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.
Highlights
Grazing pasture and croplands occupy a significant portion of land surface in the world
The window size and offset texture parameters influenced the accuracy of aboveground biomass (AGB) and canopy height (CH) estimation using both random forest (RF) and XGBoost machine learning algorithms (Table 4)
We showed that the GLCM-based texture measures derived from PlanetScope imagery enhanced the prediction accuracy of AGB and CH models compared to the performance obtained using spectral bands or vegetation indices
Summary
Grazing pasture and croplands occupy a significant portion of land surface in the world. Incorporating textural information to monitor intensively managed pasture fields is challenging, since texture varies widely depending on the landscape, types of measures, and associated parameters (e.g., window size, offset). To this date, only few studies, if not none, have explored the potential of using texture measures for monitoring pasture biomass. We assessed the feasibility of using spectral and textural information derived from high spatiotemporal resolution PlanetScope imagery for estimating and monitoring aboveground biomass (AGB) and canopy height (CH) of intensively managed mixed pastures in an ICLS in the western region of São Paulo State, Brazil. We assessed the potential for enhanced estimation accuracy by applying the XGBoost algorithm compared with the well-known ML algorithm random forest (RF)
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