Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery

Kussul, Nataliia; Lavreniuk, Mykola; Shelestov, Andrii; Skakun, Sergii

doi:10.1080/22797254.2018.1454265

Cited by 73 publications

(56 citation statements)

References 26 publications

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“…In order to avoid overfitting, an L2 regularization was used with regularization coefficient set to 0.1, and learning rate was set to 10 −3 . A committee of neural networks is used for providing crop classification and land cover maps for Ukraine using high spatial resolution Landsat 8, Sentinel-1 and Sentinel-2 imagery (Skakun et al 2015;Kussul et al 2016;Kussul et al 2018;Ghazaryan et al 2018) and appropriate in-situ data for 2000, 2010, 2016 and 2017 (Shelestov et al 2017b;Skakun et al 2016;Waldner et al 2016). The spatial resolution of the resulting maps is 30 m for 2000 and 2010, and 10 m for 2016 and 2017 ( Figure 3).…”

Section: Workflow For Calculating Indicator 1531mentioning

confidence: 99%

A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

Kussul

Lavreniuk

Kolotii

et al. 2019

International Journal of Digital Earth

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For evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1-'Forest area as proportion of total land area'; 15.3.1-'Proportion of land that is degraded over total land area'; and 2.4.1-'Proportion of agricultural area under productive and sustainable agriculture'. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows.

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Section: Workflow For Calculating Indicator 1531mentioning

confidence: 99%

A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

Kussul

Lavreniuk

Kolotii

et al. 2019

International Journal of Digital Earth

Self Cite

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“…However, as shown by other studies (Adam et al 2010;Selkowitz 2010), multi-spectral information provides an important add-on and helps decipher fuzzy borders between classes and should be considered in finescale studies and applications (St-Louis et al 2014). Moreover, other remote sensing technologies such as Synthetic Aperture Radar (SAR) help discriminate management practices and could reveal useful in improving our results (Inglada et al 2016;Torbick et al 2016;Kussul et al 2018). Yet, few studies combine multi-spectral information with animal movement data (Remelgado et al 2018) while the use of SAR is non-existent.…”

Section: Discussionmentioning

confidence: 68%

“…; Kussul et al. ). Yet, few studies combine multi‐spectral information with animal movement data (Remelgado et al.…”

Section: Discussionmentioning

confidence: 98%

From ecology to remote sensing: using animals to map land cover

Remelgado

Safi

Wegmann³

2019

Remote Sens Ecol Conserv

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Land cover is a key variable in monitoring applications and new processing technologies made deriving this information easier. Yet, classification algorithms remain dependent on samples collected on the field and field campaigns are limited by financial, infrastructural and political boundaries. Here, animal tracking data could be an asset. Looking at the land cover dependencies of animal behaviour, we can obtain land cover samples over places that are difficult to access. Following this premise, we evaluated the potential of animal movement data to map land cover. Specifically, we used 13 White Storks (Cicona cicona) individuals of the same population to map agriculture within three test regions distributed along their migratory track. The White Stork has adapted to foraging over agricultural lands, making it an ideal source of samples to map this land use. We applied a presence–absence modelling approach over a Normalized Difference Vegetation Index (NDVI) time series and validated our classifications, with high‐resolution land cover information. Our results suggest White Stork movement is useful to map agriculture, however, we identified some limitations. We achieved high accuracies (F1‐scores > 0.8) for two test regions, but observed poor results over one region. This can be explained by differences in land management practices. The animals preferred agriculture in every test region, but our data showed a biased distribution of training samples between irrigated and non‐irrigated land. When both options occurred, the animals disregarded non‐irrigated land leading to its misclassification as non‐agriculture. Additionally, we found difference between the GPS observation dates and the harvest times for non‐irrigated crops. Given the White Stork takes advantage of managed land to search for prey, the inactivity of these fields was the likely culprit of their underrepresentation. Including more species attracted to agriculture – with other land‐use dependencies and observation times – can contribute to better results in similar applications.

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“…DRs can be used to represent certain types of images using a special type of LiRA receptive fields [51], [52], [53]. One of the promising research directions could consist in using DRs in the texture segmentation problem [54], [55], [56] and in classification of satellite optical and SAR images [57], [58], [59].…”

Section: Discussionmentioning

confidence: 99%

Neural Distributed Representations of Vector Data in Intelligent Information Technologies

Gritsenko¹,

Revunova²,

Rachkovskij³

2018

Kibern. vyčisl. teh.

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Introduction. Distributed representation (DR) of data is a form of a vector representation,where each object is represented by a set of vector components, and each vector component can belong to representations of many objects. In ordinary vector representations, the meaning of each component is defined, which cannot be said about DR. However, the similarity of RP vectors reflects the similarity of the objects they represent.DR is a neural network approach based on modeling the representation of information in the brain, resulted from ideas about a "distributed" or "holographic" representations. DRs have a large information capacity, allow the use of a rich arsenal of methods developed for vector data, scale well for processing large amounts of data, and have a number of other advantages. Methods for data transformation to DRs have been developed for data of various types -from scalar and vector to graphs.The purpose of the article is to provide an overview of a part of the work of the Department of Neural Information Processing Technologies (International Center) in the field of neural network distributed representations. The approach is a development of the ideas of Nikolai Mikhailovich Amosov and his scientific school of modeling the structure and functions of the brain.Scope. The formation of distributed representations from the original vector representations of objects using random projection is considered. With the help of the DR, it is possible to efficiently estimate the similarity of the original objects represented by numerical vectors. Gritsenko V.I., Rachkovskij D.A., Revunova E.G. ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 8The use of DR allows developing regularization methods for obtaining a stable solution of discrete ill-posed inverse problems, increasing the computational efficiency and accuracy of their solution, analyzing analytically the accuracy of the solution. Thus DRs allow for increasing the efficiency of information technologies applying them.Conclusions. DRs of various data types can be used to improve the efficiency and intelligence level of information technologies. DRs have been developed for both weakly structured data, such as vectors, and for complex structured representations of objects, such as sequences, graphs of knowledge-base situations (episodes), etc. Transformation of different types of data into the DR vector format allows unifying the basic information technologies of their processing and achieving good scalability with an increase in the amount of data processed.In future, distributed representations will naturally combine information on structure and semantics to create computationally efficient and qualitatively new information technologies in which the processing of relational structures from knowledge bases is performed by the similarity of their DRs. The neurobiological relevance of distributed representations opens up the possibility of creating intelligent information technologies based on them that function similarly to...

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Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery

Cited by 73 publications

References 26 publications

A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

From ecology to remote sensing: using animals to map land cover

Neural Distributed Representations of Vector Data in Intelligent Information Technologies

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