Aghababaei Masoumeh, Ebrahimi Ataollah, Naghipour Ali Asghar, Asadi Esmaeil, Verrelst Jochem
Department of Range and Watershed Management, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord 8818634141, Iran.
Image Processing Laboratory (IPL), Parc Científic, Universitat de Valencia, 46980 Paterna, Spain.
Remote Sens (Basel). 2021 Nov 19;13(22):4683. doi: 10.3390/rs13224683.
Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.
植被类型（VTs）是重要的管理单元，其识别是土地覆盖物保护的重要工具。尽管利用地球观测技术评估和监测土地覆盖物已有很长历史，但稀疏植被类型的定量检测仍然存在问题，特别是在干旱和半干旱地区。本研究旨在确定合适的多时期数据集，以提高伊朗扎格罗斯中部异质景观中植被类型分类的准确性。为此，首先在研究区域确定了2018年、2019年和2020年各植被类型的归一化差异植被指数（NDVI）时间序列。这些数据揭示了强烈的季节性物候模式以及植被类型分离的关键时期。这使我们能够选择用于植被类型分类的最佳时间序列图像。然后，我们在谷歌地球引擎（GEE）平台内比较了Landsat 8图像的单日和多时期数据集，将其作为随机森林分类器检测植被类型的输入。单日分类的中位数总体卡帕系数（OK）和总体准确率（OA）分别为51%和64%。相反，使用多时期图像的总体卡帕准确率为74%，总体准确率为81%。因此，利用多时期数据集有利于准确的植被类型分类。此外，研究结果强调，像GEE这样可用的开放获取云计算平台有助于识别植被类型分类的最佳时期和多时期图像。
