Landscape characterization using GIS-based Landscape Character Assessment in Hulu Langat district, Malaysia

• Main Authors: Mat Isa, Masbiha, Ismail, Nor Atiah, Abdul Rasam, Abdul Rauf, Teh, Mohd Zulhaili
• Format: Article
• Published: Universiti Kebangsaan Malaysia. Fakulti Sains Sosial dan Kemanusiaan 2023
• Online Access: http://psasir.upm.edu.my/id/eprint/108972/; https://ejournal.ukm.my/gmjss/article/view/57478

Landscape Character Assessment (LCA) is rising in significance as the demand for efficient methods to manage and monitor landscapes increases around the world. LCA entails the procedure of creating maps at the desktop mapping stage to describe and assess landscapes according to the arrangement of landscape features. This study aims to pilot the mapping techniques used in the LCA desktop study, in accordance with the Malaysian Landscape Character Assessment guidebook, in the rapidly growing Hulu Langat district of Selangor. Secondary data and satellite images were used to interpret local geomorphology, hydrology, and land cover maps. The study employs the Geographical Information System (GIS) weighted sum analysis, which allows for a more accurate assessment of the spatial landscape. Six (6) landscape character types were identified in this study: (i) high-hill forest; (ii) low-hill agriculture; (iii) valley-agriculture; (iv) undulating low-land agriculture; (v) undulating lowland settlements; and (vi) low-hill settlements. These six (6) landscapes character types are important information and communication material for discussion among stakeholders in the decision-making stages for landscape development. When contemplating any kind of landscape change, the LCA mapping can be a source of guidelines and references to manage the landscape character. This study has contributed to the creation of a preliminary landscape type map for the Hulu Langat district. For future studies, it is recommended that the interpretation of landscape elements in the assessment be carried out after the evaluator has gained an understanding of the visual mapping context to avoid discrepancies for better mapping outcomes.