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Vegetation detection on agricultural lands using satellite data

Authors: Leja, Ieva;

Vegetation detection on agricultural lands using satellite data

Abstract

In this master's thesis, the challenge tackled is the identification of vegetation (fallow field or winter crop or cover crop) in agricultural lands using multispectral satellite data. The objective of the thesis is to collate established research methodologies, develop and evaluate a classification algorithm, test different features, and assess the algorithm's spatial and temporal transferibality. Two classification models were developed, a random forest and a multilayer neural network, and trained on data in Estonia from 2019 to 2021. Both models yielded comparable accuracy rates: 84 % for the random forest and 83 % for the neural network. The neural network was superior on the 2022 data and in a different region. The inclusion of 0.1 and 0.9 quantiles of each index per field to the features improved the accuracy of both models.

Maģistra darbā tiek aplūkota veģetācijas (neapsēts lauks vai ziemāji vai starpkultūra) noteikšana lauksaimniecības zemēs, izmantojot multispektrālos satelītdatus. Darba mērķis ir apkopot līdzšinējo pētījumu pieejas un, izmēģinot dažādas ieejas datu kombinācijas, izveidot un novērtēt klasifikācijas algoritmu, kā arī izpētīt šī algoritma precizitāti citā laikā un reģionā. Tika izveidoti divi klasifikācijas modeļi: gadījuma mežs un daudzslāņu neironu tīkls, kas tika apmācīti uz Igaunijas datiem no 2019. līdz 2021. gadam. Abiem modeļiem bij līdzīgīga precizitāte: 84 % gadījuma mežam un 83 % neironu tīklam, taču neironu tīkls bija pārāks uz 2022. gada datiem un citā reģionā. Abiem modeļiem precizitāti paaugstināja 0.1 un 0.9 kvantiļu katram indeksam katram laukam pievienošana ieejas datiem.

Country
Latvia
Related Organizations
Keywords

remote sensing, classification, Datorzinātne, cover crop, Sentinel-2, agriculture

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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