scholarly journals Desertification assessment of the territory of Atyrau region

Author(s):  
M S Yessenamanova ◽  
Zh S Yessenamanova ◽  
A E Tlepbergenova ◽  
R Sh Abdinov ◽  
D K Ryskalieva
Geosciences ◽  
2017 ◽  
Vol 7 (3) ◽  
pp. 50 ◽  
Author(s):  
Hicham Lahlaoi ◽  
Hassan Rhinane ◽  
Atika Hilali ◽  
Said Lahssini ◽  
Said Moukrim

Author(s):  
A. Stassopoulou ◽  
M. Petrou

We present in this paper a novel method for eliciting the conditional probability matrices needed for a Bayesian network with the help of a neural network. We demonstrate how we can obtain a correspondence between the two networks by deriving a closed-form solution so that the outputs of the two networks are similar in the least square error sense, not only when determining the discriminant function, but for the full range of their outputs. For this purpose we take into consideration the probability density functions of the independent variables of the problem when we compute the least square error approximation. Our methodoloy is demonstrated with the help of some real data concerning the problem of risk of desertification assessment for some burned forests in Attica, Greece where the parameters of the Bayesian network constructed for this task are successfully estimated given a neural network trained with a set of data.


2020 ◽  
Vol 31 (12) ◽  
pp. 1593-1607 ◽  
Author(s):  
Agostino Ferrara ◽  
Constantinos Kosmas ◽  
Luca Salvati ◽  
Antonietta Padula ◽  
Giuseppe Mancino ◽  
...  

1992 ◽  
Vol 23 (1) ◽  
pp. 81-102 ◽  
Author(s):  
J. Grunblatt ◽  
W.K. Ottichilo ◽  
R.K. Sinange

2022 ◽  
Vol 8 (2) ◽  
pp. 99-114
Author(s):  
Lamyaa Gamal EL-Deen Taha ◽  
Manar A. Basheer ◽  
Amany Morsi Mohamed

Nowadays, desertification is one of the most serious environment socioeconomic issues and sand dune advances are a major threat that causes desertification. Wadi El-Rayan is one of the areas facing severe dune migration. Therefore, it's important to monitor desertification and study sand dune migration in this area. Image differencing for the years 2000 (Landsat ETM+) and 2019 (OLI images) and Bi-temporal layer stacking was performed. It was found that image differencing is a superior method to get changes of the study area compared to the visual method (Bi-temporal layer stacking). This research develops a quantitative technique for desertification assessment by developing indicators using Landsat images. Spatial distribution of the movement of sand dunes using some spectral indices (NDVI, BSI, LDI, and LST) was studied and a Python script was developed to calculate these indices. The results show that NDVI and BSI indices are the best indices in the identification and detection of vegetation. It was found that mobile sand dunes on the southern side of the lower Wadi El-Rayan Lake caused filling up of large part of the lower lake. The indices results show that sand movement decreased the size of the lower Wadi El-Rayan Lake and there are reclamation activities in the west of the lower lake. The results show that a good result could be achieved from the developed codes compared to ready-made software (ENVI 5).


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