Towards the Issue of Allocation of the Time Frames for Growing Seasons Using Ground Observations and Remote Sensing Data

Panidi, Evgeny; Rykin, Ivan; Tsepelev, Valery

doi:10.24057/2414-9179-2018-2-24-129-140

Cited by 2 publications

(3 citation statements)

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“…Our research is conducted to investigate the possibilities for common application of remote sensing and ground-based monitoring data, and aimed on production of some synergy by fusion of ground-based data accuracy with spatial resolution of satellite observations when studying dynamics and change in climate and vegetation cover parameters. Studies performed earlier by other authors (Medvedeva et al, 2008;Miklashevich, Bartalev, 2016), as well as some results of our previously conducted research (Panidi et al, 2017;2018;, demonstrate a feasibility of integrating approach. This integration assumes investigation of change in the climatevegetation system instead of separated analysis of climate and(or) vegetation cover.…”

Section: Introductionsupporting

confidence: 75%

“…In the regions where the ground network of meteorological stations is sparse, vegetation monitoring can help to detail and interpolate spatially the estimations of climate parameters change, as the climate change is one of most significant factors of the vegetation cover change. Accordingly to our previously conducted studies (Panidi et al, 2017;2018; and to a number of the studies of other authors (Delbart et al, 2005;Sekhon et al, 2010) the Normalized Difference Water Index (NDWI) is a relevant instrument for growing seasons monitoring, while growing season length, onset and ending reflect change in surface air temperature. Analysis of the NDWI annual graphs can be applied to determine growing season parameters (Panidi, Tsepelev, 2017).…”

Section: Introductionmentioning

confidence: 94%

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Cloud-Desktop Remote Sensing Data Management to Ensure Time Series Analysis, Integration of Qgis and Google Earth Engine

Panidi

Rykin

Кикин

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Our context research is conducted to investigate the possibility of common application of the remote sensing and ground-based monitoring data to detection and observation of the dynamics and change in climate and vegetation cover parameters. We applied the analysis of the annual graphs of Normalized Difference Water Index to estimate the length and time frames of growing seasons. Basing on previously gained results, we concluded that we can use the Index-based monitoring of growing season parameters as a relevant technique. We are working on automation of computations that can be applied to processing satellite imagery, computing Normalized Difference Water Index time series (in the forms of maps and annual graphs), and estimation of growing season parameters. As currently used data amounts are big (or up-to-big) geospatial data, we use the Google Earth Engine platform to process initial datasets. Our currently described experimental work incorporates investigation of the possibilities for integration of cloud computing data storage and processing with client-side data representation in universal desktop GISs. To ensure our study needs we developed a prototype of a QGIS plugin capable to run processing in GEE and represent results in QGIS.

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Section: Introductionsupporting

confidence: 75%

Section: Introductionmentioning

confidence: 94%

Cloud-Desktop Remote Sensing Data Management to Ensure Time Series Analysis, Integration of Qgis and Google Earth Engine

Panidi

Rykin

Кикин

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…According to the (USSR climate reference book…, 1968), the growing season is defined as the transition of the surface air temperature through threshold of +5 and +10 0 С. However, in some areas where there is no well spread meteorological network, the daily graphs of the normalized difference water index (NDWI) can be used (Panidi et al, 2018;Panidi et al, 2019). Where absolute minimums are the boundaries of the beginning and end of the growing season.…”

Section: Introductionmentioning

confidence: 99%

Automated Gis-Based Technique for Evaluation of Indirect Growing Season Estimations

Rykin

Panidi

Tsepelev

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> This article is based on NDWI (Normalized Difference Water Index) which is automatically computed from the daily MODIS data. The main purpose of the article is to tell how the evaluation of NDWI-based growing season estimations can be automated. The NDWI is used as an indicator of liquid water quantity in vegetation, which is less sensitive to atmospheric scattering effect then the famous growing index (NDVI). The NDWI is computed using cloud-based platform (Google Earth Engine was applied) and compared with the daily meteorological data. The available meteorological data is collected for the past 130 years and NDWI data is collecting for the past 20 years. An automated technique has been probated on the example of republic of Komi, as it has a different climate forming factors. This approach can be used to evaluate growing season estimations for other territories that contain vegetation. Due to the accumulated amount of data, the study is relevant and has a special significance for areas with sparse hydrometeorological network.</p>

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Towards the Issue of Allocation of the Time Frames for Growing Seasons Using Ground Observations and Remote Sensing Data

Cited by 2 publications

References 1 publication

Cloud-Desktop Remote Sensing Data Management to Ensure Time Series Analysis, Integration of Qgis and Google Earth Engine

Cloud-Desktop Remote Sensing Data Management to Ensure Time Series Analysis, Integration of Qgis and Google Earth Engine

Automated Gis-Based Technique for Evaluation of Indirect Growing Season Estimations

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