Multidisciplinary Institute for Environment Studies "Ramon Margalef" University of Alicante, Edificio Nuevos Institutos, Carretera de San Vicente del Raspeig s/n San Vicente del Raspeig, 03690 Alicante, Spain.
Andalusian Center for Assessment and monitoring of global change (CAESCG), University of Almeria, 04120 Almeria, Spain.
Sensors (Basel). 2021 Jan 5;21(1):320. doi: 10.3390/s21010320.
Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped , the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.
植被通常在干旱地区零散分布。其结构、组成和空间格局是控制生物相互作用、水和养分循环的关键因素。应用分割方法对超高分辨率图像进行监测植被覆盖变化，可以为干旱地区保护生态学提供相关信息。因此，改进分割方法和了解空间分辨率对分割结果的影响是提高干旱地区植被监测的关键。我们探索和分析了基于对象的图像分析（OBIA）和基于掩模区域的卷积神经网络（Mask R-CNN）的准确性，以及这两种方法在干旱生态系统中分散植被分割中的融合。作为一个案例研究，我们绘制了，一种在欧洲最干旱地区之一的保护优先栖息地的优势灌木。我们的结果首次表明，与单独使用两种方法相比，OBIA 和 Mask R-CNN 的结果融合将分散灌木的分割准确性提高了 25%。因此，通过在超高分辨率图像上融合 OBIA 和 Mask R-CNN，可以提高植被制图的分割精度，从而更精确和敏感地监测干旱地区生物多样性和生态系统服务的变化。
