International scientific journal

ISSN: 2663-0419 (electronic version)

ISSN: 2218-8754 (print version)

International scientific journal

ISSN: 2663-0419 (electronic version)

ISSN: 2218-8754 (print version)


Detection and mapping of green-cover and landuse changes by advanced satellite image processing techniques (a case study: Azerbaijan Eastern Zangezur Economic Region)

Rasouli A.A.1, Safarov S.H.2, Asgarova M.M.3, Safarov E.S.2, Milani M.4


1 Department of Environmental Sciences, Macquarie University, Sydney, Australia Level 4, 12 Wally's Walk, Macquarie University, North Ryde, Sydney,

2 Ministry of Science and Education of the Republic of Azerbaijan Institute of Geography, Baku, Azerbaijan 115, H.Javid ave., Baku, Azerbaijan, AZ1143: (corresponding author)

3 Azerbaijan State Pedagogical University, Baku, Azerbaijan:

4 Bandirma Onyedi Eylul University, Faculty of Engineering and Natural Sciences, Bandirma, Turkey Yeni Mahalle, Shehit Astsubay Mustafa Soner Varlık Caddesi, 77, 10200, Bandirma, Turkey:



The long-term occupation of Azerbaijan territories by Armenian forces had extreme forms of socioeconomic shocks, destructive geo-environmental, ecological, and evident Green-Cover (GC) and Landuse (LU) unenthusiastic changes. During the investigations multi-spectral and high-resolution Sentinel-2 satellite images sampled from 2016 to 2021 and normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were used.  An Object-Based Image Analysis (OBIA) was adjusted in the segmentation and knowledge-based classification processes using the eCognition Developer and a Cellular Automata Markov-Chain (CA-MC) model with probabilistic rules and has been introduced within the TerrSet IDRISI Selva software.

Indexing methods indicated that significant changes in GC are taken place in the sampled rayons of Qubadli, Zangilan, and Jabrayil (located in the southern Eastern Zangezur Economic Region) over the past years. Subsequently, OBIA methods approved that majority of the negative changes are detected in LU types, predominantly on the forests (-4.7%) and pastures canopies (-4.6%) considering the last six years. In addition, a reliable CA-MA prediction map designated that there would be recognizable upbeat progressive increases in barren (+4.8%) and abandoned (+5%) lands, particularly the emergence of vegetation vulnerability in the region. Consequently, accurate image processing and mapping of the current situation of the Azerbaijan liberated lands have to be the most urgent tasks of the geographers, ecosystem scientists, and remote sensing specialists before the government starts reconstruction and rehabilitation projects.

GC/LU changes, Sentinel-2 satellite imagery, dynamic and threshold spectral indexing, knowledge-based OBIA classification, prediction CA-MC maps





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DOI: 10.33677/ggianas20220200080