morteza Sharif; S Attarchi
Abstract
The phenological cycle of plants plays an important role in the global carbon cycle. Considering the importance of the role of plants in the urban ecosystem and its role in the health of socity, it is necessary to study and monitor the phonological cycle of plants in different seasons of the year in ...
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The phenological cycle of plants plays an important role in the global carbon cycle. Considering the importance of the role of plants in the urban ecosystem and its role in the health of socity, it is necessary to study and monitor the phonological cycle of plants in different seasons of the year in urban areas at different spatio-temporal scales In this study, the two widely used NDVI and EVI indices calculated from OLI sensor of landsat satellite and MOD13Q1 product of MODIS sensor images were used to investigate the plant phenology cycle in Ahvaz metropolitan area from the period 2015 to December 2019. In this research, satellite images were retrieved and prepared through the Google Earth Engine platform. Then, according to the type of vegetation, the phenological cycle of the plants was obtained based on the vegetation indices and compared with the phenological cycle obtained from the ground surveys. Due to the possibility of noise and pixels with spectral mixing, Savitzky-Golay filter was used to smooth the phenological cycle of plants. The results show the increasing trend in the values of both NDVI and EVI indices by 0.03 and 0.04 in the OLI sensor and 0.01 in the MOD13Q1 product (annually), respectively. These changes were positive in January, March, October, November and December on both sensors. Differences were observed in both sensors during plant phenology phases. The largest difference between two sensors was observed in 2018 and 2019. This shows, in case the weather provides better condition, the plant chlorophyll content will increase. This will lead to the difference between the results of both sensors. The growing season transition periods obtained from the OLI sensor showed more detail than the MODIS medium resolution dataset. The MODIS sensor shows the growing season period start earlier than the OLI sensor. In general, according to the MODIS product, the duration of the growing season (between the beginning of the growing season (mid-winter) and the end of growing season (EOS) (early summer) is four-month. The lowest difference between the periods of the growing season of plants with ground observations in OLI and MODIS sensors, was 7 and 10 daye for Start of growing season (SOS), respectively. The biggest defference was observed at the peak of the growing season with 20 and 35 days, and for the end of growing season (EOS), 20 days later and 20 days earlier, respectively, according to ground observations. However, the length of growing season (LOS) in the OLI sensor is about five months. That the results of OLI sensor are closer to ground observations.This difference is due to the increase in heterogeneous conditions in the target phenomena and/or the spatial resolution of the MODIS sensor images. It is concluded that, the results of the OLI sensor improve our understanding of human interactions with natural environment in urban areas. Therefore, addressing them in future studies can mitigate many environmental challenges and provide more realistic information for planning.
Saba Kharyaband; S Attarchi
Abstract
In recent decades in Iran, due to the effect of climate change and population growth, the extent and depth of water in wetlands have been largely decreased. Therefore, it is worth finding the main reasons of the changes and if possible to reduce the rate of changes. Great advances in remote sensing technology ...
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In recent decades in Iran, due to the effect of climate change and population growth, the extent and depth of water in wetlands have been largely decreased. Therefore, it is worth finding the main reasons of the changes and if possible to reduce the rate of changes. Great advances in remote sensing technology offer valuable opportunities to monitor the trend of changes in natural environment. Landsat satellites from 1970s has the largest archive of remote sensing images. Remote sensing images provide data in wide area with high temporal resolution and low cost. Anzali Wetland is one of the most important international wetlands of Iran which has been registered in Ramsar Convention. In recent decades, population growth and expansion of cities and farm lands near Anzali wetland, climatic changes in this region and also changes in Caspian Sea’s water level threaten this wetland. The present study investigates wetland depth changes using Landsat imagery. Furthermore the depth changes have been thoroughly explained concerning rain fall, temperature and the Caspian Sea’s water level changes over a 30-year period from 1988 to 2018. Our Findings emphasizes that the depth of water in this wetland is more related to the changes of the Caspian Sea’s water level and the rainfall and temperature are not the main reason of decreasing of the wetland’s depth.
Sara Attarchi; Mehdi Rahnama
Abstract
Full polarimetric SAR sensors can capture full polarimetric characteristics of targets. Therefore, in comparison with single and dual polarimetric sensors they offer more capabilities in target detection. However, operation in full polarimetric mode increases complexity, data volume and need more power. ...
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Full polarimetric SAR sensors can capture full polarimetric characteristics of targets. Therefore, in comparison with single and dual polarimetric sensors they offer more capabilities in target detection. However, operation in full polarimetric mode increases complexity, data volume and need more power. Full polarimetric sensors acquire images with less swath compared to dual mode. As a result, most of SAR sensors operate in dual mode and provide dual polarimetric images. Due to high availability, dual polarimetric images are increasingly being used in many researches. In this research, the efficiency of dual polarimetric images is compared with full polarimetric mode. The main goal is to find the best combination of two polarimetric bands which has the nearest results to full polarimetric mode.One Advanced Land Observing Satellite / Phased Array L-band Synthetic Aperture Radar scene had been processed. The scene was multi-looked and converted to the backscattering coefficient (sigma nought, dB). The image was decomposed by cluode-pottier method into alpha and entropy components. Three different combination of two polarimetric bands were considered; HH-HV; HH-VV and HV-VV. Alpha and entropy of each dual polarimetric mode were also computed. Then alpha and entropy driven from full-polarimetric mode were separately compared with alpha and entropy of each dual mode. Since different land cover types (i.e. built-up, cropland, bare land and water) exist in the scene, the computations were done separately for each land cover type. The comparison among alpha values from full polarimetric mode and dual polarimetric mode reveals that HH-HV combination shows the best conformity with full polarimetric mode. HH-VV dual mode has the poorest results. Entropy values of HH-HV mode had the least difference with full polarimetric mode. Entropy values of HH-VV shows the weakest similarity. The MAE values of HH-HV, HH-VV and HV-VV were 0.06, 0.22 and 0.17, respectively. The findings of this research shows that polarimetric features driven from HH-HV combination are more compatible with full-polarimetric mode. In case, no full polarimetric image is available, this dual combination can be substituted. Based on quantitative results, HH-HV combination is recommended to be used in case no full polarimetric image is availableOne Advanced Land Observing Satellite / Phased Array L-band Synthetic Aperture Radar scene had been processed. The scene was multi-looked and converted to the backscattering coefficient (sigma nought, dB). The image was decomposed by cluode-pottier method into alpha and entropy components. Three different combination of two polarimetric bands were considered; HH-HV; HH-VV and HV-VV. Alpha and entropy of each dual polarimetric mode were also computed. Then alpha and entropy driven from full-polarimetric mode were separately compared with alpha and entropy of each dual mode. Since different land cover types (i.e. built-up, cropland, bare land and water) exist in the scene, the computations were done separately for each land cover type. The comparison among alpha values from full polarimetric mode and dual polarimetric mode reveals that HH-HV combination shows the best conformity with full polarimetric mode. HH-VV dual mode has the poorest results. Entropy values of HH-HV mode had the least difference with full polarimetric mode. Entropy values of HH-VV shows the weakest similarity. The MAE values of HH-HV, HH-VV and HV-VV were 0.06, 0.22 and 0.17, respectively. The findings of this research shows that polarimetric features driven from HH-HV combination are more compatible with full-polarimetric mode. In case, no full polarimetric image is available, this dual combination can be substituted. Based on quantitative results, HH-HV combination is recommended to be used in case no full polarimetric image is available.
