El Moussaoui El Houcine, Moumni Aicha, Khabba Saïd, Amazirh Abdelhakim, Er-Raki Salah, Chehbouni Abdelghani, Lahrouni Abderrahman
LMFE, Faculty of Sciences Semlalia, Cadi Ayyad University, 40000, Marrakesh, Morocco.
GREYC, National Graduate School of Engineering (ENSICAEN), 14000, Caen, France.
Environ Monit Assess. 2025 Jan 30;197(2):210. doi: 10.1007/s10661-025-13649-8.
In the last decades, natural and anthropogenic pressures have caused observable changes in the argan landscape despite its significance in Morocco. Remote sensing data can be used to monitor these changes over time and provide information on vegetation health and land cover changes. This study assesses the performance of supervised methods (support vector machine, maximum likelihood, and minimum distance) and unsupervised classification method (Isodata) for mapping the argan forest in the Smimou area of Essaouira province using remote sensing data from Landsat-5 and Landsat-8 (1985 and 2019). Additionally, the impact of the resampling method and the digital elevation model (DEM) integration on the classification results have been examined. The ground truth data were collected and randomly divided into two categories: 234 samples to calibrate the classification algorithms and 340 samples for validation. Maximum likelihood supervised classification achieved an overall accuracy (OA) of 89.62% (kappa = 0.84) and 87.58% (kappa = 0.81) in 1985 and 2019, respectively. Using resampling techniques on normalized difference vegetation index (NDVI) products, aiming for a 10 m resolution, the NDVI results yielded an OA of 91.60% in 1985 and 88.85% in 2019. Further integration of DEM (30-m resolution) with NDVI, which was resampled to a 10 m resolution, achieved an OA of 92.27% and 92.37% for 1985 and 2019, respectively.
在过去几十年里，尽管摩洛哥的阿甘景观具有重要意义，但自然和人为压力已使其发生了显著变化。遥感数据可用于长期监测这些变化，并提供有关植被健康状况和土地覆盖变化的信息。本研究利用Landsat - 5和Landsat - 8（1985年和2019年）的遥感数据，评估了监督方法（支持向量机、最大似然法和最小距离法）和非监督分类方法（迭代自组织数据分析技术）在索维拉省斯米穆地区绘制阿甘森林地图的性能。此外，还研究了重采样方法和数字高程模型（DEM）整合对分类结果的影响。收集了地面真值数据，并将其随机分为两类：234个样本用于校准分类算法，340个样本用于验证。最大似然监督分类在1985年和2019年分别取得了89.62%（kappa = 0.84）和87.58%（kappa = 0.81）的总体精度（OA）。对归一化植被指数（NDVI）产品采用重采样技术，目标分辨率为10米，NDVI结果在1985年和2019年分别产生了91.60%和88.85%的OA。将分辨率为30米的DEM与重采样至10米分辨率的NDVI进一步整合，在1985年和2019年分别实现了92.27%和92.37%的OA。
