ماشین بردار پشتیبان

Mapping forest types is essential for sustainable management, however, field methods are time-consuming and costly. Therefore, Modern remote sensing, especially hyperspectral imagery like PRISMA with 243 spectral bands, is valuable for mapping Hyrcanian mixed forests. However, its accuracy and capabilities must be assessed. In this study, the capability of PRISMA data was assessed using SVM, SAM, and RF classification approaches to create forest type maps for the Gorazbon district of Kheyrud Forest. A ground truth map consisting of 131 field sample plots, each measuring 45 × 45 m, was generated using Kuchler's abundance method based on the 100 trees with the largest diameters. Five forest types were identified: pure beech, mixed-beech, beech-hornbeam, hornbeam-beech, and mixed hornbeam. Geometric correction, smoothing and noise filtering was applied, then, Illumination correction was performed. Anomalies were detected using the RX algorithm. The minimum noise fraction (MNF) transformation and pixel purity index (PPI) were applied to the remaining 103 spectral bands and then used in the classification process. Additionally, indices such as NDVI, ReNDVI, MTCI, NDIred, and RTVI were incorporated into the classification process. The results showed that the SVM and RF algorithms achieved overall accuracies of 53.09% and 43.19%, with kappa coefficients of 0.38 and 0.25, respectively. The best combination of input data was derived from the spectral bands obtained through the MNF transformation. Based on the findings, high noise in PRISMA imagery and the similar spectral behavior of forest types in this region hindered the species discrimination and classification performance.

The aim of this study is to investigate the performance of various algorithms in enhancing the accuracy of land cover maps using Sentinel images.

The study area is a sheet (1:25000) encompassing an area of 15782.6 hectares. The land cover map was created using a range of algorithms - Mahalanobis distance, maximum likelihood, minimum distance, neural network, parallelepiped, support vector machine, spectral angle mapper, spectral information divergence, and binary encoding - applied to the optimal band composition derived from 12 bands (2, 3, 4, 5, 6, 7, 8, 8a, 11, 12, NDVI, SAVI). The training area was obtained from field information and satellite images. 70% of the samples were used for classification and 30% for evaluating the accuracy of the classified maps.

The results indicate that the support vector machine, neural network, and maximum likelihood classifications have the highest accuracy. The support vector machine classifier is slightly more accurate than the other two classifiers. To improve the classification, support vector machine kernels (linear, polynomial, radial basis function, and sigmoid), spectral angle mapper settings (6 modes), and parallelepiped (2 modes) were utilized. The results show that the support vector machine classifier, using the 6th degree polynomial function method, has the highest accuracy. Following this, the map was post-processed with the highest accuracy, resulting in an overall accuracy of 96.63 and a kappa coefficient of 0.9393.

An examination of 21 classification modes on Sentinel images in this study provides a comprehensive review of classification algorithms compared to previous studies.

In this study, machine learning based models (linear regression, k-nearest neighbor, support vector regression, random forest) were evaluated to estimate carbon sequestration and soil respiration in the forests of eastern Mazandaran province. After identifying the sampling points, in each plot, the diameter and height of the trees were measured and the above-ground biomass of the trees was calculated using allometric models of Hyrcanian forest. Soil samples were taken from 0-20 cm depth and soil respiration was measured by CO2-port. Soil respiration was estimated using bulk density, moisture content, texture, soil temperature, total nitrogen, available phosphorus and potassium, organic carbon and above-ground trees biomass. Soil carbon sequestration was estimated using soil temperature and moisture and above-ground tree biomass. Random forest model (RMSE = 10.47 and R2 = 0.82) and support vector machine (RMSE = 0.77 and R2 = 0.90) had the highest performance in estimating carbon sequestration and soil respiration, respectively. Soil moisture had the highest relative importance in estimating carbon sequestration (random forest model) and soil respiration (support vector machine model). According to the obtained results, it is possible to estimate the amount of carbon sequestration and soil respiration in the forest with appropriate accuracy by having the above-ground biomass of trees and basic soil characteristics.

Effective mangrove mapping needs reliable indices that can characterize mangroves from other land cover types on remote sensing data. Currently, a variety of vegetation- and mangrove indices are available, while a comparative assessment of their efficiency seems essential. The aim of this study was to evaluate six vegetation indices and seven mangrove indices developed for Sentinel-2 imagery to obtain a robust approach in mangrove mapping within Google Earth Engine (GEE) cloud computing platform. The rasterized indices were classified by support vector machine. The final maps were evaluated by area under curve (AUC) of receiver operating characteristic (ROC) in addition to common accuracy assessment criteria. Results showed that mangrove indices were more reliable than vegetation indices. Amongst the vegetation indices, Modified Chlorophyll Absorption in Reflectance Index (MCARI) (AUCmangrove from 0.91 to 0.92) achieved the highest AUC values, while MFI (Mangrove Forest Index) returned the highest values amongst the mangrove indices (AUCmangrove from 0.93 to 0.95). All in all, results revealed that MFI on Sentinel-2 imagery in GEE was efficient in mangrove mapping within the study sites.

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.

As one of the most important understory evergreen species in Hyrcanian forests of Iran, information on the distribution of Box (Buxus Hyrcana Pojark.) are essential for both forest research and practice. Here, the capability of very high spatial resolution IKONOS satellite imagery acquired in leaf-off condition was tested for mapping Box distribution in a part of Khiboos-Anjili forest reserve in Mazandaran province. The IKONOS imagery was geometrically corrected with a georefrenced panchromatic Pleaides scene, which was orthorectified using 3D ground control points obtained using differential GPS (RMSE less than one pixel). Reference data samples from three classes of non-forested area, deciduous stands without Box understory and deciduous stands with Box understory were recorded using DGPS-supported field survey. By means of a number of vegetation indices, classes seperabilities were evaluated on main and synthetic image channels by partitioning 75% training area and transformed divergence. IKONOS image was classified using both main and best-selected bands and a number of nonparametric (Maximum Likelihood, Mahalonobis distance, Minimum distance to mean and Paralell piped) and parametric (Suport Vector Machine) classifiers. Then the classified images were assessed using 25 percent of unused sample points. Results of validation using the 25% left-out test data showed the highest performance by SVM algorithm compared to other algorithms, with overall accuracy and Kappa coefficient of 97.87% and 0.96, respectively. The results also showed the potential of IKONOS imagery from leaf-off season has to map Box trees in understory layer.

Identifying forest areas susceptible to decline in order to take preventive measures can play a significant role in inhibiting this phenomenon. Previous studies suggest the high performance of modeling to identify such areas. Hence, we used 15 models to identify forest areas prone to decline in Lorestan province. For modeling, forest areas with over 50% tree mortality were used as dependent variable, and environmental factors including annual mean rainfall, annual mean temperature, relative humidity, aridity index, evapotranspiration, dust storm index, drought index, distance to surface waters and agricultural lands, slope, aspect and NDVI as independent variables were introduced into the models. AUC of each model was multiplied by its output and the mean of these models was considered as the combined model. The forest decline risk map resulted from the combined model indicated a decline trend from central parts of the Lorestan’s forests to the south and south-western parts. The Random forest and Support vector machine were recognized as the best models with AUC value of 1 and the Bioclim as the weakest model with AUC of 0.75. According to the combined model, approximately 23.7%, 7.5%, and 19.5% of the studied forests had low, medium, and high risk of decline respectively. The climatic factors including aridity index, rainfall, temperature, and evapotranspiration were the most influencing environmental factors, respectively. The present research, in addition to emphasizing the modeling efficiency in identification of forest areas susceptible to decline, indicated that the combination of models yields better result rather than their separate use.

Spatial prediction of fire risk and preparing the forest fire risk map across the natural areas are among the ways that can be used to prevent and to manage fire. The aim of this research was zonation of forest fire risk in Golestan National Park using non-parametric algorithms, namely Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF).
About 100 occurred fire points were considered for modeling the fire risk. The effective factors on fire occurring including vegetation types, physiographic, climatic, and human factors were identified and their relevant maps were prepared from different sources. To modeling purposes, initially the zone was divided into 1-ha levels of decision-making and modeling and then the pixel values of the effective factors on classes of fire occurring, across the 1-ha levels, were extracted and standardized. Based on non-parametric algorithms, fire risk was modeled with 70 percent of the fire points, as training samples. The prepared forest fire risk map was zoned in terms of four classes of low-risk, medium-risk, high-risk and dangerous. The classification accuracy of the maps, resulted from this modeling, was assessed through the overall classification accuracy given 30 percent of the remained fire points.
Results & The results indicated that RF algorithm, with the overall accuracy of 75%, was the best algorithm in predicting the fire risk compare to other ones. Likewise, after matching the fire risk occurring with the results gained from algorithms, it turned out that all algorithms were able to classify the area properly in terms of the fire risk, as more than 80 percent of fire points were placed in the high-risk and dangerous classes