Investigating the harvesting periods of wheat plots using remote sensing and Time Series : distinguishing hay bale and grain plots based on NDVI Time Series
Vasiliou, Despina (2022)
2022
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2022121530131
https://urn.fi/URN:NBN:fi:amk-2022121530131
Tiivistelmä
Remote Sensing in agriculture is a tool used for monitoring and investigating crops. Remote Sensing systems accumulate images from reflected and emitted radiation of a location, thus providing accurate and timely data.
The main aim of this thesis is to determine the harvesting periods of the hay bale and grain plots by using Remote Sensing NDVI time series analysis. ERA-TOSTHENES Centre of Excellence is a research centre based in Cyprus that commissioned the thesis topic and provided the necessary data for this re-search. This research is conducted for the Environment & Climate Departure and specifically, Agriculture.
For this to be achieved, Sentinel-2 images are processed in Google Earth Engine and with the help of vegetation index, NDVI the Time Series analysis is performed. Precisely, the study explores to visualize (time series charts) when wheat is harvested into hay bales (food for animals) and when it is harvested for its grain (food for humans). This investigation was conducted for control and monitoring purposes of crops.
Sentinel-2 images are satellite images provided in high spectral resolutions and are used to calculate the crop Vegetation Index, NDVI. This provides crop characteristics and determines the crop's condition. The Time Series analysis is a collection of the NDVI data for the study period.
The study indicates that by using Remote Sensing and NDVI time series, it is possible to observe the different harvesting periods of fields regarding hay bale and grain. The hay bale harvesting period is observed at the beginning of May compared to the grain harvesting period observed from late May to the beginning of June.
A suggestion for future research would be to improve the methodology and model used in this research in order to achieve as accurate results as possible. In addition, classification of the agricultural wheat plots for both hay bales and wheat grains using Remote Sensing and performing a crop yield estimation of these specific wheat plots using Remote Sensing would assist in an overall observation of wheat plots.
The main aim of this thesis is to determine the harvesting periods of the hay bale and grain plots by using Remote Sensing NDVI time series analysis. ERA-TOSTHENES Centre of Excellence is a research centre based in Cyprus that commissioned the thesis topic and provided the necessary data for this re-search. This research is conducted for the Environment & Climate Departure and specifically, Agriculture.
For this to be achieved, Sentinel-2 images are processed in Google Earth Engine and with the help of vegetation index, NDVI the Time Series analysis is performed. Precisely, the study explores to visualize (time series charts) when wheat is harvested into hay bales (food for animals) and when it is harvested for its grain (food for humans). This investigation was conducted for control and monitoring purposes of crops.
Sentinel-2 images are satellite images provided in high spectral resolutions and are used to calculate the crop Vegetation Index, NDVI. This provides crop characteristics and determines the crop's condition. The Time Series analysis is a collection of the NDVI data for the study period.
The study indicates that by using Remote Sensing and NDVI time series, it is possible to observe the different harvesting periods of fields regarding hay bale and grain. The hay bale harvesting period is observed at the beginning of May compared to the grain harvesting period observed from late May to the beginning of June.
A suggestion for future research would be to improve the methodology and model used in this research in order to achieve as accurate results as possible. In addition, classification of the agricultural wheat plots for both hay bales and wheat grains using Remote Sensing and performing a crop yield estimation of these specific wheat plots using Remote Sensing would assist in an overall observation of wheat plots.