Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran)

Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran) Abstract: Desertification consists of decline in production and ecological activities, which may happen due to either natural or unnatural (human) factors.This phenomenon is more evident in arid and semi-arid areas. The aim of this study is to assess the desertification trend using neural network classification and object-oriented techniques in Changouleh watershed which covers an area of 9949 hectare and is located in the south of Ilam province. For this study, TM and ETM+ satellite images of 1984 and 2013 were used. After conducting geometric and atmospheric corrections, images were classified using two neural network and object-orientedalgorithms. Moreover, to evaluate the accuracy and control the correctness of the obtained maps, typical parameters such as Kappa coefficient, the Confusion matrix, and stability of the classification were extracted for assessing the accuracy. The results show that most changes are related to increase in bare lands and decrease in poor and fair rangelands; therefore, approximately 18% of these areas has turned into desert. The results of evaluation of maps correctness show that these two methods are of high accuracy, but the object-oriented approach with Kappa coefficient (94%) and overall accuracy (96.26 %); in addition to being able to detect and categorize more classes, has a high accuracy compared to neural network method.Keywords: Neural network classification, object-oriented classification, land use changes, Changouleh watershed Sinir agi siniflandirma ve obje tabanli siniflandirma teknikleri kullanarak collesme egilim degerlendirilmesi Ozet: Collesme nedeniyle uretim ve ekolojik faaliyetlerde dusus olusur. Bu dusus dogal ya da dogal olmayan (insan) faktorlere bagli olarak ortaya cikmaktadir. Bu durum kurak ve yari kurak bolgelerde daha belirgindir. Bu calismanin amaci, 9949 hektarlik alan kaplayan ve Ilam eyaletinin guneyinde yer alan Changouleh havzasinda sinir agi siniflandirma ve nesne yonelimli teknikleri kullanarak collesme egilim degerlendirmesini ortaya koymaktir.   Bu calismada, 1984 ve 2013 yili TM ve ETM + uydu goruntuleri kullanilmistir. Geometrik ve atmosferik duzeltmeler yapildiktan sonra, goruntuler iki sinir agi ve nesne yonelimli algoritmalar kullanilarak siniflandirilmistir. Ayrica, elde edilen haritalarin dogrulugunu degerlendirmek ve kontrol etmek icin, Kappa katsayisi, Karisiklik matris ve siniflandirma istikrari gibi tipik parametreler haric tutulmustur. Sonuclar degisikliklerin cogunun ciplak topraklardaki artis ve fakir mera alanlarindaki azalma ile iliskili oldugunu gostermistir; Bu nedenle, bu alanlarin yaklasik% 18u0027i cole donusmustur. Harita dogruluk degerlendirme sonuclarina gore, her iki yontem (Kappa katsayisi (% 94))  ve (genel dogruluk (96,26%)) de yuksek dogruluk gostermektedir. Bunun yani sira nesne yonelimli yaklasim ile; daha fazla sinif kategorize etmek mumkundur ve sinir agi yontemine gore yuksek bir dogruluga sahiptir. Anahtar Kelimeler: Sinir agi siniflandirma, obje tabanli siniflandirma, arazi kullanim degisiklikleri, Changouleh havzasi. Received (Gelis):  20.07.2015 -  Revised (Duzeltme):  26.10.2015 - Accepted (Kabul):  27.10.2015 Cite (Atif):  Mohamadi, A., Heidarizadi, Z., Nourollahi, H., 2016. Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran).  Journal of the Faculty of Forestry Istanbul University  66(2): 683-690. DOI: 10.17099/jffiu.75819