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Shattri Mansor

Researcher at Universiti Putra Malaysia

Publications -  166
Citations -  3957

Shattri Mansor is an academic researcher from Universiti Putra Malaysia. The author has contributed to research in topics: Landslide & Land cover. The author has an hindex of 30, co-authored 160 publications receiving 2916 citations. Previous affiliations of Shattri Mansor include University of Dundee.

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Early detection of basal stem rot disease Ganoderma in oil palms based on hyperspectral reflectance data using pattern recognition algorithms

TL;DR: This work developed a procedure for early and accurate detection and differentiation of Ganoderma disease with different severities, based on spectral analysis and statistical models and confirmed the usefulness and efficiency of the spectrally based classification approach in rapid screening of BSR in oil palm.
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Monitoring of underground coal fires using thermal infrared data

TL;DR: In this paper, the potential utility of thermal infrared and short wavelength infrared data for detecting and mapping sub-surface high temperature sources is analyzed, and it is shown that the relatively coarse resolution of the thermal TM data enabled the detection, delineation and quantification of coal fire sources.
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NDVI Differencing and Post-classification to Detect Vegetation Changes in Halabja City, Iraq.

TL;DR: In this paper, the authors used the Normalized Difference Vegetation Index (NDVI) image differencing and post-classification techniques to detect vegetation change in Halabja city, Iraq by using Landsat-5Thematic Mapper images.

Interoperability for Smart Home Environment Using Web Services

TL;DR: The potential of Web Services technology using Simple Object Access Protocol (SOAP) in addressing the interoperability requirements for smart home environment is described and the proposed system performance is evaluated to demonstrate a complete, bi-directional realtime management of sub-systems in smartHome environment.
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Drone-based land-cover mapping using a fuzzy unordered rule induction algorithm integrated into object-based image analysis

TL;DR: The results indicate that the proposed method outperforms DT and SVM with an overall accuracy, and suggest that fuzzy rules are more appropriate than conventional crisp rules for land-cover extraction from UAV images.