Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space

Shoko, Cletah; Mutanga, Onisimo; Dube, Timothy

doi:10.1016/j.isprsjprs.2016.08.001

Cited by 71 publications

(52 citation statements)

References 118 publications

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“…The complete list can be obtained from the portal webpage (https://earthengine.google.com/datasets/). constraints [5]. This bridge has now been narrowed considerably, so one would expect researchers in such regions to take advantage of this noble innovation.…”

Section: Introductionmentioning

confidence: 99%

Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

2018

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The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent.

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Section: Introductionmentioning

confidence: 99%

Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

2018

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“…Grasslands are a major component of Earth's terrestrial ecosystems, covering over 30% of the global land area (Shoko et al, 2016). Grasslands provide essential ecosystem services, such as maintaining plant and animal biodiversity (Coppedge et al, 2004;WallisDeVries et al, 2002), controlling soil erosion (Talle et al, 2016), and regulating the terrestrial carbon cycle as a large carbon sink (Ali et al, 2016;Derner and Schuman, 2007;Scurlock and Hall, 1998).…”

Section: Introductionmentioning

confidence: 99%

“…In the last several decades, grasslands have experienced degradation due to natural factors (e.g., drought, wild fires) and anthropogenic factors (e.g., overgrazing by livestock) (Le et al, 2016;Zhou et al, 2005). Additionally, grasslands are vulnerable to climate change (Cleland et al, 2006;Shoko et al, 2016) and are threatened by invasive plants (Greer et al, 2014) and woody plant encroachment (Wang et al, 2017;Wang et al, 2018). Grassland degradation negatively affects forage and livestock production and associated social-economic functions (Kwon et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Wang

Xiao

Starks

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

235

126

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Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10-30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI < 2 m 2 /m 2 , AGB < 500 g/m 2) and optical data of LC8 and S2 at high vegetation cover (LAI > 2 m 2 /m 2 , AGB > 500 g/m 2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management.

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“…The spectral capability of remote sensing technology is also crucial in extracting species morphological and phenological characteristics, which influence their AGB variations. Most importantly, emerging sensors offer outstanding opportunities to monitor C3 and C4 grasses AGB (Shoko, Mutanga, & Dube, ). For example, the high temporal resolution of emerging sensors (e.g., Sentinel 2 at 5 days) allows multi‐temporal analysis of dynamic phenomena like species AGB in a spatially explicit manner.…”

Section: Introductionmentioning

confidence: 99%

Remotely sensed C3 and C4 grass species aboveground biomass variability in response to seasonal climate and topography

2019

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Seasonal climate and topography influence C3 and C4 grass species aboveground biomass (AGB). Climate change further threatens these grasses AGB, thereby compromising their ability to provide ecosystem goods and services. This emphasises the need to monitor their AGB for well‐informed management. New‐generation sensors, with improved resolution capabilities present an opportunity to explore C3 and C4 AGB. This study therefore investigated the response of remotely sensed C3 and C4 grasses AGB to seasonal climate and topography. Overall, the spatial and temporal responses of AGB due to seasonal climate and topography were observed across the study area. For example, in March, a marked increase in C4 AGB was associated with an increase in rainfall, with the highest significant positive relationship (R2 = 0.82, p < 0.005). Elevation had very significant positive relationship (R2 = 0.84; p < 0.005) with C3 and highest negative (R2 = −0.77; p < 0.005) with C4 AGB. During the winter fall, AGB significantly decreased from averages of 2.592 and 1.101 kg/m2 in winter (May), to 0.718 and 0.469 kg/m2 in August, for C3 and C4 grasses, respectively. These findings provide a key step in monitoring rangelands and assessing management practices to boost productivity.

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Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space

Cited by 71 publications

References 118 publications

Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Remotely sensed C3 and C4 grass species aboveground biomass variability in response to seasonal climate and topography

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