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John M. Kovacs

Researcher at Nipissing University

Publications -  51
Citations -  4286

John M. Kovacs is an academic researcher from Nipissing University. The author has contributed to research in topics: Mangrove & Rhizophora mangle. The author has an hindex of 28, co-authored 49 publications receiving 3549 citations. Previous affiliations of John M. Kovacs include University of Western Ontario & Algoma University.

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The application of small unmanned aerial systems for precision agriculture: a review

TL;DR: To provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required and the farmer should involve in the process of field design, image acquisition, image interpretation and analysis.
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Ethnobiology, socio-economics and management of mangrove forests: A review

TL;DR: There is growing research interest in the ethnobiology, socio-economics and management of mangrove forests as discussed by the authors, with harvesting efforts and impacts concentrated in stands that are closer to settlements and easiest to access (by land or by sea).
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Large-scale dieback of mangroves in Australia’s Gulf of Carpentaria: a severe ecosystem response, coincidental with an unusually extreme weather event

TL;DR: The most severe and notable instance ever reported of sudden and widespread dieback of mangrove vegetation was reported in Australia's remote Gulf of Carpentaria in 2015 and 2016 as mentioned in this paper, where the cause was not fully explained but the timing was coincident with an extreme weather event; notably one of high temperatures and low precipitation lacking storm winds.
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Assessment of red-edge vegetation indices for crop leaf area index estimation.

TL;DR: In this paper, the authors explored the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the VIS-based VIs.
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Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

TL;DR: In this paper, an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarIMetric images of an agricultural area in North-eastern Ontario, Canada.