Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Christina Karakizi"'
Publikováno v:
Remote Sensing, Vol 12, Iss 15, p 2499 (2020)
Mapping water stress in vineyards, at the parcel level, is of significant importance for supporting crop management decisions and applying precision agriculture practices. In this paper, a novel methodology based on aerial Shortwave Infrared (SWIR) d
Externí odkaz:
https://doaj.org/article/2ab669eaf8ac42eab68394f7f5e760df
Publikováno v:
Remote Sensing, Vol 10, Iss 8, p 1214 (2018)
Detailed, accurate and frequent land cover mapping is a prerequisite for several important geospatial applications and the fulfilment of current sustainable development goals. This paper introduces a methodology for the classification of annual high-
Externí odkaz:
https://doaj.org/article/f5e811e26d724cd38eb0c5551c111361
Publikováno v:
Remote Sensing, Vol 8, Iss 3, p 235 (2016)
In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, de
Externí odkaz:
https://doaj.org/article/ac3afc38f7dd4ba983f2d31599b85039
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2021, Pp 319-326 (2021)
In this work, we elaborate on the gained insights from various classification experiments towards detailed land cover mapping over four representative regions of different environmental characteristics in Greece. In particular, the proposed methodolo
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2020, Pp 1555-1562 (2020)
Freely available satellite image time-series are currently the most exploited data towards land cover mapping. In this work we assess the contribution of spectral and temporal features for the detailed, i.e., with more than thirty classes, land cover
Publikováno v:
Remote Sensing, Vol 12, Iss 2499, p 2499 (2020)
Remote Sensing; Volume 12; Issue 15; Pages: 2499
Remote Sensing; Volume 12; Issue 15; Pages: 2499
Mapping water stress in vineyards, at the parcel level, is of significant importance for supporting crop management decisions and applying precision agriculture practices. In this paper, a novel methodology based on aerial Shortwave Infrared (SWIR) d
Publikováno v:
Remote Sensing
Remote Sensing, 2018, 10 (8), pp.1214. ⟨10.3390/rs10081214⟩
Volume 10
Issue 8
Pages: 1214
Remote Sensing, Vol 10, Iss 8, p 1214 (2018)
Remote Sensing, MDPI, 2018, 10 (8), ⟨10.3390/rs10081214⟩
Remote Sensing, 2018, 10 (8), pp.1214. ⟨10.3390/rs10081214⟩
Volume 10
Issue 8
Pages: 1214
Remote Sensing, Vol 10, Iss 8, p 1214 (2018)
Remote Sensing, MDPI, 2018, 10 (8), ⟨10.3390/rs10081214⟩
International audience; Detailed, accurate and frequent land cover mapping is a prerequisite for several important geospatial applications and the fulfilment of current sustainable development goals. This paper introduces a methodology for the classi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ec62c8d92d462298ed7afc952243a03
https://inria.hal.science/hal-01959065
https://inria.hal.science/hal-01959065
Publikováno v:
IGARSS
The detailed, accurate and frequent land cover and crop-type mapping emerge as essential for several scientific communities and geospatial applications. This paper presents a methodology for the semi-automatic production of land cover and crop type m
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XL-7/W3, Pp 31-37 (2015)
An assessment of the spectral discrimination between different vine varieties was undertaken using non-destructive remote sensing observations at the véraison period. During concurrent satellite, aerial and field campaigns, in-situ reflectance data
Publikováno v:
Remote Sensing, Vol 8, Iss 3, p 235 (2016)
Remote Sensing
Volume 8
Issue 3
Pages: 235
Remote Sensing
Volume 8
Issue 3
Pages: 235
In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, de