Detection and assessment of land use dynamics on Tenerife (Canary Islands): the agricultural development between 1986 and 2010

Abstract

Since Spanish colonial times, the Canary Islands and especially Tenerife have always been used for intensive agriculture. Today almost 1/4 of the total area of Tenerife are agriculturally affected, whereas especially mountainous areas with suitable climate conditions are drastically transformed for agricultural use by building of large terraces. In recent years, political and economical developments lead to a further transformation process, especially inducted by an expansive tourism, which caused concentration- and intensification-tendencies of agricultural land use in lower altitudes as well as agricultural set-aside and rural exodus in the hinterland. The overall aim of the research at hand is to address the agricultural land use dynamics of the past decades, to statistically assess the causal reasons for those changes and to model the future agricultural land use dynamics on Tenerife. Therefore, an object-based classification procedure for recent RapidEye data (2010), Spot 4 (1998) as well as SPOT 1 (1986-88) imagery was developed, followed by a post classification comparison (PCC). Older agricultural fallow land or agricultural set-aside with a higher level of natural succession can hardly be acquired in the used medium satellite imagery. Hence, a second detection technique was generated, which allows an exact identification of the total agriculturally affected area on Tenerife, also containing older agricultural fallow land or agricultural set-aside. The method consists of an automatic texture-oriented detection and area-wide extraction of linear agricultural structures (plough furrows and field boundaries of arable land, utilised and non-utilised agricultural terraces) in current orthophotos of Tenerife. Once the change detection analysis is realised, it is necessary to identify the different driving forces which are responsible for the agricultural land use dynamics. The statistical connections between agricultural land use changes and these driving forces are identified by the use of correlation and regression analyses.