Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data

Ramoelo, Abel; Cho, Moses Azong; Mathieu, Renaud; Madonsela, Sabelo; Kerchove, Ruben Van De; Kaszta, Żaneta; Wolff, Éléonore

doi:10.1016/j.jag.2014.12.010

Cited by 158 publications

(168 citation statements)

References 45 publications

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“…However, the "black box" property of these approaches affected the model transparency [53], which prevents users from interpreting the results in physical terms [18]. In comparison, the recently popular RF method demonstrates a higher efficiency spatially and temporally compared with other machine learning methods due to its randomness and majority rule [59,72], and the model is recommended to be considered superior when combined with the hybrid inversion strategy [17,18]. The ANN method is susceptible to variation in the training data [19,73] and environmental interference information such as atmospheric scattering and background reflectance [17], which was also shown in this study and which reduced its spatial and temporal accuracy.…”

Section: Model Comparison and Study Limitationsmentioning

confidence: 99%

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Wang

Tang

et al. 2016

Remote Sensing

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Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes of northern China. Here, we compare the predictive power of regression approaches and hybrid geostatistical methods using Chinese Huanjing (HJ) satellite charge coupled device (CCD) data. The regression methods evaluated include partial least squares regression (PLSR), artificial neural networks (ANNs) and random forests (RFs). The two hybrid geostatistical methods were regression kriging (RK) and random forests residuals kriging (RFRK). The predictions were validated for different grassland types and different growing stages, and their performances were also examined by adding several groups of vegetation indices (VIs). The two hybrid geostatistical models (RK and RFRK) yielded the most accurate predictions (root mean squared error (RMSE) = 0.21 m 2 /m 2 and 0.23 m 2 /m 2 for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m 2 /m 2 ), which was the most accurate among the regression models. These three models also exhibited the best temporal performance across the duration of the growing season. The PLSR and ANN models were less accurate (RMSE = 0.33 m 2 /m 2 and 0.35 m 2 /m 2 for ANN and PLSR, respectively), and the PLSR model performed the worst (exhibiting varied temporal performance and unreliable prediction accuracy that was susceptible to ground conditions). By adding VIs to the predictor variables, the predictions of the PLSR and ANN models were obviously improved (RMSE improved from 0.35 m 2 /m 2 to 0.28 m 2 /m 2 for PLSR and from 0.33 m 2 /m 2 to 0.28 m 2 /m 2 for ANN); the RF and RFRK models did not generate more accurate predictions and the performance of the RK model declined (RMSE decreased from 0.21 m 2 /m 2 to 0.32 m 2 /m 2 ).

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Section: Model Comparison and Study Limitationsmentioning

confidence: 99%

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Wang

Tang

et al. 2016

Remote Sensing

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“…The estimation of leaf N in grass has been successful using hyperspectral data with both field spectrometer and airborne data [10][11][12][13]. The latter was possible because of the development of the second generation of vegetation indices such as the red edge position (REP) [14,15] and narrow band indices which are sensitive to subtle changes in leaf chlorophyll content in contrast to the traditional Normalized Difference Vegetation Index (NDVI) which saturates at high green vegetation canopy cover [11,[16][17][18][19]. The new generation of satellite constellations has strategically incorporated the red edge band to improve vegetation and crop condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…To date, empirical methods for estimating leaf N require basic and complex statistical analysis-from simple to machine learning regression [16][17][18]20,21]. The simple empirical approach assumes that leaf N is significantly related to specific chlorophyll-based vegetation indices [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, some methods assume that the estimation of leaf N is not dependent exclusively on one vegetation index or reflectance at a specific band, but also a combination of variables-integrated modelling using reflectance, indices and also environmental variables [10,13]. The latter approach was achieved by using stepwise multiple linear regressions (SMLR), partial least square regression (PLSR), machine learning techniques including artificial neural network (ANN) [17,18]. The machine learning techniques are non-parametric in nature and are known to perform better than the parametric ones for grass N estimation [12,17,18], and could be critical for monitoring grass nutrients using Sentinel-2.…”

Section: Introductionmentioning

confidence: 99%

“…The latter approach was achieved by using stepwise multiple linear regressions (SMLR), partial least square regression (PLSR), machine learning techniques including artificial neural network (ANN) [17,18]. The machine learning techniques are non-parametric in nature and are known to perform better than the parametric ones for grass N estimation [12,17,18], and could be critical for monitoring grass nutrients using Sentinel-2.…”

Section: Introductionmentioning

confidence: 99%

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Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model

Ramoelo

Cho

2018

Remote Sensing

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Leaf nitrogen concentration (leaf N, %) is an essential component for understanding biogeochemical cycling. Leaf N is a good indicator of grass or forage quality, which is important for understanding the movements and feeding patterns of herbivores. Leaf N can be used as input for rangeland carrying capacity and stocking rate models. The estimation of leaf N has been successful using hyperspectral and commercial high spatial resolution satellite data such as WorldView-2 and RapidEye. Empirical methods have been used successfully to estimate leaf N, on the basis that it correlates with leaf chlorophyll. As such, leaf N was estimated using red edge based indices. The new Sentinel-2 sensor has two red edge bands, is freely available, and could further improve the estimation of leaf N at a regional scale. The objective of this study is to develop red edge based Sentinel-2 models derived from an analytical spectral device (ASD) spectrometer to map and monitor leaf N using Sentinel-2 images. Field work for leaf N and ASD data were collected in 2014 (December) in and around Kruger National Park, South Africa. ASD data were resampled to the Sentinel-2 spectral configuration using the spectral response function. The Sentinel-2 data for various dates were acquired from the European Space Agency (ESA) portal. The Sentinel-2 atmospheric correction (Sen2Cor) process was implemented. Simple empirical regression was used to estimate leaf N. High leaf N prediction accuracy was achieved at the ASD level and the best model was inverted on Sentinel-2 images to explain leaf N distribution at a regional scale over time. The spatial distribution of leaf N is influenced by the underlying geological substrate, fire frequency and other environmental variables. This study is a demonstration of how ASD data can be used to calibrate Sentinel-2 for leaf N estimation and mapping.

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Mapping canopy nitrogen‐scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed‐speciesEucalyptusforests

Wagner

Baker

Moore

et al. 2021

Ecology and Evolution

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Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data

Cited by 158 publications

References 45 publications

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model

Mapping canopy nitrogen‐scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed‐speciesEucalyptusforests

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