Abstract

Remote sensing has been widely used in various fields, one of which is to provide information related to land cover classification Kedai Damar Village is one of 13 villages in the Tebing Tinggi sub-district, Serdang Bedagai Regency. The settlement of Kedai Damar is the one that is most remote from the headquarters of the high cliffs sub-district. This settlement is situated in the center of the PT.Perkebunan Nusantara-IV plantation region in Pabatu North Sumatera. As a result, several rural towns have opened shops providing food and other essentials to farmers and farmworkers in order to rely on PT. Perkebunan Nusantara-IV plantation. This research was conducted in the Kedai Damar Village administrative center area using unmanned aircraft technology and then visually interpretation of aerial photos and mapping administrative area land cover in the Kedai Damar Village administrative center area using processed data in the form of orthophoto from agisoft metashape software. Based on the results of the study, the Kedai Damar Village area has 5 types of land cover classifications that can be identified visually based on 9 elements of interpretation (hue, color, shape, size, texture, pattern, shadow, site and association) as well as ground checks or data collection and observations. In the field. there are 5 types of land cover that can be visually identified, including non-oil Palm, open land, factory Area, settlement, oil palm plantations.

Details

Title
Utilization of Unmanned Aerial Vehicle (UAV) Technology to Identify Land Cover in Kedai Damar Village, Serdang Bedagai, North Sumatera
Author
Arinah, H 1 ; Purba, M A 2 ; Affifah, N 3 

 Faculty of Forestry, Universitas Sumatera Utara , Deli Serdang, North Sumatra , Indonesia 
 Faculty of Social and Political Sciences, Public Administration Study Program, Universitas Sumatera Utara , Medan, North Sumatra , Indonesia 
 Faculty of Agriculture, Animal Husbandry Study Program, Universitas Sumatera Utara , Medan, North Sumatra , Indonesia 
First page
012053
Publication year
2024
Publication date
May 2024
Publisher
IOP Publishing
ISSN
17551307
e-ISSN
17551315
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3065004289
Copyright
Published under licence by IOP Publishing Ltd. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.