scholarly journals МОНІТОРИНГ СІЛЬСЬКОГОСПОДАРСЬКИХ КУЛЬТУР ІЗ ЗАСТОСУВАННЯМ КОСМІЧНИХ ЗНІМКІВ SENTINEL-2

2019 ◽  
pp. 99-108
Author(s):  
Максим В’ячеславович Марюшко ◽  
Руслан Едуардович Пащенко ◽  
Наталія Сергіївна Коблюк
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
European Space Agency ◽  
Remote Sensing Data ◽  
Space Images ◽  
Space Imagery ◽  
Space Agency ◽  
Vegetation Phase ◽  
Sentinel 2

The subject of the study in the article is the growing need for the use of spatial information for efficient agricultural production, due to the growing tendency of Earth remote sensing data accessibility, which, due to the spatial and temporal resolution improvement, can be used in the land cover analysis and other related jobs. The goal is to review the obtaining process of satellite multispectral space imagery from Sentinel-2 and to consider the possibility of their use for monitoring crops during the entire vegetation phase. The tasks: to study the modern needs of agricultural producers in the field of analysis of land cover occupied by agricultural crops; the analysis of the European Space Agency programs and the global land program Copernicus, which uses spatial information from Sentinel-2 for use in the agricultural sector; estimation of the constellation characteristics of Sentinel-2, imaging equipment and remote sensing data processing results by ground services received from Internet services; the use of Sentinel-2 multispectral space imagery for monitoring crops during the entire vegetation phase. The following results were obtained. After analyzing agricultural producers needs and the European Space Agency program, the feasibility of using multispectral space images taken by the Multispectral Instrument installed on satellites Sentinel-2 was established. Free access to the space imagery database is provided through the Copernicus Open Access Hub Internet Service. For the researched territory, Poltava region, Chutov district, the village of Vilkhovatka, various time space images were obtained and the normalized difference vegetation index (NDVI) was calculated. Histogram analysis of the obtained vegetation index values distribution within a single field (corn to grain) allows to reveal a quantitative and qualitative change in biomass, indicating a change in the vegetative phase. Conclusions. The approach described in this paper allows to conduct monitoring of the cropping state during the vegetation phase using both qualitative – visual analysis and quantitative – NDVI index, criteria. The change in the values of the normalized difference vegetation index can reveal a change in the biomass state. However, for calculating the NDVI index, data from near-infrared and red channels is needed, which complicates the acquisition of the original image. Therefore, in order to obtain the quantitative criteria in subsequent jobs, it is expedient to consider the possibility of using fractal dimension, which will reduce the amount of input data required for calculations.

scholarly journals IDENTIFICATION OF DROUGHT EXTENT BASED ON VCI USING SENTINEL DATA: A CASE STUDY OF THE EASTERN OF IAŞI COUNTY

2019 ◽  
Vol 13 (2) ◽  
pp. 179-186
Author(s):  
Paul Macarof ◽  
Florian Statescu ◽  
Cristian Iulian Birlica ◽  
Paul Gherasim
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
European Space Agency ◽  
Condition Index ◽  
Landsat 8 ◽  
Natural Phenomena ◽  
Space Agency ◽  
Vegetation Condition Index ◽  
Sentinel 2

In this study was analyzed zones affected by drought using Vegetation Condition Index (VCI), that is based on Normalized Difference Vegetation Index (NDVI). This fact, drought, is one of the most wide -spread and least understood natural phenomena. In this paper was used remote sensing (RS) data, kindly provided by The European Space Agency (ESA), namely Sentinel-2 (S-2) Multispectral Instrument (MSI) and wellkonwn images Landsat 8 Operational Land Imager (OLI). The RS images was processed in SNAP and ArcMap. Study Area, was considered the eastern of Iasi county. The main purpose of paper was to investigating if Sentinel images can be used for VCI analysis.

scholarly journals Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

2018 ◽  
Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Jan Musial ◽  
Alicja Malinska ◽  
Maria Budzynska ◽  
Radoslaw Gurdak ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Incidence Angle ◽  
Temporal Frequency ◽  
European Space Agency ◽  
Wetland Ecosystems ◽  
Space Agency ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Soil moisture (SM) plays an essential role in environmental studies related to wetlands, an ecosystem sensitive to climate change. Hence, there is the need for its constant monitoring. SAR (Synthetic Aperture Radar) satellite imagery is the only mean to fulfill this objective regardless of the weather. The objective of the study was to develop the methodology for SM retrieval under wetland vegetation using Sentinel-1 (S-1) satellite data. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in northeastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The NDVI (Normalized Difference Vegetation Index) was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to soil moisture retrieval for a broad range of NDVI values and soil moisture conditions. The new methodology is based on research into the effect of vegetation on backscatter () changes under different soil moisture and vegetation (NDVI) conditions. It was found that the state of the vegetation may be described by the difference between  VH and  VV, or the ratio of  VV/VH, as calculated from the Sentinel-1 images. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the new developed models and includes the derived indices based on S-1, allowed the estimation of SM for peatlands with reasonable accuracy (RMSE ~ 10 vol. %). Due to the temporal frequency of the two S-1 satellites’ (S-1A and S-1B) acquisitions, it is possible to monitor SM changes every six days. The conclusion drawn from the study emphasizes a demand for the derivation of specific soil moisture retrieval algorithms that are suited for wetland ecosystems, where soil moisture is several times higher than in agricultural areas.

scholarly journals Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 1979 ◽  
Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Jan Musial ◽  
Alicja Malinska ◽  
Maria Budzynska ◽  
Radoslaw Gurdak ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Incidence Angle ◽  
European Space Agency ◽  
Wetland Ecosystems ◽  
Space Agency ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

The objective of the study was to estimate soil moisture (SM) from Sentinel-1 (S-1) satellite images acquired over wetlands. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in north-eastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to SM retrieval for a broad range of vegetation and soil moisture conditions. The methodology is based on research into the effect of vegetation on backscatter (σ°) changes under different soil moisture and Normalized Difference Vegetation Index (NDVI) values. The NDVI was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. It was found that the state of the vegetation expressed by NDVI can be described by the indices such as the difference between σ° VH and VV, or the ratio of σ° VV/VH, as calculated from the Sentinel-1 images in the logarithmic domain. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the newly developed models using Water Cloud Model (WCM) that includes the derived indices based on S-1, allowed the estimation of SM for wetlands with reasonable accuracy (10 vol. %). The developed soil moisture retrieval algorithms based on S-1 data are suited for wetland ecosystems, where soil moisture values are several times higher than in agricultural areas.

scholarly journals MONITORING MODEL OF LAND COVER CHANGE FOR THE INDICATION OF DEVEGETATION AND REVEGETATION USING SENTINEL-2

2021 ◽  
Vol 17 (2) ◽  
pp. 163
Author(s):  
Samsul Arifin ◽  
Tatik Kartika
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Environmental Sustainability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Threshold Value ◽  
Monitoring Model ◽  
Land Changes ◽  
Sentinel 2

IInformation on land cover change is very important for various purposes, including the monitoring of changes for environmental sustainability. The objective of this study is to create a monitoring model of land cover change for the indication of devegetation and revegetation usingdata fromSentinel-2 from 2017 to 2018 of the Brantas watershed.This is one of the priority watersheds in Indonesia, so it is necessary to observe changes in its environment, including land cover change. Such change can be detected using remote sensing data. The method used is a hybrid between Normalized Difference Vegetation Index(NDVI) and Normalized Burn Ratio (NBR) which aims to detect land changes with a focus on devegetationand revegetation by determining the threshold value for vegetation index (ΔNDVI) and open land index (ΔNBR).The study found that the best thresholds to detect revegetation were ΔNDVI > 0.0309 and ΔNBR < 0.0176 and to detect devegetation ΔNDVI < -0.0206 and ΔNBR > 0.0314.It is concluded that Sentinel-2 data can be used to monitor land changes indicating devegetation and revegetation with established NDVI and NBR threshold conditions.

scholarly journals Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1546
Author(s):  
David Hernández-López ◽  
Laura Piedelobo ◽  
Miguel A. Moreno ◽  
Amal Chakhar ◽  
Damián Ortega-Terol ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Full Potential ◽  
Combined Use ◽  
Nested Grid ◽  
Sentinel 2

Earth Observation (EO) imagery is difficult to find and access for the intermediate user, requiring advanced skills and tools to transform it into useful information. Currently, remote sensing data is increasingly freely and openly available from different satellite platforms. However, the variety of images in terms of different types of sensors, spatial and spectral resolutions generates limitations due to the heterogeneity and complexity of the data, making it difficult to exploit the full potential of satellite imagery. Addressing this issue requires new approaches to organize, manage, and analyse remote-sensing imagery. This paper focuses on the growing trend based on satellite EO and the analysis-ready data (ARD) to integrate two public optical satellite missions: Landsat 8 (L8) and Sentinel 2 (S2). This paper proposes a new way to combine S2 and L8 imagery based on a Local Nested Grid (LNG). The LNG designed plays a key role in the development of new products within the European EO downstream sector, which must incorporate assimilation techniques and interoperability best practices, automatization, systemization, and integrated web-based services that will potentially lead to pre-operational downstream services. The approach was tested in the Duero river basin (78,859 km2) and in the groundwater Mancha Oriental (7279 km2) in the Jucar river basin, Spain. In addition, a viewer based on Geoserver was prepared for visualizing the LNG of S2 and L8, and the Normalized Difference Vegetation Index (NDVI) values in points. Thanks to the LNG presented in this paper, the processing, storage, and publication tasks are optimal for the combined use of images from two different satellite sensors when the relationship between spatial resolutions is an integer (3 in the case of L8 and S2).

scholarly journals Analysis of moisture conditions in the lowland areas using high resolution spectral data from the Sentinel-2 satellite and the GIS tools

2018 ◽  
Vol 44 ◽  
pp. 00048 ◽  
Author(s):  
Henryk Grzywna ◽  
PaweŁ B. Dąbek ◽  
Beata Olszewska
Keyword(s):  
Spectral Data ◽  
Vegetation Index ◽  
European Space Agency ◽  
Agricultural Drought ◽  
Spatial And Temporal Variability ◽  
Space Agency ◽  
Plant Environment ◽  
Gis Tools ◽  
Moisture Conditions ◽  
Sentinel 2

Research concerning agricultural drought issue mainly focuses on the methods based on long-term atmospheric data, temperature, precipitation and evaporation measurements. On the other hand, the scientific bibliography shows the possibilities of using spectral data for description the state of plants. The general availability and increasing resolution of the spectral and temporal data create a chance for monitoring and forecasting deficiencies of soil moisture based on spectral images. Paper presents the results of analysis of the moisture conditions in soil-plant environment in the lowland catchment area using the spectral data obtained from the Sentinel-2 European Space Agency satellite for period February-November 2016. These spectral data were used for the calculation the Normalized Differential Vegetation Index (NDVI) which provided information about moisture conditions in the soil-plant environment. Then, the values of NDVI index were compared with the data obtained from the field investigations. The analyses have showed the spatial and temporal variability of moisture conditions in the soil-plant environment determined on the background of the spectral indicators and the existence of some dependences between climatic and spectral indicators characterizing soil-plant environment.

scholarly journals Sentinel-2 Imagery Monitoring Vine Growth Related to Topography in a Protected Designation of Origin Region

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 785
Author(s):  
Dimitrios Tassopoulos ◽  
Dionissios Kalivas ◽  
Rigas Giovos ◽  
Nestor Lougkos ◽  
Anastasia Priovolou
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Climatic Conditions ◽  
Vine Growth ◽  
Protected Designation Of Origin ◽  
Growth Differences ◽  
Sentinel 2

Remote sensing satellite platforms provide accurate temporal and spatial information useful in viticulture with an increasing interest in their use. This study aims to identify the possibilities of freely available and with frequent revisit time Sentinel-2 satellites, to monitor vine growth at regional scale on a vine-growing Protected Designation of Origin (PDO) zone during the growing season of the year 2019. This study aims to: (i) investigate through several Vegetation Indices (VIs) the vine growth differences across the zone and relations with topographic parameters; (ii) identify VIs that best recognize differences on subzones of different climatic conditions; (iii) explore the effectiveness of the Sentinel-2 data monitoring management applications. A total of 27 vineyards were selected for field and satellite data collection. Several VIs have been calculated per vineyard from a 20-date time series dataset. VIs showed high negative correlation with topographic parameter of elevation on the flowering stage. The analysis of variance between the VIs of the subzones showed that these regions have statistically significant differences, that most VIs can expose on the flowering and harvest stage, and only Normalized Difference Vegetation Index (NDVI) and VIs using Red-Edge bands during the veraison period. Sentinel-2 data show great effectiveness on monitoring management applications (tillage and trimming).

scholarly journals REMOTE SENSING DATA IN MUNICIPAL GOVERNMENT

2021 ◽  
Vol 30 (4) ◽  
Author(s):  
Jaroslav Nýdrle
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Information ◽  
Relevant Literature ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Sentinel 2

This article focuses on the issue of using data obtained through remote sensing methods  in the administrative district of the municipality with extended powers of Liberec (the Czech Republic). The first part of the article discusses the question of using Earth remote sensing data for city agendas in general. Then, it presents a questionnaire, created for evaluating the needs of the Liberec municipality. This questionnaire, focusing on the use of remotely sensed data, was created on the basis of a review of relevant literature. Based on the results of the questionnaire, the following spatial information requirements were chosen to be addressed: land surface temperature map - LST (Landsat 8), vegetation index - NDVI (Sentinel 2, Planet Scope), normalized difference water index - NDWI, NDWI 2 (Sentinel 2), normalized difference built-up index - NDBI (Sentinel 2). All data obtained during the creation of this study have become part of the database of the Urban Planning and GIS Department and are available to employees of the City of Liberec.

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

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