بررسی تأثیر روش‌های مختلف تصحیح اتمسفری در برآورد زی‌توده جنگل با استفاده از شاخص‌های گیاهی

Background and objectives: 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.
Methodology: 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.
Results: 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.
Conclusion: 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.