List of Articles Landsat satellite

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  1 - Monitoring and determination of the urban green coverage threshold based on Landsat data, Case study: Zones 1 and 6 from Shiraz city
  hadi abdolazimi Hosein Roosta
  10.29252/.5.7.125
  20.1001.1.26763060.1399.5.7.11.9
  Changing the use of urban green cover over time can create various environmental hazards for the citizens of a city. Due to the importance of the subject, the present study intends to investigate the temporal and spatial changes of green cover in areas 1 and 6 of Shiraz More

  Changing the use of urban green cover over time can create various environmental hazards for the citizens of a city. Due to the importance of the subject, the present study intends to investigate the temporal and spatial changes of green cover in areas 1 and 6 of Shiraz metropolis using Landsat satellite images during five decades (1972 to 2019). For this purpose, after performing radiometric and atmospheric corrections, maps resulting from plant indices including NDVI, SAVI, OSAVI as well as the maximum likelihood algorithm were prepared in ENVI5 software and classified and evaluated in Spatial Information System (GIS). The results of this study showed that the area of the green cover in region 1 has decreased in terms of hectares in NDVI, SAVI, OSAVI indices respectively and also in the maximum likelihood algorithm has decreased from 1394 to 428, from 789 to 421, from 815 to 419, from 1402 to 439, respectively and in region 6 was decreased from 1374 to 858 (NDVI), from 1160 to 862 (SAVI), from 1149 to 884 hectares (OSAVI) and in the algorithm, the maximum likelihood of similarity has decreased from 1393 to 855 hectares. Investigation of threshold values of plant indices to identify urban green cover showed that the range of threshold values in NDVI was variable from 0.2 to 0.3, in SAVI was variable from 0.44 to 0.47 and in OSAVI was variable from 0.34 to 0.36 and using Pearson test in SPSS software, correlation coefficient values between NDVI, SAVI, OSAVI, maximum likelihood algorithm and the studied years were significant at the 1% level. The results of this test also indicated that there was no significant difference between the results of these methods in this study. This reduction of green cover is considered a serious danger for the citizens of Shiraz. Manuscript profile
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  2 - Assessment of Spatial and temporal changes in land use using remote sensing (case study: Jayransoo rangeland, North Khorasan)
  Mohabat Nadaf Reza Omidipour Hossein  Sobhani
  10.61186/jert.43616.8.14.131
  Awareness of changes process, as well as the proper management of land use in natural ecosystems, is of great importance in conservation natural resources. In this regard, the use of remote sensing has become a common approach due to the provision an extent spatial and More

  Awareness of changes process, as well as the proper management of land use in natural ecosystems, is of great importance in conservation natural resources. In this regard, the use of remote sensing has become a common approach due to the provision an extent spatial and temporal information. In this research, in order to land use mapping, first, the accuracy of three common methods of pixel-based (maximum likelihood), machine learning (support vector machine) and object-oriented methods were compared. Then, the spatial and temporal changes of land use in a period of 26 years (1997-2023) assessed using six Landsat satellite imagery. The accuracy of image classification methods was evaluated using Kappa coefficient and overall accuracy indices and the change trend was evaluated using crosstab and spatial evaluation methods. Based on the results, the support vector machine method had the highest kappa coefficient (0.71 to 0.98) and overall accuracy (86 to 99%) for all studied courses. According to the results, poor rangeland had a decreasing trend, and the land uses of very poor rangeland, bare soil, and rainfed agriculture had increasing trends. The area of poor rangeland decreased from 962 hectares (44.36%) in 1997 to 489 hectares (22.57%) in 2023, while very poor rangeland increased from 1138 hectares (52.48%) to 1606 hectares (74.05 percent) in the same period. The results of this research indicated that the trend of land use changes in Jayransoo rangeland is towards the destruction of rangelands and with the passage of time this trend is intensifying. Also, based on the results obtained from this research, it is suggested to use machine learning based classification method to prepare land use mapping in future research. Manuscript profile