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Highly discrete mapping of the growing season time frames and time dynamics. / Rykin, I.; Shagnieva, A.; Panidi, E.; Tsepelev, V.

In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 42, No. 3/W8, 20.08.2019, p. 357-361.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Rykin, I, Shagnieva, A, Panidi, E & Tsepelev, V 2019, 'Highly discrete mapping of the growing season time frames and time dynamics', International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, vol. 42, no. 3/W8, pp. 357-361. https://doi.org/10.5194/isprs-archives-XLII-3-W8-357-2019

APA

Rykin, I., Shagnieva, A., Panidi, E., & Tsepelev, V. (2019). Highly discrete mapping of the growing season time frames and time dynamics. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(3/W8), 357-361. https://doi.org/10.5194/isprs-archives-XLII-3-W8-357-2019

Vancouver

Rykin I, Shagnieva A, Panidi E, Tsepelev V. Highly discrete mapping of the growing season time frames and time dynamics. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 2019 Aug 20;42(3/W8):357-361. https://doi.org/10.5194/isprs-archives-XLII-3-W8-357-2019

Author

Rykin, I. ; Shagnieva, A. ; Panidi, E. ; Tsepelev, V. / Highly discrete mapping of the growing season time frames and time dynamics. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 2019 ; Vol. 42, No. 3/W8. pp. 357-361.

BibTeX

@article{c3ac0ea3a35241438d13cca0478be847,
title = "Highly discrete mapping of the growing season time frames and time dynamics",
abstract = "Growing season time frames can be estimated and mapped using the vegetation indexes mapping and analysis. This approach brings significant benefit consisted in the ability of detailed (highly discrete in the meaning of spatial resolution) mapping of spatial differences in growing season stage and length. In comparison with interpolation of ground air temperature (applied when using temperature to detect growing seasons), real spatial resolution raises to kilometers per pixel and higher, while nodes of ground observation network can be spaced by thousands of kilometers in some regions. Our ongoing study is devoted to design a processing chain for mapping of growing season time frames basing on vegetation indexes data with close-to-one-day time resolution. We used MOD09GA dataset as an initial data. Data processing was implemented in Google Earth Engine big geospatial data platform.",
keywords = "GIS-based Mapping, Google Earth Engine, Growing Seasons, NDWI, Vegetation Indexes",
author = "I. Rykin and A. Shagnieva and E. Panidi and V. Tsepelev",
year = "2019",
month = aug,
day = "20",
doi = "10.5194/isprs-archives-XLII-3-W8-357-2019",
language = "English",
volume = "42",
pages = "357--361",
journal = "International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences",
issn = "1682-1750",
publisher = "International Society for Photogrammetry and Remote Sensing",
number = "3/W8",
note = "2019 GeoInformation for Disaster Management, Gi4DM 2019 ; Conference date: 03-09-2019 Through 06-09-2019",

}

RIS

TY - JOUR

T1 - Highly discrete mapping of the growing season time frames and time dynamics

AU - Rykin, I.

AU - Shagnieva, A.

AU - Panidi, E.

AU - Tsepelev, V.

PY - 2019/8/20

Y1 - 2019/8/20

N2 - Growing season time frames can be estimated and mapped using the vegetation indexes mapping and analysis. This approach brings significant benefit consisted in the ability of detailed (highly discrete in the meaning of spatial resolution) mapping of spatial differences in growing season stage and length. In comparison with interpolation of ground air temperature (applied when using temperature to detect growing seasons), real spatial resolution raises to kilometers per pixel and higher, while nodes of ground observation network can be spaced by thousands of kilometers in some regions. Our ongoing study is devoted to design a processing chain for mapping of growing season time frames basing on vegetation indexes data with close-to-one-day time resolution. We used MOD09GA dataset as an initial data. Data processing was implemented in Google Earth Engine big geospatial data platform.

AB - Growing season time frames can be estimated and mapped using the vegetation indexes mapping and analysis. This approach brings significant benefit consisted in the ability of detailed (highly discrete in the meaning of spatial resolution) mapping of spatial differences in growing season stage and length. In comparison with interpolation of ground air temperature (applied when using temperature to detect growing seasons), real spatial resolution raises to kilometers per pixel and higher, while nodes of ground observation network can be spaced by thousands of kilometers in some regions. Our ongoing study is devoted to design a processing chain for mapping of growing season time frames basing on vegetation indexes data with close-to-one-day time resolution. We used MOD09GA dataset as an initial data. Data processing was implemented in Google Earth Engine big geospatial data platform.

KW - GIS-based Mapping

KW - Google Earth Engine

KW - Growing Seasons

KW - NDWI

KW - Vegetation Indexes

UR - http://www.scopus.com/inward/record.url?scp=85074279198&partnerID=8YFLogxK

U2 - 10.5194/isprs-archives-XLII-3-W8-357-2019

DO - 10.5194/isprs-archives-XLII-3-W8-357-2019

M3 - Conference article

AN - SCOPUS:85074279198

VL - 42

SP - 357

EP - 361

JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 1682-1750

IS - 3/W8

T2 - 2019 GeoInformation for Disaster Management, Gi4DM 2019

Y2 - 3 September 2019 through 6 September 2019

ER -

ID: 48957149