Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique
Autor: | Zulkiflee Abd Latif, Yousif A. Hussin, Syaza Rozali, Alan Blackburn, Biswajeet Pradhan, Nor Aizam Adnan |
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Přispěvatelé: | Department of Natural Resources, UT-I-ITC-FORAGES, Faculty of Geo-Information Science and Earth Observation |
Jazyk: | angličtina |
Rok vydání: | 2022 |
Předmět: |
010504 meteorology & atmospheric sciences
Computer science Geography Planning and Development Feature extraction 0211 other engineering and technologies 22/2 OA procedure 02 engineering and technology Object (computer science) Geological & Geomatics Engineering 01 natural sciences Random forest Support vector machine Lidar Feature (computer vision) Remote sensing (archaeology) ITC-ISI-JOURNAL-ARTICLE 0909 Geomatic Engineering Segmentation sense organs 021101 geological & geomatics engineering 0105 earth and related environmental sciences Water Science and Technology Remote sensing |
Zdroj: | Geocarto international, 3247-3264. Taylor & Francis STARTPAGE=3247;ENDPAGE=3264;ISSN=1010-6049;TITLE=Geocarto international |
ISSN: | 1010-6049 |
Popis: | 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 ( |
Databáze: | OpenAIRE |
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