EXPLORING THE POTENTIAL OF HIGH-RESOLUTION PLANETSCOPE IMAGERY FOR PASTURE BIOMASS ESTIMATION IN AN INTEGRATED CROP–LIVESTOCK SYSTEM

Abstract. Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May–August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g m−2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g m−2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring.

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VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

Remote Sensing ◽

10.3390/rs12142254 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2254 ◽

Cited By ~ 1

Author(s):

Hafiz Ali Imran ◽

Damiano Gianelle ◽

Duccio Rocchini ◽

Michele Dalponte ◽

M. Pilar Martín ◽

...

Keyword(s):

Near Infrared ◽

Spatial Scales ◽

Vegetation Indices ◽

Radiative Transfer Model ◽

Transfer Model ◽

Sharp Change ◽

Full Potential ◽

Area Index ◽

Red Edge ◽

Temporal And Spatial

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.

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Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

Remote Sensing ◽

10.3390/rs12162534 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2534

Author(s):

Aliny A. Dos Reis ◽

João P. S. Werner ◽

Bruna C. Silva ◽

Gleyce K. D. A. Figueiredo ◽

João F. G. Antunes ◽

...

Keyword(s):

Aboveground Biomass ◽

Vegetation Indices ◽

Canopy Height ◽

Gradient Boosting ◽

Remotely Sensed Data ◽

Spatiotemporal Resolution ◽

Spectral Bands ◽

Extreme Gradient Boosting ◽

Intensively Managed ◽

New Generation

Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

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Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

Remote Sensing ◽

10.3390/rs11050545 ◽

2019 ◽

Vol 11 (5) ◽

pp. 545 ◽

Cited By ~ 6

Author(s):

Dimitris Stavrakoudis ◽

Dimitrios Katsantonis ◽

Kalliopi Kadoglidou ◽

Argyris Kalaitzidis ◽

Ioannis Gitas

Keyword(s):

Near Infrared ◽

Agronomic Traits ◽

Oryza Sativa L ◽

N Uptake ◽

Vegetation Indices ◽

Growth Stages ◽

Linear Regression Models ◽

Remotely Sensed Data ◽

Cost Efficient ◽

N Management

The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.

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PEMETAAN BIOMASSA TEGAKAN HUTAN HUJAN TROPIS DI BUKIT MANDIANGIN MENGGUNAKAN CITRA SENTINEL-2 MSI

Jurnal Hutan Tropis ◽

10.20527/jht.v9i3.12318 ◽

2021 ◽

Vol 9 (3) ◽

pp. 299

Author(s):

Mufidah Asy’ari ◽

Syam’ani Syam’ani ◽

Trisnu Satriadi

Keyword(s):

Environmental Sustainability ◽

Tropical Rainforest ◽

Vegetation Indices ◽

Percentage Error ◽

Biomass Estimation ◽

Total Biomass ◽

Average Biomass ◽

Simple Ratio ◽

Estimation Models ◽

Sentinel 2

The preservation of standing biomass is one of the most vital elements for environmental sustainability and the sustainability of the forest itself. One of the actions that can be taken in an effort to maintain the sustainability of forest stand biomass is to map the distribution of biomass, and monitor changes or dynamics of stand biomass from time to time in a sustainable manner. This study aims to build a model based on remote sensing imagery to estimate the total biomass of tropical rainforest stands in Mandiangin Hill, South Kalimantan. The models developed in this study are based on vegetation indices extracted from Sentinel-2 MSI Imagery. A total of ten vegetation indices were tested in this study. For the construction process and validation of stand biomass estimation models, biomass information was measured directly in the field using a number of measuring plots. Stand biomass estimation models were made by correlating stand biomass information from the field with vegetation indices from Sentinel-2 MSI Imagery. The results showed that the most accurate model for estimating the biomass of tropical rainforest stands was 9.5806.exp (0.1454.PSSRa). Where PSSRa is Pigment Specific Simple Ratio. This model has a correlation coefficient (R2) of 0.876, a Mean Absolute Percentage Error (MAPE) of 16.8%, and a Root Mean Square Error (RMSE) of 32.6. The estimation results show that the total biomass of the Bukit Mandiangin tropical rainforest stands is between 11.7 to 998.5 Mg/ha, with an average biomass of 135.8 Mg/ha. Furthermore, the estimation of stand biomass in this study is limited to woody vegetation with a DBH of 10 cm and above. The PSSRa model with various improvements can be used to accurately estimate stand biomass

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A Log-Ratio Vegetation Index (LRVI) for Discrimination and Classification of Remote Sensing Data

Silva Lusitana ◽

10.1051/silu/20202801045 ◽

2020 ◽

Vol 28 (1) ◽

pp. 45-70

Author(s):

Francisco C. Rego ◽

Irene S.P. Cadima ◽

Eva K. Strand

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Near Infrared ◽

Vegetation Indices ◽

Ground Cover ◽

Spectral Space ◽

Electromagnetic Spectrum ◽

Remotely Sensed Data ◽

Classical Statistics ◽

Log Ratio

Discrimination and classification are integral processes for interpreting remotely sensed data. Many spectral vegetation indices have been proposed for discriminating between vegetation, soil, and other ground cover categories. Classical remote sensing show that reflectance in the red (R) and near infrared (NIR) bands of the electromagnetic spectrum have been successful in differentiating between vegetation and other ground cover classes and they are commonly used for this purpose. Here we demonstrate how Fisher’s classical statistics can be applied to develop discriminant functions for commonly used vegetation indices simply using the R and NIR bands. We derive a new vegetation index, the Log-Ratio Vegetation Index (LRVI) and demonstrate its utility in discriminating between cork oak trees and surrounding background in woodlands in Portugal. The LRVI performed better than seven previously developed vegetation indices, likely because of its linear properties in the reflectance density spectral space. The robustness and simplicity of LRVI suggests that it deserves further exploration and should be included for comparison with other vegetation indices and functions in discrimination, classification, and modelling studies. We suggest that the demonstrated approach is widely applicable to development of indices composed of other bands than R and NIR for systems or processes that correlate better with reflectance in other regions of the electromagnetic spectrum.

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Desert camouflage and what wildlife see

NIR news ◽

10.1177/0960336017738177 ◽

2017 ◽

Vol 28 (8) ◽

pp. 4-10

Author(s):

Wendy W Kuhne ◽

Martine C Duff ◽

Katie Salvaggio ◽

Nancy V Halverson ◽

Ronald Staggs

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Visual Observation ◽

Infrared Measurements ◽

Blue Wavelength ◽

Blue Range ◽

Simple Ratio ◽

Camouflage Patterns

Desert, desert-scrub, savanna and sandy beach, and lakeshore environments can be particularly tricky in terms of camouflage selection due to their low vegetative density. Therefore many companies focus on the development of paint color schemes that match the vegetation and the desert soils/sands. However another factor in the consideration of which camouflage to purchase may lie in what the animal can see. White-tailed deer and similar large mammals have been shown to have three classes of photo pigments that are sensitive to the range of blue to yellow-green during day light hours and blue to blue-green at night. Six commercially-available camouflage patterns were investigated to determine if the reflectance characteristics measured in the laboratory and under field conditions were elevated in the blue range and perhaps more likely to be seen by wildlife. The camouflage patterns were evaluated against standard vegetation indices including normalized difference vegetation index, soil adjusted vegetation index, enhanced vegetation index, and simple ratio. Only two of the patterns (S4 and S5) possessed a reflectance more like vegetation. Patterns S4, S6, S3, and S2 all showed only slight elevations in the blue wavelength range which could only have been detected by near infrared measurements instead of visual observation by the human eye.

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Predicting agronomic performance of barley using canopy reflectance data

Canadian Journal of Plant Science ◽

10.4141/p02-195 ◽

2004 ◽

Vol 84 (1) ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

T. G. Fetch ◽

Jr., B. J. Steffenson ◽

V. D. Pederson

Keyword(s):

Remote Sensing ◽

Near Infrared ◽

Vegetation Indices ◽

Kernel Weight ◽

Nondestructive Method ◽

Growth Stages ◽

Canopy Reflectance ◽

Agronomic Performance ◽

Simple Ratio ◽

Normalized Difference Vegetation Indices

The ability to accurately and rapidly predetermine agronomic performance would be desirable in most plant breeding programs. Remote sensing of canopy reflectance is a quick and nondestructive method that may be useful in the estimation of agronomic performance. Studies were conducted at Fargo and Langdon, North Dakota, to determine the effectiveness of a multispectral radiometer in estimating yield, kernel plumpness (KP), and 1000-kernel weight (TKW) in barley. Canopy reflectance was measured in eight (500–850 nm) discrete narrow-wavelength bands. Three types of reflectance models were evaluated: simple models using one to four wavelengths, simple ratio and normalized difference vegetation indices (NDVI) using green, red, and near-infrared wavelengths, and soil-adjusted vegetation indices (SAVI). The relationship between canopy reflectance and agronomic performance was significantly influenced by environment, growth stage, and plant genotype. Grain yield was best estimated near GS73 (0.84 < R2 < 0.92) at Fargo and at GS83 (0.55 < R2 < 0.81) at Langdon. In contrast, KP and TKW could be estimated at both late (GS83; 0.68 < R2 < 0.93) and early (GS24–GS47; 0.72 < R2 < 0.91) growth stages. The 550-nm and 800-nm wavelengths are critical for development of predictive models. A simple model using 550-nm, 600-nm, and 800-nm from GS47-GS73 gave significant (0.45 < R2 < 0.64) estimation of agronomic performance across all environments. In contrast, simple ratio, NDVI, and SAVI were less effective (0.05 < R2 < 0.77) in predicting agronomic performance. Remote sensing using canopy reflectance is a potential tool to estimate agronomic performance of barley, but genotypic and crop stage factors affect this method. Further studies are needed to improve the usefulness of multispectral radiometry in predicting agronomic performance. Key words: Crop yield, Hordeum vulgare, kernel plumpness, remote sensing

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Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

Remote Sensing ◽

10.3390/rs13030536 ◽

2021 ◽

Vol 13 (3) ◽

pp. 536

Author(s):

Eve Laroche-Pinel ◽

Mohanad Albughdadi ◽

Sylvie Duthoit ◽

Véronique Chéret ◽

Jacques Rousseau ◽

...

Keyword(s):

Near Infrared ◽

Water Status ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Short Wave ◽

Spatial And Temporal Scales ◽

Spectral Bands ◽

Main Challenge ◽

Spectral Scale ◽

Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

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Comparison of Brain Activation Patterns during Olfactory Stimuli between Recovered COVID-19 Patients and Healthy Controls: A Functional Near-Infrared Spectroscopy (fNIRS) Study

Brain Sciences ◽

10.3390/brainsci11080968 ◽

2021 ◽

Vol 11 (8) ◽

pp. 968

Author(s):

Roger C. Ho ◽

Vijay K. Sharma ◽

Benjamin Y. Q. Tan ◽

Alison Y. Y. Ng ◽

Yit-Shiang Lui ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Time Interval ◽

Healthy Controls ◽

Functional Near Infrared Spectroscopy ◽

Activation Patterns ◽

Identification Test ◽

Olfactory Stimuli ◽

Preliminary Study

Impaired sense of smell occurs in a fraction of patients with COVID-19 infection, but its effect on cerebral activity is unknown. Thus, this case report investigated the effect of COVID-19 infection on frontotemporal cortex activity during olfactory stimuli. In this preliminary study, patients who recovered from COVID-19 infection (n = 6) and healthy controls who never contracted COVID-19 (n = 6) were recruited. Relative changes in frontotemporal cortex oxy-hemoglobin during olfactory stimuli was acquired using functional near-infrared spectroscopy (fNIRS). The area under curve (AUC) of oxy-hemoglobin for the time interval 5 s before and 15 s after olfactory stimuli was derived. In addition, olfactory function was assessed using the Sniffin’ Sticks 12-identification test (SIT-12). Patients had lower SIT-12 scores than healthy controls (p = 0.026), but there were no differences in oxy-hemoglobin AUC between healthy controls and patients (p > 0.05). This suggests that past COVID-19 infection may not affect frontotemporal cortex function, and these preliminary results need to be verified in larger samples.

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Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Remote Sensing ◽

10.3390/rs13020233 ◽

2021 ◽

Vol 13 (2) ◽

pp. 233

Author(s):

Ilja Vuorinne ◽

Janne Heiskanen ◽

Petri K. E. Pellikka

Keyword(s):

Near Infrared ◽

Vegetation Indices ◽

Fibre Production ◽

Additive Models ◽

Leaf Biomass ◽

Ground Vegetation ◽

Perennial Crop ◽

Red Edge ◽

Agave Sisalana ◽

Sentinel 2

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small.

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