Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

Abstract

Abstract:To accurately estimate leaf area index (LAI) in mangrove areas, the selection of appropriate models and predictor variables is critical. However, there is a major challenge in quantifying and mapping LAI using multi-spectral sensors due to the saturation effects of traditional vegetation indices (VIs) for mangrove forests. WorldView-2 (WV2) imagery has proven to be effective to estimate LAI of grasslands and forests, but the sensitivity of its vegetation indices (VIs) has been uncertain for mangrove forests. Furthermore, the single model may exhibit certain randomness and instability in model calibration and estimation accuracy. Therefore, this study aims to explore the sensitivity of WV2 VIs for estimating mangrove LAI by comparing artificial neural network regression (ANNR), support vector regression (SVR) and random forest regression (RFR). The results suggest that the RFR algorithm yields the best results (RMSE = 0.45, 14.55% of the average LAI), followed by ANNR (RMSE = 0.49, 16.04% of the average LAI), and then SVR (RMSE = 0.51, 16.56% of the average LAI) algorithms using 5-fold cross validation (CV) using all VIs. Quantification of the variable importance shows that the VIs derived from the red-edge band consistently remain the most important contributor to LAI estimation. When the red-edge band-derived VIs are removed from the models, estimation accuracies measured in relative RMSE (RMSEr) decrease by 3.79%, 2.70% and 4.47% for ANNR, SVR and RFR models respectively. VIs derived from red-edge band also yield better accuracy compared with other traditional bands of WV2, such as near-infrared-1 and near-infrared-2 band. Furthermore, the estimated LAI values vary significantly across different mangrove species. The study demonstrates the utility of VIs of WV2 imagery and the selected machine-learning algorithms in developing LAI models in mangrove forests. The results indicate that the red-edge band of WV2 imagery can help alleviate the saturation problem and improve the accuracy of LAI estimation in a mangrove area.

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

“…All the regression models were run on the calibration dataset and the associated parameters were optimized using five-fold cross-validation with 10 repeated experiments. Five-fold cross validation was preferred over 10-fold cross validation because of the limited sample size [28,46]. Finally, variable importance was determined to analyze the contribution of each predictor variable to prediction accuracy of the best models.…”

“…Thus, machine learning methods based on artificial neural networks (ANNs) and random forest regression (RFR) have been utilized to capture both linear and non-linear relationships that exist between remote sensing and vegetative parameters [24][25][26]. Yuan et al [27] and Zhu et al [28] demonstrated that RFR was superior to ANNs in leaf area index (LAI) prediction due to its suitability for a relatively small number of training samples and insensitivity to noisy data [29]. Recently, Pôças et al [30] demonstrated the power of machine learning methods to support irrigation scheduling in vineyards using data from a handheld spectrometer.…”

Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery

“…2019, 11, 345 7 of 24 B5 (705 nm) bands, provided high correlations during estimation of the leaf area index and the chlorophyll content. Pu et al [59] and Zhu et al [60] introduced the operational use of this index for the Worldview-2 satellite imagery. To the best of our knowledge, this research is a novel evaluation of the NDVIre on operational Sentinel-2A imagery.…”

Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species

“…These features were selected based on their previous performances in mangrove species discriminations, mangrove biomass inversions or other vegetation studies, as highlighted in literature [14,24,25,[45][46][47][48]. EVI (enhanced vegetation index), for example, enhances vegetation signals by adding blue bands to correct soil background and aerosol scattering effects, which is suitable for areas with high leaf area index values [17]. The spectral features consisted of conventional NIR indices, red edge indices and shortwave infrared indices.…”