جستجوی مقالات مرتبط با کلیدواژه "sentinel-2" در نشریات گروه "اکولوژی"

 Food security has been the most important human concern on the planet. Food security and future changes in food prices and crop development undeniably depend on the average yield of crops. Rice is the second largest crop in terms of area under cultivation and provides food for more than half of the world's population. Due to the importance of rice production, it is important to monitor its production on a large scale. Remote sensing and geostatistical methods play an important role in spatial and temporal evaluation of climatic, soil, living factors and management methods. The study of the amount of yield obtained in province of Guilan can provide appropriate information in advancing/pursuing goals such as the study of the amount of yield gap in this region; therefore, this study was conducted to evaluate the yield of rice using two methods of geostatistics and remote sensing approach in 2016 and 2017 in Guilan province.

 In this study, different methods of preparing vegetation map to determine rice yield map were evaluated. To compare methods based on satellite-imagery and geostatistical procedure-models to estimate the rice yield of rice cultivated lands in Guilan province, a study was conducted in the 2016 and 2017. For field operations, 320 fields were surveyed to record grain yield (of total 238,000 hectares of rice-grown fields) during the physiological maturity stage. In this study, 33 statistical procedure-models were used to interpolate the amount of grain yield and then the accuracy of interpolation methods were evaluated using various statistical criteria. Satellite-imagery based methods using Landsat 8 satellite Operational Land Imager (OLI) sensor images related to the dates of June 18, August 9, August 21 in 2016, and July 23, August 8 in 2017 and the images of the Sentinel-2 satellite on June 30 and September 13, 2017, were used. Eight satellite-derived vegetation indices were calculated and the relationship between them and the yield variable was extracted using the regression relationship, and the yield map was prepared and evaluated. By fitting the peak logistics model between yield values and vegetation indices and selecting the superior index, the yield map was prepared with the help of remote sensing and the obtained yield maps were compared with different statistical criteria.

 The results of the evaluation of geostatistical interpolation methods showed the superiority of the ordinary stable kriging procedure-model over other models. In this study, the RVI vegetation index was selected as the superior index to predict actual yield throughout the Guilan Province. Comparison of geostatistical procedure-models and satellite-imagery oriented models based on the determination coefficient of regressed equations and root mean square error (RMSE) to estimate grain yield in Guilan province showed that both procedures had acceptable accuracy, however, due to the ability of remote sensing to distinguish the pointwise optical reflection of phenomena and to predict yield with high spatial resolution, this method achieved higher accuracy in yield estimating.

 Comparison of geostatistical and remote sensing methods in predicting farm yield indicated that the remote sensing method was more accurate. Early harvest forecasting based on the information extracted from this study showed that the use of image / images obtained in June and August and OLI sensor of Landsat-8 satellite in Guilan province can be used as a basis for forecasting the yield of this plant in the coming years. Such studies in Guilan province, by considering the share of Guilan province in the country's rice supply, will play an effective role in managerial decisions regarding rice supply and demand at the macro level. The results also showed that sampling in the study area based on a regular (systematic) spatial pattern can increase the accuracy of geostatistical methods in estimation of regional yield. The results of this project can provide suitable basic information for other studies such as yield gap, the reasons behind it, the relationship between land suitability and the obtained yield and forecasting and estimating yield in different time periods.

Due to the advances made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can cover all surface water areas. To monitor. In this study, the capability of Sentinel-2 and Landsat-8 satellite images to estimate the concentration of water quality parameters including acidity, electrical conductivity, total soluble solids, alkalinity and surface water temperature were investigated. First, the Sentinel-2 and Landsat-8 satellite images were pre-processed and then the appropriate bands were determined to identify a significant relationship between the values of each water quality parameter and the images were determined using linear regression and their accuracy was calculated for the actual values. The results show the superiority of Sentinel-2 images with Pearson coefficient, standard error and RMSe for PH parameter equal to 0.58, 0.28 and 0.29, EC equal to 0.847, 78.7 and 77.22, TDS equal to 0.895, 45.8 and 44.37 and for the alkalinity parameter only through Landsat-8 images with Pearson coefficient, standard error and RMSe equal to 0.473, 22 and 21.8, respectively. Both satellite images can be used for the water surface temperature parameter due to the high Pearson coefficient. These values are 0.871, 2.9 and 2.85 for Landsat-8 and 0.752 for Sentinel-2, respectively. 0, 14.4 and 4.06.

Remote sensing data can be used with reasonable accuracy today for areas without statistics to investigate natural events. One of these sudden and damaging phenomena is flooding. April flood in Golestan province has caused many damages in various areas of housing, agriculture, forestry and so on. In the present study, using satellite data and presenting flood damage detection algorithm, it is investigated. The highest difference in the NDVI index derived from Sentinel-2 data was calculated in the study period of March 2019 and late April 2019 in the study area. These results show that some areas have been severely damaged by floods, in which severity and duration of rainfall have had a significant role. These areas, which are estimated at an area of 70908 hectares, include agricultural, forest, residential, etc. Among the advantages of the proposed algorithm are the ease and speed of calculation, the capability to apply in urban areas, the high accuracy with reduced impact of other phenomena. The overall accuracy of the present algorithm is estimated to be 93.5% using ground control points. The accuracy of the results shows that the flood damage algorithm has a good accuracy to distinguish the areas under flood damage from the safe areas of danger.

Flood is among the most important environmental hazards, broadly threatening human societies and their assets. In this research, by integrating the Rational model to estimate peak runoff into Marand basin flood hazard on a sub-basin scale, assessments are accomplished using remote sensing and GIS. After determining the runoff coefficient land cover/use layers were taken from satellite images of the Sentinel 2A, and the slope map was derived from the ASTER DEM 30m and soil hydrological groups, using the specified amount of rainfall hamely intensity in mode of 1-hour peak runoff was calculated. Using linear membership function in fuzzy logic model, integrating prepared this peak runoff and elevation lines between zero and one were fuzzy and then by applying multiple weight tangles to each of these two layers we collected their results, and the flood hazards distribution map was prepared. With the implementation of prepared risk map in fifth grade the classes include very low-risk, low risk, medium, high and very high risk with the results of GIS partnership or PGIS and entering this information into the confusion matrix. The accuracy of prepared maps was determined to be about 87.83%.