TY - JOUR
T1 - Assessment of land-cover change using GIS and remotely-sensed data
T2 - A case study in Ain Snoussi area of northern Tunisia
AU - Park, Taejin
AU - Lee, Woo Kyun
AU - Woo, Su Young
AU - Yoo, Seongjin
AU - Kwak, Doo Ahn
AU - Stiti, Boutheina
AU - Khaldi, Abdelhamid
AU - Zhen, Xu
AU - Kwon, Tae Hyub
PY - 2011/6
Y1 - 2011/6
N2 - Understanding the patterns of land-cover change for biodiversity and ecology system function has been important in landscape ecology. The objective of this study was to analyze land-cover change in the Ain Snoussi area of northern Tunisia. Landsat MSS/4 and SPOT HRV/3 images were used for the analysis. To classify land-cover type into forest and non-forest area, pixel-based classification and maximum likelihood algorithm were applied to two imageries using supervised classification algorithm. After classification of images, each changed area was calculated. Thereby, analysis of distance roads and topographic factors such as elevation, slope, aspect, and Topographic Wetness Index (TWI) were performed. The results showed that the area changed into non-forest was slightly larger than that into forest. Moreover, most of the changed areas, approximately half of the total changed area, were distributed near the roads. In addition, the change from forest to non-forest area tends to have a negative and positive relationship respectively with elevation and slope. On the other hand, the change from non-forest to forest area showed the tendency to be negative in elevation, slope, and TWI. However, the slope aspect of study area did not have any particular relationship with change tendency. In conclusion, spatial pattern of land-cover change was influenced by the distance from roads and topographic characteristics of target area.
AB - Understanding the patterns of land-cover change for biodiversity and ecology system function has been important in landscape ecology. The objective of this study was to analyze land-cover change in the Ain Snoussi area of northern Tunisia. Landsat MSS/4 and SPOT HRV/3 images were used for the analysis. To classify land-cover type into forest and non-forest area, pixel-based classification and maximum likelihood algorithm were applied to two imageries using supervised classification algorithm. After classification of images, each changed area was calculated. Thereby, analysis of distance roads and topographic factors such as elevation, slope, aspect, and Topographic Wetness Index (TWI) were performed. The results showed that the area changed into non-forest was slightly larger than that into forest. Moreover, most of the changed areas, approximately half of the total changed area, were distributed near the roads. In addition, the change from forest to non-forest area tends to have a negative and positive relationship respectively with elevation and slope. On the other hand, the change from non-forest to forest area showed the tendency to be negative in elevation, slope, and TWI. However, the slope aspect of study area did not have any particular relationship with change tendency. In conclusion, spatial pattern of land-cover change was influenced by the distance from roads and topographic characteristics of target area.
KW - Digital Elevation Model
KW - Distance from roads
KW - Land-cover change
KW - Topographic Wetness Index
KW - Topographic factors
KW - Tunisia
UR - http://www.scopus.com/inward/record.url?scp=84898549589&partnerID=8YFLogxK
U2 - 10.1080/21580103.2011.573951
DO - 10.1080/21580103.2011.573951
M3 - Article
AN - SCOPUS:84898549589
SN - 2158-0103
VL - 7
SP - 75
EP - 81
JO - Forest Science and Technology
JF - Forest Science and Technology
IS - 2
ER -
