that were extremely higher or lower than other points and were easily found in the air or below ground (Luo et al., 2014). Point clouds were then classified into ground and off-ground returns in TerraScan software (TerraSolid, Ltd., Finland). Reclassification was conducted after manually checking the occurrence of misclassifications to ensure the precision of LiDAR metrics. Subsequently, LiDAR intensity was normalized (Eq. (1)) to minimize the difference among the flights (Garcia et al., 2010; Höfle and Pfeifer, 2007). Inormalized = Iraw
R2 Rs2
(1)
where Inormalized is the normalized intensity, Iraw raw intensity, R sensor–target distance, and Rs standardized flight altitude. In this study, Rs equals average flight altitude (1500 m). The ground points were aggregated to digital terrain model (DTM) with a grid size of 1 m. All of the point heights were normalized using the DTM. Offground point heights were normalized as the difference between off-ground point heights and the corresponding DTM cell heights beneath the points. A digital surface model (DSM) with 1 m resolution was generated from the maximum off-ground point height within each raster cell. Then, a surface model with normalized heights was derived as the difference between the DTM and DSM (Fig. 1(b)). 2.3. Field measurements Long-term field measurements of seven biophysical factors of maize were obtained in the study site between early May and mid-September. The biophysical factors were plant height, leaf chlorophyll concentration (Chl), LAI, FVC, biomass components, and tissue water content (W). To guarantee consistency, all of the factors were simultaneously collected on observation dates that covered nearly the entire growing season of maize (Table 1). In each growth stage, at least two periods of measurements were conducted in 14 constant plots near the flux towers; however, not all of the plots were simultaneously measured in the same period. In each stage, the average values were calculated as the field-measured values for the plots that were measured for twice. Each field plot had a large maize area, with a width and length of at least 10 m, flat terrain, and uniform growing conditions. The geographic location (latitude and longitude) of each plot was measured using GPS. In each plot, four representative maize plants were selected. The plant height was measured using a tape that extend from the ground to the canopy top. The canopy height (H) for the plot was calculated as the average plant height. The selected plants were then harvested from the root to the canopy, sealed in a plastic bag, and immediately transported to a nearby laboratory for subsequent analysis. The fresh mass of various plant organs (leaf, stem, stick, and root), and the root length were measured. Most of the plants
were harvested before fruiting period. The fresh maize organs were completely oven dried at 75 ◦ C until a constant weight mass was achieved (Wang et al., 2013). The AGB was obtained by multiplying the average dry weight of the aboveground organs (leaf, stem and stick) per plant (g/plant) by the plant density (plant/m2 ). The plant density (plant/m2 ) was determined according to the measured inter-plant row and line distances (m). The BGB was obtained from the roots using the same method. The tissue water content was determined as the ratio of the fresh-dry weight difference and the fresh weight. LAI for each plot was non-destructively measured using LAI2000 (LI-COR Inc., Lincoln, Nebraska) (Zhao et al., 2013). A series of digital photographs was collected far above the maize canopy along the plot diagonal to determine the FVC by image processing (Liu et al., 2012; Mu et al., 2013). A portable chlorophyll meter (SPAD502, New Jersey, USA) was used to measure the leaf chlorophyll concentration (Chl). Leaves at three locations, namely, the upper, middle, and lower parts of the maize plant were randomly selected; ten SPAD readings were randomly obtained at each location. SPAD readings are in arbitrary units rather than actual amounts of chlorophyll per unit area of leaf tissue (Huang et al., 2014; Krugh and Miles, 1994). Therefore, the actual amounts of chlorophyll per unit area of leaf tissue (g/cm2 ) were determined using the transformation relationship (Chl = 0.95 × SPAD value − 3.25) proposed by Wu et al. (2010) where the same SPAD meter was used. The seasonal trends and basic statistics of all the field measurements are shown in Fig. 2 and Table 2, respectively. Since the airborne campaign coincided with the tasseling stage, the data collected in this stage were used for estimation. 2.4. Crop classification data Crop classification data with 1 m resolution were produced by an object-oriented workflow using airborne Compact Airborne Spectrographic Imager (CASI) data (Xiao and Wen, 2013b). As part of a land cover survey across the entire Heihe Basin, 164 groundtruth points were collected within the study site, and were used for crop classification (Zhang et al., 2013, 2014). The ground-truth points were randomly split into 3/4 and 1/4 portions for classification training and validation, respectively. Post-classification was performed to re-classify the pixels that were difficult to identify through manual interpretation. The landscape was classified into fourteen detailed land cover types (Fig. 3(a)). Since the goal of this study was to estimate maize biomass, maize pixels were selected from the classification result (Fig. 3(b)). The classification result was validated using the ground survey points. The overall accuracy was 88.48%, and the Kappa coefficient was 0.87. The omission and commission errors of maize were 7.51% and 7.20%, respectively. 3. Methodology 3.1. Analysis of factors that affect biomass components Statistical Pearson’s correlation analysis was performed between the field-measured biomass components and other biophysical factors. The normal distribution and mutual independence of each factor were tested using Shapiro–Wilk normality test and randomness test using Statistical Package for the Social Sciences (SPSS, Chicago, IL, USA); these tests are required for
W. Li et al. / Ecological Indicators 57 (2015) 486–496
489
Fig. 2. Seasonal trends of seven field-measured factors with error bars that represent one standard error.
Pearson’s correlation (Huang et al., 2014). According to the statistical analysis, all of the measured factors followed a normal distribution (Shapiro–Wilk, p > 0.05) and exhibited randomness (runs test, p > 0.05). Factors that were significantly correlated to the biomass component were filtered through statistical analysis. However, inter-correlation that probably involves both direct and indirect effects may exist among these factors (Huang et al., 2014). Although stepwise regression can decrease the influence of inter-correlation among these factors, it provides a statistical explanation without providing a biophysical or causal relationship between the biomass component and other factors. Additionally, direct factors may occasionally be eliminated during stepwise selection, which is probably undesirable, particularly for developing field-based models. Therefore, structural equation modeling (SEM) was used to identify the relationships among
the field-measured factors to determine those directly affect biomass (Iriondo et al., 2003; Jonsson and Wardle, 2010; Pugesek and Tomer, 2003). As required by SEM, normalized Mardia’s coefficient was calculated to evaluate the multivariate normality of the dataset (Mardia, 1970). The comparative fit index (CFI) and goodness of fit index (GFI) were used to evaluate the SEM results in the AMOS program for Windows (Kim, 2010). 3.2. Estimation of maize height and LAI from LiDAR data Canopy height metrics, namely, maximum height (Hmax ), mean height (Hmean ), and percentile height (Hp ) were calculated to evaluate the maize height based on the normalized point heights within a grid size of 10 m. Laser penetration metric (LPI) which can overcome the saturation problem was calculated to estimate LAI for
Table 2 Basic statistics of the field measurements of maize in the five growth stages. Chl (g/cm2 )
LAI
FVC (%)
W (%)
13.94 23.22 17.92 3.02
35.61 54.69 48.38 6.32
0.10 0.67 0.37 0.19
1.70 8.65 4.85 2.46
74.69 84.71 77.41 3.15
14.78 246.79 70.36 76.07
1.06 48.99 10.94 14.86
Min Max Mean SD
75.11 170.56 113.11 26.74
43.63 54.12 49.99 3.11
1.79 3.45 2.51 0.59
42.30 74.20 60.64 9.84
74.81 84.07 79.67 3.34
385.72 1132.15 728.47 195.14
99.44 387.47 185.76 90.20
Tasseling
Min Max Mean SD
151.89 216.33 178.15 22.57
46.80 58.84 54.91 3.27
2.66 4.58 3.49 0.60
45.90 76.06 65.57 7.44
62.31 77.07 78.92 4.77
662.83 2225.73 1290.61 404.47
144.01 517.26 286.14 118.03
Filling
Min Max Mean SD
154.44 234.22 190.13 22.72
48.06 63.19 56.82 4.42
3.06 4.24 3.53 0.39
42.64 91.57 74.43 14.49
72.60 79.83 77.52 1.93
1083.21 2415.82 1759.28 412.23
182.84 619.04 327.34 130.02
Milking
Min Max Mean SD
160.33 236.44 192.12 23.71
34.15 57.83 47.47 6.84
2.53 4.52 3.36 0.53
35.61 79.30 68.66 12.64
68.08 76.72 72.35 2.50
1447.91 3444.51 2252.22 598.24
246.70 722.50 435.67 136.62
Stage
Statistic
Seedling
Min Max Mean SD
Jointing
H (cm)
Min is minimum value, Max is maximum value, Mean is averaged value, SD is standard deviation.
AGB (g/m2 )
BGB (g/m2 )
490
W. Li et al. / Ecological Indicators 57 (2015) 486–496
Fig. 3. Classification data of (a) all the land cover types and (b) maize in the study site.
each plot and across the entire study area based on Beer–Lambert equation (Eq. (2)) (Luo et al., 2015; Richardson et al., 2009).
4. Results 4.1. Analysis of factors that affect biomass components
I = I0 e(−k×LAI)
(2)
Hence, LAI can be calculated as: LAI = −
1 ln k
I I0
(3)
where I0 and I are the average canopy (off-ground) and ground laser intensities respectively, within a 10 m maize grid. The extinction coefficient k was set to 0.5 by assuming that the foliage angle distribution was spherical, and LiDAR conducted approximately vertical scanning (Luo et al., 2014; Richardson et al., 2009; Solberg et al., 2009). According to Richardson et al. (2009), the theoretical k value (equal to 0.5) is adequate for estimating LAI of vegetation using LiDAR data after investigating the relationship between the field-measured LAI and intensity ratio.
3.3. Models for estimating AGB and BGB Field-based models were developed to estimate the biomass components using direct factors identified by SEM analysis from the field data collected in the tasseling stage. The field-based models were subsequently tested using direct factors derived by LiDAR. As the number of field plots was insufficient to provide an independent validation dataset, we used leave-one-out cross-validation method (LOOCV) to evaluate the estimation accuracy. The estimation accuracy was quantified using coefficient of determination (R2 ), root mean squared error (RMSE) (Eq. (4)) and relative root mean squared error (rRMSE) (Eq. (5)). RMSE is related to the magnitude of the observed variables, while rRMSE is a relative value that can be used to compare the performances of different regression models. A lower rRMSE often indicates a better regression performance.
n 1 RMSE = (pi − pˆ i ) n
rRMSE =
(4)
i=1
RMSE pi
where pi is the measured value, and pˆ i is the predicted value.
(5)
High Pearson’s correlations were found between the biomass components and other field-measured factors, namely, H, Chl, LAI, FVC and W. However, inter-correlation also existed among these factors across the entire growing season (Fig. 4). The intercorrelation involved direct and indirect correlations which were subsequently clarified by SEM analysis. The explicit relationships (direct, indirect and total effects) among these factors were identified by SEM analysis as shown in Table 3. The dataset was multivariately normalized as indicated by the Mardia’s coefficient of 2.651 (a value above 3.00 indicates non-normality) (Ullman and Bentler, 2003). The theoretical model for AGB in SEM was well fitted with a CFI of 0.978, and a GFI of 0.990. The model for BGB also had a high fit according to the CFI (0.961) and GFI (0.982). The AGB and BGB were significantly influenced by H and LAI, while the amount of explained variance for BGB (69%) was lower than that in AGB (88%) (Fig. 5). Similar statistical relationships were found between the non-biomass factors in these two models with the same amount of variance explained for each factor. The amount of variance explained by the model for all dependent variables was statistically significant at p = 0.05. In addition, results showed that H and LAI directly affected the biomass components. The other three factors (Chl, FVC and W) only indirectly affected the biomass components through their direct relationships with H and LAI, despite their high correlations with biomass components (Table 3 and Fig. 4). Therefore, the two direct factors were ultimately selected to Table 3 Standardized direct, indirect and total effects in SEM analysis for AGB and BGB. Variable AGB H Chl LAI FVC W BGB H Chl LAI FVC W
Direct effect
Indirect effect
Total
0.71 0.05 ns 0.62 0.15 ns 0.06 ns
0.16 0.25 −0.07 0.00 −0.23
0.87 0.30 0.55 0.15 ns −0.17
0.61 0.15 0.54 0.04 ns 0.13 ns
0.12 0.17 −0.13 0.00 −0.23
0.73 0.32 0.41 0.04 ns −0.10
“ns” represents a non-significant effect.
W. Li et al. / Ecological Indicators 57 (2015) 486–496
491
Fig. 4. Pearson’s correlation among maize biomass components and factors that affect biomass components during the stage of (a) seedling, (b) jointing, (c) tasseling, (d) filling, and (e) milking.
develop the field-based estimation models of biomass components in the tasseling stage. 4.2. Estimation of maize height and LAI from LiDAR The field-measured canopy height (H) in the tasseling stage was compared with LiDAR height metrics. Among the height metrics, Hmax was most correlated with H followed by H90 and Hmean . Additionally, Hmax had the smallest deviation from H with a mean error (ME) of −11.38 cm, and a standard deviation (STD) of 12.28 cm (Table 4). All of the height metrics tended to underestimate maize height but significantly and linearly correlated with H. A simple linear regression model was fitted between Hmax and the fieldmeasured H at plot level. The fitted model explained 79% of the variation in H, with a RMSE of 18.48 cm (Fig. 6(a)). Similarly, a satisfactory correlation was found between the field-measured and LiDAR-derived LAI. The fitted LAI model explained 78% of the variation in the field-measured LAI, with a RMSE of 0.48 (Fig. 6(b)). 4.3. Estimation of maize biomass components Estimation models were fitted based on the linear correlations between the field-measured biomass components and the two selected biophysical factors (H and LAI) in the tasseling stage. The results from LOOCV showed that the field-based model explained 87% variance of the field-measured AGB, with an RMSE of 361.26 g/m2 , and an rRMSE of 0.28. The field-based model of BGB Table 4 Error statistics for the field-measured and LiDAR-derived maize height and LAI. Variables
Hmean
H60
H75
H90
Hmax
LAI
ME (cm) STD (cm) RMSE (cm)
−65.42 13.49 66.69
−70.71 14.22 72.02
−57.83 16.15 59.87
−38.85 15.40 41.57
−11.38 12.28 18.48
0.11 0.47 0.48
ME is mean of the error, STD is the standard deviation of error, and RMSE is the root mean squared error.
was inferior to that of AGB, with a lower R2 (0.81) and a higher rRMSE (0.41) (Table 5). Subsequently, spatially explicit H and LAI values which were derived from LiDAR (Fig. 7(a) and (b)), were used as input for the field-based models to estimate the spatial distributions of biomass across the entire study site. The spatial distributions of AGB and BGB showed similar patterns over the study area (Fig. 7(c) and (d)). Generally, a higher AGB corresponded to a higher BGB. The maize plants in the southern part of the study site exhibited a higher biomass level. Results showed that the LiDAR-derived biomass for the field plots was comparable to the field-measured biomass, with an RMSE of 288.51 g/m2 for AGB and 75.81 g/m2 for BGB; these values explain 82% and 79% of the variation, respectively (Fig. 6(c) and (d)). 5. Discussion 5.1. Factor selection for the field-based estimation models Five biophysical factors that were highly related to the maize biomass components were measured in this study. However, not all of these variables were independent and only H and LAI were found to directly affect the biomass components according to SEM. The SEM was developed based on the causal relationships between the field-measured factors; thus, background information on these factors was required. All of the field-measured factors in this study were biophysically linked at different levels. SEM helped identify the most direct factors that affect AGB/BGB to conduct a rapid and Table 5 Field-based models developed to estimate maize biomass components in the tasseling stage based on field-measurements. Variable
Equation
AGB BGB
AGB = −1658.44 + 11.997*H + 232.391*LAI BGB = −468.249 + 2.866*H + 69.797*LAI
**
p < 0.01.
R2
RMSE **
0.87 361.26 0.81** 115.34
rRMSE 0.28 0.41
492
W. Li et al. / Ecological Indicators 57 (2015) 486–496
Fig. 5. Results from SEM analysis for (a) AGB and (b) BGB. Dashed lines represent non-significant paths. Bold arrows indicate statistically significant paths with thickness reflecting the magnitude of standardized SEM coefficients listed on each path. Ellipses represent error terms for the observed variables (E1–E5).
simple field-based model, which can be applied to remotely sensed proxies over large areas. Although factors such as Chl and FVC were also highly related to the biomass components, the high correlations were partially attributed to the relationship between the direct factors (H and LAI) and biomass component. For example, the effect of FVC on AGB was directly attributed to LAI based on Beer–Lambert law. Additionally, the high correlation between Chl
and LAI increased the total effects of Chl on biomass because Chl directly influenced the growth and development of leaf area. These two structure-related factors can directly describe the growth status of maize. Similar to forest AGB, maize AGB is also primarily driven by the plant heights. From a biochemical perspective, the accumulation of nutrients in maize tissues increases the development of roots, stems and leaves, leading to variations in maize
W. Li et al. / Ecological Indicators 57 (2015) 486–496
493
Fig. 6. LiDAR-derived and field-measured (a) H (cm), (b) LAI, (c) AGB (g/m2 ), and (d) BGB (g/m2 ) of maize with coefficient of determination (R2 ) and root mean squared error (RMSE) in the tasseling stage. The dashed line (red) represents the 1:1 line, bold line (black) represents the fitted line. **p < 0.01. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
height and LAI. Maize BGB was primarily measured from the dry weight of root organs, where nutrients are exchanged between the maize plant and soil. However, biochemical parameter like soil organic carbon content in the maize field was not measured in this study, although they also affect maize biomass components particularly BGB (Huang et al., 2014). 5.2. LiDAR-derived maize height and LAI The LiDAR metrics performed quite different in estimating vegetation height and LAI in different vegetation types. Most previous studies focused on the applications of LiDAR metrics in forest areas. Our research further suggested that LiDAR metrics performed well in estimating maize heights and LAI in the tasseling stage. In this study, an underestimation of plant height occurred due to missing interaction between the laser and canopy top of maize; therefore errors were directly introduced into biomass estimations. This missing interaction is common during LiDAR detection, particularly in vegetated areas, e.g., forests and cropland. This result agrees with previous studies on other vegetation types (Hopkinson et al., 2005; Li et al., 2014; Luo et al., 2015). To reduce the height underestimation, the point density of LiDAR can be increased by repetitive flights, which are more expensive. In addition, a high small-footprint waveform sampling may decrease the probability of missing interaction. In this study, the height error (18.48 cm) was considered acceptable compared with the absolute minimum field-measured plant height (151.89 cm) in the tasseling stage. Airborne LiDAR is not recommended for monitoring maize at early growing season because the vertical resolution of laser pulse (e.g., 15 cm) is large compared with the typical plant height (e.g., 30 cm). Terrestrial laser scanning (TLS) tools can be effective alternatives to monitor short crops during the early growing period by ensuring a high point density and estimation accuracy of plant heights (Höfle, 2014). However, the spatial extent of TLS is very limited compared with airborne LiDAR, which directly limits the extension of estimation to large areas from plot level. Therefore,
the combined use of mobile laser scanning and UAS-based LiDAR, as well as other remote sensing tools may be promising alternatives. For LAI estimation, most previous studies were empirical and primarily focused on the strong relationships between different LiDAR metrics and LAI in forests (Peduzzi et al., 2012; Solberg et al., 2009; Zhao et al., 2009). In this study, the maize LAI was estimated using the LPI metric based on Beer–Lambert law. Similar estimation approaches can be found in previous studies in different vegetation areas (Luo et al., 2015; Solberg, 2010). The LPI was calculated using normalized laser intensities, and was further tested in maize fields. Compared with forests, maize has a shorter height and denser canopy, which decreased the probability that laser pulses penetrate canopy and reach ground; thus, LiDAR yielded insufficient ground returns in the maize field, leading to a lower LPI and an overestimated LAI (ME = 0.11) according to Beer–Lambert law. Influenced by the short maize canopy, LiDAR cannot discriminate between the first and subsequent returns and most of the recorded laser returns were single returns. This problem might be solved by a high-sampling full-waveform LiDAR using more detailed signal recording and waveform decomposition (e.g., echo width) (Höfle et al., 2012). Additionally, the extinction coefficient was approximated during the LAI retrieval by assuming that the foliage angle distribution was spherical and that LiDAR was approximate to vertical scanning. Although the LiDAR-derived LAIs were comparable to the field measurements, further investigations into the quantification of extinction coefficient based on remote sensing are still required to improve the estimation accuracy. 5.3. Estimation of maize biomass components using LiDAR The biomass components were estimated by LiDAR-derived heights and LAIs based on the high correlation between the fieldmeasured biomass components and two direct factors (H and LAI) in the tasseling stage. High linear correlation of biomass with the field-measured H and LAI were consistently found in the growing
494
W. Li et al. / Ecological Indicators 57 (2015) 486–496
Fig. 7. Spatial distribution map of (a) H (m), (b) LAI, (c) AGB (g/m2 ), and (d) BGB (g/m2 ) estimated by LiDAR. AGB and BGB were predicted using field-based models.
stages (Fig. 8). In a previous study, an exponential model was fitted using AGB, H, and LAI collected in a long-term observation of one maize field during the entire growing season in northern China (Gao et al., 2013). In this study, similar exponential responses of biomass to H and LAI were also found using the long-term data, as shown by the gray fitted curves in Fig. 8. However, we only used the data collected in the tasseling stage to construct the field-based estimation models because we found that the relationships among biomass and H and LAI did not follow identical trends, despite the consistently positive correlations. Most maize plants began to reach the maximum height and LAI during the tasseling stage. In some plots, the canopy height stopped increasing and LAI tended to decrease while biomass components continued to increase when the maize reached the filling and milking stages (Fig. 2). Therefore, the estimation models for individual growth stages were more reasonable compared with those for the entire growing season, particularly when remote sensing proxies e.g. LiDAR were only available in this stage. However, the exponential responses found in this study
may provide valuable insights into the inter-annual variations in maize plants when field and remote sensing data are collected over multiple-years. These provide reliable references for other ecological studies on maize in the same area. The spatial distributions of AGB and BGB showed similar patterns and consistent variations over the entire study area (Fig. 7(c) and (d)). This result indicated a stable balance in the resource allocation between the aboveground and belowground organs of maize in the tasseling stage. This result agrees with a previous study in which the root/shoot growth ratios tended to stabilize after the maize plant experienced long-term growth (Zong and Shanggunag, 2013). Information on the linkage and interaction between aboveground and belowground organs of maize remains limited (Wardle et al., 2004). After the tasseling stage, the maize height remained stable, and LAI began to decrease when the filling stage began. In the milking stage, a large proportion of organic matter and accumulated nutrients within the aboveground organs and belowground root system contributes to the growth of maize fruit, decreasing
W. Li et al. / Ecological Indicators 57 (2015) 486–496
495
Fig. 8. Relationships of the field-measured biomass components to H and LAI across the five growth stages of maize. The red markings represent the data collected in the tasseling stage, and the gray curves were fitted using long-term data. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
the growth rate of plant height and LAI. Therefore, the sensitivity of maize biomass components to H and LAI will significantly decrease after the peak growing season. Since the biomass components were estimated by the LiDAR-derived H and LAI, we suggest that airborne LiDAR is more suitable for monitoring maize during the peak growing season. This study first attempted to estimate maize BGB using only aboveground structural factors (H and LAI), which are easy to collect and monitor over space and time. However, belowground roots can be more influenced by biotic and abiotic stresses compared with aboveground organs. Estimating the indicators of these stresses, such as soil characteristics, biochemical fertilizer, and nutrients uptake, is difficult over larger space in the long-term. Our study only attempted to predict maize BGB from the perspective of maize structure, which can be rapidly and efficiently derived from remotely sensed proxies. Therefore, further studies should be performed to investigate the possibility of evaluating these rootaffecting factors based on remote sensing.
LiDAR-derived height and LAI were proved to be comparable to the field measurements, and were further used to predict the spatial pattern of the maize biomass components. Results showed that the LiDAR-estimated biomass for the field plots were comparable to the field-measured biomass, suggesting that airborne LiDAR has a high potential for estimating canopy height, LAI, and biomass components of maize during the peak growing season.
6. Conclusion
References
This study investigated the potential of using small-footprint airborne LiDAR to estimate maize AGB and BGB during the peak growing season in Zhangye City, Northwest China. Based on in situ measurements, the maize canopy height and LAI were identified as the most direct factors that affect the maize biomass components. Field-based estimation models were proposed to estimate the large-scale AGB and BGB, which can provide reliable references for other ecological studies on maize in the same area.
Acknowledgements This work was supported by China’s Special Funds for Major State Basic Research Project of China (2013CB733405); the National Natural Science Foundation of China (41201345 and 41301389), and the National High Technology Research and Development Program of China (863 Program) (2014AA06A511 and 2012AA12A304).
Becker-Reshef, I., Vermote, E., Lindeman, M., Justice, C., 2010. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens. Environ. 114, 1312–1323, http://dx. doi.org/10.1016/j.rse.2010.01.010 Brassard, B.W., Chen, H.Y.H., Bergeron, Y., 2009. Influence of environmental variability on root dynamics in northern forests. Crit. Rev. Plant Sci. 28, 179–197, http:// dx.doi.org/10.1080/07352680902776572 Franco, J.A., Banon, S., Vicente, M.J., Miralles, J., Martinez-Sanchez, J.J., 2011. Root development in horticultural plants grown under abiotic stress conditions – a review. J. Hortic. Sci. Biotechnol. 86, 543–556, http://dx.doi.org/10.1016/j.rse. 2012.04.005
496
W. Li et al. / Ecological Indicators 57 (2015) 486–496
Gao, S., Niu, Z., Huang, N., Hou, X., 2013. Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2. Int. J. Appl. Earth Observ. Geoinf. 24, 1–8, http://dx.doi.org/10.1016/j.jag.2013.02.002 Garcia, M., Riano, D., Chuvieco, E., Danson, F.M., 2010. Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sens. Environ. 114, 816–830, http://dx.doi.org/10.1016/j.rse. 2009.11.021 Höfle, B., 2014. Radiometric correction of terrestrial LiDAR point cloud data for individual maize plant detection. IEEE Geosci. Remote Sens. Lett. 11, 94–98, http:// dx.doi.org/10.1109/lgrs.2013.2247022 Höfle, B., Hollaus, M., Hagenauer, J., 2012. Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data. ISPRS J. Photogram. Rem. Sens. 67, 134–147, http://dx.doi.org/10.1016/j.isprsjprs.2011. 12.003 Höfle, B., Pfeifer, N., 2007. Correction of laser scanning intensity data: Data and model-driven approaches. ISPRS J. Photogram. Rem. Sens. 62, 415–433, http:// dx.doi.org/10.1016/j.isprsjprs.2007.05.008 Hopkinson, C., Chasmer, L.E., Sass, G., Creed, I.F., Sitar, M., Kalbfleisch, W., Treitz, P., 2005. Vegetation class dependent errors in LiDAR ground elevation and canopy height estimates in a boreal wetland environment. Can. J. Remote Sens. 31, 191–206, http://dx.doi.org/10.5589/m05-007 Huang, N., Wang, L., Guo, Y.Q., Hao, P.Y., Niu, Z., 2014. Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system. PLOS ONE 9, http://dx.doi.org/10.1371/journal.pone.0105150 Iriondo, J.M., Albert, M.J., Escudero, A., 2003. Structural equation modelling: an alternative for assessing causal relationships in threatened plant populations. Biol. Conserv. 113, 367–377, http://dx.doi.org/10.1016/s0006-3207(03)00129-0 Jonsson, M., Wardle, D.A., 2010. Structural equation modelling reveals plantcommunity drivers of carbon storage in boreal forest ecosystems. Biol. Lett. 6, 116–119, http://dx.doi.org/10.1098/rsbl.2009.0613 Kim, G., 2010. AMOS 18.0: Structural Equation Modeling. Hannarae Publishing Co., Seoul. Korhonen, L., Korpela, I., Heiskanen, J., Maltamo, M., 2011. Airborne discrete-return LiDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index. Remote Sens. Environ. 115, 1065–1080, http://dx.doi.org/ 10.1016/j.rse.2010.12.011 Krugh, B., Miles, B.L.D., 1994. The solid-state chlorophyll meter: a novel instrument for rapidly and accurately determining the chlorophyll concentrations in seedling leaves. Maize Genet. Cooper. Newslett. 68, 25. Li, W., Niu, Z., Gao, S., Huang, N., Chen, H., 2014. Correlating the horizontal and vertical distribution of LiDAR point clouds with components of biomass in a Picea crassifolia forest. Forests 5, 1910–1930, http://dx.doi.org/10.3390/f5081910 Li, X., Cheng, G., Liu, S., Xiao, Q., Ma, M., Jin, R., Che, T., Liu, Q., Wang, W., Qi, Y., Wen, J., Li, H., Zhu, G., Guo, J., Ran, Y., Wang, S., Zhu, Z., Zhou, J., Hu, X., Xu, Z., 2013. Heihe Watershed Allied Telemetry Experimental Research (HiWATER): scientific objectives and experimental design. Bull. Am. Meteorol. Soc. 94, 1145–1160, http://dx.doi.org/10.1175/bams-d-12-00154.1 Li, X., Li, X., Li, Z., Ma, M., Wang, J., Xiao, Q., Liu, Q., Che, T., Chen, E., Yan, G., Hu, Z., Zhang, L., Chu, R., Su, P., Liu, Q., Liu, S., Wang, J., Niu, Z., Chen, Y., Jin, R., Wang, W., Ran, Y., Xin, X., Ren, H., 2009. Watershed allied telemetry experimental research. J. Geophys. Res. 114, http://dx.doi.org/10.1029/2008jd011590 Liu, J., Pattey, E., Miller, J.R., McNairn, H., Smith, A., Hu, B., 2010. Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model. Remote Sens. Environ. 114, 1167–1177, http://dx.doi.org/10.1016/j.rse.2010.01.004 Liu, Y., Mu, X., Wang, H., Yan, G., 2012. A novel method for extracting green fractional vegetation cover from digital images. J. Veg. Sci. 23, 406–418, http://dx.doi.org/ 10.1111/j.1654-1103.2011.01373.x Luo, S., Wang, C., Pan, F., Xi, X., Li, G., Nie, S., Xia, S., 2015. Estimation of wetland vegetation height and leaf area index using airborne laser scanning data. Ecol. Indic. 48, 550–559, http://dx.doi.org/10.1016/j.ecolind.2014.09.024
Luo, S., Wang, C., Xi, X., Pan, F., 2014. Estimating FPAR of maize canopy using airborne discrete-return LiDAR data. Opt. Express 22, 5106–5117, http://dx.doi.org/10. 1364/OE.22.005106 Mardia, K.V., 1970. Measures of multivariate skewness and kurtosis with applications. Biometrika 57, 519–530. Mu, X., Huang, S., Chen, Y., 2013. HiWATER: Dataset of Fractional Vegetation Cover in the Middle Reaches of the Heihe River Basin. Heihe Plan Science Data Center, http://dx.doi.org/10.3972/hiwater.043.2013.db Peduzzi, A., Wynne, R.H., Fox, T.R., Nelson, R.F., Thomas, V.A., 2012. Estimating leaf area index in intensively managed pine plantations using airborne laser scanner data. Forest Ecol. Manage. 270, 54–65, http://dx.doi.org/10.3390/rs4061758 Popescu, S.C., Zhao, K., Neuenschwander, A., Lin, C., 2011. Satellite LiDAR vs. small footprint airborne LiDAR: comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sens. Environ. 115, 2786–2797, http://dx.doi.org/10.1016/j.rse.2011.01.026 Pugesek, B.H., Tomer, A., 2003. Structural Equation Modeling: Applications in Ecological and Evolutionary Biology. Cambridge University Press. Richardson, J.J., Moskal, L.M., Kim, S.-H., 2009. Modeling approaches to estimate effective leaf area index from aerial discrete-return LiDAR. Agric. Forest Meteorol. 149, 1152–1160, http://dx.doi.org/10.1016/j.agrformet.2009.02.007 Solberg, S., 2010. Mapping gap fraction LAI and defoliation using various ALS penetration variables. Int. J. Remote Sens. 31, 1227–1244, http://dx.doi.org/10.1080/ 01431160903380672 Solberg, S., Brunner, A., Hanssen, K.H., Lange, H., Næsset, E., Rautiainen, M., Stenberg, P., 2009. Mapping LAI in a Norway spruce forest using airborne laser scanning. Remote Sens. Environ. 113, 2317–2327, http://dx.doi.org/10.1016/j.rse.2009.06. 010 Ullman, J.B., Bentler, P.M., 2003. Structural Equation Modeling. Wiley Online Library. Wang, W., Huang, Y., Xu, F., Ma, C., Wang, J., 2013. HiWATER: Dataset of Biomass Observed in the Middle Reaches of the Heihe River Basin. Heihe Plan Science Data Center, http://dx.doi.org/10.3972/hiwater.122.2013.db Wardle, D.A., Bardgett, R.D., Klironomos, J.N., Setala, H., van der Putten, W.H., Wall, D.H., 2004. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633, http://dx.doi.org/10.1126/science.1094875 Wu, C., Wang, L., Niu, Z., Gao, S., Wu, M., 2010. Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images. Int. J. Remote Sens. 31, 2159–2167, http://dx.doi.org/10.1080/01431161003614382 Xiao, Q., Wen, J., 2013a. HiWATER: Airborne LiDAR-DEM&DSM Data Production in the Middle Reaches of the Heihe River Basin. Heihe Plan Science Data Center, http://dx.doi.org/10.3972/hiwater.010.2013.db Xiao, Q., Wen, J., 2013b. HiWATER: Visible and Near-Infrared Hyperspectral Radiometer (7th July, 2012). Heihe Plan Science Data Center, http://dx.doi.org/ 10.3972/hiwater.011.2013.db Zhang, M., Liu, L., Wang, Z., Bo, Y., Liu, X., 2013. HiWATER: Dataset of Vegetation Type and Plant Structure Investigation in the Middle Reaches of the Heihe River Basin. Heihe Plan Science Data Center, http://dx.doi.org/10.3972/hiwater.036. 2013.db Zhang, M., Ma, M., Wang, X., 2014. HiWATER: Land Cover Map in the Core Experimental Area of Flux Observation Matrix. Heihe Plan Science Data Center, http:// dx.doi.org/10.3972/hiwater.156.2014.db Zhao, J., Li, Y., Wang, Y., Xie, D., 2013. HiWATER: Dataset of Vegetation LAI Measured by LAI2000 in the Middle Reaches of the Heihe River Basin. Heihe Plan Science Data Center, http://dx.doi.org/10.3972/hiwater.058.2013.db Zhao, K., Popescu, S., Nelson, R., 2009. LiDAR remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sens. Environ. 113, 182–196, http://dx.doi.org/10.1016/j.rse.2008.09.009 Zheng, G., Moskal, L.M., 2009. Retrieving Leaf Area Index (LAI) using remote sensing: theories, methods and sensors. Sensors (Basel) 9, 2719–2745, http://dx.doi.org/ 10.1016/j.jag.2012.05.002 Zong, Y., Shanggunag, Z., 2013. Short term effects of evaluated atmospheric CO2 on root dynamics of water-stressed maize. J. Food Agric. Environ. 11, 1037–1041.
Contents lists available at ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China Wang Li a,b,∗ , Zheng Niu a , Ni Huang a , Cheng Wang c , Shuai Gao a,∗ , Chaoyang Wu a a b c
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China University of Chinese Academy of Sciences, Beijing 100049, China Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
a r t i c l e
i n f o
Article history: Received 25 November 2014 Received in revised form 11 April 2015 Accepted 15 April 2015 Keywords: Airborne LiDAR Maize biomass Leaf area index Canopy height
a b s t r a c t Crop biomass is an important ecological indicator of growth, light use efficiency, and carbon stocks in agroecosystems. Light detection and ranging (LiDAR) or laser scanning has been widely used to estimate forest structural parameters and biomass. However, LiDAR is rarely used to estimate crop parameters because the short, dense canopies of crops limit the accuracy of the results. The objective of this study is to explore the potential of airborne LiDAR data in estimating biomass components of maize, namely aboveground biomass (AGB) and belowground biomass (BGB). Five biomass-related factors were measured during the entire growing season of maize. The field-measured canopy height and leaf area index (LAI) were identified as the factors that most directly affect biomass components through Pearson’s correlation analysis and structural equation modeling (SEM). Field-based estimation models were proposed to estimate maize biomass components during the tasseling stage. Subsequently, the maize height and LAI over the entire study area were derived from LiDAR data and were used as input for the estimation models to map the spatial pattern of the biomass components. The results showed that the LiDAR-estimated biomass was comparable to the field-measured biomass, with root mean squared errors (RMSE) of 288.51 g/m2 (AGB), and 75.81 g/m2 (BGB). In conclusion, airborne LiDAR has great potential for estimating canopy height, LAI, and biomass components of maize during the peak growing season. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Crop biomass, which is defined as the dried weight of crop plant matter within a unit area, is an important ecological indicator of growth, light use efficiency, and carbon stocks in agro-ecosystems. Crop biomass also acts as an energy source that can be converted into various bio-fuel indicators, such as CO2 efflux, to provide indirect insight into energy exchanges at the local, regional and continental scales. A rapid, economical and quantitative estimation of crop biomass is vital for crop production and accessibility risk management, global markets, policy-making and decision-making (Becker-Reshef et al., 2010). Generally, crop biomass can be categorized into aboveground biomass (AGB) and belowground biomass (BGB), according to the distinct growing places of different plant
∗ Corresponding authors at: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, P.O. Box 9718, No. 20 Datun Road, Olympic Science & Technology Park of CAS, Beijing 100101, China. Tel.: +86 010 64889215; fax: +86 010 64889215. E-mail addresses: [email protected] (W. Li), [email protected] (S. Gao). http://dx.doi.org/10.1016/j.ecolind.2015.04.016 1470-160X/© 2015 Elsevier Ltd. All rights reserved.
components. A close relationship exists between these two biomass components through the allocation of photosynthetic resources during the growing season. Maize AGB is prone to expansion via the support of a large root system (Zong and Shanggunag, 2013), which is directly reflected in the physical appearance of the aboveground community structure and function (Wardle et al., 2004). Moreover, root biomass or belowground biomass, productivity, and mortality of maize plants are highly dynamic over space and time in a terrestrial ecosystem (Zong and Shanggunag, 2013). Because of this behavior, plant roots are easily influenced by biotic and abiotic stresses than aboveground organs (Brassard et al., 2009; Wardle et al., 2004). However, belowground biomass is not well-studied because of the limited accessibility to roots compared with the aboveground component (Franco et al., 2011). Large-scale spatial and temporal measurements of crop biomass are laborious and expensive; thus, these measurements are challenging for ecologists and agronomists. Characterized by its capability of collecting information at regional and global scales, remote sensing is the only reasonable method that can facilitate temporal estimation of biomass over large areas. Many studies have attempted to estimate crop biomass according to the close
W. Li et al. / Ecological Indicators 57 (2015) 486–496
487
Fig. 1. Location of the study site in (a) Gansu Province, Northwest China (upper left), and distributions of the field plots overlaid by a digital orthophoto map (DOM); (b) the surface model with normalized heights.
relationship between field-measured biomass and remotely sensed spectral values of the crop canopy (Liu et al., 2010). Vegetation structure is the dominant factor responsible for remotely sensed spectral reflectance of the canopy in visible and near-infrared bands (Gao et al., 2013). Forest biomass is closely related to tree structures, such as tree height and diameter at breast height according to the relative growth equation (Li et al., 2014). A similar relationship can be found between plant heights and crop biomass (Gao et al., 2013). Therefore, maize AGB is hypothesized to be highly structure-related. However, remotely sensed spectral values tend to be saturated in locations covered by dense vegetation with a high leaf area index (LAI) and AGB. In recent decades, light detection and ranging (LiDAR) or the laser scanning technique, which has a high penetration capability, is likely to overcome this problem by collecting layered canopy features. Airborne LiDAR can be a powerful tool for estimating fractional vegetation cover (FVC) (Korhonen et al., 2011), LAI (Richardson et al., 2009; Solberg, 2010; Zhao et al., 2009; Zheng and Moskal, 2009), and AGB (Li et al., 2014; Popescu et al., 2011) using LiDAR metrics in forest areas. The precision of LiDAR metrics is affected by sensor factors such as footprint size, echo-detection algorithm, wavelength, and incidence angle. Some of these factors may vary with the flight altitude and the topography of scanned targets (Höfle and Pfeifer, 2007). However, the feasibility of LiDAR metrics for estimating crop parameters still requires further investigation. Compared with forests, crops are more affected by the saturation problem because crop canopies are denser and the penetration depth of remote sensing detecting beam is limited. A high-density LiDAR point cloud is required to detect dense crop canopies and to increase the penetration probability. A higher point density will increase the cost of an airborne flight, which is currently more expensive than mobile laser scanners mounted on agricultural vehicles or unmanned aerial systems (UASs). Although terrestrial LiDAR can achieve a high point density and high detection accuracy at a lower cost for vegetation monitoring, such as individual maize plants (Höfle, 2014), its spatial detection range is small compared with airborne LiDAR. Thus, airborne LiDAR will likely be a promising technique to extend crop biomass estimations from the plot level to a larger spatial extent. Additionally, airborne LiDAR is often mounted along with many other sensors, such as microwave radiometers and hyperspectral camera (Li et al., 2009),
which facilitates the combined use of multi-source remote sensing in scientific and agro-ecological studies over large areas. Therefore, in this study, we aimed to further explore the feasibility of airborne LiDAR for estimating biomass components of maize. We first tried to extend the estimation to BGB, aside from AGB by referring to forest studies. The factors that directly affect maize biomass components were identified and used to develop field-based estimation models, which were subsequently applied to map spatial patterns of maize biomass components over the entire study site. 2. Materials 2.1. Study site The study site is located in Zhangye City, Gansu Province, China (Fig. 1). The site is a core experimental Heihe Watershed Allied Telemetry Experimental Research (HiWATER) area for flux observations, and is also part of an artificial oasis–riparian ecosystem–wetland–desert system with an average elevation of 1400 m above sea level (Li et al., 2009, 2013). The primary topography is irrigated, heterogeneous, seed maize that is interspersed with spring wheat, vegetables, orchards, and residential areas (Li et al., 2013). Most of the maize is sown in late April, and flowering occurs near mid-August, followed by harvesting between late September and early October. The mean annual temperature is 6 ◦ C with the coldest temperatures in January and the warmest in July. The mean annual precipitation is approximately to 100–250 mm, but the potential evaporation is as high as 1200–1800 mm per year. Therefore, agriculture mainly involves irrigation and agricultural development is typically influenced by drought. 2.2. Airborne LiDAR Small footprint airborne LiDAR data were acquired for the study site on July 19, 2012, aiming to measure vegetation structure parameters and derive aerodynamic roughness (Xiao and Wen, 2013a). The Leica ALS70 system was configured to emit laser pulses in the near-infrared band (1064 nm) at a scan angle of ±18◦ , a beam divergence of 0.15 mrad, and an average footprint size of 22.5 cm. The geographical coordinates (easting, northing and elevation)
488
W. Li et al. / Ecological Indicators 57 (2015) 486–496
Table 1 Acquisition date of the field measurements. Date growth stage
Seedling
Jointing
Tasseling
Filling
Milking
First period Second period
2012/05/25 2012/05/31
2012/06/23 2012/06/28
2012/07/13 2012/07/23
2012/08/03 2012/08/12
2012/08/28 –
were recorded by a dual-frequency differential global positioning system (GPS) with an inertial measurement unit. The vertical placement accuracy of the instrument was 5–30 cm. Repetitive flights with a 60% side-overlap were conducted at a nominal height of 1500 m above the ground, leading to an average pulse density of 6.7 point/m2 . All of the points were geo-referenced to the Universal Transverse Mercator (UTM) Zone 47N/WGS-84 projection. Raw LiDAR data were converted to LAS binary format files by recording the geographical coordinates, intensity, and number of returns. The raw LiDAR data were first filtered by removing outliers that were extremely higher or lower than other points and were easily found in the air or below ground (Luo et al., 2014). Point clouds were then classified into ground and off-ground returns in TerraScan software (TerraSolid, Ltd., Finland). Reclassification was conducted after manually checking the occurrence of misclassifications to ensure the precision of LiDAR metrics. Subsequently, LiDAR intensity was normalized (Eq. (1)) to minimize the difference among the flights (Garcia et al., 2010; Höfle and Pfeifer, 2007). Inormalized = Iraw
R2 Rs2
(1)
where Inormalized is the normalized intensity, Iraw raw intensity, R sensor–target distance, and Rs standardized flight altitude. In this study, Rs equals average flight altitude (1500 m). The ground points were aggregated to digital terrain model (DTM) with a grid size of 1 m. All of the point heights were normalized using the DTM. Offground point heights were normalized as the difference between off-ground point heights and the corresponding DTM cell heights beneath the points. A digital surface model (DSM) with 1 m resolution was generated from the maximum off-ground point height within each raster cell. Then, a surface model with normalized heights was derived as the difference between the DTM and DSM (Fig. 1(b)). 2.3. Field measurements Long-term field measurements of seven biophysical factors of maize were obtained in the study site between early May and mid-September. The biophysical factors were plant height, leaf chlorophyll concentration (Chl), LAI, FVC, biomass components, and tissue water content (W). To guarantee consistency, all of the factors were simultaneously collected on observation dates that covered nearly the entire growing season of maize (Table 1). In each growth stage, at least two periods of measurements were conducted in 14 constant plots near the flux towers; however, not all of the plots were simultaneously measured in the same period. In each stage, the average values were calculated as the field-measured values for the plots that were measured for twice. Each field plot had a large maize area, with a width and length of at least 10 m, flat terrain, and uniform growing conditions. The geographic location (latitude and longitude) of each plot was measured using GPS. In each plot, four representative maize plants were selected. The plant height was measured using a tape that extend from the ground to the canopy top. The canopy height (H) for the plot was calculated as the average plant height. The selected plants were then harvested from the root to the canopy, sealed in a plastic bag, and immediately transported to a nearby laboratory for subsequent analysis. The fresh mass of various plant organs (leaf, stem, stick, and root), and the root length were measured. Most of the plants
were harvested before fruiting period. The fresh maize organs were completely oven dried at 75 ◦ C until a constant weight mass was achieved (Wang et al., 2013). The AGB was obtained by multiplying the average dry weight of the aboveground organs (leaf, stem and stick) per plant (g/plant) by the plant density (plant/m2 ). The plant density (plant/m2 ) was determined according to the measured inter-plant row and line distances (m). The BGB was obtained from the roots using the same method. The tissue water content was determined as the ratio of the fresh-dry weight difference and the fresh weight. LAI for each plot was non-destructively measured using LAI2000 (LI-COR Inc., Lincoln, Nebraska) (Zhao et al., 2013). A series of digital photographs was collected far above the maize canopy along the plot diagonal to determine the FVC by image processing (Liu et al., 2012; Mu et al., 2013). A portable chlorophyll meter (SPAD502, New Jersey, USA) was used to measure the leaf chlorophyll concentration (Chl). Leaves at three locations, namely, the upper, middle, and lower parts of the maize plant were randomly selected; ten SPAD readings were randomly obtained at each location. SPAD readings are in arbitrary units rather than actual amounts of chlorophyll per unit area of leaf tissue (Huang et al., 2014; Krugh and Miles, 1994). Therefore, the actual amounts of chlorophyll per unit area of leaf tissue (g/cm2 ) were determined using the transformation relationship (Chl = 0.95 × SPAD value − 3.25) proposed by Wu et al. (2010) where the same SPAD meter was used. The seasonal trends and basic statistics of all the field measurements are shown in Fig. 2 and Table 2, respectively. Since the airborne campaign coincided with the tasseling stage, the data collected in this stage were used for estimation. 2.4. Crop classification data Crop classification data with 1 m resolution were produced by an object-oriented workflow using airborne Compact Airborne Spectrographic Imager (CASI) data (Xiao and Wen, 2013b). As part of a land cover survey across the entire Heihe Basin, 164 groundtruth points were collected within the study site, and were used for crop classification (Zhang et al., 2013, 2014). The ground-truth points were randomly split into 3/4 and 1/4 portions for classification training and validation, respectively. Post-classification was performed to re-classify the pixels that were difficult to identify through manual interpretation. The landscape was classified into fourteen detailed land cover types (Fig. 3(a)). Since the goal of this study was to estimate maize biomass, maize pixels were selected from the classification result (Fig. 3(b)). The classification result was validated using the ground survey points. The overall accuracy was 88.48%, and the Kappa coefficient was 0.87. The omission and commission errors of maize were 7.51% and 7.20%, respectively. 3. Methodology 3.1. Analysis of factors that affect biomass components Statistical Pearson’s correlation analysis was performed between the field-measured biomass components and other biophysical factors. The normal distribution and mutual independence of each factor were tested using Shapiro–Wilk normality test and randomness test using Statistical Package for the Social Sciences (SPSS, Chicago, IL, USA); these tests are required for
W. Li et al. / Ecological Indicators 57 (2015) 486–496
489
Fig. 2. Seasonal trends of seven field-measured factors with error bars that represent one standard error.
Pearson’s correlation (Huang et al., 2014). According to the statistical analysis, all of the measured factors followed a normal distribution (Shapiro–Wilk, p > 0.05) and exhibited randomness (runs test, p > 0.05). Factors that were significantly correlated to the biomass component were filtered through statistical analysis. However, inter-correlation that probably involves both direct and indirect effects may exist among these factors (Huang et al., 2014). Although stepwise regression can decrease the influence of inter-correlation among these factors, it provides a statistical explanation without providing a biophysical or causal relationship between the biomass component and other factors. Additionally, direct factors may occasionally be eliminated during stepwise selection, which is probably undesirable, particularly for developing field-based models. Therefore, structural equation modeling (SEM) was used to identify the relationships among
the field-measured factors to determine those directly affect biomass (Iriondo et al., 2003; Jonsson and Wardle, 2010; Pugesek and Tomer, 2003). As required by SEM, normalized Mardia’s coefficient was calculated to evaluate the multivariate normality of the dataset (Mardia, 1970). The comparative fit index (CFI) and goodness of fit index (GFI) were used to evaluate the SEM results in the AMOS program for Windows (Kim, 2010). 3.2. Estimation of maize height and LAI from LiDAR data Canopy height metrics, namely, maximum height (Hmax ), mean height (Hmean ), and percentile height (Hp ) were calculated to evaluate the maize height based on the normalized point heights within a grid size of 10 m. Laser penetration metric (LPI) which can overcome the saturation problem was calculated to estimate LAI for
Table 2 Basic statistics of the field measurements of maize in the five growth stages. Chl (g/cm2 )
LAI
FVC (%)
W (%)
13.94 23.22 17.92 3.02
35.61 54.69 48.38 6.32
0.10 0.67 0.37 0.19
1.70 8.65 4.85 2.46
74.69 84.71 77.41 3.15
14.78 246.79 70.36 76.07
1.06 48.99 10.94 14.86
Min Max Mean SD
75.11 170.56 113.11 26.74
43.63 54.12 49.99 3.11
1.79 3.45 2.51 0.59
42.30 74.20 60.64 9.84
74.81 84.07 79.67 3.34
385.72 1132.15 728.47 195.14
99.44 387.47 185.76 90.20
Tasseling
Min Max Mean SD
151.89 216.33 178.15 22.57
46.80 58.84 54.91 3.27
2.66 4.58 3.49 0.60
45.90 76.06 65.57 7.44
62.31 77.07 78.92 4.77
662.83 2225.73 1290.61 404.47
144.01 517.26 286.14 118.03
Filling
Min Max Mean SD
154.44 234.22 190.13 22.72
48.06 63.19 56.82 4.42
3.06 4.24 3.53 0.39
42.64 91.57 74.43 14.49
72.60 79.83 77.52 1.93
1083.21 2415.82 1759.28 412.23
182.84 619.04 327.34 130.02
Milking
Min Max Mean SD
160.33 236.44 192.12 23.71
34.15 57.83 47.47 6.84
2.53 4.52 3.36 0.53
35.61 79.30 68.66 12.64
68.08 76.72 72.35 2.50
1447.91 3444.51 2252.22 598.24
246.70 722.50 435.67 136.62
Stage
Statistic
Seedling
Min Max Mean SD
Jointing
H (cm)
Min is minimum value, Max is maximum value, Mean is averaged value, SD is standard deviation.
AGB (g/m2 )
BGB (g/m2 )
490
W. Li et al. / Ecological Indicators 57 (2015) 486–496
Fig. 3. Classification data of (a) all the land cover types and (b) maize in the study site.
each plot and across the entire study area based on Beer–Lambert equation (Eq. (2)) (Luo et al., 2015; Richardson et al., 2009).
4. Results 4.1. Analysis of factors that affect biomass components
I = I0 e(−k×LAI)
(2)
Hence, LAI can be calculated as: LAI = −
1 ln k
I I0
(3)
where I0 and I are the average canopy (off-ground) and ground laser intensities respectively, within a 10 m maize grid. The extinction coefficient k was set to 0.5 by assuming that the foliage angle distribution was spherical, and LiDAR conducted approximately vertical scanning (Luo et al., 2014; Richardson et al., 2009; Solberg et al., 2009). According to Richardson et al. (2009), the theoretical k value (equal to 0.5) is adequate for estimating LAI of vegetation using LiDAR data after investigating the relationship between the field-measured LAI and intensity ratio.
3.3. Models for estimating AGB and BGB Field-based models were developed to estimate the biomass components using direct factors identified by SEM analysis from the field data collected in the tasseling stage. The field-based models were subsequently tested using direct factors derived by LiDAR. As the number of field plots was insufficient to provide an independent validation dataset, we used leave-one-out cross-validation method (LOOCV) to evaluate the estimation accuracy. The estimation accuracy was quantified using coefficient of determination (R2 ), root mean squared error (RMSE) (Eq. (4)) and relative root mean squared error (rRMSE) (Eq. (5)). RMSE is related to the magnitude of the observed variables, while rRMSE is a relative value that can be used to compare the performances of different regression models. A lower rRMSE often indicates a better regression performance.
n 1 RMSE = (pi − pˆ i ) n
rRMSE =
(4)
i=1
RMSE pi
where pi is the measured value, and pˆ i is the predicted value.
(5)
High Pearson’s correlations were found between the biomass components and other field-measured factors, namely, H, Chl, LAI, FVC and W. However, inter-correlation also existed among these factors across the entire growing season (Fig. 4). The intercorrelation involved direct and indirect correlations which were subsequently clarified by SEM analysis. The explicit relationships (direct, indirect and total effects) among these factors were identified by SEM analysis as shown in Table 3. The dataset was multivariately normalized as indicated by the Mardia’s coefficient of 2.651 (a value above 3.00 indicates non-normality) (Ullman and Bentler, 2003). The theoretical model for AGB in SEM was well fitted with a CFI of 0.978, and a GFI of 0.990. The model for BGB also had a high fit according to the CFI (0.961) and GFI (0.982). The AGB and BGB were significantly influenced by H and LAI, while the amount of explained variance for BGB (69%) was lower than that in AGB (88%) (Fig. 5). Similar statistical relationships were found between the non-biomass factors in these two models with the same amount of variance explained for each factor. The amount of variance explained by the model for all dependent variables was statistically significant at p = 0.05. In addition, results showed that H and LAI directly affected the biomass components. The other three factors (Chl, FVC and W) only indirectly affected the biomass components through their direct relationships with H and LAI, despite their high correlations with biomass components (Table 3 and Fig. 4). Therefore, the two direct factors were ultimately selected to Table 3 Standardized direct, indirect and total effects in SEM analysis for AGB and BGB. Variable AGB H Chl LAI FVC W BGB H Chl LAI FVC W
Direct effect
Indirect effect
Total
0.71 0.05 ns 0.62 0.15 ns 0.06 ns
0.16 0.25 −0.07 0.00 −0.23
0.87 0.30 0.55 0.15 ns −0.17
0.61 0.15 0.54 0.04 ns 0.13 ns
0.12 0.17 −0.13 0.00 −0.23
0.73 0.32 0.41 0.04 ns −0.10
“ns” represents a non-significant effect.
W. Li et al. / Ecological Indicators 57 (2015) 486–496
491
Fig. 4. Pearson’s correlation among maize biomass components and factors that affect biomass components during the stage of (a) seedling, (b) jointing, (c) tasseling, (d) filling, and (e) milking.
develop the field-based estimation models of biomass components in the tasseling stage. 4.2. Estimation of maize height and LAI from LiDAR The field-measured canopy height (H) in the tasseling stage was compared with LiDAR height metrics. Among the height metrics, Hmax was most correlated with H followed by H90 and Hmean . Additionally, Hmax had the smallest deviation from H with a mean error (ME) of −11.38 cm, and a standard deviation (STD) of 12.28 cm (Table 4). All of the height metrics tended to underestimate maize height but significantly and linearly correlated with H. A simple linear regression model was fitted between Hmax and the fieldmeasured H at plot level. The fitted model explained 79% of the variation in H, with a RMSE of 18.48 cm (Fig. 6(a)). Similarly, a satisfactory correlation was found between the field-measured and LiDAR-derived LAI. The fitted LAI model explained 78% of the variation in the field-measured LAI, with a RMSE of 0.48 (Fig. 6(b)). 4.3. Estimation of maize biomass components Estimation models were fitted based on the linear correlations between the field-measured biomass components and the two selected biophysical factors (H and LAI) in the tasseling stage. The results from LOOCV showed that the field-based model explained 87% variance of the field-measured AGB, with an RMSE of 361.26 g/m2 , and an rRMSE of 0.28. The field-based model of BGB Table 4 Error statistics for the field-measured and LiDAR-derived maize height and LAI. Variables
Hmean
H60
H75
H90
Hmax
LAI
ME (cm) STD (cm) RMSE (cm)
−65.42 13.49 66.69
−70.71 14.22 72.02
−57.83 16.15 59.87
−38.85 15.40 41.57
−11.38 12.28 18.48
0.11 0.47 0.48
ME is mean of the error, STD is the standard deviation of error, and RMSE is the root mean squared error.
was inferior to that of AGB, with a lower R2 (0.81) and a higher rRMSE (0.41) (Table 5). Subsequently, spatially explicit H and LAI values which were derived from LiDAR (Fig. 7(a) and (b)), were used as input for the field-based models to estimate the spatial distributions of biomass across the entire study site. The spatial distributions of AGB and BGB showed similar patterns over the study area (Fig. 7(c) and (d)). Generally, a higher AGB corresponded to a higher BGB. The maize plants in the southern part of the study site exhibited a higher biomass level. Results showed that the LiDAR-derived biomass for the field plots was comparable to the field-measured biomass, with an RMSE of 288.51 g/m2 for AGB and 75.81 g/m2 for BGB; these values explain 82% and 79% of the variation, respectively (Fig. 6(c) and (d)). 5. Discussion 5.1. Factor selection for the field-based estimation models Five biophysical factors that were highly related to the maize biomass components were measured in this study. However, not all of these variables were independent and only H and LAI were found to directly affect the biomass components according to SEM. The SEM was developed based on the causal relationships between the field-measured factors; thus, background information on these factors was required. All of the field-measured factors in this study were biophysically linked at different levels. SEM helped identify the most direct factors that affect AGB/BGB to conduct a rapid and Table 5 Field-based models developed to estimate maize biomass components in the tasseling stage based on field-measurements. Variable
Equation
AGB BGB
AGB = −1658.44 + 11.997*H + 232.391*LAI BGB = −468.249 + 2.866*H + 69.797*LAI
**
p < 0.01.
R2
RMSE **
0.87 361.26 0.81** 115.34
rRMSE 0.28 0.41
492
W. Li et al. / Ecological Indicators 57 (2015) 486–496
Fig. 5. Results from SEM analysis for (a) AGB and (b) BGB. Dashed lines represent non-significant paths. Bold arrows indicate statistically significant paths with thickness reflecting the magnitude of standardized SEM coefficients listed on each path. Ellipses represent error terms for the observed variables (E1–E5).
simple field-based model, which can be applied to remotely sensed proxies over large areas. Although factors such as Chl and FVC were also highly related to the biomass components, the high correlations were partially attributed to the relationship between the direct factors (H and LAI) and biomass component. For example, the effect of FVC on AGB was directly attributed to LAI based on Beer–Lambert law. Additionally, the high correlation between Chl
and LAI increased the total effects of Chl on biomass because Chl directly influenced the growth and development of leaf area. These two structure-related factors can directly describe the growth status of maize. Similar to forest AGB, maize AGB is also primarily driven by the plant heights. From a biochemical perspective, the accumulation of nutrients in maize tissues increases the development of roots, stems and leaves, leading to variations in maize
W. Li et al. / Ecological Indicators 57 (2015) 486–496
493
Fig. 6. LiDAR-derived and field-measured (a) H (cm), (b) LAI, (c) AGB (g/m2 ), and (d) BGB (g/m2 ) of maize with coefficient of determination (R2 ) and root mean squared error (RMSE) in the tasseling stage. The dashed line (red) represents the 1:1 line, bold line (black) represents the fitted line. **p < 0.01. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
height and LAI. Maize BGB was primarily measured from the dry weight of root organs, where nutrients are exchanged between the maize plant and soil. However, biochemical parameter like soil organic carbon content in the maize field was not measured in this study, although they also affect maize biomass components particularly BGB (Huang et al., 2014). 5.2. LiDAR-derived maize height and LAI The LiDAR metrics performed quite different in estimating vegetation height and LAI in different vegetation types. Most previous studies focused on the applications of LiDAR metrics in forest areas. Our research further suggested that LiDAR metrics performed well in estimating maize heights and LAI in the tasseling stage. In this study, an underestimation of plant height occurred due to missing interaction between the laser and canopy top of maize; therefore errors were directly introduced into biomass estimations. This missing interaction is common during LiDAR detection, particularly in vegetated areas, e.g., forests and cropland. This result agrees with previous studies on other vegetation types (Hopkinson et al., 2005; Li et al., 2014; Luo et al., 2015). To reduce the height underestimation, the point density of LiDAR can be increased by repetitive flights, which are more expensive. In addition, a high small-footprint waveform sampling may decrease the probability of missing interaction. In this study, the height error (18.48 cm) was considered acceptable compared with the absolute minimum field-measured plant height (151.89 cm) in the tasseling stage. Airborne LiDAR is not recommended for monitoring maize at early growing season because the vertical resolution of laser pulse (e.g., 15 cm) is large compared with the typical plant height (e.g., 30 cm). Terrestrial laser scanning (TLS) tools can be effective alternatives to monitor short crops during the early growing period by ensuring a high point density and estimation accuracy of plant heights (Höfle, 2014). However, the spatial extent of TLS is very limited compared with airborne LiDAR, which directly limits the extension of estimation to large areas from plot level. Therefore,
the combined use of mobile laser scanning and UAS-based LiDAR, as well as other remote sensing tools may be promising alternatives. For LAI estimation, most previous studies were empirical and primarily focused on the strong relationships between different LiDAR metrics and LAI in forests (Peduzzi et al., 2012; Solberg et al., 2009; Zhao et al., 2009). In this study, the maize LAI was estimated using the LPI metric based on Beer–Lambert law. Similar estimation approaches can be found in previous studies in different vegetation areas (Luo et al., 2015; Solberg, 2010). The LPI was calculated using normalized laser intensities, and was further tested in maize fields. Compared with forests, maize has a shorter height and denser canopy, which decreased the probability that laser pulses penetrate canopy and reach ground; thus, LiDAR yielded insufficient ground returns in the maize field, leading to a lower LPI and an overestimated LAI (ME = 0.11) according to Beer–Lambert law. Influenced by the short maize canopy, LiDAR cannot discriminate between the first and subsequent returns and most of the recorded laser returns were single returns. This problem might be solved by a high-sampling full-waveform LiDAR using more detailed signal recording and waveform decomposition (e.g., echo width) (Höfle et al., 2012). Additionally, the extinction coefficient was approximated during the LAI retrieval by assuming that the foliage angle distribution was spherical and that LiDAR was approximate to vertical scanning. Although the LiDAR-derived LAIs were comparable to the field measurements, further investigations into the quantification of extinction coefficient based on remote sensing are still required to improve the estimation accuracy. 5.3. Estimation of maize biomass components using LiDAR The biomass components were estimated by LiDAR-derived heights and LAIs based on the high correlation between the fieldmeasured biomass components and two direct factors (H and LAI) in the tasseling stage. High linear correlation of biomass with the field-measured H and LAI were consistently found in the growing
494
W. Li et al. / Ecological Indicators 57 (2015) 486–496
Fig. 7. Spatial distribution map of (a) H (m), (b) LAI, (c) AGB (g/m2 ), and (d) BGB (g/m2 ) estimated by LiDAR. AGB and BGB were predicted using field-based models.
stages (Fig. 8). In a previous study, an exponential model was fitted using AGB, H, and LAI collected in a long-term observation of one maize field during the entire growing season in northern China (Gao et al., 2013). In this study, similar exponential responses of biomass to H and LAI were also found using the long-term data, as shown by the gray fitted curves in Fig. 8. However, we only used the data collected in the tasseling stage to construct the field-based estimation models because we found that the relationships among biomass and H and LAI did not follow identical trends, despite the consistently positive correlations. Most maize plants began to reach the maximum height and LAI during the tasseling stage. In some plots, the canopy height stopped increasing and LAI tended to decrease while biomass components continued to increase when the maize reached the filling and milking stages (Fig. 2). Therefore, the estimation models for individual growth stages were more reasonable compared with those for the entire growing season, particularly when remote sensing proxies e.g. LiDAR were only available in this stage. However, the exponential responses found in this study
may provide valuable insights into the inter-annual variations in maize plants when field and remote sensing data are collected over multiple-years. These provide reliable references for other ecological studies on maize in the same area. The spatial distributions of AGB and BGB showed similar patterns and consistent variations over the entire study area (Fig. 7(c) and (d)). This result indicated a stable balance in the resource allocation between the aboveground and belowground organs of maize in the tasseling stage. This result agrees with a previous study in which the root/shoot growth ratios tended to stabilize after the maize plant experienced long-term growth (Zong and Shanggunag, 2013). Information on the linkage and interaction between aboveground and belowground organs of maize remains limited (Wardle et al., 2004). After the tasseling stage, the maize height remained stable, and LAI began to decrease when the filling stage began. In the milking stage, a large proportion of organic matter and accumulated nutrients within the aboveground organs and belowground root system contributes to the growth of maize fruit, decreasing
W. Li et al. / Ecological Indicators 57 (2015) 486–496
495
Fig. 8. Relationships of the field-measured biomass components to H and LAI across the five growth stages of maize. The red markings represent the data collected in the tasseling stage, and the gray curves were fitted using long-term data. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
the growth rate of plant height and LAI. Therefore, the sensitivity of maize biomass components to H and LAI will significantly decrease after the peak growing season. Since the biomass components were estimated by the LiDAR-derived H and LAI, we suggest that airborne LiDAR is more suitable for monitoring maize during the peak growing season. This study first attempted to estimate maize BGB using only aboveground structural factors (H and LAI), which are easy to collect and monitor over space and time. However, belowground roots can be more influenced by biotic and abiotic stresses compared with aboveground organs. Estimating the indicators of these stresses, such as soil characteristics, biochemical fertilizer, and nutrients uptake, is difficult over larger space in the long-term. Our study only attempted to predict maize BGB from the perspective of maize structure, which can be rapidly and efficiently derived from remotely sensed proxies. Therefore, further studies should be performed to investigate the possibility of evaluating these rootaffecting factors based on remote sensing.
LiDAR-derived height and LAI were proved to be comparable to the field measurements, and were further used to predict the spatial pattern of the maize biomass components. Results showed that the LiDAR-estimated biomass for the field plots were comparable to the field-measured biomass, suggesting that airborne LiDAR has a high potential for estimating canopy height, LAI, and biomass components of maize during the peak growing season.
6. Conclusion
References
This study investigated the potential of using small-footprint airborne LiDAR to estimate maize AGB and BGB during the peak growing season in Zhangye City, Northwest China. Based on in situ measurements, the maize canopy height and LAI were identified as the most direct factors that affect the maize biomass components. Field-based estimation models were proposed to estimate the large-scale AGB and BGB, which can provide reliable references for other ecological studies on maize in the same area.
Acknowledgements This work was supported by China’s Special Funds for Major State Basic Research Project of China (2013CB733405); the National Natural Science Foundation of China (41201345 and 41301389), and the National High Technology Research and Development Program of China (863 Program) (2014AA06A511 and 2012AA12A304).
Becker-Reshef, I., Vermote, E., Lindeman, M., Justice, C., 2010. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens. Environ. 114, 1312–1323, http://dx. doi.org/10.1016/j.rse.2010.01.010 Brassard, B.W., Chen, H.Y.H., Bergeron, Y., 2009. Influence of environmental variability on root dynamics in northern forests. Crit. Rev. Plant Sci. 28, 179–197, http:// dx.doi.org/10.1080/07352680902776572 Franco, J.A., Banon, S., Vicente, M.J., Miralles, J., Martinez-Sanchez, J.J., 2011. Root development in horticultural plants grown under abiotic stress conditions – a review. J. Hortic. Sci. Biotechnol. 86, 543–556, http://dx.doi.org/10.1016/j.rse. 2012.04.005
496
W. Li et al. / Ecological Indicators 57 (2015) 486–496
Gao, S., Niu, Z., Huang, N., Hou, X., 2013. Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2. Int. J. Appl. Earth Observ. Geoinf. 24, 1–8, http://dx.doi.org/10.1016/j.jag.2013.02.002 Garcia, M., Riano, D., Chuvieco, E., Danson, F.M., 2010. Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sens. Environ. 114, 816–830, http://dx.doi.org/10.1016/j.rse. 2009.11.021 Höfle, B., 2014. Radiometric correction of terrestrial LiDAR point cloud data for individual maize plant detection. IEEE Geosci. Remote Sens. Lett. 11, 94–98, http:// dx.doi.org/10.1109/lgrs.2013.2247022 Höfle, B., Hollaus, M., Hagenauer, J., 2012. Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data. ISPRS J. Photogram. Rem. Sens. 67, 134–147, http://dx.doi.org/10.1016/j.isprsjprs.2011. 12.003 Höfle, B., Pfeifer, N., 2007. Correction of laser scanning intensity data: Data and model-driven approaches. ISPRS J. Photogram. Rem. Sens. 62, 415–433, http:// dx.doi.org/10.1016/j.isprsjprs.2007.05.008 Hopkinson, C., Chasmer, L.E., Sass, G., Creed, I.F., Sitar, M., Kalbfleisch, W., Treitz, P., 2005. Vegetation class dependent errors in LiDAR ground elevation and canopy height estimates in a boreal wetland environment. Can. J. Remote Sens. 31, 191–206, http://dx.doi.org/10.5589/m05-007 Huang, N., Wang, L., Guo, Y.Q., Hao, P.Y., Niu, Z., 2014. Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system. PLOS ONE 9, http://dx.doi.org/10.1371/journal.pone.0105150 Iriondo, J.M., Albert, M.J., Escudero, A., 2003. Structural equation modelling: an alternative for assessing causal relationships in threatened plant populations. Biol. Conserv. 113, 367–377, http://dx.doi.org/10.1016/s0006-3207(03)00129-0 Jonsson, M., Wardle, D.A., 2010. Structural equation modelling reveals plantcommunity drivers of carbon storage in boreal forest ecosystems. Biol. Lett. 6, 116–119, http://dx.doi.org/10.1098/rsbl.2009.0613 Kim, G., 2010. AMOS 18.0: Structural Equation Modeling. Hannarae Publishing Co., Seoul. Korhonen, L., Korpela, I., Heiskanen, J., Maltamo, M., 2011. Airborne discrete-return LiDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index. Remote Sens. Environ. 115, 1065–1080, http://dx.doi.org/ 10.1016/j.rse.2010.12.011 Krugh, B., Miles, B.L.D., 1994. The solid-state chlorophyll meter: a novel instrument for rapidly and accurately determining the chlorophyll concentrations in seedling leaves. Maize Genet. Cooper. Newslett. 68, 25. Li, W., Niu, Z., Gao, S., Huang, N., Chen, H., 2014. Correlating the horizontal and vertical distribution of LiDAR point clouds with components of biomass in a Picea crassifolia forest. Forests 5, 1910–1930, http://dx.doi.org/10.3390/f5081910 Li, X., Cheng, G., Liu, S., Xiao, Q., Ma, M., Jin, R., Che, T., Liu, Q., Wang, W., Qi, Y., Wen, J., Li, H., Zhu, G., Guo, J., Ran, Y., Wang, S., Zhu, Z., Zhou, J., Hu, X., Xu, Z., 2013. Heihe Watershed Allied Telemetry Experimental Research (HiWATER): scientific objectives and experimental design. Bull. Am. Meteorol. Soc. 94, 1145–1160, http://dx.doi.org/10.1175/bams-d-12-00154.1 Li, X., Li, X., Li, Z., Ma, M., Wang, J., Xiao, Q., Liu, Q., Che, T., Chen, E., Yan, G., Hu, Z., Zhang, L., Chu, R., Su, P., Liu, Q., Liu, S., Wang, J., Niu, Z., Chen, Y., Jin, R., Wang, W., Ran, Y., Xin, X., Ren, H., 2009. Watershed allied telemetry experimental research. J. Geophys. Res. 114, http://dx.doi.org/10.1029/2008jd011590 Liu, J., Pattey, E., Miller, J.R., McNairn, H., Smith, A., Hu, B., 2010. Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model. Remote Sens. Environ. 114, 1167–1177, http://dx.doi.org/10.1016/j.rse.2010.01.004 Liu, Y., Mu, X., Wang, H., Yan, G., 2012. A novel method for extracting green fractional vegetation cover from digital images. J. Veg. Sci. 23, 406–418, http://dx.doi.org/ 10.1111/j.1654-1103.2011.01373.x Luo, S., Wang, C., Pan, F., Xi, X., Li, G., Nie, S., Xia, S., 2015. Estimation of wetland vegetation height and leaf area index using airborne laser scanning data. Ecol. Indic. 48, 550–559, http://dx.doi.org/10.1016/j.ecolind.2014.09.024
Luo, S., Wang, C., Xi, X., Pan, F., 2014. Estimating FPAR of maize canopy using airborne discrete-return LiDAR data. Opt. Express 22, 5106–5117, http://dx.doi.org/10. 1364/OE.22.005106 Mardia, K.V., 1970. Measures of multivariate skewness and kurtosis with applications. Biometrika 57, 519–530. Mu, X., Huang, S., Chen, Y., 2013. HiWATER: Dataset of Fractional Vegetation Cover in the Middle Reaches of the Heihe River Basin. Heihe Plan Science Data Center, http://dx.doi.org/10.3972/hiwater.043.2013.db Peduzzi, A., Wynne, R.H., Fox, T.R., Nelson, R.F., Thomas, V.A., 2012. Estimating leaf area index in intensively managed pine plantations using airborne laser scanner data. Forest Ecol. Manage. 270, 54–65, http://dx.doi.org/10.3390/rs4061758 Popescu, S.C., Zhao, K., Neuenschwander, A., Lin, C., 2011. Satellite LiDAR vs. small footprint airborne LiDAR: comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sens. Environ. 115, 2786–2797, http://dx.doi.org/10.1016/j.rse.2011.01.026 Pugesek, B.H., Tomer, A., 2003. Structural Equation Modeling: Applications in Ecological and Evolutionary Biology. Cambridge University Press. Richardson, J.J., Moskal, L.M., Kim, S.-H., 2009. Modeling approaches to estimate effective leaf area index from aerial discrete-return LiDAR. Agric. Forest Meteorol. 149, 1152–1160, http://dx.doi.org/10.1016/j.agrformet.2009.02.007 Solberg, S., 2010. Mapping gap fraction LAI and defoliation using various ALS penetration variables. Int. J. Remote Sens. 31, 1227–1244, http://dx.doi.org/10.1080/ 01431160903380672 Solberg, S., Brunner, A., Hanssen, K.H., Lange, H., Næsset, E., Rautiainen, M., Stenberg, P., 2009. Mapping LAI in a Norway spruce forest using airborne laser scanning. Remote Sens. Environ. 113, 2317–2327, http://dx.doi.org/10.1016/j.rse.2009.06. 010 Ullman, J.B., Bentler, P.M., 2003. Structural Equation Modeling. Wiley Online Library. Wang, W., Huang, Y., Xu, F., Ma, C., Wang, J., 2013. HiWATER: Dataset of Biomass Observed in the Middle Reaches of the Heihe River Basin. Heihe Plan Science Data Center, http://dx.doi.org/10.3972/hiwater.122.2013.db Wardle, D.A., Bardgett, R.D., Klironomos, J.N., Setala, H., van der Putten, W.H., Wall, D.H., 2004. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633, http://dx.doi.org/10.1126/science.1094875 Wu, C., Wang, L., Niu, Z., Gao, S., Wu, M., 2010. Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images. Int. J. Remote Sens. 31, 2159–2167, http://dx.doi.org/10.1080/01431161003614382 Xiao, Q., Wen, J., 2013a. HiWATER: Airborne LiDAR-DEM&DSM Data Production in the Middle Reaches of the Heihe River Basin. Heihe Plan Science Data Center, http://dx.doi.org/10.3972/hiwater.010.2013.db Xiao, Q., Wen, J., 2013b. HiWATER: Visible and Near-Infrared Hyperspectral Radiometer (7th July, 2012). Heihe Plan Science Data Center, http://dx.doi.org/ 10.3972/hiwater.011.2013.db Zhang, M., Liu, L., Wang, Z., Bo, Y., Liu, X., 2013. HiWATER: Dataset of Vegetation Type and Plant Structure Investigation in the Middle Reaches of the Heihe River Basin. Heihe Plan Science Data Center, http://dx.doi.org/10.3972/hiwater.036. 2013.db Zhang, M., Ma, M., Wang, X., 2014. HiWATER: Land Cover Map in the Core Experimental Area of Flux Observation Matrix. Heihe Plan Science Data Center, http:// dx.doi.org/10.3972/hiwater.156.2014.db Zhao, J., Li, Y., Wang, Y., Xie, D., 2013. HiWATER: Dataset of Vegetation LAI Measured by LAI2000 in the Middle Reaches of the Heihe River Basin. Heihe Plan Science Data Center, http://dx.doi.org/10.3972/hiwater.058.2013.db Zhao, K., Popescu, S., Nelson, R., 2009. LiDAR remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sens. Environ. 113, 182–196, http://dx.doi.org/10.1016/j.rse.2008.09.009 Zheng, G., Moskal, L.M., 2009. Retrieving Leaf Area Index (LAI) using remote sensing: theories, methods and sensors. Sensors (Basel) 9, 2719–2745, http://dx.doi.org/ 10.1016/j.jag.2012.05.002 Zong, Y., Shanggunag, Z., 2013. Short term effects of evaluated atmospheric CO2 on root dynamics of water-stressed maize. J. Food Agric. Environ. 11, 1037–1041.