Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Study Area
2.2. Field Sampling Data
2.3. Remote Sensing Data and its Processing
2.3.1. Introduction to MODIS Productivity Data
2.3.2. Processing of Remote Sensing Data
2.4. Establishment and Verification of the Model
3. Results
3.1. Total Aboveground Biomass and its Distribution in Different Banners
3.2. Spatial Distribution of Aboveground Biomass
3.3. Interannual Variation in Aboveground Biomass
4. Discussions
4.1. Potentials Analysis of Model
- (1)
- Through the accumulation of PSNnet data every eight days from the beginning of the growing season to the peak of the growing season, a good correlation was achieved between the obtained accumulated PSNnet data and the peak growing season NDVI data for the corresponding spatial point, showing coefficients of determination (R2) up to 0.75 (Figure 7). Because there have been many previous studies on statistical models of the NDVI and aboveground biomass [35–38], we can assume a good correlation between the PSNnet data and the aboveground biomass data. Furthermore, we can assume that using the PSNnet data and the aboveground biomass data to build the model and then retrieve the aboveground biomass data is a practical method. In addition, the MODIS productivity data used in this study fully considered the effects of temperature, precipitation and other climatic factors during the process of estimating vegetation productivity. Compared with the NDVI data employed in traditional methods of biomass estimation, MODIS productivity data can better reflect the effects of environmental stresses.
- (2)
- The temporal matching between remotely sensed images and ground survey greatly affects the accuracy of remote sensing based models for grassland biomass estimation. The database of the extensive filed samples and their matching remotely sensed data is the basis of improving the model precision and stability. In this study, a sound database combining multi-year accumulated PSNnet data and ground survey biomass data with strict temporal matching was developed, which was further applied to biomass estimation models.
- (3)
- Two methods are often used to evaluate model performances. One is based on the coefficient of determination (R2), another way is to assess model error. In general, a high R2 or a low error value often indicates a good fit between the model developed and the ground survey data [39]. In this study, we compared the correlation between NDVI data and biomass, as well as accumulated PSNnet data and biomass. The result showed that R2 between calculated PSNnet data and biomass was a little higher than R2 between NDVI data and biomass, as shown in Figure 8. We used ground survey data from the Xilingol grassland for the years 2005–2012 and MODIS productivity data for the same time period to establish statistics-based models for biomass estimation, with an overall accuracy of 69%, which is close to highest accuracy (74%) by Jin, et al. [38]. In addition, NDVI data are prone to an “oversaturation” phenomenon if the vegetation coverage is higher, which decreases the sensitivity of biomass estimation, whereas MODIS productivity data can overcome this oversaturation problem. Therefore, in high vegetation cover conditions, the biomass estimation accuracy by MODIS productivity data would be higher than the biomass estimation accuracy by NDVI data.
4.2. Uncertainties of Model
4.3. Comparison with Previous Estimates
5. Conclusions
Acknowledgments
Conflicts of Interest
- Author ContributionsAll authors contributed extensively to the work. Fen Zhao, Bin Xu designed and performed experiments. Bin Xu and Yunxiang Jin reviewed the manuscript and gave comments and suggestions to the manuscript. Lang Xia performed the satellite datasets preprocessing. All authors performed the field survey. All authors participated in editing and revision of the paper.
References
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Model | R2 | F Value | RMSE | REE | Precision | |
---|---|---|---|---|---|---|
(g·m−2) | (%) | |||||
Unitary linear function | y = 1.097× − 4.776 | 0.55 | 1168.75 | 26.67 | 0.31 | 69 |
Logarithm function | y = 71.308 × lnx − 224.634 | 0.55 | 1154.46 | 26.77 | 0.40 | 60 |
Power function | y = 0.361x1.226 | 0.65 | 1739.58 | 27.43 | 0.39 | 61 |
Exponential function | y = 17.038 × e0.0178x | 0.58 | 1315.50 | 34.52 | 0.43 | 57 |
Banners | Grassland Area (km2) | Total Aboveground Biomass (Tg) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | Average | ||
East Ujimqin Banner | 45,060 | 5.72 | 3.79 | 2.49 | 3.90 | 4.06 | 4.15 | 4.11 | 5.00 | 4.15 |
Abaga Banner | 27,492 | 1.79 | 1.51 | 1.38 | 2.00 | 1.73 | 1.65 | 1.56 | 2.40 | 1.75 |
West Ujimqin Banner | 23,726 | 3.49 | 2.53 | 1.83 | 2.68 | 2.08 | 2.50 | 2.46 | 3.06 | 2.58 |
Sonid Left Banner | 34,814 | 1.21 | 1.00 | 1.01 | 1.30 | 1.20 | 0.95 | 1.05 | 1.54 | 1.15 |
Xilinhot City | 15,753 | 1.44 | 1.23 | 1.00 | 1.40 | 1.10 | 1.28 | 1.09 | 1.60 | 1.27 |
Erenhot City | 186 | 0.0029 | 0.0020 | 0.0017 | 0.0026 | 0.0020 | 0.0015 | 0.0017 | 0.0025 | 0.0021 |
Sonid Right Banner | 25,212 | 0.90 | 0.70 | 0.59 | 0.80 | 0.67 | 0.55 | 0.65 | 0.88 | 0.72 |
Zhenglan Banner | 10,281 | 1.09 | 1.05 | 0.84 | 1.15 | 0.99 | 0.87 | 1.05 | 1.15 | 1.02 |
Zhengxiangbai Banner | 6256 | 0.48 | 0.48 | 0.36 | 0.50 | 0.42 | 0.35 | 0.46 | 0.52 | 0.44 |
Xianghuang Banner | 5018 | 0.30 | 0.31 | 0.22 | 0.29 | 0.27 | 0.23 | 0.31 | 0.33 | 0.28 |
Duolun County | 3955 | 0.59 | 0.56 | 0.47 | 0.57 | 0.53 | 0.48 | 0.58 | 0.58 | 0.54 |
Taipusi Banner | 3496 | 0.43 | 0.47 | 0.36 | 0.50 | 0.39 | 0.38 | 0.46 | 0.48 | 0.43 |
Total | 201,249 | 17.44 | 13.62 | 10.56 | 15.07 | 13.42 | 13.39 | 13.79 | 17.54 | 14.35 |
Banners | Grassland Area (km2) | Aboveground Biomass Density (g·m−2) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | Average | ||
East Ujimqin Banner | 45,060 | 127.04 | 84.20 | 55.26 | 86.54 | 90.03 | 92.13 | 91.20 | 111.03 | 92.18 |
Abaga Banner | 27,492 | 65.19 | 55.07 | 50.18 | 72.66 | 62.93 | 60.09 | 56.88 | 87.03 | 63.75 |
West Ujimqin Banner | 23,726 | 147.30 | 106.82 | 77.13 | 112.89 | 87.87 | 105.18 | 103.72 | 129.01 | 108.74 |
Sonid Left Banner | 34,814 | 34.62 | 27.98 | 29.09 | 37.37 | 34.37 | 27.31 | 30.24 | 44.28 | 33.16 |
Xilinhot City | 15,753 | 91.60 | 78.25 | 63.23 | 88.41 | 69.78 | 81.17 | 69.25 | 101.70 | 80.42 |
Erenhot City | 186 | 15.54 | 10.85 | 9.40 | 14.06 | 10.80 | 7.88 | 9.13 | 13.48 | 11.39 |
Sonid Right Banner | 25,212 | 35.53 | 27.50 | 23.60 | 31.80 | 26.38 | 21.84 | 25.90 | 34.89 | 28.43 |
Zhenglan Banner | 10,281 | 105.60 | 102.60 | 81.82 | 111.67 | 95.95 | 84.87 | 101.70 | 111.58 | 99.47 |
Zhengxiangbai Banner | 6256 | 76.18 | 76.76 | 57.60 | 79.26 | 66.34 | 55.31 | 73.53 | 83.30 | 71.03 |
Xianghuang Banner | 5018 | 59.94 | 61.44 | 44.64 | 57.57 | 52.96 | 45.02 | 62.25 | 65.43 | 56.16 |
Duolun County | 3955 | 149.48 | 140.62 | 118.06 | 144.66 | 133.70 | 121.23 | 146.92 | 145.73 | 137.55 |
Taipusi Banner | 3496 | 122.19 | 135.71 | 103.88 | 141.70 | 110.21 | 108.85 | 131.52 | 138.07 | 124.02 |
Total | 201,249 | 86.66 | 67.67 | 52.48 | 74.90 | 66.66 | 66.51 | 68.53 | 87.15 | 71.32 |
Grassland Types | Grassland Area (km2) | Aboveground Biomass Density (g·m−2) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | Average | ||
Low-land meadow | 25,981 | 109.74 | 83.87 | 62.04 | 88.41 | 79.65 | 80.92 | 84.84 | 101.02 | 86.31 |
Improved grassland | 472 | 67.42 | 63.52 | 50.80 | 68.73 | 58.06 | 54.71 | 60.26 | 73.89 | 62.17 |
Montane meadow | 1581 | 202.45 | 154.89 | 123.03 | 153.47 | 145.24 | 145.58 | 159.17 | 164.75 | 156.07 |
Temperate meadow-steppe | 24,875 | 155.81 | 110.19 | 75.29 | 109.83 | 110.44 | 111.83 | 117.66 | 130.82 | 115.23 |
Temperate steppe-desert | 5108 | 24.94 | 19.78 | 19.66 | 24.91 | 23.72 | 17.07 | 20.33 | 28.69 | 22.39 |
Temperate steppe | 108,488 | 80.40 | 65.11 | 51.62 | 75.06 | 63.34 | 64.82 | 64.39 | 88.02 | 69.10 |
Temperate desert-steppe | 29,598 | 30.68 | 23.36 | 23.89 | 31.03 | 27.79 | 21.17 | 24.20 | 35.71 | 27.23 |
Temperate desert | 143 | 22.93 | 16.58 | 16.91 | 22.43 | 18.53 | 14.64 | 16.94 | 23.93 | 19.11 |
Marsh | 330 | 126.45 | 71.32 | 55.81 | 83.17 | 75.64 | 84.50 | 84.36 | 97.95 | 84.90 |
Total | 196,576 | 85.88 | 66.52 | 51.54 | 73.88 | 65.71 | 65.71 | 67.39 | 86.29 | 70.36 |
Aboveground Biomass Densities (g·m−2) | ||||
---|---|---|---|---|
Researchers | Study Area | Temperate Desert Steppe | Temperate Steppe | Temperate Meadow Steppe |
Ni, et al. [40] | China | 45.56 | 88.96 | 146.47 |
Ma, et al. [41] | Inner Mongolia | 56.5 | 133.4 | 196.7 |
Piao, et al. [42] | China | 43.57 | 91.52 | 144.9 |
Fan, et al. [43] | China | 111.11 | 151.11 | 182.22 |
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Zhao, F.; Xu, B.; Yang, X.; Jin, Y.; Li, J.; Xia, L.; Chen, S.; Ma, H. Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China. Remote Sens. 2014, 6, 5368-5386. https://doi.org/10.3390/rs6065368
Zhao F, Xu B, Yang X, Jin Y, Li J, Xia L, Chen S, Ma H. Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China. Remote Sensing. 2014; 6(6):5368-5386. https://doi.org/10.3390/rs6065368
Chicago/Turabian StyleZhao, Fen, Bin Xu, Xiuchun Yang, Yunxiang Jin, Jinya Li, Lang Xia, Shi Chen, and Hailong Ma. 2014. "Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China" Remote Sensing 6, no. 6: 5368-5386. https://doi.org/10.3390/rs6065368