TY - JOUR
T1 - Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique
AU - Rozali, Syaza
AU - Abd Latif, Zulkiflee
AU - Adnan, Nor Aizam
AU - Hussin, Y.
AU - Blackburn, Alan
AU - Pradhan, Biswajeet
N1 - Funding Information:
The authors would like to express their gratitude to the Ministry of Higher Education Malaysia for MyBrain15 and Fundamental Research Grant Scheme (FRGS) number 600-IRMI/FRGS 5/3 (319/2019) as the financial support for this research; Airborne Research & Survey Facility, UK (ARSF UK) for providing the Airborne LiDAR data and the United States Geological Survey (USGS) for providing satellite imagery.
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022/6/3
Y1 - 2022/6/3
N2 - The study involves an object-based segmentation method to extract feature changes in tropical rainforest cover using Landsat image and airborne LiDAR (ALS). Disturbance event that are represents the changes are examined by the classification of multisensor data; that is a highly accurate ALS with different resolutions of multispectral Landsat image. Disturbance Index (DI) derived from Tasseled Cap Transformation, Normalized Difference Vegetation Index (NDVI), and the ALS height are the variables for object-based segmentation process. The classification is categorized into two classes; disturbed and non-disturbed forest cover using Nearest Neighbor (NN), Random Forest (RF) and Support Vector Machine (SVM). The overall accuracy ranging from 88% to 96% and kappa ranging from 0.79 to 0.91. Mcnemar’s test p-value (<0.05) is applied to check the classification for each method used which is RF 0.03 and SVM 0.01. The accuracy increases when the integration of ALS in Landsat image (Spectral
Landsat; and Spectral
Landsat + Height
ALS).
AB - The study involves an object-based segmentation method to extract feature changes in tropical rainforest cover using Landsat image and airborne LiDAR (ALS). Disturbance event that are represents the changes are examined by the classification of multisensor data; that is a highly accurate ALS with different resolutions of multispectral Landsat image. Disturbance Index (DI) derived from Tasseled Cap Transformation, Normalized Difference Vegetation Index (NDVI), and the ALS height are the variables for object-based segmentation process. The classification is categorized into two classes; disturbed and non-disturbed forest cover using Nearest Neighbor (NN), Random Forest (RF) and Support Vector Machine (SVM). The overall accuracy ranging from 88% to 96% and kappa ranging from 0.79 to 0.91. Mcnemar’s test p-value (<0.05) is applied to check the classification for each method used which is RF 0.03 and SVM 0.01. The accuracy increases when the integration of ALS in Landsat image (Spectral
Landsat; and Spectral
Landsat + Height
ALS).
KW - ITC-ISI-JOURNAL-ARTICLE
KW - 22/2 OA procedure
UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.1080/10106049.2020.1852610
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2020/isi/hussin_est.pdf
U2 - 10.1080/10106049.2020.1852610
DO - 10.1080/10106049.2020.1852610
M3 - Article
SN - 1010-6049
SP - 3247
EP - 3264
JO - Geocarto international
JF - Geocarto international
ER -