R
Ruiliang Pu
Researcher at University of South Florida
Publications - 160
Citations - 7034
Ruiliang Pu is an academic researcher from University of South Florida. The author has contributed to research in topics: Hyperspectral imaging & Thematic Mapper. The author has an hindex of 42, co-authored 149 publications receiving 5693 citations. Previous affiliations of Ruiliang Pu include University of California & University of California, Berkeley.
Papers
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A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species
Ruiliang Pu,Shawn M. Landry +1 more
TL;DR: In this paper, the authors explored the potential of the newly developed high resolution satellite sensor, WorldView-2 (WV2) imagery for identifying and mapping urban tree species/groups in the City of Tampa, FL, USA by comparing capabilities between high resolution IKONOS (IKO, acquired on April 6, 2006) and WV2 (acquired on May 1, 2011) imagery.
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Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data
TL;DR: Field spectrometer data and leaf area index (LAI) measurements were collected on the same day as the Earth Observing 1 satellite overpass for a study site in the Patagonia region of Argentina to determine the most effective bands for forest LAI estimation.
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Land-use/land-cover change detection using improved change-vector analysis
TL;DR: Wang et al. as mentioned in this paper proposed an improved CVA, which consists of two stages, Double-Window Flexible Pace Search (DFPS) and direction cosines of change vectors for determining change direction (category).
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Monitoring plant diseases and pests through remote sensing technology: a review.
Jingcheng Zhang,Yanbo Huang,Ruiliang Pu,Pablo González-Moreno,Lin Yuan,Kaihua Wu,Wenjiang Huang +6 more
TL;DR: This review outlines the state-of-the-art research achievements in relation to sensing technologies, feature extraction, and monitoring algorithms that have been conducted at multiple scales and provides a general framework to facilitate the monitoring of an unknown disease or pest highlighting future challenges and trends.
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Conifer species recognition: An exploratory analysis of in situ hyperspectral data
Peng Gong,Ruiliang Pu,Bin Yu +2 more
TL;DR: In this paper, an artificial neural network algorithm was assessed for the identification of six conifer tree species using hyperspectral data measured above sunlit and shaded sides of canopies using a high spectral resolution radiometer.