Search published articles


Showing 3 results for Google Earth
Evaluation of Precipitation Data using CHIRPS and PERSIANN Models (Case Study: Bandar Abbas)
H. Siasar, A. Salari,
Volume 27, Issue 1 (5-2023)
Abstract
Access to large precipitation data with appropriate accuracy can play an effective role in irrigation planning and water resources management. Satellite images generate high, wide, cheap, and up-to-date data is a good way to estimate precipitation. In this research, the Google Earth engine system and precipitation products from satellite images of PERSIANN and CHIRPS models in daily, monthly, and annual time intervals were used to evaluate and validate the amount of precipitation in Bandar Abbas station during the statistical period of 1983-2020. The results showed that the precipitation estimation by PERSIANN and CHIRPS satellites on a monthly and annual scale is more accurate than the daily scale. The highest correlation coefficient and the least RMSE belonged to the PERSIANN algorithm on monthly and annual scales. The value of the correlation coefficient in the PERSIANN algorithm on daily, monthly, and annual scales is equal to 0.32, 0.83, and 0.94, respectively. The correlation coefficient in the CHIRPS algorithm in daily, monthly, and annual scales is equal to 0.24, 0.71, and 0.90, respectively. The coefficient of determination (R2) of PERSIANN and Chrips algorithms on a monthly scale were 0.89 and 0.70, respectively, and for an annual scale were 0.88 and 0.80, respectively. The general conclusion of this study indicated that the accuracy of the two algorithms in determining the spatial pattern of rainfall on a monthly and annual scale is appropriate, and the PERSIANN algorithm had a higher accuracy on a monthly time scale.

Determining the cultivated area in the Abshar Irrigation Network Using Remote Sensing
S. Afshari, H. Yazdian, A. Rezaei,
Volume 27, Issue 3 (12-2023)
Abstract
Awareness of the types of vegetation changes and human activities in different parts has particular importance as basic information for different planning. It is very difficult and expensive to collect information about the continuous changes in vegetation cover by conventional methods. Therefore, the use of new technologies such as remote sensing is very beneficial. The objective of the present research was to introduce the appropriate vegetation index and determine the vegetation cover of the Abshar network. NDVI, EVI, SAVI, and MSAVI vegetation indices were calculated from 2000 to 2021 every year and monthly in the Google Earth Engine system using Landsat 7 satellite images of the ETM+ sensor. Also, the SPI drought index was calculated using the precipitation statistics of Kohrang station in Excel software. The results of the comparison of four indices showed the superiority and higher performance of NDVI compared to the other three indices for detecting vegetation changes. Then, vegetation changes were calculated. The results showed that the trend of agricultural development in the Abshar network is downward and has a direct relationship with precipitation and the SPI drought index. Also, the results indicated that the SPI drought index was equal to -1.73in 2008, which showed a severe drought in the region. Comparing these results with the vegetation area showed that the vegetation area was 35721 hectares in this year and the year after the drought (2009), the vegetation area was 22950 hectares. Therefore, there was a decrease in precipitation and a sharp decrease in the SPI index in 2008, which led to a sharp decrease of 35% in the vegetation area in 2009.

The Estimation of Land Use Changes under Irrigation Water of Traditional Streams of Khansar City
B. Ebrahimi, M. Pasandi, H. Nilforoushan,
Volume 27, Issue 4 (12-2023)
Abstract
The different land uses in the irrigation water area of the eleven streams of Khansar city during 1969, 1995, 2014, and 2019 have been identified and their area has been determined by analysis of the aerial photos as well as the satellite images of QuickBird, and Landsat in the Google Earth Engine (GEE) environment. Then, the net and gross areas of land under irrigation water, area of non-agricultural land uses, location and area of agricultural land uses under irrigation of the streams are separated according to the type of agricultural activity (orchard or farmland) for each stream. Aerial photos of the study area dated 1969 are the basis for the assessment of agricultural conditions before the law of Fair Water Allocation. The results showed that non-agricultural and particularly urban and residential land uses have increased since 1969. In other words, land use of part of the agricultural lands has been changed to residential and urban land uses. Despite the decreasing trend of agricultural land uses in the last 50 years, these changes have not been the same between the farm and orchard land uses and the area under orchard plantation showed an increasing trend. These changes have dramatically influenced on water demand of the streams. Land use has not significantly changed from 2014 to 2019 and no noticeable change was observed in the area of the agricultural and green agricultural lands as well as the percentage of the orchard and farming lands during these years. The results of this study confirmed the significant changes in agricultural land use and consequently water consumption in the district of the eleven streams of Khansar in recent decades. This study also highlighted the high efficiency of the combined use of aerial photos, spectral satellite images with medium spatial resolution, and visible spectral satellite data with high spectral resolution, as well as using cloud system capabilities of the Google Earth Engine to study changes in agricultural land uses during last decades.

Page 1 from 1     

© 2024 CC BY-NC 4.0 | JWSS - Isfahan University of Technology

Designed & Developed by : Yektaweb