Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data

Chiarito, Eugenia; Cigna, Francesca; Cuozzo, Giovanni; Fontanelli, Giacomo; Mejía-Aguilar, Abraham; Paloscia, S.; Rossi, Mattia; Santi, Emanuele; Tapete, Deodato; Notarnicola, Claudia

doi:10.1080/22797254.2021.1901063

Cited by 18 publications

(6 citation statements)

References 35 publications

(141 reference statements)

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“…Wrapper methods: These methods use a search algorithm, such as Forward Selection (FS), Backward Elimination (BE), Recursive Feature Elimination (RFE), and Genetic Algorithms (GA) to find the optimal subset of features. They are more computationally expensive than filter methods, but they consider the interactions between features [207,210,211,218].…”

Section: Feature Selectionmentioning

confidence: 99%

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

et al. 2023

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Conventional measurement methods for above-ground biomass (AGB) are time-consuming, inaccurate, and labor-intensive. Unmanned aerial systems (UASs) have emerged as a promising solution, but a standardized procedure for UAS-based AGB estimation is lacking. This study reviews recent findings (2018–2022) on UAS applications for AGB estimation and develops a vegetation type-specific standard protocol. Analysis of 211 papers reveals the prevalence of rotary-wing UASs, especially quadcopters, in agricultural fields. Sensor selection varies by vegetation type, with LIDAR and RGB sensors in forests, and RGB, multispectral, and hyperspectral sensors in agricultural and grass fields. Flight altitudes and speeds depend on vegetation characteristics and sensor types, varying among crop groups. Ground control points (GCPs) needed for accurate AGB estimation differ based on vegetation type and topographic complexity. Optimal data collection during solar noon enhances accuracy, considering image quality, solar energy availability, and reduced atmospheric effects. Vegetation indices significantly affect AGB estimation in vertically growing crops, while their influence is comparatively less in forests, grasses, and horizontally growing crops. Plant height metrics differ across vegetation groups, with maximum height in forests and vertically growing crops, and central tendency metrics in grasses and horizontally growing crops. Linear regression and machine learning models perform similarly in forests, with machine learning outperforming in grasses; both yield comparable results for horizontally and vertically growing crops. Challenges include sensor limitations, environmental conditions, reflectance mixture, canopy complexity, water, cloud cover, dew, phenology, image artifacts, legal restrictions, computing power, battery capacity, optical saturation, and GPS errors. Addressing these requires careful sensor selection, timing, image processing, compliance with regulations, and overcoming technical limitations. Insights and guidelines provided enhance the precision and efficiency of UAS-based AGB estimation. Understanding vegetation requirements aids informed decisions on platform selection, sensor choice, flight parameters, and modeling approaches across different ecosystems. This study bridges the gap by providing a standardized protocol, facilitating widespread adoption of UAS technology for AGB estimation.

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Section: Feature Selectionmentioning

confidence: 99%

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

et al. 2023

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“…Wang, Ge, and Li [56] concluded that the accuracy of SAR data in GP can be influenced by rain due to its sensitivity to water drops on leaves. To date, SAR data use in GP has remained rudimentary, except for a few studies [66]. Therefore, a few (<5%) studies have used these sensors.…”

Section: Geographic Patternsmentioning

confidence: 99%

Remote Sensing Grassland Productivity Attributes: A Systematic Review

et al. 2023

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A third of the land on the Earth is composed of grasslands, mainly used for forage. Much effort is being conducted to develop tools to estimate grassland productivity (GP) at different extents, concentrating on spatial and seasonal variability pertaining to climate change. GP is a reliable indicator of how well an ecosystem works because of its close connection to the ecological system equilibrium. The most commonly used proxies of GP in ecological studies are aboveground biomass (AGB), leaf area index (LAI), canopy storage capacity (CSC), and chlorophyll and nitrogen content. Grassland science gains much information from the capacity of remote sensing (RS) techniques to calculate GP proxies. An overview of the studies on RS-based GP prediction techniques and a discussion of current matters determining GP monitoring are critical for improving future GP prediction performance. A systematic review of articles published between 1970 and October 2021 (203 peer-reviewed articles from Web of Science, Scopus, and DirectScience databases) showed a trend in the choice of the sensors, and the approaches to use are largely dependent on the extent of monitoring and assessment. Notably, all the reviewed articles demonstrate the growing demand for high-resolution sensors, such as hyperspectral scanners and computationally efficient image-processing techniques for the high prediction accuracy of GP at various scales of application. Further research is required to attract the synthesis of optical and radar data, multi-sensor data, and the selection of appropriate techniques for GP prediction at different scales. Mastering and listing major uncertainties associated with different algorithms for the GP prediction and pledging to reduce these errors are critical.

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“…At this site, a meadow (V1) and a pasture (P2) parcels were sampled from 2017 to 2021 throughout the vegetation-growing season. Having multiannual observations of biophysical variables is important to understand the dynamics of vegetation growth for two sites with very different management forms [40,41]. The meadow is mown twice a year (end of June, begin of September) and its dominant species is Trisetum flavescens.…”

Section: Ground Data Collectionmentioning

confidence: 99%

Insuring Alpine Grasslands Against Drought-Related Yield Losses by Sentinel-2 Satellite Data

Castelli,

Peratoner,

Pasolli

et al. 2023

Preprint

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This work estimates yield losses due to drought events in mountain grasslands in north-eastern Italy, laying the groundwork for index-based insurance. Given the high correlation between Leaf Area Index (LAI) and grassland yield, we exploit LAI as a proxy for yield. We estimate LAI by the Sentinel-2 biophysical processor and we compare different gap-filling methods, including time-series interpolation and fusion with Sentinel-1 SAR data. We derive a Forage Production Index (FPI) as the growing season cumulate of the daily product between LAI and a meteorological water stress coefficient. Finally, we calculate the drought index as the anomaly of FPI. The validation of Sentinel-2 LAI with ground measurements showed RMSE of 0.92 [m2 m-2] and R2 of 0.81, on average over all the measurement sites. The comparison between FPI and yield showed R2 of 0.56 at the pixel scale and R2 of 0.74 at the parcel scale. The developed prototype FPI index was used at the end of the growing season of the year 2022 for calculating the payments of an experimental insurance scheme that was proposed to a group of farmers in Trentino-South Tyrol.

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Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data

Cited by 18 publications

References 35 publications

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

Remote Sensing Grassland Productivity Attributes: A Systematic Review

Insuring Alpine Grasslands Against Drought-Related Yield Losses by Sentinel-2 Satellite Data

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