Estimating above-ground biomass on mountain meadows and pastures through remote sensing

Barrachina, Maria; Cristóbal, Jordi; Tulla, Antoni F.

doi:10.1016/j.jag.2014.12.002

Cited by 68 publications

(38 citation statements)

References 63 publications

(108 reference statements)

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“…In addition, it is impossible to collect all the grassland biomass information within a satellite pixel for constructing the biomass estimation model. A more rational method is to set 3 to 5 quadrats in a pixel, and then average the sampling points to estimate the pixel grassland biomass [16,[32][33][34][35][36]. As a result, spatial scale mismatching occurs between ground-based observation data and satellite data.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

et al. 2018

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Aboveground biomass is a key indicator of a grassland ecosystem. Accurate estimation from remote sensing is important for understanding the response of grasslands to climate change and disturbance at a large scale. However, the precision of remote sensing inversion is limited by a lack in the ground truth and scale mismatch with satellite data. In this study, we first tried to establish a grassland aboveground biomass estimation model at 1 m 2 quadrat scale by conducting synchronous experiments of unmanned aerial vehicle (UAV) and field measurement in three different grassland ecosystems. Two flight modes (the new QUADRAT mode and the commonly used MOSAIC mode) were used to generate point clouds for further processing. Canopy height metrics of each quadrat were then calculated using the canopy height model (CHM). Correlation analysis showed that the mean of the canopy height model (CHM_mean) had a significant linear relationship with field height (R 2 = 0.90, root mean square error (RMSE) = 19.79 cm, rRMSE = 16.5%, p < 0.001) and a logarithmic relationship with field aboveground biomass (R 2 = 0.89, RMSE = 91.48 g/m 2 , rRMSE = 16.11%, p < 0.001). We concluded our study by conducting a preliminary application of estimation of the aboveground biomass at a plot scale by jointly using UAV and the constructed 1 m 2 quadrat scale estimation model. Our results confirmed that UAV could be used to collect large quantities of ground truths and bridge the scales between ground truth and remote sensing pixels, which were helpful in improving the accuracy of remote sensing inversion of grassland aboveground biomass.

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Section: Introductionmentioning

confidence: 99%

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

et al. 2018

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“…Vegetation indices of optical satellite imagery such as NDVI (Normalised Differenced Vegetation Index) or EVI (Enhanced Vegetation Index) focus on the green vegetation component. A general relationship between vegetative ground cover and pasture biomass exists for low ground cover areas, but when the ground cover is close to 100% the cover-to-mass relationship saturates and reliable estimates are not possible even at low biomass levels [6][7][8]. Investigating this relationship, Hobbs [5] related four vegetation indices to field data and found a breakdown of biomass levels >1000 kg/ha.…”

Section: Introductionmentioning

confidence: 99%

Integration of Optical and X-Band Radar Data for Pasture Biomass Estimation in an Open Savannah Woodland

et al. 2016

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Abstract:Pasture biomass is an important quantity globally in livestock industries, carbon balances, and bushfire management. Quantitative estimates of pasture biomass or total standing dry matter (TSDM) at the field scale are much desired by land managers for land-resource management, forage budgeting, and conservation purposes. Estimates from optical satellite imagery alone tend to saturate in the cover-to-mass relationship and fail to differentiate standing dry matter from litter. X-band radar imagery was added to complement optical imagery with a structural component to improve TSDM estimates in rangelands. High quality paddock-scale field data from a northeastern Australian cattle grazing trial were used to establish a statistical TSDM model by integrating optical satellite image data from the Landsat sensor with observations from the TerraSAR-X (TSX) radar satellite. Data from the dry season of 2014 and the wet season of 2015 resulted in models with adjusted r 2 of 0.81 in the dry season and 0.74 in the wet season. The respective models had a mean standard error of 332 kg/ha and 240 kg/ha. The wet and dry season conditions were different, largely due to changed overstorey vegetation conditions, but not greatly in a pasture 'growth' sense. A more robust combined-season model was established with an adjusted r 2 of 0.76 and a mean standard error of 358 kg/ha. A clear improvement in the model performance could be demonstrated when integrating HH polarised TSX imagery with optical satellite image products.

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“…Developments in remote sensing technologies in recent years have created opportunities to provide faster and cheaper data in larger areas as an alternative to conventional biomass research, which has led many investigators to focus on biomass studies (Todd et al, 1998;Guo et al, 2000;Cho et al, 2007;He et al, 2009;Mundava et al, 2014: Dusseux et al, 2015. In these studies, where both field measurements and remote sensing data are used together, the aim is to investigate the relationship between biomass characteristics of plants and light, and to make biomass estimation in grasslands based on these relationships (Barrachina et al, 2015).…”

Section: Ratio-based Vegetation Indices For Biomass Estimation Dependmentioning

confidence: 99%

Ratio-based vegetation indices for biomass estimation depending on grassland characteristics

Karakoç¹,

Karabulut²

2019

Turk J Bot

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Aboveground biomass (AGB) is one of the key indicators of aboveground net primary productivity (ANPP). The aim of this study is to demonstrate the potential of hyperspectral remote sensing techniques to predict AGB in grasslands. In order to reach this goal, biomass properties with different ecological features and altitudes of 550 m, 1200 m, and 1400 m above sea level were investigated. Twenty-one biomass samples and hyperspectral measurements were collected from each region and a total of 63 samples were analyzed. Linear and nonlinear regression models were generated to analyze the relationships between biomass and hyperspectral vegetation indices (VIs). The results showed strong relationships between VIs and biomass variations. However, dense biomass samples indicated weaker relationships with VIs due to saturation phenomena. Findings based on the measured data showed that AGB (except dense biomass) can be estimated with high accuracy using hyperspectral vegetation indices.

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Estimating above-ground biomass on mountain meadows and pastures through remote sensing

Cited by 68 publications

References 63 publications

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

Integration of Optical and X-Band Radar Data for Pasture Biomass Estimation in an Open Savannah Woodland

Ratio-based vegetation indices for biomass estimation depending on grassland characteristics

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