Iranian Journal of Remote Sensing & GIS

Effects of Climate Change on Vegetation Phenology of Urmia Lake Basin Using AVHRR Time Series Data

farzaneh hadadi; Davod Ashourloo; Alireza Shakiba; Aliakbar Matkan

Articles in Press, Accepted Manuscript, Available Online from 17 April 2021

https://doi.org/10.48308/gisj.2021.100998

Abstract

Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various ... Read More Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various researches, remote sensing indices are used as one of the new methods in identifying climate change. One of the important indices of remote sensing is the phonological characteristics of vegetation, which in recent studies has shown great potential in identification and estimation of vegetation. In the present study, using the 5-day normalized vegetation index (NDVI) time series of NOAA-AVHRR images and plant phenology parameters, vegetation changes in rangelands and dryland areas of Lake Urmia Basin during 1984-2013 were investigated. Climatic temperature and precipitation data was obtained from the meteorological stations of Lake Urmia basin and was compared with the results of satellite images. The results of time series analysis over thirty years of statistical period in Lake Urmia basin showed that the beginning of the growing season in Oshnavieh, Saghez and Sarab started earlier in 2013 than in 1984. But in the Maragheh area it began later. The end of the growing season in Oshnaviyeh, Saghez and Takab has ended earlier. Also, the peak growth parameter in the above mentioned vegetation reached its maximum value earlier. The length of the growing season has been decreased in the cities of Oshnavieh, Maragheh and Saghez, respectively. The results of statistical analysis obtained from satellite images and climatic data showed that changes in phonological parameters are location dependent and also decreased and increased in cold nights and hot days at the beginning of the growing season, respectively. But at the end of the growing season, the warm days have increased. These changes increased the slope of the plant growth phenology curve at the time of plant aging and ultimately reduced the length of the growing season.

Employing the Convolutional LSTM Network in Crop Classification using NDVI Time Series

Mohammad Reza Gili; Davoud Ashourloo; Hossein Aghighi; Ali Akbar Matkan; Alireza Shakiba

Volume 14, Issue 1 , June 2022, , Pages 89-106

https://doi.org/10.52547/gisj.14.1.89

Abstract

Changes in crop growth at relatively short intervals, asymmetry of cultivation of similar crops, the spectral similarity between different crops at certain times of the growing season, and lack of ground data make classifying crops in satellite imagery a challenging task. Changing the amount of canopy ... Read More

Designing a Multi-objective Optimization Model of Land Use, based on Genetic Algorithms (Case study: Kerman- Rodbar Watershed)

Volume 7, Issue 1 , December 2015, , Pages 39-57

Abstract

With the increase in population and consequent increasing needs of society, land use planning is of particular importance. Land use planningdue to being involved with several conflicting aims, multi- objective evolutionary algorithm would be a useful tool to solve land use planning. But use of these ... Read More

Runoff Estimation Using Artificial Neural Network Method

Volume 6, Issue 4 , October 2014

Abstract

Runoff is one of the major components of calculating water resource processes and is the main issue in hydrology. Many concept models are used to predict the amount of runoff, which in most cases depend on topographical and hydrological data. Conventional models are not appropriate for areas in which ... Read More

Calculation of Local and Spatial Uncertainty of Precipitation Using Geostatistical SGS and CO-SGS Simulation Algorithms

Volume 6, Issue 3 , October 2014

Abstract

Among the usual interpolation methods, kriging and co-kriging are frequently used in the interpolation of precipitation data as one the best linear unbiased estimators, Despite these advantages, there models show smoothness representation and because they are based on regional averages of the data, they ... Read More

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Author = Shakiba, Alireza

Effects of Climate Change on Vegetation Phenology of Urmia Lake Basin Using AVHRR Time Series Data

Abstract

Employing the Convolutional LSTM Network in Crop Classification using NDVI Time Series

Abstract

Designing a Multi-objective Optimization Model of Land Use, based on Genetic Algorithms (Case study: Kerman- Rodbar Watershed)

Abstract

Runoff Estimation Using Artificial Neural Network Method

Abstract

Calculation of Local and Spatial Uncertainty of Precipitation Using Geostatistical SGS and CO-SGS Simulation Algorithms

Abstract