Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa.
Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, VIC, 3125, Melbourne, Australia.
Environ Monit Assess. 2024 Mar 15;196(4):370. doi: 10.1007/s10661-024-12476-7.
A large percentage of native grassland ecosystems have been severely degraded as a result of urbanization and intensive commercial agriculture. Extensive nitrogen-based fertilization regimes are widely used to rehabilitate and boost productivity in these grasslands. As a result, modern management frameworks rely heavily on detailed and accurate information on vegetation condition to monitor the success of these interventions. However, in high-density environments, biomass signal saturation has hampered detailed monitoring of rangeland condition. This issue stems from traditional broad-band vegetation indices (such as NDVI) responding to high levels of photosynthetically active radiation (PAR) absorption by leaf chlorophyll, which affects leaf area index (LAI) sensitivity within densely vegetative regions. Whilst alternate hyperspectral solutions may alleviate the problem to a certain degree, they are often too costly and not readily available within developing regions. To this end, this study evaluated the use of high-resolution Worldview-3 imagery in combination with modified NDVI indices and image manipulation techniques in reducing the effects of biomass signal saturation within a complex tropical grassland. Using the random forest algorithm, several modified NDVI-type indices were developed from all potential dual-band combinations of the Worldview-3 image. Thereafter, linear contrast stretching and histogram equalization were implemented in conjunction with Singular Value Decomposition (SVD) to improve high-density biomass estimation. Results demonstrated that both contrast enhancement techniques, when combined with SVD, improved high-density biomass estimation. However, linear contrast stretching, SVD, and modified NDVI indices developed from the red (630-690 nm), green (510-580 nm), and near-infrared 1 (770-895 nm) bands were found to produce the best biomass predictive model (R = 0.71, RMSE = 0.40 kg/m). The results generated from this research offer a means to alleviate the biomass saturation problem. This framework provides a platform to assist rangeland managers in regionally assessing changes in vegetation condition within high-density grasslands.
由于城市化和集约化商业农业的发展，很大一部分本地草原生态系统已经严重退化。广泛使用基于氮的施肥制度来恢复和提高这些草原的生产力。因此，现代管理框架严重依赖于植被状况的详细准确信息，以监测这些干预措施的成功。然而，在高密度环境中，生物量信号饱和阻碍了对牧场状况的详细监测。这个问题源于传统的宽带植被指数（如 NDVI）对叶片叶绿素吸收的高水平光合有效辐射（PAR）做出响应，这影响了高密度植被区域的叶面积指数（LAI）的敏感性。虽然替代的高光谱解决方案在一定程度上可以缓解这个问题，但它们通常过于昂贵，在发展中地区也不容易获得。为此，本研究评估了使用高分辨率 Worldview-3 图像结合改进的 NDVI 指数和图像处理技术来减少复杂热带草原中生物量信号饱和的影响。使用随机森林算法，从 Worldview-3 图像的所有潜在双波段组合中开发了几种改进的 NDVI 型指数。此后，结合奇异值分解（SVD）实施了线性对比度拉伸和直方图均衡化，以改善高密度生物量估计。结果表明，当与 SVD 结合使用时，两种对比度增强技术都可以提高高密度生物量的估计。然而，线性对比度拉伸、SVD 和从红色（630-690nm）、绿色（510-580nm）和近红外 1（770-895nm）波段开发的改进 NDVI 指数被发现可以产生最佳的生物量预测模型（R=0.71，RMSE=0.40kg/m）。本研究产生的结果提供了一种缓解生物量饱和问题的方法。该框架提供了一个平台，以帮助牧场经理在高密度草原地区评估植被状况的变化。
