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

Abstract

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.

Spatiotemporal Patterns of Pasture Quality Based on NDVI Time-Series in Mediterranean Montado Ecosystem

“…The reference measurement method for CP and NDF determinations consists of the cutting and drying of pasture samples to get the actual dry biomass per area unit, followed by specific laboratory analysis [12]. This procedure, carried out at a fine scale and based on field measurements [13], was developed for researchers and although these data are very informative, the whole process of collecting samples and processing them is laborious, destructive, expensive, and not routinely used by farmers [3]. Moreover, the limited number of samples that can be effectively processed in reference methods, based on sampling design distributions and intensity, reduces the possibility of assessing the spatial variability of pastoral resources [12,13].…”

“…This procedure, carried out at a fine scale and based on field measurements [13], was developed for researchers and although these data are very informative, the whole process of collecting samples and processing them is laborious, destructive, expensive, and not routinely used by farmers [3]. Moreover, the limited number of samples that can be effectively processed in reference methods, based on sampling design distributions and intensity, reduces the possibility of assessing the spatial variability of pastoral resources [12,13]. Therefore, an alternative approach for detailed spatial and temporal pasture monitoring is proposed based on innovative tools, such as remote sensing (data from satellite and airborne platforms) and proximal sensing (field-specific sensors) [2,3], contributing to establish sustainable grassland management systems [5].…”

Estimating stem volume of Eucalyptus sp. and Pinus sp. plantations in Brazil, using Sentinel-1B and ALOS-2/PALSAR-2 data

“…21,23,24 In addition to these algorithms, gradient boosting decision tree algorithms, and more specifically the extreme gradient boost (XGB) algorithm, have demonstrated superior performance in estimating biophysical parameters of different vegetation types. 25,26 RF is a nonlinear and non-parametric ensemble decision-tree method 27 that provides flexible, robust, and accurate predictive capabilities for high dimensional datasets. 22 RF models have been widely and successfully used for forest biophysical parameter estimation.…”

“…Random forest (RF) and support vector machine (SVM) are two machine learning algorithms commonly applied to retrieve forest biophysical parameters using different remote sensing data 21 , 23 , 24 . In addition to these algorithms, gradient boosting decision tree algorithms, and more specifically the extreme gradient boost (XGB) algorithm, have demonstrated superior performance in estimating biophysical parameters of different vegetation types 25 , 26 . RF is a nonlinear and non-parametric ensemble decision-tree method 27 that provides flexible, robust, and accurate predictive capabilities for high dimensional datasets 22 .…”

Assessing the Wall-To-Wall Spatial and Qualitative Dynamics of the Brazilian Pasturelands, between 2010 and 2018, Based on the Analysis of the Landsat Data Archive

“…Most of these studies used moderate spatial resolution data (MODIS) (Aguiar et al, 2017;Arantes et al, 2018; O. J. R. . More recently, high spatial resolution data have been used in pasture quality assessments and biomass estimates (Brito et al, 2018;Chen et al, 2021;Dos Reis et al, 2020). Among the products most used for these qualitative analyzes is the NDVI (Normalized Difference Vegetation Index), considered a "proxy" of vegetative vigor and forage productivity (Arantes et al, 2016).…”