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European Journal of Remote Sensing is a peer-reviewed journal published by Taylor & Francis, covering Remote Sensing in Agriculture, Remote Sensing and LiDAR Applications, Remote-Sensing Image Classification.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." European Journal of Remote Sensing 12 (3): 45–58.
Formats any DOI in European Journal of Remote Sensing style. No sign-up. |
| Publishes research in | Remote Sensing in Agriculture Remote Sensing and LiDAR Applications Remote-Sensing Image Classification Remote Sensing and Land Use Land Use and Ecosystem Services |
| ISSN | 2279-7254 |
| Citation impact (2-yr) | 3.35 |
| h-index | 67 |
| i10-index | 487 |
| Total citations | 20,824 |
| Article processing charge | $1,060 |
| Open access | Yes |
| Top institutions publishing here | National Research Council |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
The present study focuses on determining the relationship of estimated land surface temperature (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) for Florence and Naples cities in Italy using Landsat 8 data. The study also classifies different land use/land cover LU–LC) types using NDVI and NDBI threshold values, iterative self-organizing…
This paper reviewed major remote sensing image classification techniques, including pixel-wise, sub-pixel-wise, and object-based image classification methods, and highlighted the importance of incorporating spatio-contextual information in remote sensing image classification. Further, this paper grouped spatio-contextual analysis techniques into three major categories, including 1) texture extraction, 2) Markov random fields (MRFs) modeling, and 3) image segmentation…
Knowledge of tree species composition in a forest is an important topic in forest management. Accurate tree species maps allow for much more detailed and in-depth analysis of biophysical forest variables. The paper presents a comparison of three classification algorithms: support vector machines (SVM), random forest (RF) and artificial neural networks (ANN) for tree species…
The increased availability of mapped environmental data calls for better tools to analyze the spatial characteristics and information contained in those maps. Publicly available, user-friendly and universal tools are needed to foster the interdisciplinary development and application of methodologies for the extraction of image object information properties contained in digital raster maps. That is the…
Remote Sensing (RS) data and techniques, in combination with GIS and landscape metrics, are fundamental to analyse and characterise Land Cover (LC) and its changes. The case study here described, has been conducted in the area of Avellino (Southern Italy). To characterise the dynamics of changes during a fifty year period (1954–2004), a multitemporal set…
Remote Sensing Letters
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