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    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Geocarto International on 27/12/2020, available online: https://www.tandfonline.com/doi/full/10.1080/10106049.2020.1852610

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Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using an object-based technique

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Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using an object-based technique. / Rozali, Syaza; Abd Latif, Zulkiflee; Adnan, Nor Aizam et al.
In: Geocarto International, 27.12.2020.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Rozali S, Abd Latif Z, Adnan NA, Hussin Y, Blackburn A, Pradhan B. Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using an object-based technique. Geocarto International. 2020 Dec 27. Epub 2020 Dec 27. doi: 10.1080/10106049.2020.1852610

Author

Rozali, Syaza ; Abd Latif, Zulkiflee ; Adnan, Nor Aizam et al. / Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using an object-based technique. In: Geocarto International. 2020.

Bibtex

@article{176e929d72634fc1bec63007a76227ea,
title = "Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using an object-based technique",
abstract = "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{\textquoteright}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 (SpectralLandsat; and SpectralLandsat + HeightALS).",
keywords = "Object-based segmentation, airborne LiDAR, remote sensing, Random Forest, support vector machine",
author = "Syaza Rozali and {Abd Latif}, Zulkiflee and Adnan, {Nor Aizam} and Yousif Hussin and Alan Blackburn and Biswajeet Pradhan",
year = "2020",
month = dec,
day = "27",
doi = "10.1080/10106049.2020.1852610",
language = "English",
journal = "Geocarto International",
issn = "1010-6049",
publisher = "Taylor and Francis Ltd.",

}

RIS

TY - JOUR

T1 - Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using an object-based technique

AU - Rozali, Syaza

AU - Abd Latif, Zulkiflee

AU - Adnan, Nor Aizam

AU - Hussin, Yousif

AU - Blackburn, Alan

AU - Pradhan, Biswajeet

PY - 2020/12/27

Y1 - 2020/12/27

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 (SpectralLandsat; and SpectralLandsat + HeightALS).

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 (SpectralLandsat; and SpectralLandsat + HeightALS).

KW - Object-based segmentation

KW - airborne LiDAR

KW - remote sensing

KW - Random Forest

KW - support vector machine

U2 - 10.1080/10106049.2020.1852610

DO - 10.1080/10106049.2020.1852610

M3 - Journal article

JO - Geocarto International

JF - Geocarto International

SN - 1010-6049

ER -