Zone II • Versions EN1
Abstract: Aboveground biomass (AGB) reflects the forage availability and herbivore carrying capacity of grasslands, and is also an important component of grassland carbon stocks. Therefore, long-term AGB data is important information for the development of sustainable animal husbandry, and the formulation of grassland ecosystem management policies. In addition, it is fundamental to understanding carbon storage and the bio-geochemical dynamics of terrestrial ecosystems. In this study, we developed empirical remote sensing inversion AGB estimation models, based on field-observed AGB data and long-term normalized difference vegetation index (NDVI) data. Yearly AGB data were generated for the northern temperate and Tibetan Plateau alpine grasslands of China, from 1982 to 2015, using AGB estimation models. The dataset can be used for studies of grassland productivity and carbon storage, and for related decision-making in China.
Keywords: China; grassland biomass; productivity; NDVI; remote sensing model
|Title||A dataset for aboveground biomass of the northern temperate and Tibetan Plateau alpine grasslands in China, based on field investigation and remote sensing inversion (1982 – 2015)|
|Corresponding author||Yu Guirui (firstname.lastname@example.org)|
|Data authors||Jiao Cuicui, Yu Guirui, Chen Zhi, He Nianpeng|
|Geographical scope||Grasslands in Inner Mongolia, Qinghai, Tibet, Xinjiang, Gansu, and Ningxia|
|Spatial resolution||8 km||Data volume||81 MB|
|Data format||ArcGIS TIFF|
|Data service system||<http://www.cnern.org.cn/data/meta?id=40577>;|
|Source(s) of funding||National Key Research and Development Program of China (2016YFA0600104); Early Career Foundation of Sichuan University of Science & Engineering (2017RCSK19).|
|Dataset composition||The dataset consists of 38 data documents, 34 of which store yearly AGB for the northern temperate and Tibetan Plateau alpine grasslands in China. Each data document is recorded as NGTP_YYYY_AGB.tif. NGTP, YYYY, and AGB denote the northern temperate grasslands and Tibetan Plateau alpine grasslands in China, year, and aboveground biomass, respectively. The other 4 data documents, recorded as NGTP_1980sAGB.tif, NGTP_1990sAGB.tif, NGTP_2000sAGB.tif, and NGTP_2010sAGB.tif, store averaged AGB values covering the periods 1982–1989, 1990–1999, 2000–2009, and 2010–2015, respectively. The data unit is g·m-2.|
Biomass is defined as the mass of living biological organisms per unit of space at a given time. Aboveground biomass (AGB) can indicate the growth status of vegetation, forage production1-2 and ecosystem quality3 in grassland ecosystems. In addition, AGB is also a key component of terrestrial carbon stocks.4 Despite representing a lower proportion in carbon stocks, AGB is closely linked to nutrient cycles and energy flow in grasslands,5 which in China cover approximately 3.9 million km2, making this stock the second largest area of grasslands globally. The northern temperate and Tibetan Plateau alpine grasslands account for 79% of Chinese grasslands, forming both an important basis for animal husbandry, and ecological buffer zones for northern China.6 Therefore, evaluating AGB dynamics for the Chinese northern temperate and Tibetan Platea alpine grasslands is important for the protection of grassland resources, for livestock husbandry development, for water and soil conservation, and to facilitate sustainable natural resource use.1-2
Various grassland AGB estimation methods have been proposed in previous studies. Biomass harvesting is a common and reliable method for estimating grassland AGB, however, it has limitations due to its intense field sampling and sample processing requirements.1, 7-8 Combining AGB field observations with remote sensing techniques has proven effective for evaluating grassland AGB, at regional or global scales.8-9 Disturbance and pressures on Chinese northern temperate and Tibetan Plateau alpine grasslands have been continuous, due to China's rapid economic and social development over the past four decades, and the dynamics of grassland AGB changes are a good indicator of this development.
Exploring temporal variation of AGB is basic information required to make policy decisions for the sustainable management of grasslands. However, previous research on AGB estimations for Chinese grasslands primarily concentrated on reporting mean AGB values during given periods,10 and a few studies have reported on the dynamics of grassland AGB over time periods extending over a decade to two decades.11 Until now, there has not been a publicly accessible, long-term AGB dataset for the Chinese northern temperate and Tibetan Plateau alpine grasslands covering the past three decades (1982-2015), and this has hindered relevant grassland research, to some degree.
In this study, grassland AGB estimation models were developed, based on field observations and long-term Normalized Difference Vegetation Index (NDVI) time series data. Then, annual AGB stocks for the Chinese northern temperate and Tibetan Plateau alpine grasslands during the past three decades were calculated, using the developed AGB estimation models. Mean grassland AGB values during 1980s (1982–1989), 1990s (1990–1999), 2000s (2000–2009), and 2010s (2010–2015), were generated, based on yearly AGB data. Finally, a long-term spatial AGB dataset was obtained for the Chinese northern temperate and Tibetan Plateau alpine grasslands. This dataset will be public and freely available, which will assist those wishing to conduct relevant grassland research, such as forage production, herbivore carrying capacity, and carbon stock inventories.
2.1 Data collection and study area
2.1.1 Study area
The Chinese northern temperate and Tibetan Plateau alpine grasslands are widely distributed, extending over approximately 23° of latitudes and 50° of longitude. The grasslands located in Inner Mongolia, Qinghai, Xizang, Xinjiang, Gansu, and Ningxia were those mainly considered, while those in areas west of the northern end of Liaodong Bay, and in Shaanxi, were not included in this study, in line with previous studies on northern Chinese grasslands.11-12 Northern temperate grasslands included in this study extend from the Greater Khingan Range and the Lesser Khingan Range in the W, to international boundaries located in western Xinjiang, in the SW. The Tibetan Plateau alpine grasslands cover most areas of Xizang and Qinghai, and small regions of Gansu and Xinjiang.12 There are mainly six grassland vegetation types: montane meadows, meadow steppes, typical steppes, desert steppes, alpine steppes, and alpine meadows, in the Chinese northern temperate and Tibetan Plateau alpine grasslands, according to the 1:1000000-scale vegetation map of China (Figure 1).13
The geographical extent of the Chinese northern temperate grasslands and the Tibetan Plateau alpine grasslands was identified by rationalizing the collated spatial extents for Inner Mongolia, Qinghai, Xizang, Xinjiang, Gansu, and Ningxia, with the spatial extent of grasslands based on the 1:1000000-scale vegetation map of China13. The geographical extent of the Tibetan Plateau alpine grasslands of China was identified through rationalizing the spatial extent of the Tibetan Plateau (details in the section 2.1.4) with the spatial extent of grasslands in China. The geographical extent of the northern temperate grasslands was identified by overlaying the geographical extent of both the northern temperate grasslands and the Tibetan Plateau alpine grasslands with the geographical extent of the Tibetan Plateau alpine grasslands in China.
2.1.2 Field AGB observations
Field AGB observations were obtained through the traditional harvesting method. There were primarily two sources for these field-observed AGB data: (1) 787 AGB observations from 230 publications cited in the Web of Science (www.Webofknowledge.com), and China National Knowledge Infrastructure (http://epub.cnki.net), and (2) 630 AGB observations contributed by researchers in this study. In total, 1583-site annual AGBobservations, from 1395 field sites, were initially collected for the study area, covering the past three decades (1982–2015). Details of the field AGB observations have been described in Jiao et al.,14-15 Ma et al.10, 16 and Xu et al.17
Three criteria were employed to eliminate unsuitable observations: (1) insufficient site-description, such as missing latitude or longitude information; (2) insufficient information on specific sampling times; and (3) outliers, that is, observations that fell outside the range of mean ± 3 standard deviations for each vegetation type. Finally, field-observed AGB for 1259 siteyears, from 1104 sites, were used in this study (Figure 1).
2.1.3 Long-term NDVI time-series data
Long-term, NDVI time series data, for the period 1982 to 2015, were acquired from third-generation NDVI datasets (NDVI3g.1) produced by the Global Inventory Modelling and Mapping Studies (GIMMS).18 NDVI3g.1 data19 were generated by applying the 15-day maximum value composite (MVC) to images obtained by Advanced Very High Resolution Radiometers (AVHRRs) aboard National Oceanic and Atmospheric Administration (NOAA) satellites.18 NDVI3g.1 data were at a spatial resolution of 0.083° (⁓ 8 km × 8 km), and at a biweekly temporal resolution.18, 20-21
2.1.4 Spatial vector data
The 1:1000000-scale vegetation map of China generated by the Editorial Committee of Vegetation Map of China, Chinese Academy of Sciences13 was downloaded from the Resource and Environment Data Cloud Platform.22 The geographical extent of the Tibetan Plateau was also sourced from the Resource and Environment Data Cloud Platform.22
2.2 Development of the AGB estimation model
2.2.1 Data preprocessing
The following three data preprocessing steps were implemented, before developing the grassland AGB estimation models:
(1) The MVC approach (Equation (1)23 was applied to biweekly NDVI series data, to generate monthly NDVI data.
In (1), i is the month, from 1–12, is the maximum of two NDVI images available for month, and and are NDVI images for the first and second halves of month, respectively.
(2) Then, annual NDVI series data, input parameters for the AGB estimation models, were obtained from monthly NDVI time series data. Annual maximum NDVI values, or average NDVI values during the growing season, have been used as annual NDVI values for estimating grassland AGB in previous studies. The growing season usually extends from April to October, in the grasslands, however, the optimal monthly time period for obtaining annual NDVI values for grassland AGB estimation varied, due to spatial heterogeneities, such as different climate or vegetation conditions, across the study area.24-25
Thus, the optimal monthly NDVI time period might from April–October, from May–October, from June–October, from July–October, from April–September, from May–September, from June–September, from July–September, from April–August, from May–August, from June–August, or from July–August. Therefore, there were 13 different annual NDVI values generated by calculating the annual maximum NDVI, and 12 different averaged NDVI values for the various composite periods within the overall range of April–October (Figure 2, Appendix S1).
(3) 13 annual NDVI values for each field AGB observation were extracted, according to the corresponding field-observed, AGB sampling year, and the geographical locations. In turn, a dataset was generated in which every record included one field AGB observation (AGBobs), and 13 corresponding annual NDVI values.
Approximately 75% of the field AGB observations were randomly selected for development of the AGB estimation model, with the remaining 25% used for model validation. Only grasslands with an annual maximum NDVI value of > 0.1 were analyzed in this study, due to NDVI images of sparsely vegetated areas being affected by the spectral characteristics of soils.
2.2.2 Model development
Regression correlations between AGBobs and 13 corresponding annual NDVI values were analyzed, based on the preprocessed AGBobs and NDVI data, respectively. In total, 52 regression models were developed, including linear, exponential, power, and logarithmic models (Appendix S1). Coefficients of determination (R2 , Equation (2)), and root mean square errors (RMSE, Equation (3)) were employed to assess the performance of each of the 52 regression models.
In (2) and (3), R2 is the coefficient of determination between modeled AGB data (AGBmod ) and field-observed AGB data (AGBobs ), which denotes a similar pattern between AGBmod and AGBobs, and the fraction of AGBobs variation that can be explained by the model. RMSE is the root mean square error between AGBmod and AGBobs , which represents biases that cause modeled AGB data to differ from field-observed AGB data. The symbol n represents the number of field AGB observations included in the dataset for model validation.
The best AGB estimation model was identified as the model with the highest R2 and lowest RMSE, of the 52 regression models developed.26-27 The 52 models needed to be optimized when no model satisfied both maximum R2 and minimum RMSE criteria26, and this was achieved by calculating averaged results for AGB estimations obtained from the model with the maximum R2 , and the model with the minimum RMSE, at the pixel scale of this study.
As stated above, we developed AGB estimation models for the Chinese northern temperate and Tibetan Plateau alpine grasslands. Then, the performance of the models was assessed, based on the remaining 25% of the field AGB observations. Accuracy assessments indicated that the models with both the highest R2 and the lowest RMSE were not among the 52 regression models developed for either the northern temperate grasslands, or the Tibetan Plateau alpine grasslands of China (Appendix S1).
According to methods for developing an AGB estimation model, the optimal AGB estimation model was composed of the fitted regressions satisfying the highest R2 and meeting the lowest RMSE criteria, among the 52 regression models for both the northern temperate grasslands, and the Tibetan Plateau alpine grasslands, respectively (Appendix S1, bold fonts). More details on the development for grassland AGB estimation models have been reported in Jiao et al.,14 and a technical flowchart for the development of grassland AGB estimation models in this study can be seen in Figure 2.
Optimized AGB estimation models for the northern temperate grasslands (AGB-RSMNG, Equation (4)), and the Tibetan Plateau alpine grasslands (AGB-RSMTP, Equation (5)) were developed.
In (4) and (5), x denotes the geographic position, and t is the year, from 1982 to 2015. and separately denote the grassland AGB at position in year for the northern temperate grasslands, and for the Tibetan Plateau alpine grasslands, respectively. , ,anddenote, respectively, the averaged NDVI values of the period from July–October, from June–September, from April–August, and from May–August, for year t.
Spatial yearly AGB data for the Chinese northern temperate and Tibetan Plateau alpine grasslands were generated through a series of data processing steps. Averaged AGB values during the 1980s (1982–1989), 1990s (1990–1999), 2000s (2000–2009), and 2010s (2010–2015), were calculated, based on yearly AGB data. Finally, a long-term spatial AGB dataset was obtained for the grasslands. In this dataset, the data unit is g·m-2, the data format is ARCGIS TIFF, and the data spatial resolution is 8 km. The spatial patterns of averaged AGB values, during 1980s, 1990s, 2000s, and 2010s, are shown in Figure 3.
In this study, data acquisition, data preprocessing, model development, and model validation for AGB estimation were in accordance with the following criteria, designed to ensure AGB data quality.
Field-observed AGB data generated by the harvesting method were primarily obtained from two sources: (1) from publications, and (2) directly from field surveys. As part of the process of acquiring data from the literature, the keywords for searches, data acquisition methods, and data processing approaches were authenticated by specialists. In addition, data unit uniformity, data cross-checking, and data outlier elimination were conducted by the research team. When acquiring field observation data, quadrat design, sampling, and sample processing were completed using uniform technical specifications.
NDVI data used in AGB estimation models were obtained from NDVI3g.1, produced by GIMMS. GIMMS NDVI3g.1 has been widely used to monitor long-term vegetation dynamics.28-30 Previous research had indicated that GIMMS NDVI3g.1 data was corrected for sensor degradation, intersensor differences, cloud cover, solar zenith angle, and viewing angle effects resulting from satellite drift, as well as for the presence of volcanic aerosols. In addition, files on data quality control were provided in GIMMS NDVI3g.1.
In most empirical grassland AGB estimation models developed with field AGB observations and NDVI data, NDVI values from a predefined time period were used on the basis of the "normal," or mean, growing season, or on a subjective time period, such as calendar months—such as April–October, May–September, etc.31-33 The situation whereby the optimal monthly NDVI composite period used here for obtaining annual NDVI values for AGB estimation varied with vegetation and climate condition across the study area, was considered appropriate for this study. 13 different annual NDVI values, for various periods (Figure 2, Appendix S1), were analyzed to scientifically confirm the optimal monthly NDVI time period for the AGB estimation models applied to the northern temperate grasslands and to the Tibetan Plateau alpine grasslands. That process optimized the empirical grassland AGB estimation models, to some degree.
The modeled AGB data generated by AGB-RSMNG and AGB-RSMTP were compared with corresponding field-observed AGB data (remaining 25% of field AGB observations for model validation) to directly determine the accuracy of the AGB dataset (Figure 4). Comparisons indicated that AGB-RSMNG and AGB-RSMTP effectively simulated AGB variations in the study areas. The R2 and RMSE between the modeled AGB data and field-observed AGB data were 0.63 and 55.38 g·m-2, respectively, in the northern temperate grasslands (Figure 4 (a)), and 0.64 and 56.43 g·m-2, respectively, for the Tibetan Plateau alpine grasslands (Figure 4 (b)).
The spatial AGB dataset for the northern temperate and Tibetan Plateau alpine grasslands, from 1982 to 2015, is available in the data resources service website of the Synthesis Research Center of Chinese Ecosystem Research Network (CERN) (http://www.cnern.org.cn). After logging in, click the data for papers icon on the home page or select paper data in the data resources section, and then the dataset can be downloaded. The format of the data is ArcGIS TIFF, and the unit is g·m-2.
It should be noted that some uncertainties exist in the dataset. Due to the long time-series of this dataset, field AGB observations from both multiple field surveys, and multiple inventory datasets from previous studies, were used when AGB-RSMNG and AGB-RSMTP were developed, in order to make use of available field data. Multiple field surveys did not apply consistent sampling designs although field technical specifications were obeyed during all field sampling surveys. This could be a source of uncertainties in this dataset, and unfortunately, a quantitative assessment of the sampling quality could not be conducted.
Spatial heterogeneities were considered only for climate, rather than on vegetation condition, across the study area, due to inconsistencies with field AGB observations. There were six grassland vegetation types in the northern temperate grasslands and the Tibetan Plateau alpine grasslands. For two of the six, alpine steppes and alpine meadows, located in the Tibetan Plateau region, modeled AGB data was obtained using the AGB-RSMTP. For the other four grassland vegetation types, located in the northern temperate region, modeled AGB data was generated by the AGB-RSMNG. Spatial heterogeneities for the various vegetation types were not taken into account when developing AGB-RSMNG and AGB-RSMTP, which could be another source of uncertainty in the dataset.
We are grateful to Zhongmin Hu from the South China Normal University, and to Mei Huang, from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, for providing some grassland AGB field observations in China. In addition, we thank Global Inventory Modelling and Mapping Studies (GIMMS) for offering long-term NDVI data, and the Resource and Environment Data Cloud Platform for offering the 1:1000000-scale vegetation map of China, and data on the spatial extent of the Tibetan Plateau.
Jobbagy E & Sala O. Controls of grass and shrub aboveground production in the Patagonian steppe.Ecological Applications 10(2000): 541-549.
White R, Murray S, Rohweder M et al.Pilot Analysis of Global Ecosystems: Grassland Ecosystems . Washington, DC: World Resources Institute, 2000.
Zhai P. Estimation of vegetation biomass based on remote sensing and its bearing capacity evaluation. Master’s Dissertation, North China University of Science and Technology, 2017.
Schlesinger W. Carbon balance in terrestrial detritus.Annual Review of Ecology and Systematics 8(1977): 51-81.
Scurlock J, Johnson K & Olson R. Estimating net primary productivity from grassland biomass dynamics measurements.Global Change Biology 8(2002): 736-753.
Zhang R, Feng Q, Guo J et al. Spatio-temporal changes of NDVI and climatic factors of grassland in northern China from 2000 to 2012. Journal of Desert Research 35(2015): 1403-1412.
Singh J, Lauenroth W & Steinhorst R. Review and assessment of various techniques for estimating net aerial primary production in grasslands from harvest data. Botanical Review 41(1975): 181-232.
Lauenroth W, Hunt H, Swift D et al. Estimating aboveground net primary production in grasslands - a simulation approach. Ecological Modelling 33(1986): 297-314.
JiangY, Tao J, Huang Y et al. The spatial pattern of grassland aboveground biomass on Xizang Plateau and its climatic controls. Journal of Plant Ecology 8(2015): 30-40.
Ma A, He N, Yu G et al. Carbon storage in Chinese grassland ecosystems: Influence of different integrative methods. Scientific Reports 6(2016): 21378.
Ma W, Fang J, Yang Y et al. Biomass carbon stocks and their changes in northern China's grasslands during 1982-2006. Science in China Series - C Life Sciences 53(2010): 841-850.
Sun H. China Encyclopedia of Resources Science. Qingdao: China University of Petroleum Press, 2000.
Editorial Committee of Vegetation Map of China Chinese Academy of Sciences. Vegetation map of The People’s Republic of China (1:1000 000). Beijing: Geological Publishing House, 2007.
Jiao C, Yu G, Ge J et al. Analysis of spatial and temporal patterns of aboveground net primary productivity in the Eurasian steppe region from 1982 to 2013. Ecology and Evolution7 (2017): 5149-5162.
Jiao C, Yu G, He N et al. Spatial pattern of grassland aboveground biomass and its environmental controls in the Eurasian steppe. Journal of Geographical Sciences 27(2017): 3-22.
Ma A, He N, Xu L et al. Grassland restoration in northern China is far from complete: evidence from carbon variation in the last three decades.Ecosphere 8(2017): e01750.
Xu L, Yu G, He N et al. Carbon storage in China’s terrestrial ecosystems: A synthesis. Scientific Reports 8(2018): 2806.
Tucker C, Pinzon J, Brown M et al. An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing 26(2005): 4485-4498.
Global Inventory Monitoring And Modeling Studies, National Aeronautics and Space Administration. the third-generation NDVI dataset (NDVI3g.1). Available at: <https://ecocast.arc.nasa.gov/data/pub/gimms/>.
Beck H, Mcvicar T, Van Dijk albert I et al. Global evaluation of four AVHRR–NDVI data sets: Intercomparison and assessment against Landsat imagery. Remote Sensing of Environment 115(2011): 2547-2563.
Zhu Z, Bi J, Pan Y et al. Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sensing 5(2013): 927-948.
The Resource and Environment Data Cloud Platform. Vegetation Map of The People’s Republic of China (1:1000 000)/Spatial Extent of the Tibetan Plateau. Available at: <http://www.resdc.cn>.
Holben B. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing 7(1986): 1417-1434.
Mkhabela M, Bullock P, RAJ S et al. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology 151(2011): 385-393.
An N, Price K & Blair J. Estimating above-ground net primary productivity of the tallgrass prairie ecosystem of the Central Great Plains using AVHRR NDVI. International Journal of Remote Sensing 34(2013): 3717-3735.
Taylor K. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research-Atmospheres 106(2001): 7183–7192.
Ji L & Peters A. Performance evaluation of spectral vegetation indices using a statistical sensitivity function.Remote Sensing of Environment 106(2007): 59-65.
Piao S, Fang J, Zhou L et al. Changes in vegetation net primary productivity from 1982 to 1999 in China. Global Biogeochemical Cycles 19(2005): GB2027.
Jongen M, Pereira J, Igreja A et al. The effects of drought and timing of precipitation on the inter-annual variation in ecosystem-atmosphere exchange in a Mediterranean grassland. Agricultural and Forest Meteorology 151(2011): 595-606.
Wu X & Liu H. Consistent shifts in spring vegetation green-up date across temperate biomes in China, 1982-2006.Global Change Biology 19(2013): 870-880.
Gu Y, Wylie B & Bliss N. Mapping grassland productivity with 250-m eMODIS NDVI and SSURGO database over the Greater Platte River Basin, USA. Ecological Indicators 24(2013): 31-36.
Guo Q, Hu Z, Li S et al. Spatial variations in aboveground net primary productivity along a climate gradient in Eurasian temperate grassland: effects of mean annual precipitation and its seasonal distribution.Global Change Biology 18(2012): 3624-3631.
1. Jiao C, Yu G, Chen Z et al. A dataset for aboveground biomass of the northern temperate and Tibetan Plateau alpine grasslands in China, based on field investigation and remote sensing inversion (1982 – 2015). Science Data Bank. DOI: 10.11922/sciencedb.601 (2018).
How to cite this article
Jiao C, Yu G, Chen Z et al. A dataset for aboveground biomass of the northern temperate and Tibetan Plateau alpine grasslands in China, based on field investigation and remote sensing inversion (1982 – 2015). China Scientific Data 4(2019). DOI: 10.11922/csdata.2018.0029.zh