Land Use and Cover Mapping with Airborne Hyperspectral Imager in Setiu, Malaysia


  •  Kamaruzaman Jusoff    

Abstract

In recent years, land use and land cover plays a pivotal role in global environmental change. Under these circumstances,
the need of a new dimension for detecting land use and cover is getting more imperative for conservation and effective
management of land use and cover types. Importantly, the use of information technology to support decision making in
detecting land use and cover is essential and recent. One of the technologies used is Airborne Remote Sensing. The
objective of this study is to identify, quantify, classify and map land use and land cover mapping in Setiu, Terengganu
using UPM-APSB’s AISA airborne hyperspectral remote sensing. Detection of land use and cover was performed using
airborne hyperspectral imaging data taken on 20 April 2006 with the support of existing land use and cover maps. The
size of the study area is 100 ha. The image was displayed in ENVI 4.0 Software using bands 202217 (RGB)
combination. The data were then enhanced and classified for different land use and cover classes. From the data
analysis, the image can be classified into eight classes. The classes are 2-3 years old oil palm plantation, 4-5 years old
oil palm plantation, young (3-4 years old) rubber plantation, matured (15-17 years old) rubber plantation, vegetation
crops, open area, road and river. The land use and land cover classes area distribution of the plots under study in Setiu,
Terengganu were 4.18 ha, 8.58 ha, 6.26 ha, 70.43 ha, 2.98 ha, 2.31 ha, 2.78 ha, and 2.48 ha. Overall, the classification
accuracy of interpretation of the airborne imagery for land use and cover in Setiu, Terengganu is 89.51 and kappa
coefficient is 0.86. This study shows that, airborne hyperspectral remote sensing technique is capable in identifying,
quantifying, classifying and mapping land use and cover in Setiu, Terengganu, hence a good decision support tool in
land use and cover planning and management.


This work is licensed under a Creative Commons Attribution 4.0 License.