Landscape sensitivity-based scenario analysis using flus model: a case of Asarsuyu watershed

Abstract

The landscape is a dynamic and multidimensional concept which includes natural and/or cultural components, ecosystems, relations and processes. The sustainability of landscapes depends on the harmony of these ecosystems. Particularly, it is important to identify areas where the landscape is sensitive within the basin and to restrict urban growth in these areas. However, the land cover change that occurs in the form of transformation from natural areas to cultural areas undermines the operability of the processes within the landscape. In this study, two different scenarios regarding land cover change have been developed using the FLUS model for Asarsuyu Watershed between the years 2018-2036 and 2036-2054. With these scenarios, the areas where landscape is sensitive are revealed and the urban growth is restricted in areas where landscape sensitivity is very high and high. In this respect, landscape sensitivity studies provide an important tool for guiding urban growth in an ecological framework.

Delineation of Urban Development Boundary and Carbon Emission Effects in Xuzhou City, China

“…In 2018, Liang et al further expanded the model by adding the UGB (urban growth boundary) module [44], which enables effective land use simulation and UDB delineation. Due to the advantages of higher accuracy, faster processing speed, and ease of use, the FLUS model has gained widespread use in many countries such as China [45], Myanmar [46], Turkey [47], and Colombia [48]. The FLUS model consists of three components: (1) Probability-of-occurrence estimation using artificial neural network; (2) Cellular automata based on self-adaptive inertia and competition mechanism; (3) Boundary delineation based on morphological erosion and dilation.…”

Multi-Temporal Change of LULC and Its Impact on Carbon Storage in Jiangsu Coastal, China

“…However, recent year's studies on LULC at home and abroad mainly focused on exploring the mechanism of change, influence, and prediction [13][14][15]. The research on the mechanism of the LULC model mainly includes CLUES [16,17], ESMs [18], SD [19], logistic-CA-Markov [20], FLUS [21], and PLUS [22]. Compared with other LULC dynamic models, the PLUS model is based on an adaptive competition strategy, containing land expansion strategies and CA models for LULC dynamic simulation.…”