Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet Plateau based on validated MODIS NDVI

Liu, Shiliang; Cheng, Fangyan; Dong, Shikui; Zhao, Haidi; Hou, Xiyong; Wu, Xue

doi:10.1038/s41598-017-04038-4

Cited by 79 publications

(27 citation statements)

References 58 publications

(68 reference statements)

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“…We estimated the primary productivity of alpine ecosystems based on the normalized difference vegetation index (NDVI), an indirect measure of vegetation cover and biomass related to the physical properties of plants (Cihlar et al , Pettorelli et al ). Despite its limitations, the NDVI has been widely used as a proxy for ecosystem properties including global grassland productivity (Gao et al ) and above ground biomass in the alpine belt (Liu et al ). To minimize the problems related to single‐date remote sensing studies of vegetation (Holben ), we calculated the maximum NDVI value at each pixel of 30 × 30 m using Landsat 8 Annual Greenest‐Pixel images (from 2013 to 2018) in Google Earth Engine.…”

Section: Methodsmentioning

confidence: 99%

Global distribution and bioclimatic characterization of alpine biomes

2020

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Although there is a general consensus on the distribution and ecological features of terrestrial biomes, the allocation of alpine ecosystems in the global biogeographic system is still unclear. Here, we delineate a global map of alpine areas above the treeline by modelling regional treeline elevation at 30 m resolution, using global forest cover data and quantile regression. We then used global datasets to 1) assess the climatic characteristics of alpine ecosystems using principal component analysis, 2) define bioclimatic groups by an optimized cluster analysis and 3) evaluate patterns of primary productivity based on the normalized difference vegetation index. As defined here, alpine biomes cover 3.56 Mkm 2 or 2.64% of land outside Antarctica. Despite temperature differences across latitude, these ecosystems converge below a sharp threshold of 5.9°C and towards the colder end of the global climatic space. Below that temperature threshold, alpine ecosystems are influenced by a latitudinal gradient of mean annual temperature and they are climatically differentiated by seasonality and continentality. This gradient delineates a climatic envelope of global alpine biomes around temperate, boreal and tundra biomes as defined in Whittaker's scheme. Although alpine biomes are similarly dominated by poorly vegetated areas, world ecoregions show strong differences in the productivity of their alpine belt irrespectively of major climate zones. These results suggest that vegetation structure and function of alpine ecosystems are driven by regional and local contingencies in addition to macroclimatic factors.

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Section: Methodsmentioning

confidence: 99%

Global distribution and bioclimatic characterization of alpine biomes

2020

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“…The present study used the NDVI/MODIS Terra data series to estimate pasture growth-related metrics and integrated them through multicriteria and categorization by Hurst exponents using a simplified expert system employed to demonstrate long-term memory processes in conjunction with vegetation phenological parameters expressed in time series. Several other metrics and parameters related to NDVI variations may be evaluated through Hurst statistics, e.g., aboveground biomass [47].…”

Section: Discussionmentioning

confidence: 99%

“…In economics, when applied to price series, investments, and capital markets, the Hurst exponent is used to detect memory effects and trends [26,[44][45][46]. It has also been used in academic, environmental, and remote sensing applications [47]. Power-law relations for spatiotemporal transects in two types of pasture in China have been estimated [48].…”

Section: Hurst Exponentmentioning

confidence: 99%

Analysis of Grassland Degradation in Zona da Mata, MG, Brazil, Based on NDVI Time Series Data with the Integration of Phenological Metrics

et al. 2019

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There is currently a lot of interest in determining the state of Brazilian grasslands. Governmental actions and programs have recently been implemented for grassland recovery in Brazilian states, with the aim of improving production systems and socioeconomic indicators. The aim of this study is to evaluate the vegetative growth, temporal vigor, and long-term scenarios for the grasslands in Zona da Mata, Minas Gerais State, Brazil, by integrating phenological metrics. We used metrics derived from the normalized difference vegetation index (NDVI) time series from moderate resolution imaging spectroradiometer (MODIS) data, which were analyzed in a geographic information system (GIS), using multicriteria analysis, the analytical hierarchy process, and a simplified expert system (ESS). These temporal metrics, i.e., the growth index (GI) for 16-day periods during the growing season; the slope; and the maximum, minimum, and mean for the time series, were integrated to investigate the grassland vegetation conditions and degradation level. The temporal vegetative vigor was successfully described using the rescaled range (R/S statistic) and the Hurst exponent, which, together with the metrics estimated for the full time series, imagery, and field observations, indicated areas undergoing degradation or areas that were inadequately managed (approximately 61.5%). Time series analysis revealed that most grasslands showed low or moderate vegetative vigor over time with long-term persistence due to farming practices associated with burning and overgrazing. A small part of the grasslands showed high and sustainable plant densities (approximately 8.5%). A map legend for grassland management guidelines was developed using the proposed method with remote sensing data, which were applied using GIS software and a field campaign.

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“…Different data sources were employed to mapping grassland AGB, e.g., Landsat [14,23], SPOT [1], MERIS [26], and Sentinel [27,28]. The long-term coverage and high revisiting frequency make MODIS an unparalleled data source, and it has been widely used in grassland AGB estimation [2,13,28,29]. However, the performance of physically-based method on MODIS data has not been assessed.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery

Yin

et al. 2019

Remote Sensing

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The estimation of aboveground biomass (AGB), an important indicator of grassland production, is crucial for evaluating livestock carrying capacity, understanding the response and feedback to climate change, and achieving sustainable development. Most existing grassland AGB estimation studies were based on empirical methods, in which field measurements are indispensable, hindering their operational use. This study proposed a novel physically-based grassland AGB retrieval method through the inversion of PROSAIL model against MCD43A4 imagery. This method relies on the basic understanding that grassland is herbaceous, and therefore AGB can be represented as the product of leaf dry matter content (Cm) and leaf area index (LAI), i.e., AGB = Cm × LAI. First, the PROSAIL model was parameterized according to the literature regarding grassland parameters retrieval, then Cm and LAI were retrieved using a lookup table (LUT) algorithm, finally, the retrieved Cm and LAI were multiplied to obtain the AGB. The method was assessed in Zoige Plateau, China. Results show that it could reproduce the reference AGB map, which is generated by upscaling the field measurements, in terms of magnitude (with RMSE and R-RMSE of 60.06 g·m−2 and 18.1%, respectively) and spatial distribution. The estimated AGB time series also agreed reasonably well with the expected temporal dynamic trends of the grassland in our study area. The greatest advantage of our method is its fully physical nature, i.e., no field measurement is needed. Our method has the potential for operational monitoring of grassland AGB at regional and even larger scales.

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Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet Plateau based on validated MODIS NDVI

Cited by 79 publications

References 58 publications

Global distribution and bioclimatic characterization of alpine biomes

Global distribution and bioclimatic characterization of alpine biomes

Analysis of Grassland Degradation in Zona da Mata, MG, Brazil, Based on NDVI Time Series Data with the Integration of Phenological Metrics

Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery

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