This study sought to evaluate the utility of remotely sensed data in estimating the impact of wattle invasion and clearance on native grass species productivity using Sentinel-2 multispectral instrument (MSI) imaging and the partial least squares regression (PLSR) algorithm. Therefore this study assessed grass above ground biomass (AGB) at various levels of wattle invasion In assessing the impacts of wattle invasion on grass AGB the study found that, wattle invasion significantly reduces grass AGB when, compared with uninvaded and cleared plots. Mean grass AGB was 89.64 g m−2, 43.87 g m−2 and 83.36 g m−2 for the cleared, moderately invaded and uninvaded, respectively. The study further found no significant differences between cleared and uninvaded plots (p = 0.826). However, moderately invaded plots were significantly lower than the cleared (p < 0.0001) and uninvaded plots (p = 0.001). In assessing the applicability of remotely sensed data, the findings of this study showed that the most influential variables in estimating biomass were red-edge-based VIs. Specifically, the simple ratio VI (band5/band2) was the most optimal variable for predicting grass AGB across various levels of wattle invasion yielding high accuracies (root mean square error of prediction [RMSEP] = 19.11 g m−2 and R 2 = 0.83). Additionally, PLSR results showed that the moderately invaded treatment was most optimally predicted with RMSEP of 13.06 g m−2. Overall, the results underscore the utility of remotely sensed data in monitoring grassland degradation and restoration.

中文翻译：

---

使用遥感数据量化草地生产力：草地恢复效益评估

本研究旨在使用 Sentinel-2 多光谱仪器 (MSI) 成像和偏最小二乘回归 (PLSR) 算法评估遥感数据在估计荆棘入侵和清除对原生草种生产力的影响方面的效用。因此，本研究评估了各种荆棘入侵水平的地上草生物量 (AGB) 在评估荆棘入侵对草 AGB 的影响时，研究发现，与未入侵和清除的地块相比，荆棘入侵显着降低了草 AGB。清除、中度入侵和未入侵的平均草 AGB 分别为 89.64 gm-2、43.87 gm-2 和 83.36 gm-2。该研究进一步发现清除地块和未入侵地块之间没有显着差异（p = 0.826）。然而，中度侵入地块显着低于清除地块（p < 0.0001) 和未入侵的地块 (p = 0.001)。在评估遥感数据的适用性时，本研究的结果表明，估计生物量最有影响的变量是基于红边的 VI。具体来说，简单的比值 VI (band5/band2) 是预测草 AGB 的最佳变量，用于在各种水平的荆棘入侵中产生高精度（预测的均方根误差 [RMSEP] = 19.11 gm-2 和 R 2 = 0.83） . 此外，PLSR 结果显示中度侵入处理最理想地预测为 RMSEP 为 13.06 gm-2。总体而言，结果强调了遥感数据在监测草地退化和恢复方面的效用。这项研究的结果表明，估计生物量最有影响的变量是基于红边的 VI。具体来说，简单的比值 VI (band5/band2) 是预测草 AGB 的最佳变量，用于在各种水平的荆棘入侵中产生高精度（预测的均方根误差 [RMSEP] = 19.11 gm-2 和 R 2 = 0.83） . 此外，PLSR 结果显示中度侵入处理最理想地预测为 RMSEP 为 13.06 gm-2。总体而言，结果强调了遥感数据在监测草地退化和恢复方面的效用。这项研究的结果表明，估计生物量最有影响的变量是基于红边的 VI。具体来说，简单的比值 VI (band5/band2) 是预测草 AGB 的最佳变量，用于在各种水平的荆棘入侵中产生高精度（预测的均方根误差 [RMSEP] = 19.11 gm-2 和 R 2 = 0.83） . 此外，PLSR 结果显示中度侵入处理最理想地预测为 RMSEP 为 13.06 gm-2。总体而言，结果强调了遥感数据在监测草地退化和恢复方面的效用。简单比率 VI (band5/band2) 是预测草 AGB 的最佳变量，用于预测各种水平的荆棘入侵，产生高精度（预测的均方根误差 [RMSEP] = 19.11 gm-2 和 R 2 = 0.83）。此外，PLSR 结果显示中度侵入处理最理想地预测为 RMSEP 为 13.06 gm-2。总体而言，结果强调了遥感数据在监测草地退化和恢复方面的效用。简单比率 VI (band5/band2) 是预测草 AGB 的最佳变量，用于预测各种水平的荆棘入侵，产生高精度（预测的均方根误差 [RMSEP] = 19.11 gm-2 和 R 2 = 0.83）。此外，PLSR 结果显示中度侵入处理最理想地预测为 RMSEP 为 13.06 gm-2。总体而言，结果强调了遥感数据在监测草地退化和恢复方面的效用。