Estimating biophysical variables of pasture cover using sentinel-1 data

Over the years, different optical remote sensing platforms and data have been used to estimate aboveground pasture biomass in a variety of landscapes, both heterogeneous and homogenous and at varying spatial scales. Optical methods are often confounded by target visibility, namely presence of cloud cover and haze, and are constrained to daylight conditions. In this study, we used the synthetic aperture radar data from the European Space Agency Sentinel-1 mission to estimate pasture biomass, sward height and leaf area index of a complex extensive grazing ‘farmscape’ comprising of a range of grass vegetation communities We observed that the quality of digital elevation model used in radar data pre-processing significantly influences the ability of eigenvector scattering decomposition in estimating biomass, sward height and leaf area index

Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass

Above ground biomass (AGB) is a parameter commonly used for assessment of grassland systems. Destructive AGB measurements, although accurate, are time consuming and are not easily undertaken on a repeat basis or over large areas. Structure-from-Motion (SfM) photogrammetry and Terrestrial Laser Scanning (TLS) are two technologies that have the potential to yield precise 3D structural measurements of vegetation quite rapidly. Recent advances have led to the successful application of TLS and SfM in woody biomass estimation, but application in natural grassland systems remains largely untested. The potential of these techniques for AGB estimation is examined considering 11 grass plots with a range of biomass in South Dakota, USA. Volume metrics extracted from the TLS and SfM 3D point clouds, and also conventional disc pasture meter settling heights, were compared to destructively harvested AGB total (grass and litter) and AGB grass plot measurements. Although the disc pasture meter was the most rapid method, it was less effective in AGB estimation (AGBgrass r2 = 0.42, AGBtotal r2 = 0.32) than the TLS (AGBgrass r2 = 0.46, AGBtotal r2 = 0.57) or SfM (AGBgrass r2 = 0.54, AGBtotal r2 = 0.72) which both demonstrated their utility for rapid AGB estimation of grass systems

Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass

Above ground biomass (AGB) is a parameter commonly used for assessment of grassland systems. Destructive AGB measurements, although accurate, are time consuming and are not easily undertaken on a repeat basis or over large areas. Structure-from-Motion (SfM) photogrammetry and Terrestrial Laser Scanning (TLS) are two technologies that have the potential to yield precise 3D structural measurements of vegetation quite rapidly. Recent advances have led to the successful application of TLS and SfM in woody biomass estimation, but application in natural grassland systems remains largely untested. The potential of these techniques for AGB estimation is examined considering 11 grass plots with a range of biomass in South Dakota, USA. Volume metrics extracted from the TLS and SfM 3D point clouds, and also conventional disc pasture meter settling heights, were compared to destructively harvested AGB total (grass and litter) and AGB grass plot measurements. Although the disc pasture meter was the most rapid method, it was less effective in AGB estimation (AGBgrass r 2 = 0.42, AGBtotal r 2 = 0.32) than the TLS (AGBgrass r 2 = 0.46, AGBtotal r 2 = 0.57) or SfM (AGBgrass r 2 = 0.54, AGBtotal r 2 = 0.72) which both demonstrated their utility for rapid AGB estimation of grass systems

Methods and Applications of 3D Ground Crop Analysis Using LiDAR Technology: A Survey

Light Detection and Ranging (LiDAR) technology is positioning itself as one of the most effective non-destructive methods to collect accurate information on ground crop fields, as the analysis of the three-dimensional models that can be generated with it allows for quickly measuring several key parameters (such as yield estimations, aboveground biomass, vegetation indexes estimation, perform plant phenotyping, and automatic control of agriculture robots or machinery, among others). In this survey, we systematically analyze 53 research papers published between 2005 and 2022 that involve significant use of the LiDAR technology applied to the three-dimensional analysis of ground crops. Different dimensions are identified for classifying the surveyed papers (including application areas, crop species under study, LiDAR scanner technologies, mounting platform technologies, and the use of additional instrumentation and software tools). From our survey, we draw relevant conclusions about the use of LiDAR technologies, such as identifying a hierarchy of different scanning platforms and their frequency of use as well as establishing the trade-off between the economic costs of deploying LiDAR and the agronomically relevant information that effectively can be acquired. We also conclude that none of the approaches under analysis tackles the problem associated with working with multiple species with the same setup and configuration, which shows the need for instrument calibration and algorithmic fine tuning for an effective application of this technology.Fil: Micheletto, Matías Javier. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz | Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz | Universidad Nacional de la Patagonia "san Juan Bosco". Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz; ArgentinaFil: Chesñevar, Carlos Iván. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentin

Estimation of Productivity in Dryland Mediterranean Pastures: Long-Term Field Tests to Calibration and Validation of the Grassmaster II Probe

The estimation of pasture productivity is of great interest for the management of animal grazing. The standard method of assessing pasture mass requires great effort and expense to collect enough samples to accurately represent a pasture. This work presents the results of a long-term study to calibrate a Grassmaster II capacitance probe to estimate pasture productivity in two phases: (i) the calibration phase (2007–2018), which included measurements in 1411 sampling points in three parcels; and (ii) the validation phase (2019), which included measurements in 216 sampling points in eight parcels. A regression analysis was performed between the capacitance (CMR) measured by the probe and values of pasture green matter and dry matter (respectively, GM and DM, in kg ha−1). The results showed significant correlations between GM and CMR and between DM and CMR, especially in the early stages of pasture growth cycle. The analysis of the data grouped by classes of pasture moisture content (PMC) shows higher correlation coefficients for PMC content >80% (r = 0.775; p 80% showed a good approximation between GM or DM measured and GM or DM predicted (r = 0.959; p 80%

Forage mass estimation in silvopastoral and full sun systems: evaluation through proximal remote sensing applied to the SAFER model.

Abstract: The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′S, 47°50′W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was able to promisingly express the behavior of forage variables, with a significant correlation with all of them. The variables that obtained the best estimation performance model were the dry mass of leaves and stems and the dry mass of leaves in silvopastoral and full sun systems, respectively. It was concluded that the association of the SAFER model with the proximal sensor allowed us to obtain a fast, precise and accurate forage estimation method

Examination of the Potential of Structure-from-Motion Photogrammetry and Terrestrial Laser Scanning for Rapid Nondestructive Field Measurement of Grass Biomass

Above ground biomass (AGB) is a parameter commonly used for assessment of grassland systems. While destructive sampling of AGB is highly accurate, it is time consuming and often precludes repeat temporal sampling or sampling in sensitive ecosystems. Consequently, a number of nondestructive techniques that relate grass structural properties to AGB have been developed. This study investigated the application of two recent technologies, Terrestrial Laser Scanning (TLS) and Structurefrom- Motion (SfM), in the development of rapid nondestructive AGB estimation of grassland plots. TLS and SfM volume metrics generated using a rasterized surface differencing method were linearly related to destructively measured total AGB and grass AGB excluding all litter, and results were compared to the conventional disc pasture meter. The linear models were assessed using a leave-one-out cross validation scheme. The disc pasture meter was found to be the least reliable method in assessing total AGB (r2 = 0.32, RMSELOOCV = 269 g/m2). SfM (r2 = 0.74, RMSELOOCV = 169 g/m2) outperformed TLS (r2 = 0.56, RMSELOOCV = 219 g/m2), though a much larger slope in SfM regressions suggests an increased sensitivity to error. Litter removal decreased the effectiveness of AGB estimation for both TLS (r2 = 0.49) and SfM (r2 = 0.51) but increased the fit of disc pasture meter estimations (r2 = 0.42), highlighting the complex relationship between litter accumulation and AGB. TLS and SfM derived volumes were shown to be insensitive to cell dimensions when calculating volume provided cell dimensions were large enough to ensure no empty cells occurred. Using observed ground surfaces in volumetric calculations rather than an estimated ground plane increased r2 to 0.63 for TLS and 0.77 for SfM. Though the disc pasture meter was found to be the most rapid of the three methods, TLS and SfM both out performed it and have clearly demonstrated their potential utility for AGB estimation of grass systems. Their ability to systematically collect measurements over larger spatial extents than those investigated here could greatly outpace the disc pasture meter’s predictive capabilities and speed

Non-Destructive Biomass Estimation in Mediterranean Alpha Steppes: Improving Traditional Methods for Measuring Dry and Green Fractions by Combining Proximal Remote Sensing Tools

The Mediterranean region is experiencing a stronger warming effect than other regions, which has generated a cascade of negative impacts on productivity, biodiversity, and stability of the ecosystem. To monitor ecosystem status and dynamics, aboveground biomass (AGB) is a good indicator, being a surrogate of many ecosystem functions and services and one of the main terrestrial carbon pools. Thus, accurate methodologies for AGB estimation are needed. This has been traditionally done by performing direct field measurements. However, field-based methods, such as biomass harvesting, are destructive, expensive, and time consuming and only provide punctual information, not being appropriate for large scale applications. Here, we propose a new non-destructive methodology for monitoring the spatiotemporal dynamics of AGB and green biomass (GB) of M. tenacissima L. plants by combining structural information obtained from terrestrial laser scanner (TLS) point clouds and spectral information. Our results demonstrate that the three volume measurement methods derived from the TLS point clouds tested (3D convex hull, voxel, and raster surface models) improved the results obtained by traditional field-based measurements. (Adjust-R2 = 0.86–0.84 and RMSE = 927.3–960.2 g for AGB in OLS regressions and Adjust-R2 = 0.93 and RMSE = 376.6–385.1 g for AGB in gradient boosting regression). Among the approaches, the voxel model at 5 cm of spatial resolution provided the best results; however, differences with the 3D convex hull and raster surface-based models were very small. We also found that by combining TLS AGB estimations with spectral information, green and dry biomass fraction can be accurately measured (Adjust-R2 = 0.65–0.56 and RMSE = 149.96–166.87 g in OLS regressions and Adjust-R2 = 0.96–0.97 and RMSE = 46.1–49.8 g in gradient boosting regression), which is critical in heterogeneous Mediterranean ecosystems in which AGB largely varies in response to climatic fluctuations. Thus, our results represent important progress for the measurement of M. tenacissima L. biomass and dynamics, providing a promising tool for calibration and validation of further studies aimed at developing new methodologies for AGB estimation at ecosystem regional scales