لندست 8

The evaluation of disturbances using satellite imagery is a crucial sub-field in natural resource science, serving as a tool for monitoring changes such as fire and livestock grazing in forest and pasture ecosystems. Grazing intensity and fires are significant disturbances in arid and semi-arid rangelands. Remote sensing for burned area mapping has been extensively studied. Spectral indices are widely used to monitor vegetation cover changes, particularly post-fire, and to generate burned area maps. Given the vastness and inaccessibility of Iran's mountain rangelands, multi-spectral remote sensing data was employed to identify burned areas. This research aimed to determine the most effective auxiliary data to enhance classification accuracy for identifying and delineating burned semi-steppe rangelands, and to estimate burned pasture extent for post-fire management using Landsat-8 imagery.

Burned rangeland sites were selected based on information from the Chaharmahal and Bakhtiari Province Department of Natural Resources Protection and local expert input. Old fire occurrence and grazing intensity at selected sites were determined. Twenty-seven burned sites, representing old fire occurrences and high to medium grazing intensity, were delineated using GPS polygons. Maximum likelihood classification (MLC) in Idrisi TerrSet software was used to identify burned pasture areas. Auxiliary data, including pan-sharpened raw bands, Tasseled Cap transformation components, digital elevation model (DEM) derivatives, principal component analysis (PCA) components, and the Normalized Burn Ratio-Thermal (NBRT) index, were evaluated to improve classification accuracy. Overall and Kappa accuracies were assessed using error matrices, and Friedman's rank test was used to compare the effectiveness of different data combinations.

The combination of DEM derivatives and the NBRT index significantly improved the classification accuracy of burned areas with varying grazing intensities, achieving an overall accuracy of 66% and a Kappa accuracy of 63%. The NBRT index, based on near-infrared (NIR), short-wave infrared (SWIR1 and SWIR2), and thermal infrared (Thermal1) bands, effectively distinguished burned semi-arid rangelands with different fire ages and grazing intensities. The high sensitivity of these bands to post-fire vegetation changes contributes to the NBRT index's effectiveness.

Multi-spectral remote sensing data, particularly Landsat-8 imagery, combined with auxiliary data layers, effectively identifies and delineates burned semi-steppe pastures. MLC demonstrated capability despite spectral similarities between regenerated vegetation and surrounding areas. The NBRT index's effectiveness stems from the high sensitivity of its constituent bands (NIR, SWIR1, SWIR2, and Thermal1) to post-fire vegetation changes, enabling accurate identification of burned semi-steppe grasslands.

Forest Canopy Cover (FCC) is one of the most important structural characteristics in monitoring health and quality of forest stands. Measuring this characteristic based on ground methods requires a lot of cost and time, and it is extremely difficult to implement on a large scale. Today, use of remote sensing techniques can compensate for the limitations of terrestrial methods. The present study was conducted with the aim of investigating the efficiency of Landsat 9 satellite in modeling and estimating the canopy cover of the northern Zagros oak forests. In order to collect FCC field data, 79 square plots with dimensions of 45×45 m were implemented based on systematic-random sampling method with 200×200 m grid size in summer 2023 in a part of Baneh forests, Kurdistan province. In each plot, the large diameter and the perpendicular diameter of the crown of trees or shoots were measured using a tape measure. Finally, FCC values were calculated for each plot based on the crown area of each tree or stem. In the present study, a frame of the OLI-2 sensor data of the Landsat 9 satellite at the Collection 2 Level 1 correction level was received on July 23, 2023. After verifying the geometrical quality of the image and performing atmospheric correction using FLAASH method, Vegetation Indices, Principal Component Analysis (PCA), and Tasseled Cap Transformation were performed on the image. In total, the number of 35 computational bands and 7 original usage bands and the spectral values of these 42 spectral variables obtained from Landsat 9 data were extracted using the map of field plots. In the following, FCC using stepwise regression (MLR) and non-parametric Random Forest (RF) and Support Vector Machine (SVM) statistical models based on three sets of spectral variables (original bands, computational bands and combination of original and computational bands) and 70% of the data were modeled. Finally, the regression models were evaluated and fitted using the coefficient of determination (R2), root mean square error (RMSE) and root mean square relative error (rRMSE) statistics using cross-validation method based on 30% of data. Investigation of FCC data measured on the field data on descriptive statistics showed that the researched forest is in a semi-dense condition. Pearson's correlation analysis conducted to investigate the relationship between FCC and spectral variables showed that vegetation indices are more sensitive to FCC changes. The results of modeling using 70% of the samples showed that the model obtained from RF is based on the combination of the original and computational bands with R2 = 0.88 and rRMSE = 16.95 as the optimal model in estimating the amount of canopy. The evaluation of the relative importance of the spectral variables used in the obtained model showed that NDNIR.SWIR2 has the highest influence in the RF modeling process and PCA of band 7 (PCA.B7) has the least importance. Validation of the obtained models using cross-validation showed that the RF model obtained from the original bands of the Landsat 9 image with of R2 = 0.85 and rRMSE = 15.65 was selected as the best model for forest canopy estimation and mapping. In general, this research showed that by using the original bands of Landsat 9 satellite data based on the RF machine learning algorithm, it is possible to estimate and map the FCC in the traditionally managed forests of North Zagros with reasonable accuracy.

Earth's surface reflection is significantly affected by atmospheric conditions such as water vapor and particulate matter; therefore, atmospheric correction is needed to minimize these effects and convert digital number values to surface reflection. Therefore, as a remote sensing approach, atmospheric correction is required to minimize these effects and convert digital number (DN) values ​​to surface reflectance. The main objective of this research was to study of four atmospheric correction models, including (1) dark object subtraction (DOS), (2) fast line-of-sight atmospheric analysis of spectral hypercube (FLAASH), (3) the second simulation of the satellite signal in solar spectrum (6SV), (4) atmospheric topographic correction (ATCOR) model and their comparison with the OLI original image for estimation of the aboveground biomass (AGB) in the forests of Avardim of the Shafarood watershed, Guilan province.

In order to estimate of biomass, 246 plots (3600 m2) were established as systematic-random sampling pattern with 300m×300m dimensions and DBH, height of trees and shrubs were measured. A handheld GPS device (Garmin GPS MAP 64s with an accuracy of ±3 meters) was used for sampling and finding the sample pieces, and for this purpose, before starting the statistics, the latitude and longitude of the points (all sample pieces) were entered into the GPS device. And then, using the above device, the sample parts were determined in the field and the characteristics of the diameter at the chest, the height of the existing trees and shrubs (diameter more than 7.5 cm) were measured and then recorded in the relevant forms. The OLI sensor images of Landsat 8 satellite were extracted from the USGS global site. The selection of images was done according to the season, the amount of minimum cloud cover and also in the growing season close to the time of maximum greenness. These images are presented at L1T level and fully compatible with digital maps. In this research, 7 OLI sensor bands of Landsat 8 satellite related to pass/row number 166/34 have been used. Before the Landsat 8 satellite passes over Iran, Terra satellite prepares images with a time difference of about half an hour to local time. Due to the stability of the atmospheric conditions, it is possible to use MODIS information in step with Landsat 8. Also, in this research, three daily MODIS products were used for each of the Landsat 8 images with a spatial resolution of 500 meters, including: MOD04 (optical thickness of suspended particles in the range of 550 micrometers), MOD05 (water vapor) and MOD07 (total ozone). The DEM obtained from ASTER with a spatial resolution of 30 meters was obtained from the USGS global site. The DEM model was directly used in the SV6 atmospheric correction method. Also, DEM model was used for the atmospheric effect correction method of ATCOR to prepare the map of slope, direction, sky visibility.

The results showed that the atmospheric correction model based on 6SV radiative transfer code had a good performance in most of the plant indices obtained from the OLI sensor data of Landsat 8 satellite. The ARVI index obtained from the 6SV atmospheric correction model has the highest correlation analysis results with a correlation coefficient of 0.801. Also, in the case of using the FLAASH method, ARVI (0.779) and RVI (0.586) indices have the highest and lowest correlations, respectively. In the DOS or dark object atmospheric correction method, the highest and lowest correlations are related to GARI (0.762) and EVI (0.518) indices, respectively, and finally, in the ATCOR method, the highest and lowest correlations are respectively related to NDVI indices (732.0) and GNDVI (0.454). In general, in estimating forest biomass, 6SV atmospheric correction model showed the best performance with the lowest RMSE percentage (15.04%), followed by FLAASH, ATCOR and DOS models.

Estimating and monitoring the amount of biomass on land is necessary for climate change studies, carbon cycle production, food allocation and fuel accumulation, fire behavior studies, etc. in the ecosystem. Also, applying atmospheric corrections on the main bands of the images in the pre-processing process before classifying and extracting plant indices is necessary and unavoidable to remove the unwanted effects of the atmosphere and it improves the accuracy of the results. From the results obtained in the present study, it can be suggested that the 6SV atmospheric correction model, with the integration of water vapor and aerosol optical depth obtained from MODIS products, is more suitable for the estimation of terrestrial zinc based on remote sensing data, especially when using the data in are obtained in summer, when water vapor and temperature are both high and the forest canopy is in full development. Finally, it is suggested to use the 6SV atmospheric correction model to estimate of aboveground biomass based on remote sensing data.

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.

Knowledge on the area and amount of forest coverage at landscape scale can be one of the most important indicators in forest sustainable development. In this study, we used Landsat-8 full coverage imagery across the Guilan Province and the supervised classification method for forest canopy cover mapping in the summer of 2014. Field data were collected by a two-stage sampling method and 316 number of 0.5-ha plots. Subsequently, information on the types of land use and the canopy density (the ratio of the level of forest floor lightness per unit area) were recorded. With an overall accuracy of 91.8% and kappa coefficient of 0.80, results showed that 498804 ha of the total land area of Guilan Province is covered by forests, from which dense, semi-dense, and scattered forests account for about 42.1, 41.5, and 16.4% of the forested areas, respectively. This study demonstrated the negative effect of spectral similarity between farmlands with scatter and semi-dense forests in the accuracy of forest classification. This study demonstrated the proper performance of Landsat 8 data in providing thematic maps such as density and forest cover. Therefore, these data and information can be recommended for use in forest management decision-making, conservation, and restoration.

In the current study, the capability of OLI-Landsat 8 data was investigated for estimating the tree above-ground woody biomass (AGWB) in Hyrcanian Fagus orientalis stands. The quality of images in term of geometric error and existing cloud was surveyed. Principle component analysis (PCA), tasseled cap transformation, rationing and fusion in both PCA and color transform approaches were employed on the images. 65 square sample plots (45×45 m2) were established in the study area and 45 sample plots were used for modeling with multiple linear regression (MLR) as stepwise. The model was validated with remaining 20 sample plots. Pearson correlation results showed most relationship between tree AGWB and corresponding spectral responds from PCA on spectral bands of 1 to 7. The same result was observed using MLR. So, this component was the most efficient variable in tree AGWB estimation in pure Fagus stands in the study area with adjusted-R2= 0.122, RMSE=14.3%, and bias=1.8%. The error of this model can be accepted on the large scale such as watersheds.

This research aims at investigating the feasibility of Landsat8 images for poplar plantation mapping. Three areas in Talesh and Sumehsara regions, Guilan province, with a total 1025.35 ha with different vegetation mixtures were chosen as study area. Five image-sets were selected from growth and autumn seasons. Studying the images showed no radiometric and geometric distortions. The image enhancements like rationing, PCA and Tasseled-Cap transformation were performed. By preparing training samples in each region and choosing the suitable band-sets, the images were classified using maximum likelihood algorithm. The ground truth was prepared for Hefzabad and Toularoud in Talesh using GPS. It was extracted for Haft-Deghnan and Sheikhneshin from available update-maps. To ensure suitability of the training samples, assessing the accuracy of classification were performed in the training samples. After ensuring this, the accuracy of classification was assessed in whole areas of all images. The results in all areas and dates were almost close to each other. However, the best results were always obtained in growth season, especially in June 2013. Using calculated enhanced bands along with the original bands in classifications in Talesh region showed slight improvement in accuracies, with overall accuracy and Kappa about 95% and 71%, respectively. In Sumehsara region, the best results were obtained in June with 83% overall accuracy and the relatively low Kappa 0.43. While, the results of accuracy assessment in training samples in all cases were about 0.89 to 0.99. It could be stated that the Landsat 8 images had medium capability for poplar plantation mapping. More investigations are needed to create better poplar plantation mapping.

This research focused on determining the possibility of separating different percents of Beech and Hornbeam species based on two methods of spectral reflectance assessment and classification using landsat imagery in a northern forest of Iran. To do this, Landsat7 ETM+ imagery of the study area for 24 June, 2000 (middle of germination season) were provided. The 100×100 m (1 ha) sample plots with 90m distances from each other over 3 areas containing Beach and Hornbeam mixtures were transmitted from forest type map on a 1:25000 scale. Four groups were determined after field inventory, I) pure Beech, II) 80% Beech and 20% Hornbeam, III) 70% Beech and 30% Hornbeam and IV) 60% Beech and 40% Hornbeam. To avoid road reflectance interference with tree reflectance, sample plots were selected with 60 m distance from roads. Followed by digitizing the roads and plots, the images were geometrically corrected at the ortho level using 13 ground control points. Then, the digitized roads and sample plots were overlaid on 6 bands (1, 2, 3, 4, 5 and 7) of the ETM+ imagery and their pixel data were extracted. In the next step, the obtained data was statistically analyzed. The differences among the selected groups were compared via t-test method. The results showed the capability of ETM+ imagery in separating the pure Beech from the mixture of 70% Beech and 30% Hornbeam and 60% Beech and 40% Hornbeam. Then, the images were classified using a Maximum Likelihood Algorithm. Based on the classification results, the maximum likelihood were measured with 63% total accuracy and 44% kappa coefficient. The results of comparing four above mentioned groups using maximum likelihood classification method were in accordance with the results of applying their pixel values.

Classifying age classes in a large area using remotely sensed data has considerable significance for forest sustainable management. In this research, Landsat ETM+ data from Loveh forest, dating July 2002, were analyzed to investigate the potential of this sensor for age class mapping. We applied a systematic cluster sampling method to collect field data. We used 99 plots so that contained 32 plot. In stands with 25-45 years, 33 plots in stand with 5-25 years and 34 plots in stands with >45 years. The quality of the image was first evaluated for radiometric noises. Separability of three age classes 5-25, 25-45 and >45 years, using a supervised classification and four algorithm of maximum likelihood, minimum distance, parallel piped and linear discriminate analysis (Fisher). The results showed that maximum likelihood in three and two age classes with overall accuracy and kappa coefficient were (79% and 94%) and (0.68 and 0.86), respectively. Signature separability, producer and user accuracies showed the highest spectral similarity between 5-25 and >45 age classes. By merging the two classes, the overall accuracy and kappa coefficient became equal to 94% and 0.86, respectively. These results demonstrate that the reflectance values recorded by ETM+ sensor are related to forest stands. This information could also be used to estimate forest biomass and carbon content, identify locations within the stands that might require treatment and plan other management activities.