منیژه رجب پور رحمتی

Knowing the forest canopy height is essential for evaluating the health and dynamics of forest ecosystems, as well as for monitoring and modeling the carbon cycle and biodiversity. However, measuring canopy height through ground surveys is costly and time-consuming. Since 2018, the ICESat-2 satellite, equipped with the ATLAS laser sensor, has enabled the direct measurement of tree height. Although ICESat-2 is specifically designed to estimate ice height, it also provides significant data on vegetation height on the Earth's surface. This study aims to investigate the ability of the ATLAS sensor to accurately estimate forest canopy height in northern Iran. For this purpose, the vegetation height data from the ATLAS sensor (ATL08) was evaluated in the Kheyroud experimental forest. To validate the accuracy of the estimated forest canopy height by ATLAS, the forest height data obtained from the satellite were compared with the maximum tree height measured in 121 plots collocated with LiDAR footprints. The estimated forest canopy height by ATLAS and the maximum measured height of trees were compared using a t-test. The RMSE, rRMSE, and R2 values were 0.87 m, 2.7%, and 0.98, respectively, indicating the high accuracy of the ATLAS sensor in forest canopy height estimation. The results of the t-test showed that the mean difference between the measured maximum height of trees in the plots and the corresponding values extracted from ICESat-2 satellite data is not statistically significant (P > 0.05). This study demonstrates that the satellite estimates forest canopy height with very good accuracy in the forests of northern Iran, with a slight overestimation in areas with low-height trees.

Information on forest types and their spatial distribution are valuable for sustainable forest management and planning. The use of remote sensing technology and geographic information system for providing such fundamental information specially in mountainous and remote areas, has been considered by many researchers and forest managers. The current study aims to investigate the capability of Landsat-8 and Sentinel-2 satellite data to generate forest type map in the Kojur watershed of Hyrcanian forests. The performance of some parametric and non-parametric classification methods was also compared.

Following quality assessment, some preprocessing techniques including vegetation indices (VI) extraction, tasseled cap transformation (TCT), principal component analysis (PCA) and fusion were applied on the satellite imagery. Field information collected in September 2018 plus available field data from September 2013 and May 2014, in total 60 sample plots, were used to produce a ground truth map. Forest type was determined through Gorji Bahri approach in each plot. Based on forest types separability, six types were identified (pure beech, mixed beech, beech-hornbeam, mixed hornbeam, pure eastern hornbeam, and eastern hornbeam-Persian oak) to be classified using satellite data. The performance of some classifiers like support vector machine (SVM), random forest (RF), artificial neural network (ANN) and maximum likelihood (ML) was analyzed using two different training datasets.

The results indicated that the sentinel-2 dataset performed better than Landsat-8 for producing forest type map specially when the number of classes increases. It was also found that image fusion methods on sentinel-2 and landsat-8, appropriately improved the result of classifications. This research confirms the effectiveness of number of training samples on the performance of classifiers. Respecting the accuracy assessment criterion, the SVM and RF algorithms showed better result while only 22% of field data was used as training samples. By increasing the number of training samples to 50% of field measurements, the highest accuracy was obtained using RF algorithm applying on all datasets from two satellites.

The Landsat-8 and Sentinel-2 satellite data have moderate capability (overall accuracy around 75% for four-class classification) for mapping forest types in the Hyrcanian forest. The SVM and RF produced more stable and accurate results in comparison with two other algorithms, ANN and ML. Complementary studies are recommended in different forest sites while considering phenology of species and topographic attributes.