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Spatial upscaling of green aboveground biomass derived from MODIS-based NDVI in arid and semiarid grasslands
Accepted Manuscript Spatial upscaling of green aboveground biomass derived from MODIS-based NDVI in arid and semiarid grasslands Juanjuan Xue, Yonghui Ge, Hongrui Ren PII: DOI: Reference:
S0273-1177(17)30526-4 http://dx.doi.org/10.1016/j.asr.2017.07.016 JASR 13326
To appear in:
Advances in Space Research
Received Date: Revised Date: Accepted Date:
27 February 2017 16 May 2017 10 July 2017
Please cite this article as: Xue, J., Ge, Y., Ren, H., Spatial upscaling of green aboveground biomass derived from MODIS-based NDVI in arid and semiarid grasslands, Advances in Space Research (2017), doi: http://dx.doi.org/ 10.1016/j.asr.2017.07.016
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Spatial upscaling of green aboveground biomass derived from MODIS-based NDVI in arid and semiarid grasslands Juanjuan Xue, Yonghui Ge, Hongrui Ren* College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China Corresponding author: Hongrui Ren Tel: +86 351 6014472 Fax: +86 351 6014472 E-mail address: [email protected] Mailing address: College of Mining Engineering, Taiyuan University of Technology, No. 79, West Yingze Street, Taiyuan 030024, China
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Abstract
Accurate estimation of green aboveground biomass is important for sustainable use of
grassland resources in arid and semiarid grasslands. Nevertheless, it is difficult to achieve spatial upscaling of green aboveground biomass estimation using traditional spatial upscaling methods in arid and semiarid grasslands due to its inherent heterogeneity. In the study, a new spatial upscaling algorithm was proposed to estimate green aboveground biomass in the desert steppe of Inner Mongolia. The algorithm was successfully employed for spatial upscaling of green aboveground biomass estimation from MOD13Q1 NDVI (fine resolution) to MOD13A2 NDVI (coarse resolution) based on field measurements in the desert steppe. Results showed that, the correlation between distributed green aboveground biomass (obtained from fine resolution) and lumped green aboveground biomass (obtained from coarse resolution) was improved, and root mean squared error and relative error decreased after upscaling. Statistical analyses performing on the slopes and intercepts of the fitted lines between distributed green aboveground biomass and lumped green aboveground biomass demonstrated that, there was no significant difference (P > 0.05) between the fitted line and the 1:1 line after upscaling, and there was significant difference (P < 0.05) between the fitted line and the 1:1 line before upscaling. These indicated that, lumped green aboveground biomass after upscaling was much closer to distributed aboveground green biomass than lumped green aboveground biomass before upscaling. The algorithm proposed in the study could play an important role in large-scale green aboveground biomass investigation in arid and semiarid grasslands.
Keywords Spatial upscaling; Green aboveground biomass; Normalized difference vegetation index; Arid and semiarid grasslands
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1. Introduction The retrieval algorithms and models based on remote sensing information are all established in small scale with homogeneous land surface (Wu et al., 2009; Ren et al., 2011; Ren and Zhou, 2012). Error will appear if we use these algorithms and models in large scale with heterogeneous land surface (Ehleringer and Field, 1993). Nevertheless, only in large scale biophysical and biochemical parameters derived from these algorithms and models are of great significance for human activities, such as weather forecasting, environment monitoring, crop yield estimation, and vegetation investigation (Curran and Foody, 1994; Waring and Running, 1998; Csillag et al., 2000). Therefore, the spatial upscaling of these parameters must be considered in the application of remote sensing. The concept of scale is a subject to many studies of natural science, such as geography, ecology, biology, physics, and astronomy. Spatial scaling means a process of bridging the gaps in quantitative information derived at different spatial scales (Simic et al., 2004). The process from small scale (fine resolution) to large scale (coarse resolution) is referred to as upscaling. Otherwise, the process from large scale (coarse resolution) to small scale (fine resolution) is referred to as downscaling. Transferring algorithms from one scale to another without causing considerable errors is one of the greatest challenges in the process of spatial scaling (Chen, 1999). This scaling challenge could be attributed to spatial heterogeneity of land surface (Ehleringer and Field, 1993). Two approaches have been employed to quantify spatial heterogeneity: first, textural parameters method (Hu and Islam, 1997), and second, contextural parameters method (Chen, 1999). For the former, based on the variability in brightness of pixels, spatial heterogeneity is quantified by different parameters, such as autocorrelation indices (Qi and Wu, 1996) and
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variance and covariance (Hall et al., 1992; Friedl et al., 1995; Atkinson et al., 1996; Hay et al., 1997; Wu and Dennis, 2000). The later approach describes the size and shape of features showed on an image including the areas, distributions, and patterns of the features (Chen, 1999; Anita et al., 2004). Specifically, various land cover types as subpixel information are considered in this approach (Mayaux and Lambin, 1995; Chen, 1999; Anita et al., 2004; Kevin et al., 2005; Jin et al., 2007). While the textural parameters method provided just approximations of the scaling effect, the contextural parameters method was found to be far more effective in improving leaf area index mapping (Chen, 1999) in the same study. Some other works also showed that, texture-based method was generally used for characterizing the surface heterogeneity (Rastetter et al., 1992; Bonan et al., 1993; Friedl, 1996). Grasslands in arid and semiarid areas has been described as inherently heterogeneous owing to composition, productivity, and diversity varying across multiple scales. The spatial heterogeneity of the land surface could introduce great uncertainties in large spatial-scale analyses in arid and semiarid grasslands (Ehleringer and Field, 1993). Therefore, it is critical to scale land surface parameters from one resolution to another for ecosystem processes and biogeochemistry models in arid and semiarid grasslands (Hu and Islam, 1997; Ren and Zhou, 2014a, 2014b). Nevertheless, above two approaches maybe lose their capability for quantifying spatial heterogeneity in arid and semiarid grasslands due to very lower vegetation cover. Texture parameters method cannot accurately describe spatial relationships, distribution patterns, and fragmentation degrees of features between different spatial resolutions (Rastetter et al., 1992; Bonan et al., 1993; Friedl, 1996) in arid and semiarid grasslands. It is also difficult to get subpixel information of vegetation cover types, and contexture method cannot be employed to quantify spatial heterogeneity in arid
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and semiarid grasslands (Zhang et al., 2009). Therefore, how to achieve spatial scaling of vegetation parameters in arid and semiarid grasslands is one of the greatest challenges in remote sensing of grasslands. Besides the spatial scaling, there are other reasons causing the uncertainty of estimating green aboveground biomass in arid and semiarid grasslands. The most important ones of which are the number and representativeness of the sampling sites, the temporal consistency between the sites and remotely sensed data, and the satellite-derived vegetation indices (Liang et al., 2016). Over the past 30 years, lots of studies have been conducted to explore the effects of these factors on the accuracy of green aboveground biomass estimation in arid and semiarid grasslands (Eisfelder et al., 2014; Liang et al., 2016). Nevertheless, few studies have been conducted to explore the spatial scaling in arid and semiarid grasslands (Tueller, 1987; Lu, 2006; Guli et al., 2009; Zhang et al., 2009). Thus, the objective of this study was to establish a spatial scaling algorithm in arid and semiarid grasslands. Specifically, a spatial upscaling approach was proposed to estimate green aboveground biomass, an important vegetation parameter, in the desert steppe of Inner Mongolia, where very lower vegetation cover and higher spatial heterogeneity, using MODIS normalized difference vegetation index (NDVI) with different spatial resolutions. To achieve the objective, a large-scale field survey was conducted to measure green aboveground biomass during the growing season of 2013 in the desert steppe of Inner Mongolia.
2. Materials and methods 2.1. Study area Field survey was conducted in the desert steppe of Xilingol League, Inner Mongolia
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Autonomous Region, China. As shown in Fig. 1, the desert steppe is mainly located in the East Sunite Banner and West Sunite Banner of Xilingol League. There is a arid and semiarid temperate continental climate in the study area, where precipitation is the principal environmental factor controlling vegetation growth. Based on long-term meteorological data (1961-2000) from weather stations in East Sunite Banner and West Sunite Banner, mean annual precipitation is approximately 140 mm, with 85% distributed in the growing seasons, and mean annual temperature is 4.1 ℃ with mean monthly temperature ranging from -15.9 ℃ in January to 22.5 ℃ in July. The soil in the study area is classified as argids (suborder) of aridisols (order). As a typical temperate desert steppe, vegetation is dominated by Stipa klemenzii Roshev. and Stipa gobica Roshev. The main species include Caragana microphylia Lam., Artemisia frigida Willd.Sp.Pl., Agropyron desertorum (Fisch.) Schut., and Cleistogenes squarrosa (Trin.) Keng. [Insert Fig. 1] 2.2. Data collection Our field survey was conducted in late September 2013. As shown in Fig. 1, 39 sampling sites (500 m × 500 m) were randomly selected in the study area. Within each sampling site, vegetation is evenly distributed. Longitude and latitude information of these sites was also obtained by differential global position system (GPS). A total of 20 plots (0.5 m × 0.5 m) were randomly established within each sampling site to measure green aboveground biomass. All living plants within each plot were clipped at the ground surface, and fresh weight recorded on an electronic balance with a sensitivity of 0.01 g. Green aboveground biomass (g/m2) of each plot was then calculated by dividing the weight of living plants by the area of the plot. Green aboveground biomass from 20 plots for each sampling site was averaged to provide a single green aboveground
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biomass for the sampling site. MODIS-based NDVI (normalized difference vegetation index) used in the study was derived from Terra MODIS reflectance composite products MOD13Q1 and MOD13A2, which were all generated from 16 days with maximum value composite (MVC). The day of year (DOY) for each NDVI image represents the first day of the period of the 16-day composite. The spatial resolutions were 250 m and 1000 m for MOD13Q1 NDVI and MOD13A2 NDVI, respectively. According to the location of study area, the 16-day composite products MOD13Q1 and MOD13A2 from September 14, 2013 to September 29, 2013, numbered as h25v04 and h26v04 in global sinusoidal projection system, were downloaded from the National Aeronautic and Space Administration Land Process Distributed Active Archive Center. In the study, the products were further processed with MODIS Reprojection Tool (MRT Version 4.0). The data format was converted from HDF to GeoTIFF, and the projection was converted from sinusoidal projection to WGS84/Albers projection. 2.3. Spatial upscaling algorithm In the study, a spatial upscaling method was proposed to estimate green aboveground biomass in the desert steppe of Inner Mongolia using MODIS-based NDVI with different spatial resolutions. Specifically, the spatial upscaling of green aboveground biomass estimation from MOD13Q1 NDVI (250 m × 250 m) to MOD13A2 NDVI (1000 m × 1000 m) was achieved by employing two kinds of green aboveground biomass with spatial resolution of 1000 m × 1000 m (Fig. 2), namely, distributed green aboveground biomass (BiomassD) and lumped green aboveground biomass (BiomassL). As shown in Fig. 2, BiomassD at a spatial resolution of 1000 m × 1000 m was obtained from
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the average value of green aboveground biomass estimated from NDVI with spatial resolution of 250 m × 250 m. BiomassL at a spatial resolution of 1000 m × 1000 m was directly estimated from NDVI with spatial resolution of 1000 m × 1000 m based on estimation model established at spatial resolution of 250 m × 250 m. Specifically, BiomassD and BiomassL were calculated by the following equation.
BiomassD
1 16 1 16 Biomass f(NDVIi ) i 16 i 1 16 i 1
(1)
BiomassL f(NDVI)
(2)
where NDVIi is NDVI with resolution of 250 m × 250 m obtained from MOD13Q1, Biomassi is green aboveground biomass with resolution of 250 m × 250 m, NDVI is NDVI with resolution of 1000 m × 1000 m obtained from MOD13A2, and f(x) is estimation model of green aboveground biomass established at spatial resolution of 250 m × 250 m. To reduce mismatch error and to avoid pixel contamination, The NDVIi was derived from the MOD13Q1 pixel (250 m × 250 m) located as close as possible to the centre of the sampling site (500 m × 500 m). The difference between BiomassL and BiomassD was the error of green aboveground biomass estimation due to spatial heterogeneity. In the study, the objective of spatial upscaling was to make BiomassL as close to or equal to BiomassD as possible. With statistical methods, the spatial upscaling of green aboveground biomass estimation was explored from MOD13Q1 NDVI (fine resolution) to MOD13A2 NDVI (coarse resolution) in the desert steppe. [Insert Fig. 2]
3. Results 3.1. Establishment of spatial upscaling algorithm
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The performance of MOD13Q1 NDVI for estimating green aboveground biomass of all sampling sites is presented in Fig. 3. The regression relation was statistically significant at P < 0.001. The NDVI could account for 66% of the variability in measured green aboveground biomass of all sampling sites. [Insert Fig. 3] On the basis of the linear regression model as shown in Fig. 3, BiomassD and BiomassL can be calculated in the study by the following equations:
BiomassD f(NDVI250) 839.9NDVI250 107.2 839.9NDVI250 107.2 BiomassL f(NDVI1000) 839.9NDVI1000 107.2
(3)
(4)
where NDVI250 is average value of NDVI with spatial resolution of 250 m × 250 m from 16 pixels, and NDVI1000 is NDVI with spatial resolution of 1000 m × 1000 m. The objective in the study was to make BiomassL as close to or equal to BiomassD as possible. Nevertheless, according to the mathematical characteristics of Eqs. (3) (4), the objective was simplified to explore correlation between NDVI at fine resolution (250 m × 250 m) and NDVI at coarse resolution (1000 m × 1000 m). Based on land cover data in the study area in 2013 obtained from the MODIS land cover product (MCD12Q1, type 5), 100 samples with grassland cover were extracted from MOD13Q1 NDVI and MOD13A2 NDVI imageries above, respectively, using by systemic-random sampling. A sample was a pixel for MOD13A2 NDVI, while a sample was 16 pixels for MOD13Q1 NDVI corresponding to a sample for MOD13A2 NDVI. As shown in Fig. 4, regression analysis was conducted between MOD13Q1 NDVI and MOD13A2 NDVI, and linear relationship showed a
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good agreement (R2 = 0.83, P < 0.001). The regression model was described as follows. NDVI250 0.87NDVI1000 0.02
(5)
Accordingly, the spatial upscaled BiomassL (BiomassL') could be calculated by the following equations:
BiomassL 839.9 (0.87NDVI1000 0.02) 107.2
(6)
BiomassL 730.7NDVI1000 90.4
(7)
[Insert Fig. 4] 3.2. Validation of spatial upscaling algorithm Another independent 100 samples with grassland cover were also extracted from MOD13Q1 NDVI and MOD13A2 NDVI imageries above, respectively, using by systemic-random sampling. The validation of the spatial upscaling algorithm was conducted using these independent samples. Firstly, BiomassD, BiomassL, and BiomassL' for these samples were computed with the Eqs. (3) (4) (7). Then regression analyses were conducted between BiomassD and BiomassL and BiomassD and BiomassL', respectively. The relative performance of regression models were evaluated by coefficient of determination (R2), root mean square error (RMSE), and relative error (RE). Compared with regression model (R2 = 0.79, RMSE = 10.3 g/m2, RE = 25.3%) between BiomassD and BiomassL (Fig. 5(a)), the regression model between BiomassD and BiomassL' yielded higher R2 (0.84), lower RMSE (7.6 g/m2) and RE (18.7%). [Insert Fig. 5] The Student's t-test was also employed to assess whether the slope and intercept of the regression line between BiomassD and BiomassL/BiomassL' differed statistically from 1 and 0,
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respectively, against the ideal regression line (1:1 line). The test was performed at a confidence level of 95%. As shown in Table 1, before spatial upscaling, the slope and intercept of the fitted line between BiomassL and BiomassD were significantly different from 1 and 0 (P < 0.05), respectively. Nevertheless, after spatial upscaling, the slope and intercept of the fitted line between BiomassL' and BiomassD were not significantly different from 1 and 0 (P > 0.05), respectively. In other words, the fitted line between BiomassL and BiomassD was significantly different from the 1:1 line, and the fitted line between BiomassL' and BiomassD was not significantly different from the 1:1 line. These results showed that BiomassL' after spatial upscaling was much closer to BiomassD than BiomassL before spatial upscaling. [Insert Table 1] As shown in Fig. 6, the green aboveground biomass at the time of field survey in the desert steppe was also estimated with BiomassD (Eq. (3)), BiomassL (Eq. (4)), and BiomassL' (Eq. (7)), respectively. The spatial distribution tendencies of the green aboveground biomass were overall consistent for three methods. However, before spatial scaling, the green aboveground biomass calculated from BiomassL was obviously more than that from BiomassD. And after spatial scaling, the green aboveground biomass calculated from BiomassL' was in good agreement with that from BiomassD. Further analyses indicated that, the mean values in the desert steppe were 52.8 g/m2 , 71.6 g/m2, and 49.8 g/m2 for green aboveground biomass estimated from BiomassD, BiomassL, and BiomassL', respectively. This result once again showed that BiomassL' after spatial upscaling was much closer to BiomassD than BiomassL before spatial upscaling. [Insert Fig. 6]
4. Discussion 11
Two types of measures, texture-based and contexture-based approaches, have often been employed to perform spatial scaling (Hu and Islam, 1997; Chen, 1999). However, due to lower vegetation cover and higher spatial heterogeneity, these approaches cannot apply to arid and semiarid grasslands, for example, the desert steppe of Inner Mongolia in the study. According to our survey data, green vegetation cover was less than 30% within all sampling sites in the study area. As also presented in Fig. 3 and Fig. 4, the values of NDVIs derived from MOD13Q1 and MOD13A2 within all sampling sites were less than 0.26, which indicated a very lower vegetation greenness and density in the study area. The dependence on land cover types and nonlinearity of the algorithms are two major issues in the texture-based and contexture-based approaches (Hu and Islam, 1997; Chen, 1999; Anita, et al., 2004). Compared with the texture-based and contexture-based methods, the spatial upscaling algorithm, used in the study, does not need to quantify the distribution patterns and fragmentation degrees, and get subpixel information (land cover types) of land surface in arid and semiarid grasslands. And that the algorithm is also linear in the spatial upscaling (Fig. 4). Our results confirmed that, the spatial upscaling algorithm was useful for spatial upscaling of green aboveground biomass estimation in the desert steppe of Inner Mongolia. In the study, the proposed spatial upscaling algorithm was executed with 16-day MOD13Q1 NDVI (250 m × 250 m) and MOD13A2 NDVI (1000 m × 1000 m). Without doubt, other remotely sensed data with different spatial and temporal resolutions could also be used in the spatial upscaling algorithm. Nevertheless, due to the large spatial scale, low cost, and temporal continuity, MODIS products have long been the primary remotely sensed data sources in grassland resources surveys (Eisfelder et al., 2012).
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NDVI (Rouse et al., 1974) has been widely used for monitoring green aboveground biomass in grasslands (Eisfelder et al., 2012). Nevertheless, it may lose its ability for green aboveground biomass estimation in arid and semiarid areas due to the effect of bare soil (Boschetti et al., 2007). Based on NDVI algorithm, many soil-adjusted vegetation indices were established to overcome the soil noise. The most common of these indices are SAVI (soil-adjusted vegetation index) (Huete, 1988), MSAVI (modified soil-adjusted vegetation index) (Qi et al., 1994), OSAVI (optimized soil-adjusted vegetation index) (Rondeaux et al., 1996), TSAVI (transformed soil-adjusted vegetation index) (Baret et al., 1989), ATSAVI (adjusted transformed soil-adjusted vegetation index) (Baret and Guyot, 1991), and PVI (perpendicular vegetation index) (Richardson and Wiegand, 1977). Theoretically, the performance of these soil-adjusted vegetation indices is better than that of NDVI for green aboveground biomass estimation in the study area. With MOD09Q1 data, we further evaluated the performance of these indices. Unexpected, the accuracy of NDVI (R2 = 0.71, RMSE = 13.95 g/m2) was better than that of these soil-adjusted vegetation indices (SAVI: R2 = 0.61, RMSE = 16.62 g/m2; MSAVI: R2 = 0.56, RMSE = 17.24 g/m2; OSAVI: R2 = 0.67, RMSE = 14.58 g/m2; TSAVI: R2 = 0.58, RMSE = 16.75 g/m2; ATSAVI: R2 = 0.56, RMSE = 17.46 g/m2; PVI: R2 = 0.47, RMSE = 18.94 g/m2). Therefore, NDVI was employed to explore the spatial upscaling of estimate green aboveground biomass in the study area. To overcome the mismatch between the sampling sites and the pixels, the ideal approaches are to select the sampling sites in very homogeneous areas (Sannier et al., 2002; Wessels et al., 2006) or to use a minimum of sampling plots within each pixel (Baccini et al., 2008). Due to low vegetation cover and coarse spatial resolution, these sampling methods cannot be employed in the study area. Thus, we used a different sampling method in the study. We overlaid the sampling
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plots within each sampling site (500 m × 500 m) on MOD13Q1 NDVI imagery (250 m × 250 m) to selected pixel located as close as possible to the centre of the sampling site. This approach could avoid pixel contamination and reduce the mismatch error in arid and semiarid areas.
5. Conclusions In the study, a new spatial upscaling algorithm was established to perform spatial upscaling of land surface parameters in arid and semiarid grasslands. We have demonstrated the good utility of this algorithm for green aboveground biomass estimation in the desert steppe of Inner Mongolia. The algorithm is a sample and practical method for filling the gap of spatial scaling in arid and semiarid grasslands. We hope future studies would utilize the proposed algorithm to estimate other vegetation parameters such as vegetation cover, chlorophyll content, and leaf area index with various spatial and temporal resolutions remotely sensed data in arid and semiarid areas.
Acknowledgements This research was jointly supported by Program for the Innovative Talents of Higher Learning Institutions of Shanxi, and Natural Science Foundation of Shanxi. We are also grateful to the anonymous reviewers for their valuable comments and suggestions on this paper.
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Grass Forage Sci. 70, 611-619. http://dx.doi.org/10.1111/gfs.12152. Richardson, A.J., Wiegand, C.L., 1977. Distinguishing vegetation from soil background information. Photogramm. Eng. Rem. S. 43, 1541-1552. Rondeaux, G., Steven, M., Baret, F., 1996. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 55, 95-107. http://dx.doi.org/10.1016/0034-4257(95)00186-7. Rouse J.E., Haas, R.H., Schell, J.A., Deering, D.W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. In: Freden S.C., Mercanti E.P., Becker M.A. (eds). Third ERTS Symposium. Washington, DC: NASA SP-351 I pp. 309-317. Sannier, C.A.D., Taylor, J.C., Plessis, W.D., 2002. Real-time monitoring of vegetation biomass with NOAA-AVHRR in Etosha National Park, Namibia, for fire risk assessment. Int. J. Remote Sens. 23, 71-89. http://dx.doi.org/10.1018/01431160010006863. Simic, A., Chen, J.M., Liu, J., Csillag, F., 2004. Spatial scaling of net primary productivity using subpixel information. Remote Sens. Environ. 93, 246-258. http://dx.doi.org/10.1016/j.rse.200 4.07.008. Tueller, P.T., 1987. Remote sensing science application in arid environments. Remote Sens. Environ. 23, 143-154. http://dx.doi.org/10.1016/0034-4257(87)90034-4. Waring, R.H., Running, S.W., 1998. Forest ecosystem: Analysis t multiple scales. San Diego, CA: Academic Press. Wessels, K.J., Prince, S.D., Zambatis, N., Macfadyen, S., Frost, P.E., Van Zyl, D., 2006. Relationship between herbaceous biomass and 1-km2 advances very high resolution radiometer (AVHRR) NDVI in Kruger National Park, South Africa. Int. J. Remote Sens. 27, 951-973. http://dx.doi.org/10.1080/01431160500169098.
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Wu, J., Dennis, E., 2000. Multiscale analysis of landscape heterogeneity: scale variance and pattern metrics. Geogr. Inform. Sci. 6, 6-9. http://dx.doi.org/10.1080/10824 000009480529. Wu, H., Jiang, X.G., Xi, X.H., Li, C.R., Li, Z.L., 2009. Comparison and analysis of two general scaling methods for remotely sensed information. J. Remote Sens. 13(2), 183-189. http://dx.do i.org/1007-4619(2009)02-0183-07. Zhang, H.B., Yang, G.X., Li, G., Chen, B.R., Xin, X.P., 2009. Study on the MODIS NDVI and NOAA NDVI based spatial scaling method—A case study in Inner Mongolia. Pratacultural Sci. 26(10), 39-45.
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Fig. 1 Location of study area and distribution of 39 sampling sites for green aboveground biomass measurements.
Fig. 2 Spatial upscaling algorithm for eatimating green aboveground biomass in the desert steppe of Inner Mongolia.
Fig. 3 linear regression analysis between green aboveground biomass and MOD13Q1 NDVI (250 m × 250 m).
Fig. 4 Linear regression analysis between MOD13Q1 NDVI and MOD13A2 NDVI.
Fig. 5 Linear regression analysis between BiomassD and BiomassL before spatial upscaling (a) and between BiomassD and BiomassL' after spatial upscaling (b).
Fig. 6 Green aboveground biomass at the time of field survey estimated from BiomassD, BiomassL', and BiomassL in the desert steppe of Inner Mongolia.
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Table 1 Tests on slopes (not significantly different from 1) and intercepts (not significantly different from 0) of the fitted lines between BiomassD and BiomassL/BiomassL'.
Slope
Intercept
Lumped vs Distributed value
t
P
value
t
P
BiomassL vs BiomassD
0.804*
4.801
<0.05
4.568*
2.263
<0.05
BiomassL' vs BiomassD
0.995
0.121
>0.05
-1.554
-0.782
>0.05
*Significant difference from 1 (slope) or 0 (intercept).
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S0273-1177(17)30526-4 http://dx.doi.org/10.1016/j.asr.2017.07.016 JASR 13326
To appear in:
Advances in Space Research
Received Date: Revised Date: Accepted Date:
27 February 2017 16 May 2017 10 July 2017
Please cite this article as: Xue, J., Ge, Y., Ren, H., Spatial upscaling of green aboveground biomass derived from MODIS-based NDVI in arid and semiarid grasslands, Advances in Space Research (2017), doi: http://dx.doi.org/ 10.1016/j.asr.2017.07.016
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Spatial upscaling of green aboveground biomass derived from MODIS-based NDVI in arid and semiarid grasslands Juanjuan Xue, Yonghui Ge, Hongrui Ren* College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China Corresponding author: Hongrui Ren Tel: +86 351 6014472 Fax: +86 351 6014472 E-mail address: [email protected] Mailing address: College of Mining Engineering, Taiyuan University of Technology, No. 79, West Yingze Street, Taiyuan 030024, China
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Abstract
Accurate estimation of green aboveground biomass is important for sustainable use of
grassland resources in arid and semiarid grasslands. Nevertheless, it is difficult to achieve spatial upscaling of green aboveground biomass estimation using traditional spatial upscaling methods in arid and semiarid grasslands due to its inherent heterogeneity. In the study, a new spatial upscaling algorithm was proposed to estimate green aboveground biomass in the desert steppe of Inner Mongolia. The algorithm was successfully employed for spatial upscaling of green aboveground biomass estimation from MOD13Q1 NDVI (fine resolution) to MOD13A2 NDVI (coarse resolution) based on field measurements in the desert steppe. Results showed that, the correlation between distributed green aboveground biomass (obtained from fine resolution) and lumped green aboveground biomass (obtained from coarse resolution) was improved, and root mean squared error and relative error decreased after upscaling. Statistical analyses performing on the slopes and intercepts of the fitted lines between distributed green aboveground biomass and lumped green aboveground biomass demonstrated that, there was no significant difference (P > 0.05) between the fitted line and the 1:1 line after upscaling, and there was significant difference (P < 0.05) between the fitted line and the 1:1 line before upscaling. These indicated that, lumped green aboveground biomass after upscaling was much closer to distributed aboveground green biomass than lumped green aboveground biomass before upscaling. The algorithm proposed in the study could play an important role in large-scale green aboveground biomass investigation in arid and semiarid grasslands.
Keywords Spatial upscaling; Green aboveground biomass; Normalized difference vegetation index; Arid and semiarid grasslands
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1. Introduction The retrieval algorithms and models based on remote sensing information are all established in small scale with homogeneous land surface (Wu et al., 2009; Ren et al., 2011; Ren and Zhou, 2012). Error will appear if we use these algorithms and models in large scale with heterogeneous land surface (Ehleringer and Field, 1993). Nevertheless, only in large scale biophysical and biochemical parameters derived from these algorithms and models are of great significance for human activities, such as weather forecasting, environment monitoring, crop yield estimation, and vegetation investigation (Curran and Foody, 1994; Waring and Running, 1998; Csillag et al., 2000). Therefore, the spatial upscaling of these parameters must be considered in the application of remote sensing. The concept of scale is a subject to many studies of natural science, such as geography, ecology, biology, physics, and astronomy. Spatial scaling means a process of bridging the gaps in quantitative information derived at different spatial scales (Simic et al., 2004). The process from small scale (fine resolution) to large scale (coarse resolution) is referred to as upscaling. Otherwise, the process from large scale (coarse resolution) to small scale (fine resolution) is referred to as downscaling. Transferring algorithms from one scale to another without causing considerable errors is one of the greatest challenges in the process of spatial scaling (Chen, 1999). This scaling challenge could be attributed to spatial heterogeneity of land surface (Ehleringer and Field, 1993). Two approaches have been employed to quantify spatial heterogeneity: first, textural parameters method (Hu and Islam, 1997), and second, contextural parameters method (Chen, 1999). For the former, based on the variability in brightness of pixels, spatial heterogeneity is quantified by different parameters, such as autocorrelation indices (Qi and Wu, 1996) and
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variance and covariance (Hall et al., 1992; Friedl et al., 1995; Atkinson et al., 1996; Hay et al., 1997; Wu and Dennis, 2000). The later approach describes the size and shape of features showed on an image including the areas, distributions, and patterns of the features (Chen, 1999; Anita et al., 2004). Specifically, various land cover types as subpixel information are considered in this approach (Mayaux and Lambin, 1995; Chen, 1999; Anita et al., 2004; Kevin et al., 2005; Jin et al., 2007). While the textural parameters method provided just approximations of the scaling effect, the contextural parameters method was found to be far more effective in improving leaf area index mapping (Chen, 1999) in the same study. Some other works also showed that, texture-based method was generally used for characterizing the surface heterogeneity (Rastetter et al., 1992; Bonan et al., 1993; Friedl, 1996). Grasslands in arid and semiarid areas has been described as inherently heterogeneous owing to composition, productivity, and diversity varying across multiple scales. The spatial heterogeneity of the land surface could introduce great uncertainties in large spatial-scale analyses in arid and semiarid grasslands (Ehleringer and Field, 1993). Therefore, it is critical to scale land surface parameters from one resolution to another for ecosystem processes and biogeochemistry models in arid and semiarid grasslands (Hu and Islam, 1997; Ren and Zhou, 2014a, 2014b). Nevertheless, above two approaches maybe lose their capability for quantifying spatial heterogeneity in arid and semiarid grasslands due to very lower vegetation cover. Texture parameters method cannot accurately describe spatial relationships, distribution patterns, and fragmentation degrees of features between different spatial resolutions (Rastetter et al., 1992; Bonan et al., 1993; Friedl, 1996) in arid and semiarid grasslands. It is also difficult to get subpixel information of vegetation cover types, and contexture method cannot be employed to quantify spatial heterogeneity in arid
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and semiarid grasslands (Zhang et al., 2009). Therefore, how to achieve spatial scaling of vegetation parameters in arid and semiarid grasslands is one of the greatest challenges in remote sensing of grasslands. Besides the spatial scaling, there are other reasons causing the uncertainty of estimating green aboveground biomass in arid and semiarid grasslands. The most important ones of which are the number and representativeness of the sampling sites, the temporal consistency between the sites and remotely sensed data, and the satellite-derived vegetation indices (Liang et al., 2016). Over the past 30 years, lots of studies have been conducted to explore the effects of these factors on the accuracy of green aboveground biomass estimation in arid and semiarid grasslands (Eisfelder et al., 2014; Liang et al., 2016). Nevertheless, few studies have been conducted to explore the spatial scaling in arid and semiarid grasslands (Tueller, 1987; Lu, 2006; Guli et al., 2009; Zhang et al., 2009). Thus, the objective of this study was to establish a spatial scaling algorithm in arid and semiarid grasslands. Specifically, a spatial upscaling approach was proposed to estimate green aboveground biomass, an important vegetation parameter, in the desert steppe of Inner Mongolia, where very lower vegetation cover and higher spatial heterogeneity, using MODIS normalized difference vegetation index (NDVI) with different spatial resolutions. To achieve the objective, a large-scale field survey was conducted to measure green aboveground biomass during the growing season of 2013 in the desert steppe of Inner Mongolia.
2. Materials and methods 2.1. Study area Field survey was conducted in the desert steppe of Xilingol League, Inner Mongolia
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Autonomous Region, China. As shown in Fig. 1, the desert steppe is mainly located in the East Sunite Banner and West Sunite Banner of Xilingol League. There is a arid and semiarid temperate continental climate in the study area, where precipitation is the principal environmental factor controlling vegetation growth. Based on long-term meteorological data (1961-2000) from weather stations in East Sunite Banner and West Sunite Banner, mean annual precipitation is approximately 140 mm, with 85% distributed in the growing seasons, and mean annual temperature is 4.1 ℃ with mean monthly temperature ranging from -15.9 ℃ in January to 22.5 ℃ in July. The soil in the study area is classified as argids (suborder) of aridisols (order). As a typical temperate desert steppe, vegetation is dominated by Stipa klemenzii Roshev. and Stipa gobica Roshev. The main species include Caragana microphylia Lam., Artemisia frigida Willd.Sp.Pl., Agropyron desertorum (Fisch.) Schut., and Cleistogenes squarrosa (Trin.) Keng. [Insert Fig. 1] 2.2. Data collection Our field survey was conducted in late September 2013. As shown in Fig. 1, 39 sampling sites (500 m × 500 m) were randomly selected in the study area. Within each sampling site, vegetation is evenly distributed. Longitude and latitude information of these sites was also obtained by differential global position system (GPS). A total of 20 plots (0.5 m × 0.5 m) were randomly established within each sampling site to measure green aboveground biomass. All living plants within each plot were clipped at the ground surface, and fresh weight recorded on an electronic balance with a sensitivity of 0.01 g. Green aboveground biomass (g/m2) of each plot was then calculated by dividing the weight of living plants by the area of the plot. Green aboveground biomass from 20 plots for each sampling site was averaged to provide a single green aboveground
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biomass for the sampling site. MODIS-based NDVI (normalized difference vegetation index) used in the study was derived from Terra MODIS reflectance composite products MOD13Q1 and MOD13A2, which were all generated from 16 days with maximum value composite (MVC). The day of year (DOY) for each NDVI image represents the first day of the period of the 16-day composite. The spatial resolutions were 250 m and 1000 m for MOD13Q1 NDVI and MOD13A2 NDVI, respectively. According to the location of study area, the 16-day composite products MOD13Q1 and MOD13A2 from September 14, 2013 to September 29, 2013, numbered as h25v04 and h26v04 in global sinusoidal projection system, were downloaded from the National Aeronautic and Space Administration Land Process Distributed Active Archive Center. In the study, the products were further processed with MODIS Reprojection Tool (MRT Version 4.0). The data format was converted from HDF to GeoTIFF, and the projection was converted from sinusoidal projection to WGS84/Albers projection. 2.3. Spatial upscaling algorithm In the study, a spatial upscaling method was proposed to estimate green aboveground biomass in the desert steppe of Inner Mongolia using MODIS-based NDVI with different spatial resolutions. Specifically, the spatial upscaling of green aboveground biomass estimation from MOD13Q1 NDVI (250 m × 250 m) to MOD13A2 NDVI (1000 m × 1000 m) was achieved by employing two kinds of green aboveground biomass with spatial resolution of 1000 m × 1000 m (Fig. 2), namely, distributed green aboveground biomass (BiomassD) and lumped green aboveground biomass (BiomassL). As shown in Fig. 2, BiomassD at a spatial resolution of 1000 m × 1000 m was obtained from
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the average value of green aboveground biomass estimated from NDVI with spatial resolution of 250 m × 250 m. BiomassL at a spatial resolution of 1000 m × 1000 m was directly estimated from NDVI with spatial resolution of 1000 m × 1000 m based on estimation model established at spatial resolution of 250 m × 250 m. Specifically, BiomassD and BiomassL were calculated by the following equation.
BiomassD
1 16 1 16 Biomass f(NDVIi ) i 16 i 1 16 i 1
(1)
BiomassL f(NDVI)
(2)
where NDVIi is NDVI with resolution of 250 m × 250 m obtained from MOD13Q1, Biomassi is green aboveground biomass with resolution of 250 m × 250 m, NDVI is NDVI with resolution of 1000 m × 1000 m obtained from MOD13A2, and f(x) is estimation model of green aboveground biomass established at spatial resolution of 250 m × 250 m. To reduce mismatch error and to avoid pixel contamination, The NDVIi was derived from the MOD13Q1 pixel (250 m × 250 m) located as close as possible to the centre of the sampling site (500 m × 500 m). The difference between BiomassL and BiomassD was the error of green aboveground biomass estimation due to spatial heterogeneity. In the study, the objective of spatial upscaling was to make BiomassL as close to or equal to BiomassD as possible. With statistical methods, the spatial upscaling of green aboveground biomass estimation was explored from MOD13Q1 NDVI (fine resolution) to MOD13A2 NDVI (coarse resolution) in the desert steppe. [Insert Fig. 2]
3. Results 3.1. Establishment of spatial upscaling algorithm
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The performance of MOD13Q1 NDVI for estimating green aboveground biomass of all sampling sites is presented in Fig. 3. The regression relation was statistically significant at P < 0.001. The NDVI could account for 66% of the variability in measured green aboveground biomass of all sampling sites. [Insert Fig. 3] On the basis of the linear regression model as shown in Fig. 3, BiomassD and BiomassL can be calculated in the study by the following equations:
BiomassD f(NDVI250) 839.9NDVI250 107.2 839.9NDVI250 107.2 BiomassL f(NDVI1000) 839.9NDVI1000 107.2
(3)
(4)
where NDVI250 is average value of NDVI with spatial resolution of 250 m × 250 m from 16 pixels, and NDVI1000 is NDVI with spatial resolution of 1000 m × 1000 m. The objective in the study was to make BiomassL as close to or equal to BiomassD as possible. Nevertheless, according to the mathematical characteristics of Eqs. (3) (4), the objective was simplified to explore correlation between NDVI at fine resolution (250 m × 250 m) and NDVI at coarse resolution (1000 m × 1000 m). Based on land cover data in the study area in 2013 obtained from the MODIS land cover product (MCD12Q1, type 5), 100 samples with grassland cover were extracted from MOD13Q1 NDVI and MOD13A2 NDVI imageries above, respectively, using by systemic-random sampling. A sample was a pixel for MOD13A2 NDVI, while a sample was 16 pixels for MOD13Q1 NDVI corresponding to a sample for MOD13A2 NDVI. As shown in Fig. 4, regression analysis was conducted between MOD13Q1 NDVI and MOD13A2 NDVI, and linear relationship showed a
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good agreement (R2 = 0.83, P < 0.001). The regression model was described as follows. NDVI250 0.87NDVI1000 0.02
(5)
Accordingly, the spatial upscaled BiomassL (BiomassL') could be calculated by the following equations:
BiomassL 839.9 (0.87NDVI1000 0.02) 107.2
(6)
BiomassL 730.7NDVI1000 90.4
(7)
[Insert Fig. 4] 3.2. Validation of spatial upscaling algorithm Another independent 100 samples with grassland cover were also extracted from MOD13Q1 NDVI and MOD13A2 NDVI imageries above, respectively, using by systemic-random sampling. The validation of the spatial upscaling algorithm was conducted using these independent samples. Firstly, BiomassD, BiomassL, and BiomassL' for these samples were computed with the Eqs. (3) (4) (7). Then regression analyses were conducted between BiomassD and BiomassL and BiomassD and BiomassL', respectively. The relative performance of regression models were evaluated by coefficient of determination (R2), root mean square error (RMSE), and relative error (RE). Compared with regression model (R2 = 0.79, RMSE = 10.3 g/m2, RE = 25.3%) between BiomassD and BiomassL (Fig. 5(a)), the regression model between BiomassD and BiomassL' yielded higher R2 (0.84), lower RMSE (7.6 g/m2) and RE (18.7%). [Insert Fig. 5] The Student's t-test was also employed to assess whether the slope and intercept of the regression line between BiomassD and BiomassL/BiomassL' differed statistically from 1 and 0,
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respectively, against the ideal regression line (1:1 line). The test was performed at a confidence level of 95%. As shown in Table 1, before spatial upscaling, the slope and intercept of the fitted line between BiomassL and BiomassD were significantly different from 1 and 0 (P < 0.05), respectively. Nevertheless, after spatial upscaling, the slope and intercept of the fitted line between BiomassL' and BiomassD were not significantly different from 1 and 0 (P > 0.05), respectively. In other words, the fitted line between BiomassL and BiomassD was significantly different from the 1:1 line, and the fitted line between BiomassL' and BiomassD was not significantly different from the 1:1 line. These results showed that BiomassL' after spatial upscaling was much closer to BiomassD than BiomassL before spatial upscaling. [Insert Table 1] As shown in Fig. 6, the green aboveground biomass at the time of field survey in the desert steppe was also estimated with BiomassD (Eq. (3)), BiomassL (Eq. (4)), and BiomassL' (Eq. (7)), respectively. The spatial distribution tendencies of the green aboveground biomass were overall consistent for three methods. However, before spatial scaling, the green aboveground biomass calculated from BiomassL was obviously more than that from BiomassD. And after spatial scaling, the green aboveground biomass calculated from BiomassL' was in good agreement with that from BiomassD. Further analyses indicated that, the mean values in the desert steppe were 52.8 g/m2 , 71.6 g/m2, and 49.8 g/m2 for green aboveground biomass estimated from BiomassD, BiomassL, and BiomassL', respectively. This result once again showed that BiomassL' after spatial upscaling was much closer to BiomassD than BiomassL before spatial upscaling. [Insert Fig. 6]
4. Discussion 11
Two types of measures, texture-based and contexture-based approaches, have often been employed to perform spatial scaling (Hu and Islam, 1997; Chen, 1999). However, due to lower vegetation cover and higher spatial heterogeneity, these approaches cannot apply to arid and semiarid grasslands, for example, the desert steppe of Inner Mongolia in the study. According to our survey data, green vegetation cover was less than 30% within all sampling sites in the study area. As also presented in Fig. 3 and Fig. 4, the values of NDVIs derived from MOD13Q1 and MOD13A2 within all sampling sites were less than 0.26, which indicated a very lower vegetation greenness and density in the study area. The dependence on land cover types and nonlinearity of the algorithms are two major issues in the texture-based and contexture-based approaches (Hu and Islam, 1997; Chen, 1999; Anita, et al., 2004). Compared with the texture-based and contexture-based methods, the spatial upscaling algorithm, used in the study, does not need to quantify the distribution patterns and fragmentation degrees, and get subpixel information (land cover types) of land surface in arid and semiarid grasslands. And that the algorithm is also linear in the spatial upscaling (Fig. 4). Our results confirmed that, the spatial upscaling algorithm was useful for spatial upscaling of green aboveground biomass estimation in the desert steppe of Inner Mongolia. In the study, the proposed spatial upscaling algorithm was executed with 16-day MOD13Q1 NDVI (250 m × 250 m) and MOD13A2 NDVI (1000 m × 1000 m). Without doubt, other remotely sensed data with different spatial and temporal resolutions could also be used in the spatial upscaling algorithm. Nevertheless, due to the large spatial scale, low cost, and temporal continuity, MODIS products have long been the primary remotely sensed data sources in grassland resources surveys (Eisfelder et al., 2012).
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NDVI (Rouse et al., 1974) has been widely used for monitoring green aboveground biomass in grasslands (Eisfelder et al., 2012). Nevertheless, it may lose its ability for green aboveground biomass estimation in arid and semiarid areas due to the effect of bare soil (Boschetti et al., 2007). Based on NDVI algorithm, many soil-adjusted vegetation indices were established to overcome the soil noise. The most common of these indices are SAVI (soil-adjusted vegetation index) (Huete, 1988), MSAVI (modified soil-adjusted vegetation index) (Qi et al., 1994), OSAVI (optimized soil-adjusted vegetation index) (Rondeaux et al., 1996), TSAVI (transformed soil-adjusted vegetation index) (Baret et al., 1989), ATSAVI (adjusted transformed soil-adjusted vegetation index) (Baret and Guyot, 1991), and PVI (perpendicular vegetation index) (Richardson and Wiegand, 1977). Theoretically, the performance of these soil-adjusted vegetation indices is better than that of NDVI for green aboveground biomass estimation in the study area. With MOD09Q1 data, we further evaluated the performance of these indices. Unexpected, the accuracy of NDVI (R2 = 0.71, RMSE = 13.95 g/m2) was better than that of these soil-adjusted vegetation indices (SAVI: R2 = 0.61, RMSE = 16.62 g/m2; MSAVI: R2 = 0.56, RMSE = 17.24 g/m2; OSAVI: R2 = 0.67, RMSE = 14.58 g/m2; TSAVI: R2 = 0.58, RMSE = 16.75 g/m2; ATSAVI: R2 = 0.56, RMSE = 17.46 g/m2; PVI: R2 = 0.47, RMSE = 18.94 g/m2). Therefore, NDVI was employed to explore the spatial upscaling of estimate green aboveground biomass in the study area. To overcome the mismatch between the sampling sites and the pixels, the ideal approaches are to select the sampling sites in very homogeneous areas (Sannier et al., 2002; Wessels et al., 2006) or to use a minimum of sampling plots within each pixel (Baccini et al., 2008). Due to low vegetation cover and coarse spatial resolution, these sampling methods cannot be employed in the study area. Thus, we used a different sampling method in the study. We overlaid the sampling
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plots within each sampling site (500 m × 500 m) on MOD13Q1 NDVI imagery (250 m × 250 m) to selected pixel located as close as possible to the centre of the sampling site. This approach could avoid pixel contamination and reduce the mismatch error in arid and semiarid areas.
5. Conclusions In the study, a new spatial upscaling algorithm was established to perform spatial upscaling of land surface parameters in arid and semiarid grasslands. We have demonstrated the good utility of this algorithm for green aboveground biomass estimation in the desert steppe of Inner Mongolia. The algorithm is a sample and practical method for filling the gap of spatial scaling in arid and semiarid grasslands. We hope future studies would utilize the proposed algorithm to estimate other vegetation parameters such as vegetation cover, chlorophyll content, and leaf area index with various spatial and temporal resolutions remotely sensed data in arid and semiarid areas.
Acknowledgements This research was jointly supported by Program for the Innovative Talents of Higher Learning Institutions of Shanxi, and Natural Science Foundation of Shanxi. We are also grateful to the anonymous reviewers for their valuable comments and suggestions on this paper.
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Fig. 1 Location of study area and distribution of 39 sampling sites for green aboveground biomass measurements.
Fig. 2 Spatial upscaling algorithm for eatimating green aboveground biomass in the desert steppe of Inner Mongolia.
Fig. 3 linear regression analysis between green aboveground biomass and MOD13Q1 NDVI (250 m × 250 m).
Fig. 4 Linear regression analysis between MOD13Q1 NDVI and MOD13A2 NDVI.
Fig. 5 Linear regression analysis between BiomassD and BiomassL before spatial upscaling (a) and between BiomassD and BiomassL' after spatial upscaling (b).
Fig. 6 Green aboveground biomass at the time of field survey estimated from BiomassD, BiomassL', and BiomassL in the desert steppe of Inner Mongolia.
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Table 1 Tests on slopes (not significantly different from 1) and intercepts (not significantly different from 0) of the fitted lines between BiomassD and BiomassL/BiomassL'.
Slope
Intercept
Lumped vs Distributed value
t
P
value
t
P
BiomassL vs BiomassD
0.804*
4.801
<0.05
4.568*
2.263
<0.05
BiomassL' vs BiomassD
0.995
0.121
>0.05
-1.554
-0.782
>0.05
*Significant difference from 1 (slope) or 0 (intercept).
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