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.
morteza Sharif; aboozar kiani
Abstract
Forest fires worldwide cause severe damage to vegetation, soil and natural habitats, resulting in direct and indirect negative environmental impacts such as deforestation, climate change and drought. Therefore, identifying and determining the hazards of vegetation that suffer from fire is crucial for ...
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Forest fires worldwide cause severe damage to vegetation, soil and natural habitats, resulting in direct and indirect negative environmental impacts such as deforestation, climate change and drought. Therefore, identifying and determining the hazards of vegetation that suffer from fire is crucial for their management and development. The proliferation of remote sensing images such as the active fire products of the Terra and Aqua satellites over the past two decades has been one of the most essential methods in detecting these fires. However, the active fire product of the MODIS sensor in previous studies has shown that they alone do not provide good results in fire-affected areas. Therefore, it is necessary to evaluate vegetation with basic maps. The aim of this study was to investigate two types of plant products and to discover the active fire of MODIS sensor and FNF-JAXA forest and non-forest cover maps for better separation of burnt areas of vegetation in Iran between July 1 and 160 2020. The results show the highest area of fire on Julius 144 with more than 49 thousand hectares and Julius 128 with more than 45 thousand hectares. However, the largest area of the fire, forest cover is estimated at 120 to 160 in 2020 with more than 14 thousand hectares. Khuzestan province had the highest area of fires in the period under study that most of these areas in agricultural lands and the three provinces of Fars, Kohgiluyeh and Boyer-Ahmad and Bushehr had the highest area of fires in forest cover. The highest frequency of fires was observed in agricultural lands, the main reason for which could be human intervention. The evaluation of the results showed that the use of the FNF-JAXA product (accuracy of 87.4% and a Kappa coefficient of 0.85) compared to MODIS products (accuracy of 80.3% and a Kappa coefficient of 0.78) in the separation of forest areas has better capabilities. However, the ability of MODIS products to distinguish between pasture and agricultural vegetation is an important advantage, which the FNF-JAXA product does not have. In general, the findings of the research show that the MODIS product and FNF-JAXA maps can be used as reference maps to distinguish different types of vegetation that are subject to fire, in damage assessment and management.
