جستجوی مقالات مرتبط با کلیدواژه « landsat 9 » در نشریات گروه « جنگلداری »

Using landscape metrics, it is possible to study the quantification of spatial patterns of environmental elements and changes in their extent at different scales. The result of this study can lead to a good understanding of how the landscape changes, which is required for sustainable management and planning. Due to the importance of the subject, the purpose of this study is to use remote sensing and evaluate the efficiency of landscape metrics in analyzing changes in forest areas and land cover in part of theNorthernZagros forest areas. 

Land cover map related to different years (1998, 2009, 2020) was prepared through supervised classification of satellite images and visual interpretation of Google Earth images and field visits. The ArcGIS Patch Analyst extension and FRAGSTAT software were used in order to monitor and evaluate the landscape metrics. Nine landscape metrics including four metrics of the number of patches (NP), patch density (PD), percentage of each class (PLAND) and area of each class (CA) at the class level and five metrics at the landscape level including number of patches (NP), Mean Patch Size (MPS), Shannon Diversity Index (SHDI), Largest patch Index (LPI), Continuity (contag) were calculated.

The results of measuring the area of the class and the percentage of each class in the period of 1998, 2009, 2020 at the class level showed a decrease in the forest area and an increase in the percentage of residential and agricultural cover during the period; So that in 1998, the forest cover was 78.82% of the area, and in 2020, this amount decreased to 77.36% (The area of forests has decreased by 0.066% annually during 22 years). During the period, we have seen an increase in the number of residential patches (13-17) and agricultural patches (19-42) and a decrease in the number of forest patches (21-18). The comparison of the patch density metric also shows the increase of this metric in all the land covers except the forest. The results of the survey at the surface of the land show an increase in the number of patches and the Shannon diversity index and a decrease in the continuity index, the mean patch size and the largest patch size metrices.

The results of this study show the expansion of residential areas and agricultural uses and the reduction of forest areas, which indicates the destruction of the integrity of the land due to the increase in the number and classes of man-made areas (residential and agricultural).

The goal of this study is to see how well the forest canopy density (FCD) model and the GLAMA mobile ‎app estimate the canopy cover density of Zagros forests in Sardasht province. Landsat 9 Operational Land ‎Imager-2 (OLI-2) in 2022 was employed for this purpose. Four indices were created to run the model: 1- Advanced ‎Vegetation Index, 2- Bare Soil Index, 3- Shadow Index, and 4- Thermal Index. The Scaled Shadow Index and Vegetation Density index were then computed by integrating and synthesizing indices one and two, as well as indices three and four, and the FCD model map was created by combining these two indices. The created model was validated using GLAMA mobile app and ‎hemispherical photographs. For this purpose, 100 square sample plots in Sardasht city with varying canopy ‎cover were chosen, and photographs of the canopy cover were taken at five positions on each sample ‎plot. The overall accuracy of the FCD model generated for Sardasht Province was 76%, with a Kappa ‎coefficient of 0.697. Furthermore, the correlation results demonstrate a strong (R2‎ = 0.985) and significant ‎‎(p-value = <0.0001) correlation between the average canopy cover index values estimated by ‎hemispherical photography using GLAMA software and the values acquired by the FCD model. According ‎to the findings of this study, the FCD model developed using Landsat 9 satellite data and the GLAMA ‎mobile app performs very well in estimating forest land canopy density in Zagros forests.

Hyrcanian forests play an important role in absorbing carbon dioxide and reducing the severity of climate change and global warming. The accurate estimation of actual evapotranspiration can help to plan for conservation and management of these forests and their water resources. Direct measurement methods for evapotranspiration have limitations, one of the most important of them is the pointed measurements. Methods for distance measuring have been developed to address these constraints. The purpose of this study was to prepare and estimate actual instantaneous () and daily () evapotranspiration maps from Landsat 8 satellite imagery and SEBAL model in Hyrcanian forests. For this purpose, Landsat 8 satellite images were prepared in two different dates, i.e. 2014/7/26 and 2016/5/28 in 200 thousand hectares of Hyrcanian forests in the cities of Pol-Sefid and Kiasar, and after processing and calculations, latent heat flux was estimated from images and from that basis,  and  maps were prepared. To evaluate these maps,  and land values were calculated using weather data of Pol-Sefid and Kiasar stations and the FAO Penman Monteith method. The estimated  and  means at the Pol-Sefid station were 0.53 and 5.39 and at Kiasar station, 0.48 and 4.98 respectively. The calculated mean of these two parameters, respectively, were 0.60 and 6.16, at the Pol-sefid station and 0.55 and 5.82, at the Kiasar station. The results showed that the percentage of estimated relative differences for  and is 0.068 and 0.809 respectively, and mean percentage of estimated relative differences for  and is 11.99% and 13.56%, respectively, which in total shows the high ability of the used approach for evapotranspiration maps.

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.