A5082681779- Remote Sensing in Agriculture
- Land Use and Ecosystem Services
- Diverse Scientific Research in Ukraine
- Remote-Sensing Image Classification
- Smart Agriculture and AI
- Land Use and Management
- Remote Sensing and Land Use
- Agriculture and Biological Studies
- Environmental Sustainability and Technology
- Soil and Environmental Studies
- Scientific Research and Studies
- Advanced Vision and Imaging
- Remote Sensing and LiDAR Applications
- Advanced Image Fusion Techniques
- Agriculture Market Analysis Ukraine
- Geochemistry and Geologic Mapping
- Environmental and Biological Research in Conflict Zones
- Soil and Land Suitability Analysis
- Image Processing Techniques and Applications
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Air Quality Monitoring and Forecasting
- Rangeland Management and Livestock Ecology
- Geographic Information Systems Studies
- Industrial Vision Systems and Defect Detection
- Scientific Research Methodologies and Applications
Space Research Institute
2015-2024
State Space Agency of Ukraine
2016-2022
National Academy of Sciences of Ukraine
2016-2022
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
2017-2022
Institute of Physics
2017-2022
Space Research Institute
2021
Taras Shevchenko National University of Kyiv
2015-2016
Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the are unsupervised neural network (NN) used optical imagery segmentation missing data restoration due to clouds shadows, an ensemble supervised NNs. As basic NN architecture, we use traditional fully...
Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale mapping requires processing management of large amount heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective this study is explore efficiency using the Google Earth Engine (GEE) platform when classifying multi-temporal with potential apply for larger (e.g. country level)...
For many applied problems in agricultural monitoring and food security, it is important to provide reliable crop classification maps. Satellite imagery extremely valuable source of data maps a timely way at moderate high spatial resolution. Information on parcel boundaries that takes into account the context may improve quality compared pixel-based approaches. In general, parcels contain several plots with different crops such situations should be taken when using boundaries. this paper, we...
Ukraine is one of the most developed agricultural countries in world. For many applications, it extremely important to provide reliable crop maps taking into account diversity cropping systems used Ukraine. The use optical imagery only limited due cloud cover, and previous studies showed particular difficulties discriminating summer crops such as maize, soybeans, sunflower, sugar beet. This paper focuses on exploring feasibility assessing efficiency using multitemporal satellite...
Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing mapping methodologies over contrasting Joint Experiment Crop Assessment and Monitoring (JECAM) sites medium to large average field size using the time series 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red near-infrared channels). Different strategies were devised assess accuracy classification methods: confusion matrices derived indicators...
For evaluating the progresses towards achieving Sustainable Development Goals (SDGs), a global indicator framework was developed by UN Inter-Agency and Expert Group on Indicators. In this paper, we propose an improved methodology set of workflows for calculating SDGs indicators. The main improvements consist using moderate high spatial resolution satellite data state-of-the-art deep learning land cover classification assessing productivity. Within European Network Observing our Changing...
Accurate classification and mapping of crops is essential for supporting sustainable land management. Such maps can be created based on satellite remote sensing; however, the selection input data optimal classifier algorithm still needs to addressed especially areas where field scarce. We exploited intra-annual variation temporal signatures remotely sensed observations used prior knowledge crop calendars development a two-step processing chain classification. First, Landsat-based time-series...
For accurate crop classification, it is necessary to use time-series of high-resolution satellite data better discriminate among certain types. This task brings the following challenges: a large amount for download, Big processing and computational resources utilization state-of-the-art classification approaches. solving these problems, we have developed an automated workflow, which based on machine-learning techniques. By deployment workflow cloud platform, can overcome challenges...
Along the season crop classification maps based on satellite data is a challenging task for countries with large diversity of agricultural crops different phenology (crop calendars). In this paper, we investigate feasibility delivering early and along specific using available free over multiple years, including Landsat-8, Sentinel-1 Sentinel-2. For study, test site in Kyiv region (Ukraine) selected, which have been collecting ground types every year since 2011. Crop type are generated...
Abstract. One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is presence clouds and shadows that result having missing values data sets. In this paper, a new approach to classification multi-temporal satellite imagery due proposed. First, self-organizing Kohonen maps (SOMs) are used restore pixel time series imagery. SOMs trained each spectral band separately using nonmissing values. Missing restored through special procedure substitutes input...
Abstract. Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims assessing the efficiency (in terms of prediction error minimization) satellite biophysical model based predictors assimilation into winter models different (region, county field) for one regions in central part Ukraine. Vegetation index NDVI, as well parameters (LAI fAPAR) derived from data WOFOST growth are considered model. Due to very short time series...
For many applied problems in agricultural monitoring and food security it is important to provide reliable crop classification maps national or global scale. Large amount of satellite data for large scale mapping generate a "Big Data" problem. The main idea this paper was comparison pixel-based approaches Ukraine exploring efficiency the Google Earth Engine (GEE) cloud platform solving problem providing high resolution map territory. study carried out Joint Experiment Crop Assessment...
In the paper we propose methodology for solving large scale classification and area estimation problems in remote sensing domain on basis of deep learning paradigm. It is based a hierarchical model that includes self-organizing maps (SOM) data preprocessing segmentation (clustering), ensemble multi-layer perceptrons (MLP) heterogeneous fusion geospatial analysis post-processing. The proposed applied generation high resolution land cover use territory Ukraine from 1990 to 2010 2015.
While people are aware that there is a continuing conflict in Ukraine, little understanding of its impact. The military South-Eastern Ukraine has been on-going since 2014, with major socio-economic impact on the Donetsk and Luhansk regions. In this study, we quantify land cover use changes those regions related to cropland changes. Cropland areas account for almost 50% regions, declining industry between 2014–2017, role agriculture regional economy increased. We freely available satellite...
Crop rotation is an important determining factor of crop productivity. Sustainable agriculture requires correct rules rotation. Failure to comply with these can lead deterioration soil biochemical characteristics and land degradation. In Ukraine as well in many other countries, sunflower monocropping common practice the impact this fact should be studied find most precise save economic potential minimize degradation factors. This research provides evaluation effect on vegetation indices...
In this paper we propose a new methodology to automatically generate retrospective high resolution land cover maps on regular basis for the whole territory of Ukraine. An ensemble neural networks, in particular multilayer perceptrons (MLPs), is used multi-temporal Landsat-4/5/7 satellites imagery classification with previously restored missing data due clouds, shadows and non-regular coverage. This was obtain Ukraine three decades, namely 1990s, 2000s 2010s, overall accuracy more than 97%.
In this paper, we propose a new approach to pixel and parcel-based classification of multi-temporal optical satellite imagery. We first restore missing data due clouds shadows based on vector raster fusion in different phases methodology. Pixel-based maps are derived from an ensemble neural networks, particular multilayer perceptrons (MLPs). The proposed is applied for regional scale crop using Landsat-8 images the JECAM site Kyivska oblast Ukraine 2013. obtained results area estimates also...
The accurate delineation of agricultural field boundaries from satellite imagery is vital for land management and crop monitoring. However, current methods face challenges due to limited dataset sizes, resolution discrepancies, diverse environmental conditions. We address this by reformulating the task as instance segmentation introducing Field Boundary Instance Segmentation - 22M (FBIS-22M), a large-scale, multi-resolution comprising 672,909 high-resolution image patches (ranging 0.25 m 10...
There is a growing recognition of the interdependencies among supply systems that rely upon food, water and energy. Billions people lack safe sufficient access to these systems, coupled with rapidly global demand increasing resource constraints. Modeling frameworks are considered one few means available understand complex interrelationships sectors, however development nexus related has been limited. We describe three open-source models well known in their respective domains (i.e. TerrSysMP,...
For many applied problems in agricultural monitoring and food security, it is important to provide reliable crop classification maps. In this paper, we aim compare performance of different filters available ESA SNAP toolbox them with our approach applying reduce speckle multitemporal synthetic-aperture radar (SAR) Sentinel-1 imagery. this, evaluate an impact SAR data filtering on accuracy. We have found that overall accuracy without any 82.6% whilst the use despeckling methods achieves gain...