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Academic literature on the topic 'Sentinel-2 imagery'

Author: Grafiati

Published: 21 February 2023

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Journal articles on the topic "Sentinel-2 imagery"

Gašparović, M., D. Medak, I. Pilaš, L. Jurjević, and I. Balenović. "FUSION OF SENTINEL-2 AND PLANETSCOPE IMAGERY FOR VEGETATION DETECTION AND MONITORING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 155–60. http://dx.doi.org/10.5194/isprs-archives-xlii-1-155-2018.

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Abstract. Different spatial resolutions satellite imagery with global almost daily revisit time provide valuable information about the earth surface in a short time. Based on the remote sensing methods satellite imagery can have different applications like environmental development, urban monitoring, etc. For accurate vegetation detection and monitoring, especially in urban areas, spectral characteristics, as well as the spatial resolution of satellite imagery is important. In this research, 10-m and 20-m Sentinel-2 and 3.7-m PlanetScope satellite imagery were used. Although in nowadays research Sentinel-2 satellite imagery is often used for land-cover classification or vegetation detection and monitoring, we decided to test a fusion of Sentinel-2 imagery with PlanetScope because of its higher spatial resolution. The main goal of this research is a new method for Sentinel-2 and PlanetScope imagery fusion. The fusion method validation was provided based on the land-cover classification accuracy. Three land-cover classifications were made based on the Sentinel-2, PlanetScope and fused imagery. As expected, results show better accuracy for PS and fused imagery than the Sentinel-2 imagery. PlanetScope and fused imagery have almost the same accuracy. For the vegetation monitoring testing, the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and fused imagery was calculated and mutually compared. In this research, all methods and tests, image fusion and satellite imagery classification were made in the free and open source programs. The method developed and presented in this paper can easily be applied to other sciences, such as urbanism, forestry, agronomy, ecology and geology.

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Dalla Marta, Anna, Giovanni Battista Chirico, Salvatore Falanga Bolognesi, et al. "Integrating Sentinel-2 Imagery with AquaCrop for Dynamic Assessment of Tomato Water Requirements in Southern Italy." Agronomy 9, no. 7 (2019): 404. http://dx.doi.org/10.3390/agronomy9070404.

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A research study was conducted in an open field tomato crop in order to: (i) Evaluate the capability of Sentinel-2 imagery to assess tomato canopy growth and its crop water requirements; and (ii) explore the possibility to predict crop water requirements by assimilating the canopy cover estimated by Sentinel-2 imagery into AquaCrop model. The pilot area was in Campania, a region in the south west of Italy, characterized by a typical Mediterranean climate, where field campaigns were conducted in seasons 2017 and 2018 on processing tomato. Crop water use and irrigation requirement were estimated by means of three different methods: (i) The AquaCrop model; (ii) an irrigation advisory service based on Sentinel-2 imagery known as IRRISAT and (iii) assimilating the canopy cover estimated by Sentinel-2 imagery into AquaCrop model Sentinel-2 imagery proved to be effective for monitoring canopy growth and for predicting irrigation water requirements during mid-season stage of the crop, when the canopy is fully developed. Conversely, the integration of the Sentinel-2 imagery with a crop growth model can contribute to improve the irrigation water requirement predictions in the early and development stage of the crop, when the soil evaporation is not negligible with respect to the total evapotranspiration.

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Shepherd, James D., Jan Schindler, and John R. Dymond. "Automated Mosaicking of Sentinel-2 Satellite Imagery." Remote Sensing 12, no. 22 (2020): 3680. http://dx.doi.org/10.3390/rs12223680.

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Repeat frequencies of optical remote sensing satellites have been increasing over the last 40 years, but there is still dependence on clear skies to acquire usable imagery. To increase the quality of data, composited mosaics of satellite imagery can be used. In this paper, we develop an automated method for clearing clouds and producing different types of composited mosaics suitable for use in cloud-affected countries, such as New Zealand. We improve the Tmask algorithm for cloud detection by using a parallax method to produce an initial cloud layer and by using an object-based cloud and shadow approach to remove false cloud detections. We develop several parametric scoring approaches for choosing best-pixel composites with minimal remaining cloud. The automated mosaicking approach produced Sentinel-2 mosaics of New Zealand for five successive summers, 2015/16 through 2019/20, with remaining cloud being less than 0.1%. Contributing satellite overpasses were typically of the order of 100. In comparison, manual methods for cloud clearing produced mosaics with 5% remaining cloud and from satellite overpasses typically of the order of 20. The improvements to cloud clearing enable the use of all possible Sentinel-2 imagery to produce automatic mosaics capable of regular land monitoring, at a reasonable cost.

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Traganos, Dimosthenis, and Peter Reinartz. "Mapping Mediterranean seagrasses with Sentinel-2 imagery." Marine Pollution Bulletin 134 (September 2018): 197–209. http://dx.doi.org/10.1016/j.marpolbul.2017.06.075.

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Han, Dong, Shuaibing Liu, Ying Du, et al. "Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery." Sensors 19, no. 18 (2019): 4013. http://dx.doi.org/10.3390/s19184013.

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This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites.

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Ayala, C., C. Aranda, and M. Galar. "TOWARDS FINE-GRAINED ROAD MAPS EXTRACTION USING SENTINEL-2 IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021 (June 17, 2021): 9–14. http://dx.doi.org/10.5194/isprs-annals-v-3-2021-9-2021.

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Abstract. Nowadays, it is highly important to keep road maps up-to-date since a great deal of services rely on them. However, to date, these labours have demanded a great deal of human attention due to their complexity. In the last decade, promising attempts have been carried out to fully-automatize the extraction of road networks from remote sensing imagery. Nevertheless, the vast majority of methods rely on aerial imagery (< 1 m), whose costs are not yet affordable for maintaining up-to-date maps. This work proves that it is also possible to accurately detect roads using high resolution satellite imagery (10 m). Accordingly, we have relied on Sentinel-2 imagery considering its freely availability and the higher revisit times compared to aerial imagery. It must be taken into account that the lack of spatial resolution of this sensor drastically increases the difficulty of the road detection task, since the feasibility to detect a road depends on its width, which can reach sub-pixel size in Sentinel-2 imagery. For that purpose, a new deep learning architecture which combines semantic segmentation and super-resolution techniques is proposed. As a result, fine-grained road maps at 2.5 m are generated from Sentinel-2 imagery.

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Howe, Alexander A., Sean A. Parks, Brian J. Harvey, Saba J. Saberi, James A. Lutz, and Larissa L. Yocom. "Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America." Remote Sensing 14, no. 20 (2022): 5249. http://dx.doi.org/10.3390/rs14205249.

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Accurate assessment of burn severity is a critical need for an improved understanding of fire behavior and ecology and effective post-fire management. Although NASA Landsat satellites have a long history of use for remotely sensed mapping of burn severity, the recently launched (2015 and 2017) European Space Agency Sentinel-2 satellite constellation offers increased temporal and spatial resolution with global coverage, combined with free data access. Evaluations of burn severity derived from Landsat and Sentinel generally show comparable results, but these studies only assessed a small number of fires with limited field data. We used 912 ground calibration plots from 26 fires that burned between 2016 and 2019 in western North America to compare Sentinel- and Landsat-derived burn severity estimates with the field-based composite burn index. We mapped burn severity using two methods; the well-established paired scene approach, in which a single pre- and post-fire scene are selected for each fire, and also a mean image compositing approach that automatically integrates multiple scenes using the cloud-based remote sensing platform Google Earth Engine. We found that Sentinel generally performed as well or better than Landsat for four spectral indices of burn severity, particularly when using atmospherically corrected Sentinel imagery. Additionally, we tested the effects of mapping burn severity at Sentinel’s finer spatial resolution (10 m) on estimates of the spatial complexity of stand-replacing fire, resulting in a 5% average reduction per-fire in area mapped as high-severity patch interiors (24,273 ha total) compared to mapping at the resolution of Landsat (30 m). These findings suggest Sentinel may improve ecological discrimination of fine-scale fire effects, but also warrant caution when comparing estimates of burn severity spatial patterns derived at different resolutions. Overall, these results indicate that burn severity mapping will benefit substantially from the integration of Sentinel imagery through increased imagery availability, and that Sentinel’s higher spatial resolution improves opportunities for examining finer-scale fire effects across ecosystems.

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Su Wei, 苏伟, 张明政 Zhang Mingzheng, 蒋坤萍 Jiang Kunping, 朱德海 Zhu Dehai, 黄健熙 Huang Jianxi, and 王鹏新 Wang Pengxin. "Atmospheric Correction Method for Sentinel-2 Satellite Imagery." Acta Optica Sinica 38, no. 1 (2018): 0128001. http://dx.doi.org/10.3788/aos201838.0128001.

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Hu, Bin, Yongyang Xu, Xiao Huang, et al. "Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery." ISPRS International Journal of Geo-Information 10, no. 8 (2021): 533. http://dx.doi.org/10.3390/ijgi10080533.

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Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery.

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Dobrinić, D., D. Medak, and M. Gašparović. "INTEGRATION OF MULTITEMPORAL SENTINEL-1 AND SENTINEL-2 IMAGERY FOR LAND-COVER CLASSIFICATION USING MACHINE LEARNING METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 91–98. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-91-2020.

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Abstract. Using space-borne remote sensing data is widely used for land-cover classification (LCC) due to its ability to provide a big amount of data with a regular temporal revisit time. In recent years, optical and synthetic aperture radar (SAR) imagery have become available for free, and their integration in time series have improved LCC. This research evaluates the classification accuracy using multitemporal (MT) Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Pixel-based LCC is made for S1 and S2 imagery, and for a combination of both datasets with Random Forest (RF) and Extreme Gradient Boosting (XGBoost; XGB). The extent of the study area, is located in the south-east of France, in Lyon. Regardless of LCC using single-date or MT data, the highest classification results were achieved with integrated S1 and S2 imagery and XGB method, whereas overall accuracy (OA) and Kappa coefficient (Kappa) increased from 85.51% to 91.09%, and from 0.81 to 0.88, respectively. Furthermore, the integration of MT imagery significantly improved the classification of urban areas and reduced misclassification between forest and low vegetation. In this paper, in terms of the pixel-based classification, XGB produced slightly better results than RF, and outperformed it in terms of computational time. This research improved LCC with integration of radar and optical MT imagery, which can be useful for areas hampered by a frequent cloud cover. Future work should use the aforementioned data for specific applications in remote sensing, as well as evaluate the classification performance with different approaches, such as neural networks or deep learning.

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Dissertations / Theses on the topic "Sentinel-2 imagery"

Sangiorgi, Nicola <1991&gt. "Use of Sentinel-2 satellite imagery for forest site evaluation and forest harvesting detection." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amsdottorato.unibo.it/9437/1/Tesi_dottorato_Nicola_Sangiorgi_reviewed.pdf.

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The access to Earth Observation data leaded researcher to a different point of view in the forest sector. Immediately tropical forest deforestation drawn the majority of interests (Perbet et al., 2019; Tang et al., 2019; Shimizu et al., 2017; Asner et al., 2009), heading to the development of many different tools for tropical forest monitoring. This study was focused on the application of satellite remote sensing data (derived from Sentinel-2) to two cardinal aspect for Italian forest. Since wood production plays a key role in developing a rural economy and stimulating the use of sustainable raw material, an increment of Douglas-fir plantation is desirable because of his great growth potential. Therefore, it was necessary to investigate good indices in order to assess the Douglas-fir land suitability and fertility indices. Empirical models were developed and validated using different sets of variables derived from remote sensing data and field survey. Models validation reached good results for Site Index ranging from 0.63 to 0.97 R2 and Current Annual Increment ranging from 0.50 to 0.98 R2. Furthermore, remote sensing data were applied to calibrate and validate different approaches for forest change detection. Knowing where and when forest harvests are done is crucial for correctly applying sustainable forest management and for controlling illegal logging. In this study was demonstrated that there are already tools developed in tropical forest that they could be applied to Italian forest. The best method was the basic one, which uses only summer images avoiding the seasonal noise problem in the time series but losing near-real time ability. If the temporal accuracy is essential the best method for removing time series seasonality resulted the harmonic model fitting, but further analyses are needed expanding the validation area in order to corroborate these results.

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Skogsmo, Markus. "A Scalable Approach for Detecting Dumpsites using Automatic Target Recognition with Feature Selection and SVM through Satellite Imagery." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-418792.

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Throughout the world, there is a great demand to map out the increasing environmental changes and life habitats on Earth. The vast majority of Earth Observations today, are collected using satellites. The Global Watch Center (GWC) initiative was started with the purpose of producing a global situational awareness of the premises for all life on Earth. By collecting, studying and analyzing vast amounts of data in an automatic, scalable and transparent way, the GWC aims are to work towards reaching the United Nations (UN) Sustainable Development Goals (SDG). The GWC vision is to make use of qualified accessible data together with leading organizations in order to lay the foundation of the important decisions that have the biggest potential to make an actual difference for the common awaited future. As a show-case for the initiative, the UN strategic department has recommended a specific use-case, involving mapping large accumulation of waste in areas greatly affected, which they believe will profit the initiative very much. This Master Thesis aim is, in an automatic and scalable way, to detect and classify dumpsites in Kampala, the capital of Uganda, by using available satellite imagery. The hopes are that showing technical feasibility and presenting interesting remarks will aid in spurring further interest in coming closer to a realization of the initiative. The technical approach is to use a lightweight version of Automatic Target Recognition. This is conventionally used in military applications but is here used, to detect and classify features of large accumulations of solid-waste by using techniques from the field of Image Analysis and Data Mining. Choice of data source, this study's area of interest as well as choice of methodology for Feature Extraction and choice of the Machine Learning algorithm Support Vector Machine will all be described and implemented. With a classification precision of 95 percent will technical results be presented, with the ambition to promote further work and contribute to the GWC initiative with valuable information for later realization.

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Cavonius, Johansson Hanna, and Jens Henriksson. "Skattning av skogliga variabler genom satellitbilder från Sentinel 2 : Estimation of forest variables using satellite images from Sentinel 2." Thesis, Linnéuniversitetet, Institutionen för skog och träteknik (SOT), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-94004.

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Stora arealer skog behöver övervakas. Att göra detta på ett kostnadseffektivt sätt är något som skogssektorn efterfrågar. Syftet med studien var att undersöka möjligheten att skatta skogliga variabler med satellitbilder från Sentinel 2. Korrelationen mellan granskogens uppmätta reflektans i satellitbilder från Sentinel 2 och uppmätta variablerna i fält har beräknats och analyserats. Resultatet visar att styrkan i korrelation skiljer sig mellan olika rumsliga upplösningar, vilken tid på året satellitbilderna är tagna, vilka spektrala band och vegetationsindex som används samt vilka skogliga variabler som avses uppskattas. Att använda enskilda satellitbilders värden från Sentinel 2 ger inte tillräckligt tillförlitliga data för att uppskatta skogliga variabler.

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Marques, Francisco José Mendonça. "Utilidade agronómica dos índices NDVI e NDWI obtidos por imagem dos satélites Sentinel - 2: estudos de caso nas culturas de trigo, brócolo e arroz." Master's thesis, Universidade de Évora, 2018. http://hdl.handle.net/10174/24272.

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Este estudo analisa a potencial utilidade agronómica dos satélites Sentinel-2 nas culturas do Trigo, Brócolo e Arroz, através de técnicas de detecção remota, recorrendo aos índices NDVI (Índice de Vegetação por Diferença Normalizada) e NDWI (Índice de Diferença Normalizada da Água). Para as culturas do Trigo e do Brócolo, instaladas na zona de Évora, recorreu-se a imagens de satélite (NDVI e NDWI), em tempo real, para auxiliar na identificação de áreas com maior e menor desenvolvimento vegetativo. Efectuaram-se ainda medições no campo, ao nível da fisiologia das plantas, bem como da sua capacidade produtiva. A cultura do arroz, instalada na zona de Coruche, foi estudada através do histórico de imagens NDVI e NDWI. Os resultados permitiram relacionar nos casos das culturas cerealíferas, diferentes padrões de clorofila com condições edáficas distintas, explicando os distintos níveis de produtividade. No caso do brócolo foi possível associar padrões de desenvolvimento a factores edafo-climáticos e culturais; Agronomic utility of NDVI and NDWI indices obtained through Sentinel- 2 satellite images: study cases in Wheat, Broccoli and Rice Crops ABSTRACT: This study analyzes the potential agronomic utility of Sentinel-2 satellites in Wheat, Broccoli and Rice crops through remote sensing techniques using NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) indices. Wheat and Broccoli crops (Évora area), satellite images (NDVI and NDWI) were used in real time to help identify areas with higher and lower vegetative development. In these crops, measurements were also made in the field, at physiology plant level, as well as productive capacity level. Rice crop (Coruche area) was studied through NDVI and NDWI images historical. The results allowed to associate different chlorophyll patterns with distinct soil conditions in Rice and Wheat crops, explaining the different yield levels. In Broccoli, the use of Sentinel-2 helped in establishing a relation between crop development, climatic and soil-related factors and farming procedures.

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Saavedra, Nuno José Cardoso. "Avaliação do potencial das imagens dos Satélites Sentinel 2 na monitorização do cumprimento de alguns dos requisitos da PAC." Master's thesis, Universidade de Évora, 2020. http://hdl.handle.net/10174/27710.

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A Política Agrícola Comum (PAC) é um sistema de ajudas e programas de apoio aos agricultores da União Europeia (UE). Para receber apoios no âmbito das medidas da PAC, cada agricultor deve, anualmente, submeter à autoridade nacional competente, em Portugal o Instituto de Financiamento da Agricultura e Pescas, I.P. (IFAP), um formulário com os dados referentes à sua exploração. Posteriormente estes dados são sujeitos a ações de controlo administrativo e em alguns casos a inspeções físicas no terreno. De acordo com as atuais regras da PAC, cada estado membro deve, segundo uma análise de risco, realizar uma série de inspeções físicas no terreno de modo a garantir que os critérios de elegibilidade e os compromissos assumidos são cumpridos. Com o objetivo de simplificar e modernizar a Política Agrícola Comum, a Comissão Europeia, em 22 de maio de 2018, adotou novas regras que permitem o uso dos dados dos satélites Sentinel do programa Copernicus, como evidência principal na verificação do cumprimentos de alguns dos requisitos das ajudas da PAC. Pretende-se que as evidências digitais obtidas remotamente reduzam significativamente o número de inspeções físicas no terreno, diminuindo os custos e a burocracia associada ao processo de controlo. Neste estudo é proposta uma abordagem que pretende avaliar o potencial dos dados das imagens do satélite Sentinel 2 para confirmar, com recurso a um índice de vegetação simples (NDVI, “Normalised Difference Vegetation Index”), a presença da cultura de milho numa amostra de parcelas fornecidas pelo IFAP. A confirmação no terreno da presença de um determinado tipo de cultura, é fundamental para concluir da elegibilidade dessas áreas a determinada ajuda ou regime de apoio. Os resultados obtidos demonstraram que a aplicação de uma metodologia hierárquica, priorizando numa primeira abordagem a utilização de um índice simples e fácil de aplicar (NDVI), pode permitir a confirmação da cultura presente e desta forma excluir com relativa segurança uma percentagem significativa de parcelas da amostra de controlo, reduzindo consideravelmente o número de inspeções físicas no terreno; EVALUATION OF THE SENTINEL 2 SATELLITE IMAGE POTENTIAL IN MONITORING COMPLIANCE WITH SOME OF THE CAP REQUIREMENTS. ABSTRACT: The Common Agricultural Policy (CAP) is a system of subsidies and support programs for farmers in the European Union (EU). In order to receive support under the CAP measures, each farmer must submit annually its declaration to the competent national authority, in Portugal the Instituto de Financiamento da Agricultura e Pescas, I.P. (IFAP). Subsequently these declarations are subject to administrative control actions and in some cases to field inspections. Under current CAP rules, each member state must, according to a risk analysis, conduct a series of physical checks on farms to ensure that the eligibility criteria and commitments are met. In order to simplify and modernize the Common Agricultural Policy, the European Commission on 22 May 2018 adopted new rules allowing the use of the data from the EU’s Copernicus Sentinel satellites, as main evidence when checking farmers’ fulfilment of requirements under the CAP payments. Is intended that evidences obtained by digital means will significantly reduce the number of field inspections, reducing costs and bureaucracy associated with the control process. This study proposes an approach that aims to assess the potential of Sentinel 2 satellite image data to confirm, using a standardized satellite index (NDVI), the presence of maize crop in a sample of parcels provided by IFAP. Confirmation on the field of the presence of a particular crop type is crucial for concluding the eligibility of these areas for a particular subsidy payment scheme. The results show that the application of a hierarchical methodology, prioritizing in the first approach the use of a simple and easy to apply index (NDVI), can allow the field confirmation of the crop type, and thus relatively safely exclude a significant percentage of plots of the sample control, considerably reducing the number of physical field inspections.

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Hunger, Sebastian, Pierre Karrasch, and Christine Wessollek. "Evaluating the potential of image fusion of multispectral and radar remote sensing data for the assessment of water body structure." SPIE, 2016. https://tud.qucosa.de/id/qucosa%3A34859.

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The European Water Framework Directive (Directive 2000/60/EC) is a mandatory agreement that guides the member states of the European Union in the field of water policy to fulfil the requirements for reaching the aim of the good ecological status of water bodies. In the last years several work ows and methods were developed to determine and evaluate the haracteristics and the status of the water bodies. Due to their area measurements remote sensing methods are a promising approach to constitute a substantial additional value. With increasing availability of optical and radar remote sensing data the development of new methods to extract information from both types of remote sensing data is still in progress. Since most limitations of these data sets do not agree the fusion of both data sets to gain data with higher spectral resolution features the potential to obtain additional information in contrast to the separate processing of the data. Based thereupon this study shall research the potential of multispectral and radar remote sensing data and the potential of their fusion for the assessment of the parameters of water body structure. Due to the medium spatial resolution of the freely available multispectral Sentinel-2 data sets especially the surroundings of the water bodies and their land use are part of this study. SAR data is provided by the Sentinel-1 satellite. Different image fusion methods are tested and the combined products of both data sets are evaluated afterwards. The evaluation of the single data sets and the fused data sets is performed by means of a maximum-likelihood classification and several statistical measurements. The results indicate that the combined use of different remote sensing data sets can have an added value.

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Pineda, Ancco Ferdinand Edgardo. "A generative adversarial network approach for super resolution of sentinel-2 satellite images." Master's thesis, Pontificia Universidad Católica del Perú, 2020. http://hdl.handle.net/20.500.12404/16137.

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Recently, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a Peruvian satellite, which serve as the reference for the superresolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR), the Structural Similarity (SSIM) and the Erreur Relative Globale Adimensionnelle de Synth`ese (ERGAS). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well. Trabajo de investigación

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Denize, Julien. "Evaluation of time-series SAR and optical images for the study of winter land-use." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S062.

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L'étude de l'utilisation hivernale du sol représente un enjeu majeur afin de préserver et d'améliorer la qualité des sols et des eaux de surfaces. Cependant la connaissance des dynamiques spatio-temporelles associées à l'utilisation du sol en période hivernale demeure aujourd'hui encore un défi pour la communauté scientifique. C'est dans ce contexte que s'inscrivent ces travaux de thèse dont l'objectif est d'évaluer le potentiel de séries temporelles d'images optiques et RSO à haute résolution spatiale pour l'étude de l'utilisation des sols en période hivernale à une échelle locale et régionale. Pour se faire, une méthodologie a été établie afin : (i) de déterminer la méthode de classification la plus adaptée pour identifier l'usage des sols en hiver; (ii) de comparer des images RSO Sentinel-1 et optiques Sentinel-2; (iii) de définir la configuration RSO la plus adaptée en comparant trois séries temporelles d'images (Alos-2, Radarsat-2 et Sentinel-1).Les résultats ont tout d'abord mis en évidence l'intérêt de l'algorithme de classification Random Forest pour discriminer à une échelle fine les types d'usage des sols en hiver qui sont très variés. Dans un second temps, ils ont souligné l'intérêt des données Sentinel-2 pour cartographier l'utilisation hivernale des sols à une échelle locale et régionale. Enfin, ils ont permis de déterminer qu'une série temporelle dense d'images Sentinel-1 était la configuration RSO la plus adaptée afin d'identifier l'utilisation hivernale du sol. De manière générale, si cette thèse a permis de montrer que les données Sentinel-2 sont les plus adaptées pour étudier l'utilisation du sol en période hivernale, les images RSO ont tout leur intérêt dans les régions où le couvert nuageux est important, les séries temporelles denses Sentinel- 1 ayant été définies comme les plus performantes The study of winter land-use is a major challenge in order to preserve and improve the quality of soils and surface water. However, knowledge of the spatio-temporal dynamics associated with winter land-use remains a challenge for the scientific community. In this context, the objective of this study is to evaluate the potential of time series of high spatial resolution optical and SAR images for the study of winter land-use at a local and regional scale. For that purpose, a methodology has been established to: (i) determine the most suitable classification method for identifying winter land-use ; (ii) compare Sentinel-1 SAR and Sentinel-2 optical images; (iii) define the most suitable SAR configuration by comparing three image time-series (Alos-2, Radarsat-2 and Sentinel-1).The results first of all highlighted the interest of the Random Forest classification algorithm to discriminate at a fine scale the different types of land use in winter. Secondly, they showed the value of Sentinel-2 data for mapping winter land-use at a local and regional scale. Finally, they determined that a dense time series of Sentinel-1 images was the most appropriate SAR configuration to identify winter land-use. In general, while this thesis has shown that Sentinel-2 data are best suited to studying land use in winter, SAR images are of great interest in regions with significant cloud cover, dense Sentinel-1 time-series having being defined as the most efficient

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Solari, Raphael Alberto Fuhr. "Aplicação de métodos de classificação supervisionada em imagens do Sentinel-2, como suporte ao cadastro ambiental rural." reponame:Repositório Institucional da UnB, 2017. http://repositorio.unb.br/handle/10482/32350.

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Dissertação (mestrado)—Universidade de Brasília, Faculdade de Planaltina , Programa de Pós-Graduação em Ciências Ambientais, 2017. Submitted by Raquel Viana (raquelviana@bce.unb.br) on 2018-07-24T17:34:37Z No. of bitstreams: 1 2017_RaphaelAlbertoFuhrSolari.pdf: 8787413 bytes, checksum: 25d55756045855c48e838734e8f13a85 (MD5) Approved for entry into archive by Raquel Viana (raquelviana@bce.unb.br) on 2018-07-24T19:34:55Z (GMT) No. of bitstreams: 1 2017_RaphaelAlbertoFuhrSolari.pdf: 8787413 bytes, checksum: 25d55756045855c48e838734e8f13a85 (MD5) Made available in DSpace on 2018-07-24T19:34:55Z (GMT). No. of bitstreams: 1 2017_RaphaelAlbertoFuhrSolari.pdf: 8787413 bytes, checksum: 25d55756045855c48e838734e8f13a85 (MD5) Previous issue date: 2018-07-24 O Cadastro Ambiental Rural (CAR) é uma ferramenta de controle da situação ambiental rural nacional, principalmente para Áreas de Preservação Permanentes (APP) e Reservas Legais (RL). Nas análises de uso e ocupação do solo realizadas durante a elaboração do CAR, são utilizadas imagens de satélites, como não há um procedimento definido para extração de dados, esta dissertação visa avaliar seis métodos de classificação supervisionada: Spectral Angler Mapper (Mapeamento pelo Ângulo Espectral) (SAM), Spectral Correlation Mapper (Mapeamento pela Correlação Espectral) (SCM), Máxima Verossimilhança (Maxver), Minimum Distance (Distância Mínima), Mahalanobis Distance (Distância Mahalanobis), Feature Space (Feição Espacial), a partir de imagens do satélite Sentinel-2, em vinte assentamentos localizados no norte do estado do Mato Grosso. A fim de identificar o método que melhor se adeque para dar suporte ao CAR. A classificação de cada assentamento foi extraída separadamente da classificação global. Tais resultados foram analisados por meio da Matriz de Confusão, Índice Kappa, Accuracy Assessment, (Avaliação de Precisão) e Image Difference (Diferença entre Imagens), para avaliar o nível de acurácia de cada método em todos os assentamentos. Todos os métodos demonstraram ótimos resultados no nível de acurácia e Índice Kappa, mas, para suporte ao CAR, o método que apresentou melhor acurácia e variância mínima entre as classes foi o SCM. The Environmental Rural Registry (CAR, in Portuguese) is a tool to control the national rural environmental situation, mainly for Permanent Preservation Areas (PPA) and Legal Reserves (LR). In the analysis of land use and occupation made during the elaboration of the CAR, satellite images are used. As there is no set procedure for this, this dissertation aims to test the application of six methods of supervised classification: Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), Maximum Likelihood (Maxver), Minimum Distance, Mahalanobis Distance, Feature Space, in images of the Sentinel-2 satellite, in twenty settlements located in the north of the state of Mato Grosso. To obtain the level of accuracy of each method, in all the individual settlements, the results were analyzed through the Confusion Matrix, Kappa Index, Accuracy Assessment, Image Difference. All methods demonstrated good averages in the Accuracy Assessment and Kappa Index. However, to be the method that gives better support to the CAR, it is necessary to present a minimum variance between the classes used in this work (vegetation and anthropized area) and to have consistent results throughout the study area. Under these conditions, the methods that obtained the best result were the Mahalanobis Distance and the SCM.

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Alexandre, Cyprien. "Estimation de la biomasse fourragère des prairies : apports du couplage entre modèles dynamiques de croissance et imagerie satellitaire : exemple de La Réunion et du Kalahari." Thesis, La Réunion, 2017. http://www.theses.fr/2017LARE0050/document.

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Cette étude a eu pour but d'étudier la possibilité de couplage de modèles dynamiques de croissance de l'herbe avec des données de télédétection, et ce pour deux terrains contrastés : La Réunion et le Kalahari (Afrique du Sud). Deux phases se sont succédé. Une première phase exploratoire, basée sur des images SPOT5 et SPOT5take5 (satellites désorbités en cours d'étude) a permis de tirer plusieurs enseignements. A La Réunion l'ajustement d’un modèle empirique entre indices de végétation et biomasse engendre trop d'erreur. Il est en revanche possible d'estimer le Leaf Area Index (LAI) grâce au NDVI (Normalized Difference Vegetation Index). Les parcours du Kalahari, plus complexes, avec différentes strates de végétation (graminées, arbustes, arbres) n'ont pas permis d'estimer l'état du couvert de graminées. Cette phase a ouvert la voie au travail effectué sur un capteur plus pérenne dans le temps, Sentinel-2. Les données Sentinel-2 ont permis d'estimer le LAI des prairies réunionnaises avec une RMSE (Root Mean Square Error) de 0,63 (r²=0,82). Le LAI ainsi estimé a été utilisé dans le couplage du modèle dynamique permettant une baisse générale de la RMSE de l'ordre de 40% par rapport au modèle sans couplage. Ces résultats ont été obtenus durant l'hiver austral, la saison sèche. Durant la période d'été austral les pluies plus abondantes accélèrent la croissance des plantes et les cycles de pousse se raccourcissent. Les images satellites sans couvert nuageux se font plus rares. La prise en compte de cette combinaison de facteurs pouvant impacter les prédictions de biomasse fourragère fera partie des principale perspectives de ce travail The purpose of this study was to explore the possibility of coupling dynamic models of grass growth with remote sensing data for two contrasting countries: Reunion Island and Kalahari (South Africa). Two phases followed one another. A first exploratory phase, based on SPOT5 and SPOT5take5 images (desorbed satellites under study) allowed us to learn from this experience. In Reunion the adjustment of an empirical model between vegetation indices and biomass generates too much error. However it is possible to estimate the Leaf Area Index (LAI) thanks to the NDVI (Normalized Difference Vegetation Index). More complex Kalahari rangelands with different vegetation strata (grasses, shrubs, trees) failed to estimate grass cover conditions. This phase set the stage to work on a more durable sensor over time, Sentinel-2. Sentinel-2 data made it possible to estimate the LAI of Reunion Island grasslands with a RMSE (Root Mean Square Error) of 0.63 (r² = 0.82). The LAI thus estimated was used in the coupling of the dynamic model, allowing a general decrease of the RMSE of the order of 40% compared to the model without coupling. These results were obtained during the austral winter, the dry season. During the austral summer, the more abundant rains speed up the growth of the plants and the growth cycles become shorter. Satellite images without cloud cover are becoming scarce. Taking into account this combination of factors that may impact predictions of forage biomass will be one of the main perspectives of this work

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Book chapters on the topic "Sentinel-2 imagery"

Digra, Amritpal, Charanjeet Singh Nijjar, R. Setia, S. K. Gupta, and B. Pateriya. "Mapping Orchards and Crops Using Sentinel-2 Imagery." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7698-8_13.

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Pereira-Pires, João E., Valentine Aubard, G. Baldassarre, José M. Fonseca, João M. N. Silva, and André Mora. "Fuel Break Monitoring with Sentinel-2 Imagery and GEDI Validation." In Internet of Things. Technology and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96466-5_5.

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Leenstra, Marrit, Diego Marcos, Francesca Bovolo, and Devis Tuia. "Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery." In Pattern Recognition. ICPR International Workshops and Challenges. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68787-8_42.

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Velayarce, Diego, Manuel Alvarez, Diego Guevara, and Victor Murray. "Analysis of Deforestation in Ucayali-Peru Using Satellite Imagery from Sentinel-2." In Proceedings of the 6th Brazilian Technology Symposium (BTSym’20). Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75680-2_35.

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Lantzanakis, G., Z. Mitraka, and N. Chrysoulakis. "Comparison of Physically and Image Based Atmospheric Correction Methods for Sentinel-2 Satellite Imagery." In Perspectives on Atmospheric Sciences. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-35095-0_36.

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Chymyrov, Akylbek, Florian Betz, Ermek Baibagyshov, Alishir Kurban, Bernd Cyffka, and Umut Halik. "Floodplain Forest Mapping with Sentinel-2 Imagery: Case Study of Naryn River, Kyrgyzstan." In Vegetation of Central Asia and Environs. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99728-5_14.

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Bayas, Shiwani, Suraj Sawant, Ishwari Dhondge, Priyanka Kankal, and Amit Joshi. "Land Use Land Cover Classification Using Different ML Algorithms on Sentinel-2 Imagery." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0840-8_59.

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Słapek, Michał, Krzysztof Smykała, and Bogdan Ruszczak. "Brassica Napus Florescence Modeling Based on Modified Vegetation Index Using Sentinel-2 Imagery." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20915-5_8.

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Kamusoko, Courage. "Mapping Urban Land Cover Using Multi-seasonal Sentinel-2 Imagery, Spectral and Texture Indices." In Springer Geography. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5149-6_3.

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Virnodkar, Shyamal, V. K. Pachghare, and Sagar Murade. "A Technique to Classify Sugarcane Crop from Sentinel-2 Satellite Imagery Using U-Net Architecture." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6353-9_29.

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Conference papers on the topic "Sentinel-2 imagery"

Plutalova, T. G., K. Teshebaeva, D. N. Balykin, et al. "Central Yamal vegetation monitoring based on Sentinel-2 and Sentinel-1 imagery." In Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.18.29.040.

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In this study fusion of optical (Sentinel-2) and radar (Sentinel-1) imagery is presented for vegetation cover classification in polar Arctic environment of the Western Siberia. Sentinel-1 and Sentinel-2 images were analyzed using parametric rule classification. Results showed significantly improved land cover classification results based on contextual analysis. Synergy of Sentinel-2 bands 4 and 3 and Sentinel-1 dual polarization VV and VH images increased the classification accuracy significantly. Specifically, classification accuracy increased for two classes — Erect dwarf-shrub tundra with 6% and Fresh Water with 10%. The classification accuracy as well test sites were analyzed using in situ data collected during three fieldwork campaigns in August-September (2016–2018) in the surrounding of Bovanenkovo settlement.

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Levering, Alex, Diego Marcos, Sylvain Lobry, and Devis Tuia. "Interpretable Scenicness from Sentinel-2 Imagery." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9323706.

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Notarnicola, C., S. Asam, A. Jacob, C. Marin, M. Rossi, and L. Stendardi. "Mountain crop monitoring with multitemporal Sentinel-1 and Sentinel-2 imagery." In 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). IEEE, 2017. http://dx.doi.org/10.1109/multi-temp.2017.8035225.

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Langevin, Scott, Chris Bethune, Philippe Horne, et al. "Useable machine learning for Sentinel-2 multispectral satellite imagery." In Image and Signal Processing for Remote Sensing XXVII, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2021. http://dx.doi.org/10.1117/12.2599951.

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Rahman, Shahriar, Hsing-Chung Chang, Kerrie Tomkins, and Warwick Hehir. "Bushfire Severity Mapping Using Sentinel-1 and ‐2 Imagery." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9323421.

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WANG, LIMEI, GUOWANG JIN, and XIN XIONG. "Improvement in Land Cover Classification Using Multitemporal Sentinel-1 and Sentinel-2 Satellite Imagery." In ITCC 2022: 2022 4th International Conference on Information Technology and Computer Communications. ACM, 2022. http://dx.doi.org/10.1145/3548636.3548639.

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Cheng, Keli, Trevor M. Bajkowski, and Grant J. Scott. "Evaluation of Sentinel-2 Data for Automatic Maasai Boma Mapping." In 2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE, 2021. http://dx.doi.org/10.1109/aipr52630.2021.9762131.

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Ayala, C., C. Aranda, and M. Galar. "Sub-Pixel Width Road Network Extraction Using Sentinel-2 Imagery." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9555128.

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Zhang, Yiming, Sergii Skakun, and Victor Prudente. "Detection of Changes in Impervious Surface Using Sentinel-2 Imagery." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9323327.

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Reports on the topic "Sentinel-2 imagery"

Wilford, J., and D. Roberts. Sentinel-2 Barest Earth imagery for soil and lithological mapping. Geoscience Australia, 2021. http://dx.doi.org/10.11636/146125.

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Gikov, Alexander, Petar Dimitrov, Lachezar Filchev, Eugenia Roumenina, and Georgi Jelev. Crop Type Mapping Using Multi-date Imagery from the Sentinel-2 Satellites. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2019. http://dx.doi.org/10.7546/crabs.2019.06.11.

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Lasko, Kristofer, and Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42402.

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Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines.

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Becker, Sarah, Megan Maloney, and Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42003.

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Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI). Previous testing focused on one study site using 2-m resolution commercial MSI from WorldView-2 and 30-m resolution imagery from Landsat-7. New testing in this work used 3-m imagery from PlanetScope and 10-m imagery from Sentinel-2 in imagery in sites across 12 biomes in South and Central America and North Korea. Overall accuracy ranged between 23% and 97% for Sentinel-2 imagery and between 51% and 98% for PlanetScope imagery. Future research will focus on automating the identification of the threshold that separates tree from other land covers, exploring use of the output for machine learning applications, and incorporating ancillary data such as digital surface models and existing tree cover maps.

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Saltus, Christina, Molly Reif, and Richard Johansen. waterquality for ArcGIS Pro Toolbox. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42240.

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Monitoring water quality of small inland lakes and reservoirs is a critical component of USACE water quality management plans. However, limited resources for traditional field-based monitoring of numerous lakes and reservoirs that cover vast geographic areas often leads to reactional responses to harmful algal bloom (HAB) outbreaks. Satellite remote sensing methodologies using HAB indicators is a good low-cost option to traditional methods and has been proven to maximize and complement current field-based approaches while providing a synoptic view of water quality (Beck et al. 2016; Beck et al. 2017; Beck et al. 2019; Johansen et al. 2019; Mishra et al. 2019; Stumpf and Tomlinson 2007; Wang et al. 2020; Xu et al. 2019; Reif 2011). To assist USACE water quality management, we developed an ESRI ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro) that was founded on the design and research established in the waterquality R software package (Johansen et al. 2019; Johansen 2020). The toolbox enables the detection, monitoring, and quantification of HAB indicators (chlorophyll-a, phycocyanin, and turbidity) using Sentinel-2 satellite imagery. Four tools are available 1) to automate the download of Sentinel-2 Level-2A imagery, 2) to create stacked image with options for cloud and non-water features masks, 3) to apply water quality algorithms to generate relative estimations of one to three water quality parameters (chlorophyll-a, phycocyanin, and turbidity), and 4) to create linear regression graphs and statistics comparing in situ data (from field-based water sampling) to relative estimation data. This document serves as a user's guide for the waterquality for ArcGIS Pro toolbox and includes instructions on toolbox installation and descriptions of each tool's inputs, outputs, and troubleshooting guidance.

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Saltus, Christina, Molly Reif, and Richard Johansen. waterquality for ArcGIS Pro Toolbox : user's guide. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45362.

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Monitoring water quality of small inland lakes and reservoirs is a critical component of the US Army Corps of Engineers (USACE) water quality management plans. However, limited resources for traditional field-based monitoring of numerous lakes and reservoirs covering vast geographic areas often leads to reactional responses to harmful algal bloom (HAB) outbreaks. Satellite remote sensing methodologies using HAB indicators is a good low-cost option to traditional methods and has been proven to maximize and complement current field-based approaches while providing a synoptic view of water quality (Beck et al. 2016; Beck et al. 2017; Beck et al. 2019; Johansen et al. 2019; Mishra et al. 2019; Stumpf and Tomlinson 2007; Wang et al. 2020; Xu et al. 2019; Reif 2011). To assist USACE water quality management, we developed an Environmental Systems Research Institute (ESRI) ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro) founded on the design and research established in the waterquality R software package (Johansen et al. 2019; Johansen 2020). The toolbox enables the detection, monitoring, and quantification of HAB indicators (chlorophyll-a, phycocyanin, and turbidity) using Sentinel-2 satellite imagery. Four tools are available: (1) automating the download of Sentinel-2 Level-2A imagery, (2) creating stacked image with options for cloud and non-water features masks, (3) applying water quality algorithms to generate relative estimations of one to three water quality parameters (chlorophyll-a, phycocyanin, and turbidity), and (4) creating linear regression graphs and statistics comparing in situ data (from field-based water sampling) to relative estimation data. This document serves as a user’s guide for the waterquality for ArcGIS Pro toolbox and includes instructions on toolbox installation and descriptions of each tool’s inputs, outputs, and troubleshooting guidance.

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Choe, B.-H., A. Blais-Stevens, S. Samsonov, and J. Dudley. RADARSAT Constellation Mission (RCM) InSAR preliminary observations of slope movements in British Columbia, Alberta, and Nunavut. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331099.

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The Geological Survey of Canada (GSC)mp;gt;'s Public Safety Geoscience Program (PSGP) has collaborated with the Canada Centre for Remote Sensing (CCRS) to assess the performance of new RCM data for monitoring slope movements. The PSGP has the mandate to study natural hazards and provide baseline geoscience information to help stakeholders and decision-makers mitigate against potential risk. This report provides preliminary results observed from new RCM InSAR data acquired over 21 sites in British Columbia (BC), Alberta (AB), and Nunavut (NU) from April 2020 to September 2021. , In some cases, comparisons with RCM imagery were made with RADARSAT-2 and Sentinel-1 observations. A total of 13 sites in BC, two sites in AB, and six sites in NU that are located close to communities and/or infrastructure were investigated. From these, we acquired a total of 1235 RCM single look complex (SLC) images of HH polarization (ascending: 514, descending: 721) from April 2020 to September 2021. Most were acquired with 3 m very-high-resolution and/or 5 m high-resolution modes. Based on the preliminary observations, the advantages and limitations of RCM InSAR for landslide monitoring are highlighted.

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Hodul, M., H. P. White, and A. Knudby. A report on water quality monitoring in Quesnel Lake, British Columbia, subsequent to the Mount Polley tailings dam spill, using optical satellite imagery. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330556.

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In the early morning on the 4th of August 2014, a tailings dam near Quesnel, BC burst, spilling approximately 25 million m3 of runoff containing heavy metal elements into nearby Quesnel Lake (Byrne et al. 2018). The runoff slurry, which included lead, arsenic, selenium, and vanadium spilled through Hazeltine Creek, scouring its banks and picking up till and forest cover on the way, and ultimately ended up in Quesnel Lake, whose water level rose by 1.5 m as a result. While the introduction of heavy metals into Quesnel Lake was of environmental concern, the additional till and forest cover scoured from the banks of Hazeltine Creek added to the lake has also been of concern to salmon spawning grounds. Immediate repercussions of the spill involved the damage of sensitive environments along the banks and on the lake bed, the closing of the seasonal salmon fishery in the lake, and a change in the microbial composition of the lake bed (Hatam et al. 2019). In addition, there appears to be a seasonal resuspension of the tailings sediment due to thermal cycling of the water and surface winds (Hamilton et al. 2020). While the water quality of Quesnel Lake continues to be monitored for the tailings sediments, primarily by members at the Quesnel River Research Centre, the sample-and-test methods of water quality testing used, while highly accurate, are expensive to undertake, and not spatially exhaustive. The use of remote sensing techniques, though not as accurate as lab testing, allows for the relatively fast creation of expansive water quality maps using sensors mounted on boats, planes, and satellites (Ritchie et al. 2003). The most common method for the remote sensing of surface water quality is through the use of a physics-based semianalytical model which simulates light passing through a water column with a given set of Inherent Optical Properties (IOPs), developed by Lee et al. (1998) and commonly referred to as a Radiative Transfer Model (RTM). The RTM forward-models a wide range of water-leaving spectral signatures based on IOPs determined by a mix of water constituents, including natural materials and pollutants. Remote sensing imagery is then used to invert the model by finding the modelled water spectrum which most closely resembles that seen in the imagery (Brando et al 2009). This project set out to develop an RTM water quality model to monitor the water quality in Quesnel Lake, allowing for the entire surface of the lake to be mapped at once, in an effort to easily determine the timing and extent of resuspension events, as well as potentially investigate greening events reported by locals. The project intended to use a combination of multispectral imagery (Landsat-8 and Sentinel-2), as well as hyperspectral imagery (DESIS), combined with field calibration/validation of the resulting models. The project began in the Autumn before the COVID pandemic, with plans to undertake a comprehensive fieldwork campaign to gather model calibration data in the summer of 2020. Since a province-wide travel shutdown and social distancing procedures made it difficult to carry out water quality surveying in a small boat, an insufficient amount of fieldwork was conducted to suit the needs of the project. Thus, the project has been put on hold, and the primary researcher has moved to a different project. This document stands as a report on all of the work conducted up to April 2021, intended largely as an instructional document for researchers who may wish to continue the work once fieldwork may freely and safely resume. This research was undertaken at the University of Ottawa, with supporting funding provided by the Earth Observations for Cumulative Effects (EO4CE) Program Work Package 10b: Site Monitoring and Remediation, Canada Centre for Remote Sensing, through the Natural Resources Canada Research Affiliate Program (RAP).

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Johansen, Richard A., Christina L. Saltus, Molly K. Reif, and Kaytee L. Pokrzywinski. A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing. U.S. Army Engineer Research and Development Center, 2022. http://dx.doi.org/10.21079/11681/44523.

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Harmful Algal Blooms (HABs) continue to be a global concern, especially since predicting bloom events including the intensity, extent, and geographic location, remain difficult. However, remote sensing platforms are useful tools for monitoring HABs across space and time. The main objective of this review was to explore the scientific literature to develop a near-comprehensive list of spectrally derived empirical algorithms for satellite imagers commonly utilized for the detection and quantification HABs and water quality indicators. This review identified the 29 WorldView-2 MSI algorithms, 25 Sentinel-2 MSI algorithms, 32 Landsat-8 OLI algorithms, 9 MODIS algorithms, and 64 MERIS/Sentinel-3 OLCI algorithms. This review also revealed most empirical-based algorithms fell into one of the following general formulas: two-band difference algorithm (2BDA), three-band difference algorithm (3BDA), normalized-difference chlorophyll index (NDCI), or the cyanobacterial index (CI). New empirical algorithm development appears to be constrained, at least in part, due to the limited number of HAB-associated spectral features detectable in currently operational imagers. However, these algorithms provide a foundation for future algorithm development as new sensors, technologies, and platforms emerge.

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Nedkov, Roumen. Quantitative Assessment of Forest Degradation after Fire Using Ortogonalized Satellite Images from Sentinel-2. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.01.11.

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