فهرست مطالب p. fatehi

Accurate digital elevation models (DEMs) are crucial for effective forest planning and management. Various methods exist for generating DEM data, with handheld mobile laser scanners being the most efficient and precise approach. The raw point clouds obtained from these scanners require several preprocessing steps, one of which involves separating ground and non-ground points. Errors in this part of the process can lead to the generation of an inaccurate digital model with high uncertainty and errors. Various algorithms, such as voxel-based segmentation, simulation filters, and deep learning-based approaches, have been developed for this purpose. This study evaluates the performance of the OBS algorithm in automatically separating ground points from non-ground points in handheld laser scanner data. The study area comprised five different sections within the Karaj Botanical Garden, covering a total area of 7.2 hectares. These areas contained forest stands characterized by heterogeneous structures and multi-story tree layers. Data were acquired using a handheld GeoSLAM laser scanner. To generate a reliable reference for evaluating the algorithm's results, ground points were manually separated. The performance of the algorithm was evaluated by comparing it with the manually separated ground truth using statistical metrics, including Matthew's correlation coefficient, Kappa coefficient, and Intersection over Union (IoU). The statistical metrics across the five study areas demonstrated the effectiveness of the OBS algorithm in separating ground points from non-ground points, with Matthew's correlation coefficient, Kappa coefficient, and IoU values of 0.895, 0.891, and 0.902, respectively. Additionally, the optimal voxel size for the algorithm was determined to be within the range of 15 to 22 centimeters.We conclude that the OBS algorithm, when configured with optimal input parameters, provides high performance in automatically separating ground points from non-ground points, especially in heterogeneous forested environments. The importance of configuring the optimal input parameters is also highlighted.

Leaf area index (LAI) is a vital biophysical characteristic to assess the condition, describe forest structure and function of forest ecosystems. LAI is a key input in modeling global climate change, carbon fluxes, water cycle, photosynthesis, and interception processes. The estimation of LAI in forests through remote sensing data, using machine learning models, has gained widespread attention, particularly for large-scale LAI mapping. This method is favored for its efficiency, involving minimal time investment, cost-effectiveness, and a non-destructive approach. This study aimed to investigate the potential of Sentinel-2 data for estimating the LAI of northern Zagros forests, employing the Gaussian Process Regression (GPR) method.

LAI field data were collected in June and July 2023 from a coppice forest in the Marivan and Sarvabad counties of Kurdistan province, Iran. A total of 93 square plots, each measuring 20×20 square meters, were randomly selected. The location of each plot was recorded using a DGPS device. The LAI within each plot was measured using the hemispherical photography method. Five photos were captured within each sample using a Coolpix4500+FC-E8 camera equipped with a fisheye lens. The LAI was then calculated for each hemispherical photo and averaged for each sample plot using the “hemispheR” package in the R programming language. A cloud-free Sentinel-2B image with L1C correction level was acquired on July 2, 2023. After verifying the radiometric and geometric quality of the image, the Sen2Cor processor was used to apply atmospheric correction. Different input data, including spectral bands and spectral indices (Vegetation Indices, Tasseled Cap Transformation, and Principal Component Analysis) were generated from the Sentinel-2 image. These datasets, i.e., the spectral bands, spectral indices, and a combination of spectral bands and spectral indices, were used to estimate LAI. The modeling process was carried out using the GPR algorithm based on 65 sample plots (70% of the dataset). The performance of the models was finally evaluated using 28 plots (30% of the dataset) with different metrics such as the coefficient of determination (R2), root mean square error (RMSE), relative root mean square error (rRMSE), and Akaike Information Criterion (AIC).

The descriptive statistics for the measured LAI showed that the minimum, maximum, average, and standard deviation values of the leaf area index over the study area were 0.33, 3.88, 2.129, and 0.627 m2.m-2, respectively. The Pearson correlation analysis between forest LAI and spectral variables (including original bands and spectral indices) indicated a stronger correlation between LAI and spectral indices (i.e., GNDVI, SAVI, and TCTV) than the original bands. Thirty percent of field sample plots were randomly selected and used to evaluate the forest LAI model generated using the GPR machine learning algorithm based on three datasets: original bands, spectral indices, and a combination of original bands and spectral indices, all derived from Sentinel-2 imagery. The evaluation outcomes revealed that the model derived from the main bands of the Sentinel-2 satellite achieved R2 = 0.81, RMSE = 0.21 m2.m-2, rRMSE = 9.14%, and AIC = 103.65. This performance was deemed satisfactory when compared to the performance of models built using the other two datasets (i.e., spectral indices, and a combination of original bands and spectral indices) to estimate LAI. Using the best-performing model, a comprehensive LAI map of the study area was generated using data derived from the main bands of Sentinel-2 imagery.

This study provides preliminary evidence of the potential of Sentinel-2 satellite data in evaluating the leaf area index in the North Zagros coppice forests. However, the integration of ground data of leaf area index and Sentinel-2 data from various growing seasons could potentially enhance the robustness of the results and mitigate uncertainties, thereby paving the way for future research endeavors. This approach could lead to more accurate and reliable assessments of forest health and productivity.

Climate change and human interventions as a whole have had negative and remarkable effects on the quantity and quality of forests. Beside the change in the forests extent, which has always been considered and monitored, its phenological changes have also been investigated in the last decade. The NDVI Vegetation Index derived from satellite data is an appropriate proxy for quantifying and expressing forest status including phenological changes. This study aimed to characterize the trend of start, end, and length of growing season using NDVI satellite time series dataset over 18-year time periods and then assess their relationship with precipitation and temperature parameters. This study was carried out over the southern Zagros forests using MODIS-NDVI time series with temporal and spatial resolution of 16-day and 250 meters, respectively. The precipitation and temperature datasets were also collected from regional synoptic meteorological stations. After preprocessing steps, 414 NDVI images during 2000-2017 were analyzed pixel by pixel to extract the start, end, and length of the growing season using Midpoint method, considering 50% and 35% thresholds of NDVI annual amplitude for start and end of the growing season, respectively. Then, the statistical significance of phenological metrics was assessed. Based on the results, the mean dates for the start and the end of growing season were 16th March and 15th August, respectively, with the mean length of growing season of 151 days in the study period from 2000 to 2017. Considering 35% of NDVI annual amplitude for the end of growing season, the mean date for the end of growing season was 8th September and the mean length of growing season was 190 days. The seasonal trend showed that the start and the end of growing season has occurred respectively 0.02 and 1.04 days earlier per year in the southern Zagros forests during 2000-2017. The length of growing season has been shortened 1.02 day per year. However, variation in the start and the end dates and the length of growing season have not been significant in 93%, 81% and 81% of the region, respectively, at 90 % confident level. Generally, the change in the occurrence of the end of growing season was greater than the start of growing season. A weak correlation was observed between phenological changes and climate parameters like temperature and precipitation in the study area.

Knowing the tree species combination of forests provides valuable information for studying the forest’s economic value, fire risk assessment, biodiversity monitoring, and wildlife habitat improvement. Fieldwork is often time-consuming and labor-required, free satellite data are available in coarse resolution and the use of manned aircraft is relatively costly. Recently, unmanned aerial vehicles (UAV) have been attended to be an easy-to-use, cost-effective tool for the classification of trees. In fact, given the cost-efficient nature of UAV derived SfM, coupled with its ease of application, it became a popular choice. The type of imagery is an important factor in classification analysis because the spatial and spectral resolution can influence the accuracy of classification. On the other hand, classification algorithms also play an important role in the accuracy of tree species identification. So, this study investigated the performance of four classifiers for tree species classification using UAV-based high-resolution imagery in broadleaf forests and takes a comparative approach to examine the three non-parametric classifiers including support vector machines (SVM), random forest (RF), artificial neural network (ANN), and one parametric classifier including linear discriminant analysis (LDA) classifiers in heterogeneous forests of Noor city located in Mazandaran province. In June 2019, the study area was photographed. The field survey was carried out to record the species and position of the mature overstory trees which were clearly identifiable on the orthomosaics. Individual tree crowns were clipped by one-meter buffer and the digit numbers were summarized at for each tree by computing descriptive statistics from the orthomosaics. Using zonal statistics, mean, standard deviation, variance, unique, range, mode, and median were calculated for raw bands (Red, Green, Blue), vegetation indices (NRB, NGB), and band ratios (G/R, R/B) from RGB orthomosaics. We classified the tree into 4 classes: Parrotia persica (Ironwood tree), Populus capsica (Caspian poplar), Ulmus minor (Common Elm), and Quercus castaneifolia (Chestnut-leaved oak). Finally, the classification algorithms were applied using R software. The classification accuracy for identified trees was performed using 10-fold cross-validation by computing the producer’s accuracy, user’s accuracy, and Overall accuracy. All algorithms resulted in overall accuracies above 80%. Of course, the results showed that, as a parametric algorithm, LDA with an overall accuracy of 0.87 provided the best results for tree classification, because it does not require the tuning of free parameters. As for parameter value, the mean was the most important that this can be related to the similarity of this feature in any sample. Caspian poplar with user accuracy of 0.97 and Ironwood tree with user accuracy of 0.72 had the highest and lowest classification accuracy, respectively. Caspian poplar high accuracy is probably due to its crown color which is quite different from the other species. The main error (misclassification) is a classification between “Ironwood tree” and “Common Elm” classes. This may be caused by the fact that the spectral signatures between Ironwood tree and Common Elm trees are very similar. In general, our study showed that UAV derived orthomosaic can be used for tree classification with very high accuracy in mix broadleaf forests by different algorithms.

Urban vegetation monitoring can play a vital role in sustainable city management because of their diverse environmental, social, cultural, and economic functions. In this study, the vegetation trend was assessed using the Normalized Vegetation Difference Index (NDVI) obtained from the Landsat 5 and 8 at a maximum vegetation greenness time by applying a parametric approach (i.e. Ordinary Least Square Linear Regression) and a non-parametric approach (i.e. Theil-Sen and Mann-Kendall) over Tehran city during 2008 - 2019. The MOD13Q1 NDVI product was used as a complementary data due to the lack of appropriate Landsat data for 2011 and 2012. The data collected from four synoptic meteorological stations located in Tehran were used to extract the mean temperature and the total precipitation parameters. The relationship between NDVI variations and climatic characteristics, i.e. the mean temperature and total precipitation of one month before and one year before the maximum NDVI values were analyzed. The NDVI trend analysis showed a slight increase in Tehran vegetation cconditions during 12 years, however, the result of the Mann-Kendall test was not statistically significant (α = 0.05). Trend analysis for 22 individual districts showed a significant negative trend for five districts, and the remaining districts showed a non-significant trend. The NDVI was negatively correlated with temperature, and there was a positive correlation between NDVI and precipitation. The NDVI variations showed a more similar trend to the climate data of one month before NDVI data-set than one year.