0000000000567977

AUTHOR

Masoumeh Aghababaei

showing 3 related works from this author

Introducing ARTMO's Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape.

2022

Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible . To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To…

General Earth and Planetary SciencesAutomated Radiative Transfer Models Operator; machine-learning classification toolbox; Gaussian process classifier; plant types; Sentinel-2Remote sensing
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Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

2021

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong se…

Earth observation010504 meteorology & atmospheric sciencesComputer scienceNDVIScienceQvegetation types classification04 agricultural and veterinary sciences15. Life on landTime optimal01 natural sciencesNormalized Difference Vegetation IndexRandom forestIdentification (information)Vegetation typesmachine learning040103 agronomy & agriculturevegetation types classification; multi-temporal images; machine learning; Google Earth Engine; NDVI0401 agriculture forestry and fisheriesGeneral Earth and Planetary SciencesGoogle Earth EngineCartographymulti-temporal images0105 earth and related environmental sciencesRemote Sensing
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Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms.

2021

Plant Ecological Unit’s (PEUs) are the abstraction of vegetation communities that occur on a site which similarly respond to management actions and natural disturbances. Identification and monitoring of PEUs in a heterogeneous landscape is the most difficult task in medium resolution satellite images datasets. The main objective of this study is to compare pixel-based classification versus object-based classification for accurately classifying PEUs with four selected different algorithms across heterogeneous rangelands in Central Zagros, Iran. We used images of Landsat-8 OLI that were pan-sharpened to 15 m to classify four PEU classes based on a random dataset collected in the field (40%). …

PixelEcologyComputer scienceprincipal component analysisScienceQPerceptronObject (computer science)Field (computer science)Statistical classificationplant ecological units mappingmachine learning algorithmsPrincipal component analysisClassifier (linguistics)General Earth and Planetary Sciencesobject-based classificationTest dataRemote sensing
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