Mechanics, Materials Science & Engineering, May 2016
ISSN 2412-5954
Monitoring the Natural Factors Influence on Vegetation Development by Using Moderate-Resolution Imaging Spectroradiometer (Modis) Images with OBIA Method in Uzbekistan Sh. B. Akmalov1, J. V. Gerts2, D. B. Omonov3 1
Lille 1 University of Science and Technology Villeneuve d'ASCQ, Paris, France
2
Tashkent Institute of Irrigation and Melioration, Tashkent, Uzbekistan
3
Tashkent State Agrarian University, Tashkent, Uzbekistan DOI 10.13140/RG.2.1.1185.1920
Keywords: Syrdarya, remote sensing, object based image analysis (OBIA), eCognition, moderate-resolution imaging spectroradiometer (MODIS), NDVI.
ABSTRACT. In the study, natural and anthropogenic effects on vegetation are discussed and degree of their influence are shown in Syrdarya province (Uzbekistan). A statistical model of integrated meteo- and hydro- remote sensing data was developed. By the use of this model the correlation of various natural factors in vegetation period was analyzed and scale-dependency of spatial relationships between NDVI and three climatic factors were investigated. MODIS NDVI images have been used for the study area and OBIA method was applied via eCognition software.
Introduction. Agriculture is a vital industry in Syrdarya province (Uzbekistan) and it plays a key role in supporting the greatest part of population. However, hot summer winds drain the soil and harm the plants. Intense evaporation in summer cause salinization and other negative processes on the surface of the field [1]. Nowadays, remote sensing techniques are widely used in updating land cover information, environmental protection and ecological monitoring. The use of RS data prove to be useful for observation anthropogenic effects, natural and ecological processes on a large scale [2]. RS allows observation of processes over long timescales and at the same time helps us to solve many difficulties and necessities, existing in traditional ecological analyzing method [3]. The high temporal resolution of the MODIS datasets can provide an efficient and consistent way for monitoring of biomass, vegetation and above-mentioned factors. [4] Consequently, such high temporal and medium spatial resolution sensor like MODIS could be a very useful tool for such investigations on the regional scale. Methods and materials. Study area. Syrdarya is one of twelve provinces of Uzbekistan, which borders on Kazakhstan Republic in the north, on Tashkent province in the east, on Tajikistan Republic on the south and on Jizzakh province in the west. Gulistan city is the center of the province. The province is located on the left riverside of Syrdarya, the main source of irrigation water in the province. (Figure 1). The climate of the region is continental with hot and long summers, and short winters with little snow. The long-term annual average temperature in the region is + 14.75 up to 38-2012). Precipitations mainly occur during the winter-spring months showing and averages at 340 mm per year. Relative moisture of the air at wintertime forms 74-78%, but in a year, it is about 29-31%, at average annual rate of 56%. Annual evaporability is 1500 mms [5] (Figure 2).
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Mechanics, Materials Science & Engineering, May 2016
ISSN 2412-5954
Fig. 1. Study area. (Source: Google earth image and GIS shape file of Syrdarya province)
Fig. 2. Average annual meteorological data of Syrdarya province. (Source: UzGidroMet data) Data. Some meteodata was selected for correlation analysis of MODIS NDVI value to natural factors. This data (2000-2012) has been provided by UZGIDROMET center and includes the following: air temperature, runoff value of Syrdarya River, participations. MODIS NDVI data. Analysis requered Terra (MOD13Q1) MODIS vegetation index products which are provided every 16 days at a spatial resolution of 250 meters as gridded level-3 products in the Sinusoidal projection. The Terra 16-day period starts from the day 001. In the study 154 images from the period between 2000 and 2012 were taken in sequence of one image per month. Georeferenced MODIS NDVI images were provided free of charge by glovis.usgs.gov. Tools and software. OBIA method was used during the analysis, which was performed by eCognition program. With this software, MODIS NDVI images have been segmented up to the homogenous object of the surface for the whole Syrdarya province. Segmentation parameters were chosen as follows in Table 1. After the segmentation, monthly average values of NDVI have been copied from the window of "image object information" and pasted to Excel file. Then, the water volume of river, average values of temperature and monthly total sum of precipitation were added to the data base in accordance with months in a year. MMSE Journal. Open Access www.mmse.xyz
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Mechanics, Materials Science & Engineering, May 2016
ISSN 2412-5954
Results and discussion. By statistical analysis, the correlation coefficients (R) of monthly NDVI changes have been found. Table 2 shows the correlation of vegetation with natural factors within the last 12 years. Table 1. Segmentation parameter of MODIS NDVI images Name
MODIS NDVI
Number of Image images layer weight 154
1
Scale Shape Parameter 80
0,1
Compactness Number of object
0,9
1 Province
Table 2. Correlation results
Amount of water (entrance to Rainfall Syrdarya reg.) m3/s mm Mean NDVI of province
-0.73
-0.47
0.87
In accordance to the results, the precipitation almost do not have an impact on vegetation period, since the correlation coefficient between them is equal to R=-0.47, which means the absence of any relation. It could be explained by the fact that precipitation in province mostly falls in the nonvegetation period and the greatest part of agriculture is based on irrigation. At the same time, NDVI has strong negative correlation with the change of water volume of the Syrdarya river. In spite of the fact that this river is among ice-fed rivers R is equal to -0.73.
Fig. 3. Annual average volume of water of Syrdarya river. (Source: UzGidroMet data) We imply that this fact appears because two water reservoirs in the up-flow part of the river (Tohtagul reservoir in Kyrgyzstan and Kayrakkum reservoir in Tajikistan) collect water intensively during MMSE Journal. Open Access www.mmse.xyz
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Mechanics, Materials Science & Engineering, May 2016
ISSN 2412-5954
vegetation period and in non-vegetation period a huge amount of water is dropped to Syrdarya in order to produce electrical energy (Figure 3). Water do not almost influence the vegetation period in province, because it is dropped in down-stream and drain water reaches the river which flows straight away to the neighboring Kazakhstan. However, correlation index of vegetation period with temperature is very high (0.87), which means that the vegetation period occurs in hot seasons of the year. Conclusion and recomendations. Satellite information (in particular MODIS data) is very important if we are going to solve the influence of natural and anthropogenic factors on vegetation development. For such issues remote sensing provides a wealth of time-consuming information at a global scale. Nowdays, MODIS images are in open access, and the analysis program of eCognition Developer is not very expensive, which means their accessibility for the researches. Besides, MODIS images have NDVI band, which saves us from the difficulties of calculation. In accrodance with analysis result, vegetation development has high levels of concordance with temperature, but at the same time negative relations with water volume and precepitations revialed. Influences of hot summer, artificial irrigation and water shortage in agricultural areas were chosen as a main factors for that. Trans boundary water problems are international issues requiring global interferences. It could be recommended to continue current investigations by gathering field experiments with results and conducting future analysis of factors influencing the natural changes. References administrative division of the people, history, government structure, economy, education, science, health, life, cultural life]. "National encyclopedy of Uzbekistan" State Scientific Publishing, Tashkent. 2007. [2] Gyuris,
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[3] Giniyatullina, O. L., V. P. Potapov, and E. L. Schactli [4] Brigante, Raffaella, and Fabio Radicionis.
Sensors with High Spatial
Uzbekistan, Tashkent. [6] Law, Beverly E., Tim Arkebauer, John L. Campbell, Jing Chen, Osbert Sun, Mark Schwartz,
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