Remote Sensing-Based Biomass Estimation and Its  Spatio-Temporal Variations in Temperate Grassland,  Northern China

Jin, Ye; Xiao, Yang; Qiu, Jianjun; Li, Jinya; Gao, Tian; Wu, Qiong; Zhao, Fen; Ma, Hailong; Huo, Yongxin; Xu, Bin

doi:10.3390/rs6021496

Cited by 134 publications

(104 citation statements)

References 33 publications

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“…The spatial heterogeneity observed in all the maps on a larger scale, agreed with the results found in other studies [48,52]. Even though in our study we found evidence of spatial gradients, these were not as marked as the ones found at the larger scale.…”

Section: Discussionsupporting

confidence: 79%

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Mapping Land Cover and Estimating the Grassland Structure in a Priority Area of the Chihuahuan Desert

Rodríguez-Maturino

Martínez-Guerrero

Chairez-Hernández

et al. 2017

Land

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Abstract:A field characterization of the grassland vegetation structure, represented by the coverage of grass canopy (CGC) and the grass height, was carried out during three years (2009)(2010)(2011) in a priority area for the conservation of grasslands of North America. Landsat Thematic Mapper (TM5) images were selected and the information of reflectance was obtained based on the geographical location of each field-sampling site. Linear models, constructed with field and satellite data, with high coefficients of determination for CGC (R 2 = 0.81, R 2 = 0.81 and R 2 = 0.72) and grass height (R 2 = 0.82, R 2 = 0.79 and R 2 = 0.73) were obtained. The maps showed a good level of CGC (>25%) and grass height (>25 cm), except for the year 2009, which presented the lowest values of grass height in the area. According to the Kappa Index, a moderate concordance among the three CGC maps was presented (0.49-0.59). Conversely, weak and moderate concordances were found among the grass height maps (0.36-0.59). It was observed that areas with a high CGC do not necessarily correspond to areas with greater grass height values. Based on the data analyzed in this study, the grassland areas are highly dynamic, structurally heterogeneous and the spatial distribution of the variables does not show a definite pattern. From the information generated, it is possible to determine those areas that are the most important for monitoring to then establish effective strategies for the conservation of these grasslands and the protection of threatened migratory bird species.

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Section: Discussionsupporting

confidence: 79%

“…Jin et al [48] found similar results for biomass, which is directly related to CGC and grass height. The greatest quantitative variation was registered for the variable height, which was possibly due to the fact that this variable is more susceptible to changes than CGC.…”

Section: Discussionsupporting

confidence: 67%

Mapping Land Cover and Estimating the Grassland Structure in a Priority Area of the Chihuahuan Desert

Rodríguez-Maturino

Martínez-Guerrero

Chairez-Hernández

et al. 2017

Land

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“…Secondly, each model for grassland biomass estimation uses different equations to describe the correlations between vegetation indices and grassland biomass. The prevalent MODfrm includes linear, polynomial, power, logarithmic, and exponential function forms (Gao et al, 2013a;Jin et al, 2014;Yang et al, 2009). These regression models have performed well in Sahel of Africa (Tucker et al, 1985), Patagonia of Argentina (Gaitan et al, 2013), Colombia (Anaya et al, 2009), and China (Jin et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…f AGB and f BGB denote the functions used to estimate AGB and BGB, respectively. AGB estimation function used one of the three regression relationships between NDVI max and AGB field measurements, including linear (Yang et al, 2009), power (Jin et al, 2014) and exponential (Gao et al, 2013b) forms (see Eqs. (A1)-(A3) in Appendix A).…”

Section: Establishment and Empirical Validation Of Estimation Modelsmentioning

confidence: 99%

“…The prevalent MODfrm includes linear, polynomial, power, logarithmic, and exponential function forms (Gao et al, 2013a;Jin et al, 2014;Yang et al, 2009). These regression models have performed well in Sahel of Africa (Tucker et al, 1985), Patagonia of Argentina (Gaitan et al, 2013), Colombia (Anaya et al, 2009), and China (Jin et al, 2014). Furthermore, a considerable amount of uncertainties remain in model parameters such as the ratios of root and shoot (R/S) (Wang et al, 2010;Yang et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Jia

Liu

Yang

et al. 2016

Ecological Indicators

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a b s t r a c tAccurate estimation of grassland biomass and its dynamics are crucial not only for the biogeochemical dynamics of terrestrial ecosystems, but also for the sustainable use of grassland resources. However, estimations of grassland biomass on large spatial scale usually suffer from large variability and mostly lack quantitative uncertainty analyses. In this study, the spatial grassland biomass estimation and its uncertainty were assessed based on 265 field measurements and remote sensing data across Northern China during [2001][2002][2003][2004][2005]. Potential sources of uncertainty, including remote sensing data sources (DATsrc), model forms (MODfrm) and model parameters (biomass allocation, BMallo, e.g. root:shoot ratio), were determined and their relative contribution was quantified. The results showed that the annual grassland biomass in Northern China was 1268.37 ± 180.84 Tg (i.e., 532.02 ± 99.71 g/m 2 ) during 2001-2005, increasing from western to eastern area, with a mean relative uncertainty of 19.8%. There were distinguishable differences among the uncertainty contributions of three sources (BMallo > DATsrc > MODfrm), which contributed 52%, 27% and 13%, respectively. This study highlighted the need to concern the uncertainty in grassland biomass estimation, especially for the uncertainty related to BMallo.

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Analysis of spatial and temporal patterns of aboveground net primary productivity in the Eurasian steppe region from 1982 to 2013

Jiao

et al. 2017

Ecology and Evolution

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To explore the importance of the Eurasian steppe region (EASR) in global carbon cycling, we analyzed the spatiotemporal dynamics of the aboveground net primary productivity (ANPP) of the entire EASR from 1982 to 2013. The ANPP in the EASR was estimated from the Integrated ANPPNDVI model, which is an empirical model developed based on field‐observed ANPP and long‐term normalized difference vegetation index (NDVI) data. The optimal composite period of NDVI data was identified by considering spatial heterogeneities across the study area in the Integrated ANPPNDVI model. EASR's ANPP had apparent zonal patterns along hydrothermal gradients, and the mean annual value was 43.78 g C m−2 yr−1, which was lower than the global grasslands average. Compared to other important natural grasslands, EASR's ANPP was lower than the North American, South American, and African grasslands. The total aboveground net primary productivity (TANPP) was found to be 378.97 Tg C yr−1, which accounted for 8.18%–36.03% of the TANPP for all grasslands. In addition, EASR's TANPP was higher than that of the grasslands in North America, South America, and Africa. The EASR's TANPP increased in a fluctuating manner throughout the entire period of 1982–2013. The increasing trend was greater than that for North American and South American and was lower than that for African grasslands over the same period. The years 1995 and 2007 were two turning points at which trends in EASR's TANPP significantly changed. Our analysis demonstrated that the EASR has been playing a substantial and progressively more important role in global carbon sequestration. In addition, in the development of empirical NDVI‐based ANPP models, the early–middle growing season averaged NDVI, the middle–late growing season averaged NDVI and the annual maximum NDVI are recommended for use for semi‐humid regions, semi‐arid regions, and desert vegetation in semi‐arid regions, respectively.

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Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China

Cited by 134 publications

References 33 publications

Mapping Land Cover and Estimating the Grassland Structure in a Priority Area of the Chihuahuan Desert

Mapping Land Cover and Estimating the Grassland Structure in a Priority Area of the Chihuahuan Desert

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Analysis of spatial and temporal patterns of aboveground net primary productivity in the Eurasian steppe region from 1982 to 2013

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