| Yazarlar |
Orkan Özcan
İstanbul Teknik Üniversitesi, Türkiye |
Doç. Dr. Gül Aslı BOZBAY
İstanbul Ticaret Üniversitesi, Türkiye |
|
Esra Erten
İstanbul Teknik Üniversitesi, Türkiye |
|
Nebiye Musaoğlu
İstanbul Teknik Üniversitesi, Türkiye |
|
Müfit Çetin
Yalova Üniversitesi, Türkiye |
| Özet |
| To identify policies that will promote positive effects and mitigate negative ones of grazing is a major challenge in the Silvo-pastoral system. This paper presents the role of examining land-cover change trajectories by remote sensing imagery in grazing policy monitoring. The study was conducted for Duzlercami forest ecosystem located in the Mediterranean geographical region of Turkey and administrated by the General Directorate of Forestry (GDF) of the Ministry of Forestry and Water Affairs. Time series land-cover datasets from Landsat images between 1988 and 2016 were collected and classified. To link the conversions among trajectories and grazing policy, class level landscape metrics derived from the classified images were used. To validate the approach, yearly grazing-plans managed by GDF and populations of livestock were used. Results of this research have indicated that even though there is a yearly grazing plan, overgrazing can happen on the pilot site, and it can be easily identified by the destruction of woody vegetation. The notable correlation (r 2 = 0.89) between degraded woody vegetation and cattle population has occurred in the last 30 years in the landscape, and Landsat imagery can effectively support the grazing policy mapping and monitoring. |
| Anahtar Kelimeler |
| Degradation | Grazing | Mediterranean | Remote sensing |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | ADVANCES IN SPACE RESEARCH |
| Dergi ISSN | 0273-1177 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2019 |
| Cilt No | 63 |
| Sayı | 1 |
| Sayfalar | 160 / 171 |
| Doi Numarası | 10.1016/j.asr.2018.09.009 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S0273117 |
