Hyperspectral Analysis of Leaf Pigments and Nutritional Elements in Tallgrass Prairie Vegetation

Understanding the spatial distribution of forage quality is important to address critical research questions in grassland science. Due to its efficiency and accuracy, there has been a widespread interest in mapping the canopy vegetation characteristics using remote sensing methods. In this study, foliar chlorophylls, carotenoids, and nutritional elements across multiple tallgrass prairie functional groups were quantified at the leaf level using hyperspectral analysis in the region of 470–800 nm, which was expected to be a precursor to further remote sensing of canopy vegetation quality. A method of spectral standardization was developed using a form of the normalized difference, which proved feasible to reduce the interference from background effects in the leaf reflectance measurements. Chlorophylls and carotenoids were retrieved through inverting the physical model PROSPECT 5. The foliar nutritional elements were modeled empirically. Partial least squares regression was used to build the linkages between the high-dimensional spectral predictor variables and the foliar biochemical contents. Results showed that the retrieval of leaf biochemistry through hyperspectral analysis can be accurate and robust across different tallgrass prairie functional groups. In addition, correlations were found between the leaf pigments and nutritional elements. Results provided insight into the use of pigment-related vegetation indices as the proxy of plant nutrition quality

Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression

The use of spectral features to estimate leaf area index (LAI) is generally considered a challenging task for hyperspectral data. In this study, the hyperspectral reflectance of winter wheat was selected to optimize the selection of spectral features and to evaluate their performance in modeling LAI at various growth stages during 2008 and 2009. We extracted hyperspectral features using different techniques, including reflectance spectra and first derivative spectra, absorption and reflectance position and vegetation indices. In order to find the best subset of features with the best predictive accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to estimated LAI values. The results indicated that the red edge–NIR spectral region (680 nm–1300 nm) was the most sensitive to LAI. Most features in this region exhibited a high correlation with LAI and had higher VIP values, especially the first derivative waveband at 750 nm (r = 0.900, VIP = 1.144). Adding a large number of features would not significantly improve the accuracy of the PLSR model. The PLSR model based on the fourteen features with the highest VIP values predicted LAI with a mean bootstrapped R2 value of 0.880 and a mean RMSE of 0.943 on the validation dataset and produced an estimated LAI result better than that, including the entire 54-feature dataset with a mean R2 of 0.875 and a mean RMSE of 0.965. The results of this study thus suggest that the use of only a few of the best features by VIP values is sufficient for LAI estimation

Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation

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Spatial water table level modelling with multi-sensor unmanned aerial vehicle data in boreal aapa mires

Peatlands have been degrading globally, which is increasing pressure on restoration measures and monitoring. New monitoring methods are needed because traditional methods are time-consuming, typically lack a spatial aspect, and are sometimes even impossible to execute in practice. Remote sensing has been implemented to monitor hydrological patterns and restoration impacts, but there is a lack of studies that combine multi-sensor ultra-high-resolution data to assess the spatial patterns of hydrology in peatlands. We combine optical, thermal, and topographic unmanned aerial vehicle data to spatially model the water table level (WTL) in unditched open peatlands in northern Finland suffering from adjacent drainage. We predict the WTL with a linear regression model with a moderate fit and accuracy (R2 = 0.69, RMSE = 3.85 cm) and construct maps to assess the spatial success of restoration. We demonstrate that thermal-optical trapezoid-based wetness models and optical bands are strongly correlated with the WTL, but topography-based wetness indices do not. We suggest that the developed method could be used for quantitative restoration assessment, but before-after restoration imagery is required to verify our findings

Remote Sensing Energy Balance Model for the Assessment of Crop Evapotranspiration and Water Status in an Almond Rootstock Collection

One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.info:eu-repo/semantics/publishedVersio

The utility of very-high resolution unmanned aerial vehicles (UAV) imagery in monitoring the spatial and temporal variations in leaf moisture content of smallholder maize farming systems.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Maize moisture stress, resulting from rainfall variability, is a primary challenge in the production of rain-fed maize farming, especially in water-scarce regions such as southern Africa. Quantifying maize moisture variations throughout the growing season can support agricultural decision-making and prompt the rapid and robust detection of smallholder maize moisture stress. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit near real-time information for determining maize moisture content at farm scale. Therefore, this study evaluated the utility of UAV derived multispectral imagery in estimating maize leaf moisture content indicators on smallholder farming systems throughout the maize growing season. The first objective of the study was to conduct a comparative analysis in order to evaluate the performance of five regression techniques (support vector regression, random forest regression, decision trees regression, artificial neural network regression and the partial least squares regression) in predicting maize water content indicators (i.e. equivalent water thickness (EWT), fuel moisture content (FMC) and specific leaf area (SLA)), and determine the most suitable indicator of smallholder maize water content variability based on multispectral UAV data. The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising maize moisture indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC and SLA were derived from the random forest regression algorithm with a relative root mean square error (rRMSE) of 3.13%, 1% and 3.48 %, respectively. Additionally, EWT and FMC yielded the highest predictive performance of maize leaf moisture and demonstrated the best correlation with remotely sensed data. The study’s second objective was to evaluate the utility of UAVderived multispectral imagery in estimating the temporal variability of smallholder maize moisture content across the maize growing season using the optimal maize moisture indicators. The findings illustrated that the NIR and red-edge wavelengths were influential in characterising maize moisture variability with the best models for estimating maize EWT and FMC resulting in a rRMSE of 2.27 % and 1%, respectively. Furthermore, the early reproductive stage was the most optimal for accurately estimating maize EWT and FMC using UAVproximal remote sensing. The findings of this study demonstrate the prospects of UAV- derived multispectral data for deriving insightful information on maize moisture availability and overall health conditions. This study serves as fundamental step towards the creation of an early maize moisture stress detection and warning systems, and contributes towards climate change adaptation and resilience of smallholder maize farming

Dados espectrorradiométricos de campo e índices de vegetação para estimar porcentagem de cobertura vegetal verde de pastagens cultivadas

Dissertação (mestrado)—Universidade de Brasília, Instituto de Geociências, Pós-graduação em Geociências Aplicadas, 2015.Pastagens cultivadas correspondem a principal atividade antrópica do Cerrado, concentrandose principalmente em Goiás, Mato Grosso do Sul, Mato Grosso e Minas Gerais. Mais de 50% das áreas de pastagens apresentam algum tipo de degradação, os quais são difíceis de serem mapeados com base em imagens de satélite. Diante de uma necessidade de quantificar os parâmetros biofísicos das pastagens do Cerrado, esta pesquisa objetivou analisar o potencial de dados espectrorradiométricos de campo para estimar porcentagem de cobertura vegetal verde (%CV) de pastagens cultivadas como Brachiaria, no campo experimental da Embrapa Cerrados em Planaltina/DF. Dados espectrorradiométricos foram obtidos por meio do espectrorradiômetro portátil FieldSpec da Analytical Spectral Devices (ASD) Inc. (que adquire dados de reflectância na faixa espectral de 325 - 1075 nm, com 1 nm de intervalo e 25º de IFOV), nos meses de julho, setembro, outubro e novembro de 2013, além de janeiro de 2014 (50 pontos de amostragem para cada mês). Nesses mesmos pontos de amostragem, foram obtidas fotografias verticais do terreno que foram classificadas pelo método de classificação supervisionada por máxima verossimilhança para estimar a correspondente %CV. Os dados espectrorradiométricos foram ainda convertidos para índices de vegetação, os quais têm apresentado boa correlação com parâmetros da vegetação em diversos estudos anteriores. Os dados espectrais e de %CV foram comparados estatisticamente por meio de coeficientes de correlação (r), em termos de: comprimentos de onda (faixa espectral de 400 a 900 nm); 12 diferentes índices de vegetação (IV); e faixas espectrais de oito plataformas orbitais com larguras de bandas estreitas e largas. Resultados dessa pesquisa indicaram que: a estimativa de %CV é mais favorável na estação chuvosa que na estação seca, setembro foi o mês em que a %CV esteve mais baixa (~23%), o mês de janeiro foi o que apresentou maior %CV verde (~ 57%). A faixa espectral do vermelho e do infravermelho próximo mostraram ser as mais apropriadas para estimar %CV. O Soil-Adjusted Vegetation Index (SAVI) e o Enhanced Vegetation Index (EVI) foram os Índices de Vegetação que apresentaram melhores correlações com a %CV. E as variações nas larguras das bandas das oito plataformas orbitais não interferiram significativamente na correlação entre IV e %CV.Cultivated pastures are the main anthropogenic activity in the Cerrado, mainly concentrated in Goiás, Mato Grosso do Sul, Mato Grosso and Minas Gerais. More than 50% of the areas under pasture show some type of degradation, which are difficult to map using satellite images. Faced with a need to quantify the biophysical parameters of the Cerrado grasslands, this study aims to analyze the potential of field spectro-radiometric data to estimate percentage of green vegetation cover (% CV) of cultivated pastures, like Brachiaria at the experimental field of Embrapa-Cerrados in Planaltina, DF. Spectro-radiometric data were obtained through the portable spectro-radiometer FieldSpec of Analytical Spectral Devices (ASD) Inc. (acquiring reflectance data in the spectral range of 325-1075 nm, 1 nm range and 25° of IFOV), in July, September, October and November 2013, and January 2014 (50 sampling points for each month). In these same sampling points, vertical terrain photographs were taken, that were classified by the classification method using maximum likelihood estimation to obtain the corresponding% CV. The spectro-radiometric data were also converted to vegetation indices that have shown good correlation with vegetation parameters in several previous studies. The spectral data and CV% were statistically compared by means of correlation coefficients (r), in terms of: wavelength (spectral range of 400 to 900 nm); 12 different vegetation indices (VI); and spectral bands of eight orbital platforms with narrow and broad wavebands. Results from this study indicated that: the estimation of% CV is more favorable in the rainy season than in the dry season, September being the month in which the% CV was lowest (~ 23%), the month of January showing the highest% CV (~ 57%). The red and near infrared wavelengths were the most appropriate spectra to estimate CV%. The Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI) were the Vegetation Indices that were best correlated with the% CV. The variations in the widths of the bands of the eight orbital platforms did not influence significantly the correlation between VI and CV%

Modelling leaf area index in a tropical grassland using multi-temporal hyperspectral data.

Master of Science in Environmental science.Abstract available in PDF file

High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields