Quantifying Aboveground Biomass of Shrubs Using Spectral and Structural Metrics Derived from UAS Imagery

Poley, Lucy G.; Laskin, David; McDermid, Gregory J.

doi:10.3390/rs12142199

Cited by 16 publications

(6 citation statements)

References 87 publications

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“…In this study, CV is a proxy to DW, and CV×EVI a proxy to FW. Other studies similarly used VIs [21,26] or combination of structural and spectral information [43,51,52] in modelling biomass, which is also a proxy-based approach. It could not be claimed with the available data in the study that the reported simple linear model relationships would hold exactly same every year, nevertheless the metrics CV and CV×EVI could certainly be used as a proxy to screen crop genotypes with higher or lower biomass and monitor their growth patterns over time.…”

Section: Discussionmentioning

confidence: 99%

“…Using both spectral (VIs) and structural (surface models) information obtained from UAVs could be a useful approach for not only estimating biomass but also other crop traits such as leaf area index, chlorophyll and nitrogen content, plant lodging, plant density, and counting head numbers. Though few studies have combined spectral (VIs) and structural (CHMs) data in estimating biomass in barley [43], pastures [51], and shrubs-dominated ecosystems [52]. However, the potential of combining spectral and structural information for the estimation of dry and fresh biomass in the agronomically important food crop wheat is yet to be investigated.…”

Section: Introductionmentioning

confidence: 99%

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Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

2020

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Efficient, precise and timely measurement of plant traits is important in the assessment of a breeding population. Estimating crop biomass in breeding trials using high-throughput technologies is difficult, as reproductive and senescence stages do not relate to reflectance spectra, and multiple growth stages occur concurrently in diverse genotypes. Additionally, vegetation indices (VIs) saturate at high canopy coverage, and vertical growth profiles are difficult to capture using VIs. A novel approach was implemented involving a fusion of complementary spectral and structural information, to calculate intermediate metrics such as crop height model (CHM), crop coverage (CC) and crop volume (CV), which were finally used to calculate dry (DW) and fresh (FW) weight of above-ground biomass in wheat. The intermediate metrics, CHM (R2 = 0.81, SEE = 4.19 cm) and CC (OA = 99.2%, Κ = 0.98) were found to be accurate against equivalent ground truth measurements. The metrics CV and CV×VIs were used to develop an effective and accurate linear regression model relationship with DW (R2 = 0.96 and SEE = 69.2 g/m2) and FW (R2 = 0.89 and SEE = 333.54 g/m2). The implemented approach outperformed commonly used VIs for estimation of biomass at all growth stages in wheat. The achieved results strongly support the applicability of the proposed approach for high-throughput phenotyping of germplasm in wheat and other crop species.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

2020

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“…Although digital cameras do not directly record reflectance, the increased use of structure-from-motion in various research disciplines has led to the use of imagery from consumer cameras for calculating spectral indices (e.g. Torres-Sánchez and others, 2014; Poley and others, 2020). Unlike true reflectance data, the digital signature of consumer cameras is affected by differences in camera settings and lighting conditions.…”

Section: Discussionmentioning

confidence: 99%

A semi-automated, GIS-based framework for the mapping of supraglacial hydrology

Bash

Shellian²,

Dow³

et al. 2022

J. Glaciol.

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Supraglacial drainage networks play an integral role in both glacier dynamics and run-off timing, and mapping them provides insight into their role in glacial systems. Here we present a reproducible approach for semi-automated mapping of supraglacial hydrologic features, which complements existing work in automated and manual mapping by providing clear definitions for identification of features. This framework uses a digital terrain model (DTM) to identify potential flow routes on the glacier surface, which are then classified using a set of standardized rules based on the DTM and an orthomosaic. We found that the normalized difference water index calculated from digital imagery was influenced by image brightness and introduce a new approach using average RGB values to correct for this. Using this framework we mapped supraglacial drainage networks at Nàłùdäy and Thores Glacier, Canada. The framework was easier to implement with high-resolution (0.5 m) imagery and DTMs, compared to data with lower resolution (10 m), due to the increased detail in topography and feature boundaries at high-resolution. Lower-resolution data captured larger streams (>2 pixels wide), however, indicating that the framework can still be used at this resolution. Mapping supraglacial hydrology using standardized methods opens possibilities for investigating many questions relating to changes in supraglacial hydrology over time.

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“…Equipped with highresolution sensors and advanced image processing techniques, UAVs can capture high-quality remote sensing imagery, offering comprehensive data on shrub vegetation [38,39]. They are capable of capturing extensive areas of shrub coverage from a high-altitude overhead perspective, enabling the rapid and accurate identification and extraction of shrub objects [37,[40][41][42]. Additionally, UAVs have higher spatial resolution and flexibility compared to traditional aerial remote sensing techniques, allowing for the capture of finer details of shrub structure and characteristics [43].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Mapping of Shrub Biomass in Arid and Semi-Arid Environments by Integrating Remote Sensing Observations across Multiple Spatial Scales

Liu,

Wang,

et al. 2024

Preprint

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Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. However, capturing the shrub biomass accurately by satellite observations is challenging due to the short and sparse features of shrubs. In this study, we presented a framework to estimate shrub biomass at a 10m spatial resolution by integrating ground in-situ data, unmanned aerial vehicle (UAV), Sentinel-2, and Sentinel-1 observations. The pilot study was conducted in the Helan mountains of Ningxia province, China. First, the spatial distribution map of shrubland and other land cover types was generated in 2023. Then, a prediction model of shrub biomass was developed in a Random Forest Regression (RFR) approach driven by different predicted variable datasets based on in-situ measurements, UAV, and satellite images. Finally, the developed model was used to produce the biomass map of shrubland in 2023. The uncertainty was characterized by creating a standard deviation (SD) map using the Leave-One-Out Cross-Validation (LOOCV) method in the shrub biomass estimates. The resultant shrubland distribution map in 2023 has an F1 score of 0.92. The shrub biomass model driven by spectral bands and vegetation indices (R2= 0.62) demonstrated superior performance than that only driven by spectral bands (R2=0.33) or vegetation indices (R2=0.55). The uncertainty of the estimated biomass was lower than 4%, with the lowest values (<2%) happening in the high shrub coverage (>30%) and biomass production (>30kg/m2) regions. Furthermore, the findings show that the magnitude of shrub biomass was affected by the aridity and precipitation significantly in the study region. This study provides a workflow to accurately monitor the biomass of shrublands in a complicated environment based on remote sensing images from multi-platform observations.

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Quantifying Aboveground Biomass of Shrubs Using Spectral and Structural Metrics Derived from UAS Imagery

Cited by 16 publications

References 87 publications

Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

A semi-automated, GIS-based framework for the mapping of supraglacial hydrology

High-Resolution Mapping of Shrub Biomass in Arid and Semi-Arid Environments by Integrating Remote Sensing Observations across Multiple Spatial Scales

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