Automated annual cropland mapping  from reconstructed time series of Landsat data

Плотников, Д.Е.; Kolbudaev, P.A.; Барталев, С.А.; Loupian, Е.А.

doi:10.21046/2070-7401-2018-15-2-112-127

Cited by 6 publications

(2 citation statements)

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“…Our finding that multi-seasonal Landsat images give greater accuracy in the classification of vegetation cover than mono-seasonal images is in agreement with the results of the studies conducted in different regions of the world [8][9][10]. In works [11][12][13] was investigated the selection of the appropriate algorithm and features for the classification of land cover, including the vegetation in the Mediterranean region, using Landsat images.…”

Section: Resultssupporting

confidence: 89%

A comparison of mono-seasonal and multiseasonal Landsat images for vegetation cover classification in the Mediterranean region: a case study in Latakia, Syria

Malinnikov

Khatib

2021

E3S Web Conf.

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Providing constantly updated information on vegetation serves as a basis for studies of natural resources and ecological issues. This paper discusses the question related to an appropriate season(s) for classification vegetation cover in the Mediterranean region and detecting its changes using Landsat imagery. Autumn, spring, and multi-seasonal satellite images, captured in 2017, were used to classify vegetation cover in a part of the Lattakia province, Syria. The satellite images were classified using the random forest algorithm, and high spatial resolution satellite images Google Earth Pro were used as reference data. The results indicate better effectiveness of the autumn images over spring ones for vegetation cover classification with 73.6% and 62.4% overall accuracy, respectively. In addition, a comparison of autumn and multi-seasonal Landsat images indicates no significant statistical difference in the accuracy of vegetation cover classification at the significance level of 0.05, which illustrates the effectiveness of using autumn images to classify the vegetation cover of the Mediterranean region. Furthermore, the obtained results show the necessity of using additional features as the spectral channels may not be sufficient for mapping vegetation cover in the Mediterranean region with high accuracy.

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Section: Resultssupporting

confidence: 89%

A comparison of mono-seasonal and multiseasonal Landsat images for vegetation cover classification in the Mediterranean region: a case study in Latakia, Syria

Malinnikov

Khatib

2021

E3S Web Conf.

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“…However, such methods are difficult to apply for Landsat data because of the low observation frequency of this satellite system. Computationally intensive methods, based on modelling of the annual dynamics of field's reflectance, are required to implement "MODIS-like" processing scheme on Landsat imagery [26,27].…”

Section: Introductionmentioning

confidence: 99%

Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus

et al. 2022

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Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels.

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Abandoned field determination using MODIS data

Denisova

Чернов

2021

2021 International Conference on Information Technology and Nanotechnology (ITNT)

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Automated annual cropland mapping from reconstructed time series of Landsat data

Cited by 6 publications

References 1 publication

A comparison of mono-seasonal and multiseasonal Landsat images for vegetation cover classification in the Mediterranean region: a case study in Latakia, Syria

A comparison of mono-seasonal and multiseasonal Landsat images for vegetation cover classification in the Mediterranean region: a case study in Latakia, Syria

Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus

Abandoned field determination using MODIS data

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