Relevant bibliographies by topics / Time series of satellite images

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  2. Dissertations / Theses
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Academic literature on the topic 'Time series of satellite images'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Time series of satellite images"

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Ghosh, Tilottama, Kimberly E. Baugh, Christopher D. Elvidge, Mikhail Zhizhin, Alexey Poyda, and Feng-Chi Hsu. "Extending the DMSP Nighttime Lights Time Series beyond 2013." Remote Sensing 13, no. 24 (December 9, 2021): 5004. http://dx.doi.org/10.3390/rs13245004.

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Data collected by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) sensors have been archived and processed by the Earth Observation Group (EOG) at the National Oceanic and Atmospheric Administration (NOAA) to make global maps of nighttime images since 1994. Over the years, the EOG has developed automatic algorithms to make Stable Lights composites from the OLS visible band data by removing the transient lights from fires and fishing boats. The ephemeral lights are removed based on their high brightness and short duration. However, the six original satellites collecting DMSP data gradually shifted from day/night orbit to dawn/dusk orbit, which is to an earlier overpass time. At the beginning of 2014, the F18 satellite was no longer collecting usable nighttime data, and the focus had shifted to processing global nighttime images from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data. Nevertheless, it was soon discovered that the F15 and F16 satellites had started collecting pre-dawn nighttime data from 2012 onwards. Therefore, the established algorithms of the previous years were extended to process OLS data from 2013 onwards. Moreover, the existence of nighttime data from three overpass times for the year 2013–DMSP satellites F18 and F15 from early evening and pre-dawn, respectively, and the VIIRS from after midnight, made it possible to intercalibrate the images of three different overpass times and study the diurnal pattern of nighttime lights.
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Wang, Ruifu, Dongdong Teng, Wenqing Yu, Xi Zhang, and Jinshan Zhu. "Improvement and Application of a GAN Model for Time Series Image Prediction—A Case Study of Time Series Satellite Cloud Images." Remote Sensing 14, no. 21 (November 2, 2022): 5518. http://dx.doi.org/10.3390/rs14215518.

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Predicting the shape evolution and movement of remote sensing satellite cloud images is a difficult task requiring the effective monitoring and rapid prediction of thunderstorms, gales, rainstorms, and other disastrous weather conditions. We proposed a generative adversarial network (GAN) model for time series satellite cloud image prediction in this research. Taking time series information as the constraint condition and abandoning the assumption of linear and stable changes in cloud clusters in traditional methods, the GAN model is used to automatically learn the data feature distribution of satellite cloud images and predict time series cloud images in the future. Through comparative experiments and analysis, the Mish activation function is selected for integration into the model. On this basis, three improvement measures are proposed: (1) The Wasserstein distance is used to ensure the normal update of the GAN model parameters; (2) establish a multiscale network structure to improve the long-term performance of model prediction; (3) combined image gradient difference loss (GDL) to improve the sharpness of prediction cloud images. The experimental results showed that for the prediction cloud images of the next four times, compared with the unimproved Mish-GAN model, the improved GDL-GAN model improves the PSNR and SSIM by 0.44 and 0.02 on average, and decreases the MAE and RMSE by 18.84% and 7.60% on average. It is proven that the improved GDL-GAN model can maintain good visualization effects while keeping the overall changes and movement trends of the prediction cloud images relatively accurate, which is helpful to achieve more accurate weather forecast. The cooperation ability of satellite cloud images in disastrous weather forecasting and early warning is enhanced.
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Liu, Yu, Wenqing Li, Li Li, and Naiqun Zhang. "Extraction of Long Time-Series Vegetation Indices from Combined Multisource Satellite Imagery." Computational Intelligence and Neuroscience 2022 (May 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/3901372.

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Extracting vegetation cover information by combining multisource satellite images can improve the time scale of vegetation cover monitoring, realize encrypted observation in short period, and shorten the regional vegetation remote sensing monitoring cycle. The NDVI and RVI datasets from 2007–2019 were extracted using 9 phases of multisource satellite images (Landsat TM/OLI, Sentinel-2 MSI, and GF-1 PMS) covering Xiaxi, Sichuan. Three typical validation sites representing higher vegetation cover in mountains and no vegetation cover in water bodies in the region, respectively, were selected to extract NDVI and RVI at the corresponding locations. Linear regression and Spearman correlation coefficient (ρ) analysis were used to verify the correlation between NDVI and RVI from multisource images. The results showed that the vegetation indices fluctuated smoothly in the time series within the validation sites, and the vegetation indices of multisource satellite images were good measures of long-term vegetation cover in the region; the vegetation indices of the same satellite images showed significant correlations (both R2 and ρ exceeded 0.8), and the vegetation indices of different satellite images (PSM and MSI, PSM and OLI) showed more significant correlations (both R2 and ρ exceeded 0.7); the smaller the difference between the original resolutions of satellite images, the more significant the correlation between the extracted NDVI and RVI.
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Erena, Manuel, José A. Domínguez, Joaquín F. Atenza, Sandra García-Galiano, Juan Soria, and Ángel Pérez-Ruzafa. "Bathymetry Time Series Using High Spatial Resolution Satellite Images." Water 12, no. 2 (February 14, 2020): 531. http://dx.doi.org/10.3390/w12020531.

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The use of the new generation of remote sensors, such as echo sounders and Global Navigation Satellite System (GNSS) receivers with differential correction installed in a drone, allows the acquisition of high-precision data in areas of shallow water, as in the case of the channel of the Encañizadas in the Mar Menor lagoon. This high precision information is the first step to develop the methodology to monitor the bathymetry of the Mar Menor channels. The use of high spatial resolution satellite images is the solution for monitoring many hydrological changes and it is the basis of the three-dimensional (3D) numerical models used to study transport over time, environmental variability, and water ecosystem complexity.
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Guyet, Thomas, and Hervé Nicolas. "Long term analysis of time series of satellite images." Pattern Recognition Letters 70 (January 2016): 17–23. http://dx.doi.org/10.1016/j.patrec.2015.11.005.

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Li, Jianzhou, Jinji Ma, and Xiaojiao Ye. "A Batch Pixel-Based Algorithm to Composite Landsat Time Series Images." Remote Sensing 14, no. 17 (August 29, 2022): 4252. http://dx.doi.org/10.3390/rs14174252.

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Compositing is a fundamental pre-processing for remote sensing images. Landsat series optical satellite images are influenced by cloud coverage, acquisition time, sensor types, and seasons, which make it difficult to obtain continuous cloud-free observations. It limits the potential use and analysis of time series images. Therefore, global change researchers urgently need to ‘composite’ multi-sensor and multi-temporal images. Many previous studies have used isolated pixel-based algorithms to composite Landsat images; however, this study is different and develops a batch pixel-based algorithm for composing continuous cloud-free Landsat images. The algorithm chooses the best scene as the reference image using the user-specified image ID or related parameters. Further, it accepts all valid pixels in the reference image as the main part of the result and develops a priority coefficient model. Development of this model is based on the criteria of five factors including cloud coverage, acquisition time, acquisition year, observation seasons, and sensor types to select substitutions for the missing pixels in batches and to merge them into the final composition. This proposed batch pixel-based algorithm may provide reasonable compositing results on the basis of the experimental test results of all Landsat 8 images in 2019 and the visualization results of 12 locations in 2020. In comparison with the isolated pixel-based algorithms, our algorithm eliminates band dispersion, requires fewer images, and enhances the composition’s pixel concentration considerably. The algorithm provides a complete and practical framework for time series image processing for Landsat series satellites, and has the potential to be applied to other optical satellite images as well.
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Silva, B. L. C., F. C. Souza, K. R. Ferreira, G. R. Queiroz, and L. A. Santos. "SPATIOTEMPORAL SEGMENTATION OF SATELLITE IMAGE TIME SERIES USING SELF-ORGANIZING MAP." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 255–61. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-255-2022.

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Abstract. Nowadays, researchers have free access to an unprecedentedly large amount of remote sensing images collected by satellites and sensors with different spatial, temporal, and spectral resolutions. This scenario has promoted the use of satellite image time series for spatiotemporal analysis. This paper presents a methodology for spatiotemporal segmentation of satellite image time series. Spatiotemporal segmentation finds homogeneous regions in space and time from remote sensing images based on spectral features. The proposed approach is unsupervised based on the self-organizing map (SOM) neural network and hierarchical clustering algorithm. It was implemented and applied to a region in the Mato Grosso state, Brazil. The results were evaluated using qualitative and quantitative approaches. In the qualitative approach, visual analysis was performed based on the land use and land cover map of the TerrraClass Cerrado project. In the quantitative approach, supervised and geometric metrics were used to analyze the quality of the produced segments. The results obtained are promising since the segments produced were homogeneous and with a low occurrence of over-segmentation.
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PETITJEAN, FRANÇOIS, FLORENT MASSEGLIA, PIERRE GANÇARSKI, and GERMAIN FORESTIER. "DISCOVERING SIGNIFICANT EVOLUTION PATTERNS FROM SATELLITE IMAGE TIME SERIES." International Journal of Neural Systems 21, no. 06 (December 2011): 475–89. http://dx.doi.org/10.1142/s0129065711003024.

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Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors.
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Vitkovskaya, I. S. "SATELLITE DATA PROCESSING ALGORITHM IN THE PROCESS OF FORMATION OF THE TIME SERIES OF VEGETATION INDEXES." Eurasian Physical Technical Journal 18, no. 2 (June 11, 2021): 90–95. http://dx.doi.org/10.31489/2021no2/90-95.

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The diverse spectral indexes computed from the satellite images are used extensively in the world practice of remote sensing of the Earth from space. This approach proved its validity for the satellite monitoring of the underlying terrain, detection of ongoing changes and trends of their dynamic patters. Accumulated prodigious amount of satellite data, the state-of-the-art methods of thematic interpretation gave rise to creation of services providing free access to both images and to image processing results. Notwithstanding the foregoing, in the furtherance of the local and regional scale it turns out that usage of the end products of thematic processing of space information supplied by the known available services was not efficient on all occasions. Consequently, we may need to generate our own archives of the long-term series of satellite indexes. The volume of files containing the digital index matrices computed based on the MODIS satellite low resolution data subject to the complete coverage of the territory of Kazakhstan surpasses 4 Gb. This often results in the delayed computations, and on frequent occasions in infeasibility of computation of a full matrix when the medium specs computers are employed. This article is focused on the satellite data processing algorithm in the process of formation of the time series of vegetation indexes. As a consequence, the multi-year archive of vegetation indexes (over a period of 2001-2020), which provided a basis for trend analysis of the underlying terrain, determination of their future trends and forecasting of their changes was created within the territory of the Republic.
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Zhou, Z. G., P. Tang, and M. Zhou. "DETECTING ANOMALY REGIONS IN SATELLITE IMAGE TIME SERIES BASED ON SESAONAL AUTOCORRELATION ANALYSIS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 303–10. http://dx.doi.org/10.5194/isprsannals-iii-3-303-2016.

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Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they are generally designed for detecting inter-annual or abrupt land cover changes, but are not focusing on detecting spatial-temporal changes in continuous images. In order to identify spatial-temporal dynamic processes of unexpected changes of land cover, this study proposes a method for detecting anomaly regions in each image of satellite image time series based on seasonal autocorrelation analysis. The method was validated with a case study to detect spatial-temporal processes of a severe flooding using Terra/MODIS image time series. Experiments demonstrated the advantages of the method that (1) it can effectively detect anomaly regions in each of satellite image time series, showing spatial-temporal varying process of anomaly regions, (2) it is flexible to meet some requirement (e.g., z-value or significance level) of detection accuracies with overall accuracy being up to 89% and precision above than 90%, and (3) it does not need time series smoothing and can detect anomaly regions in noisy satellite images with a high reliability.
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Dissertations / Theses on the topic "Time series of satellite images"

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Vázquez, Navarro Margarita R. "Life cycle of contrails from a time series of geostationary satellite images." kostenfrei, 2009. http://edoc.ub.uni-muenchen.de/10913/.

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Vazquez, Navarro Margarita R. "Life cycle of contrails from a time series of geostationary satellite images." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-109135.

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Kalinicheva, Ekaterina. "Unsupervised satellite image time series analysis using deep learning techniques." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS335.

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Cette thèse présente un ensemble d'algorithmes non-supervisés pour l'analyse générique de séries temporelles d'images satellites (STIS). Nos algorithmes exploitent des méthodes de machine learning et, notamment, les réseaux de neurones afin de détecter les différentes entités spatio-temporelles et leurs changements éventuels dans le temps. Nous visons à identifier trois types de comportement temporel : les zones sans changements, les changements saisonniers, les changements non triviaux (changements permanents comme les constructions, la rotation des cultures agricoles, etc).Par conséquent, nous proposons deux frameworks : pour la détection et le clustering des changements non-triviaux et pour le clustering des changements saisonniers et des zones sans changements. Le premier framework est composé de deux étapes : la détection de changements bi-temporels et leur interprétation dans le contexte multi-temporel avec une approche basée graphes. La détection de changements bi-temporels est faite pour chaque couple d’images consécutives et basée sur la transformation des features avec les autoencodeurs (AEs). A l’étape suivante, les changements à différentes dates qui appartiennent à la même zone géographique forment les graphes d’évolution qui sont par la suite clusterisés avec un modèle AE de réseaux de neurones récurrents. Le deuxième framework présente le clustering basé objets de STIS. Premièrement, la STIS est encodée en image unique avec un AE convolutif 3D multi-vue. Dans un deuxième temps, nous faisons la segmentation en deux étapes en utilisant à la fois l’image encodée et la STIS. Finalement, les segments obtenus sont clusterisés avec leurs descripteurs encodés
This thesis presents a set of unsupervised algorithms for satellite image time series (SITS) analysis. Our methods exploit machine learning algorithms and, in particular, neural networks to detect different spatio-temporal entities and their eventual changes in the time.In our thesis, we aim to identify three different types of temporal behavior: no change areas, seasonal changes (vegetation and other phenomena that have seasonal recurrence) and non-trivial changes (permanent changes such as constructions or demolishment, crop rotation, etc). Therefore, we propose two frameworks: one for detection and clustering of non-trivial changes and another for clustering of “stable” areas (seasonal changes and no change areas). The first framework is composed of two steps which are bi-temporal change detection and the interpretation of detected changes in a multi-temporal context with graph-based approaches. The bi-temporal change detection is performed for each pair of consecutive images of the SITS and is based on feature translation with autoencoders (AEs). At the next step, the changes from different timestamps that belong to the same geographic area form evolution change graphs. The graphs are then clustered using a recurrent neural networks AE model to identify different types of change behavior. For the second framework, we propose an approach for object-based SITS clustering. First, we encode SITS with a multi-view 3D convolutional AE in a single image. Second, we perform a two steps SITS segmentation using the encoded SITS and original images. Finally, the obtained segments are clustered exploiting their encoded descriptors
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Wegner, Maus Victor, Gilberto Camara, Marius Appel, and Edzer Pebesma. "dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R." Foundation for Open Access Statistics, 2019. http://epub.wu.ac.at/6808/1/v88i05.pdf.

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The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat. This package provides an implementation of the time-weighted dynamic time warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis. Package dtwSat is available from the Comprehensive R Archive Network (CRAN) and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualizing and assessing the results.
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LI, YUANXUN. "SVM Object Based Classification Using Dense Satellite Imagery Time Series." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233340.

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Sanchez, Eduardo Hugo. "Learning disentangled representations of satellite image time series in a weakly supervised manner." Thesis, Toulouse 3, 2021. http://www.theses.fr/2021TOU30032.

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Cette thèse se focalise sur l'apprentissage de représentations de séries temporelles d'images satellites via des méthodes d'apprentissage non supervisé. Le but principal est de créer une représentation qui capture l'information la plus pertinente de la série temporelle afin d'effectuer d'autres applications d'imagerie satellite. Cependant, l'extraction d'information à partir de la donnée satellite implique de nombreux défis. D'un côté, les modèles doivent traiter d'énormes volumes d'images fournis par les satellites. D'un autre côté, il est impossible pour les opérateurs humains d'étiqueter manuellement un tel volume d'images pour chaque tâche (par exemple, la classification, la segmentation, la détection de changement, etc.). Par conséquent, les méthodes d'apprentissage supervisé qui ont besoin des étiquettes ne peuvent pas être appliquées pour analyser la donnée satellite. Pour résoudre ce problème, des algorithmes d'apprentissage non supervisé ont été proposés pour apprendre la structure de la donnée au lieu d'apprendre une tâche particulière. L'apprentissage non supervisé est une approche puissante, car aucune étiquette n'est nécessaire et la connaissance acquise sur la donnée peut être transférée vers d'autres tâches permettant un apprentissage plus rapide avec moins d'étiquettes. Dans ce travail, on étudie le problème de l'apprentissage de représentations démêlées de séries temporelles d'images satellites. Le but consiste à créer une représentation partagée qui capture l'information spatiale de la série temporelle et une représentation exclusive qui capture l'information temporelle spécifique à chaque image. On présente les avantages de créer des représentations spatio-temporelles. Par exemple, l'information spatiale est utile pour effectuer la classification ou la segmentation d'images de manière invariante dans le temps tandis que l'information temporelle est utile pour la détection de changement. Pour ce faire, on analyse plusieurs modèles d'apprentissage non supervisé tels que l'auto-encodeur variationnel (VAE) et les réseaux antagonistes génératifs (GANs) ainsi que les extensions de ces modèles pour effectuer le démêlage des représentations. Considérant les résultats impressionnants qui ont été obtenus par les modèles génératifs et reconstructifs, on propose un nouveau modèle qui crée une représentation spatiale et une représentation temporelle de la donnée satellite. On montre que les représentations démêlées peuvent être utilisées pour effectuer plusieurs tâches de vision par ordinateur surpassant d'autres modèles de l'état de l'art. Cependant, nos expériences suggèrent que les modèles génératifs et reconstructifs présentent des inconvénients liés à la dimensionnalité de la représentation, à la complexité de l'architecture et au manque de garanties sur le démêlage. Pour surmonter ces limitations, on étudie une méthode récente basée sur l'estimation et la maximisation de l'informations mutuelle sans compter sur la reconstruction ou la génération d'image. On propose un nouveau modèle qui étend le principe de maximisation de l'information mutuelle pour démêler le domaine de représentation. En plus des expériences réalisées sur la donnée satellite, on montre que notre modèle est capable de traiter différents types de données en étant plus performant que les méthodes basées sur les GANs et les VAEs. De plus, on prouve que notre modèle demande moins de puissance de calcul et pourtant est plus efficace. Enfin, on montre que notre modèle est utile pour créer une représentation qui capture uniquement l'information de classe entre deux images appartenant à la même catégorie. Démêler la classe ou la catégorie d'une image des autres facteurs de variation permet de calculer la similarité entre pixels et effectuer la segmentation d'image d'une manière faiblement supervisée
This work focuses on learning data representations of satellite image time series via an unsupervised learning approach. The main goal is to enforce the data representation to capture the relevant information from the time series to perform other applications of satellite imagery. However, extracting information from satellite data involves many challenges since models need to deal with massive amounts of images provided by Earth observation satellites. Additionally, it is impossible for human operators to label such amount of images manually for each individual task (e.g. classification, segmentation, change detection, etc.). Therefore, we cannot use the supervised learning framework which achieves state-of-the-art results in many tasks.To address this problem, unsupervised learning algorithms have been proposed to learn the data structure instead of performing a specific task. Unsupervised learning is a powerful approach since no labels are required during training and the knowledge acquired can be transferred to other tasks enabling faster learning with few labels.In this work, we investigate the problem of learning disentangled representations of satellite image time series where a shared representation captures the spatial information across the images of the time series and an exclusive representation captures the temporal information which is specific to each image. We present the benefits of disentangling the spatio-temporal information of time series, e.g. the spatial information is useful to perform time-invariant image classification or segmentation while the knowledge about the temporal information is useful for change detection. To accomplish this, we analyze some of the most prevalent unsupervised learning models such as the variational autoencoder (VAE) and the generative adversarial networks (GANs) as well as the extensions of these models to perform representation disentanglement. Encouraged by the successful results achieved by generative and reconstructive models, we propose a novel framework to learn spatio-temporal representations of satellite data. We prove that the learned disentangled representations can be used to perform several computer vision tasks such as classification, segmentation, information retrieval and change detection outperforming other state-of-the-art models. Nevertheless, our experiments suggest that generative and reconstructive models present some drawbacks related to the dimensionality of the data representation, architecture complexity and the lack of disentanglement guarantees. In order to overcome these limitations, we explore a recent method based on mutual information estimation and maximization for representation learning without relying on image reconstruction or image generation. We propose a new model that extends the mutual information maximization principle to disentangle the representation domain into two parts. In addition to the experiments performed on satellite data, we show that our model is able to deal with different kinds of datasets outperforming the state-of-the-art methods based on GANs and VAEs. Furthermore, we show that our mutual information based model is less computationally demanding yet more effective. Finally, we show that our model is useful to create a data representation that only captures the class information between two images belonging to the same category. Disentangling the class or category of an image from other factors of variation provides a powerful tool to compute the similarity between pixels and perform image segmentation in a weakly-supervised manner
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Wang, Zhihao. "Land Cover Classification on Satellite Image Time Series Using Deep Learning Models." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu159559249009195.

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Karasiak, Nicolas. "Cartographie des essences forestières à partir de séries temporelles d’images satellitaires à hautes résolutions : stabilité des prédictions, autocorrélation spatiale et cohérence avec la phénologie observée in situ." Thesis, Toulouse, INPT, 2020. http://www.theses.fr/2020INPT0115.

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La forêt a un rôle essentiel sur terre, que ce soit pour stocker le carbone et ainsi lutter contre le réchauffement climatique ou encore fournir un habitat à de nombreuses espèces. Or la composition de la forêt (la localisation des essences ou leur diversité) a une influence sur les services écologiques rendus. Dans ce contexte, il est important de cartographier les forêts et les essences qui la composent. La télédétection, en particulier à partir d’images satellitaires, apparat comme le moyen le plus adéquat pour caractériser un vaste territoire. Avec l’arrivée de constellations satellitaires comme Sentinel-2 ou Landsat-8 et leur gratuité d’acquisition pour l’utilisateur, il devient possible d’envisager l’usage de séries temporelles d’images satellites à haute résolution spatiale, spectrale et temporelle à l’aide d’algorithmes d’apprentissage automatique. Si de nombreux travaux ont étudié le potentiel des images satellitaires pour identifier les essences, rares sont ceux qui utilisent des séries temporelles (plusieurs images par an) avec une haute résolution spatiale et en tenant compte de l’autocorrélation spatiale des références, i.e. la ressemblance des échantillons spatialement proches les uns des autres. Or, en ne prenant pas en compte ce phénomène, des biais d’évaluation peuvent survenir et ainsi surestimer la qualité des modèles d’apprentissage. Il s’agit aussi de mieux cerner les verrous méthodologiques afin de comprendre pourquoi il peut être facile ou compliqué pour un algorithme d’identifier une essence d’une autre. L’objectif général de la thèse vise à étudier le potentiel et les verrous concernant la reconnaissance des essences forestières à partir des séries temporelles d’images satellite à haute résolution spatiale, spectrale, et temporelle. Le premier objectif consiste à étudier la stabilité temporelle des prédictions à partir d’une archive de neuf ans du satellite Formosat-2. Plus particulièrement, les travaux portent sur la mise en place d’une méthode de validation qui soit le plus fidèle à la qualité observée des cartographies. Le second objectif s’intéresse au lien entre les évènements phénologiques in situ (pousse des feuilles en début de saison, ou perte et coloration des feuilles en fin de saison) et ce qu’il est observable par télédétection. Outre la capacité de détecter ces évènements, il sera étudié si ce qui permet aux algorithmes de différencier les essences les unes des autres est lié à des comportements spécifiques par espèce
Forests have a key role on earth, whether to store carbon and so reducing the global warming or to provide a place for many species. However, the composition of the forest (the location of the tree species or their diversity) has an influence on the ecological services provided. In this context, it seems critical to map tree species that make it up the forest. Remote sensing, especially from satellite images, appears to be the most appropriate way to map large areas. Thanks to the satellite constellations such as Sentinel-2 or Landsat-8 and their free acquisition for the user, the use of time series of satellite images with high spatial, spectral and temporal resolution using automatic learning algorithms is now easy to access. While many works have studied the potential of satellite images to identify tree species, few use time series (several images per year) with high spatial resolution and taking into account the spatial autocorrelation of references, i.e. the spectral similarity of spatially close samples. However, by not taking this phenomenon into account, evaluation biases may occur and thus overestimate the quality of the learning models. It is also a question of better identifying the methodological barriers in order to understand why it can be easy or complicated for an algorithm to identify one species from another. The general objective of the thesis is to study the potential and the obstacles concerning the idenficiation of forest tree species from satellite images time series with high spatial, spectral and temporal resolution. The first objective is to study the temporal stability of predictions from a nine-year archive of the Formosat-2 satellite. More specifically, the work focuses on the implementation of a validation method that is as faithful as possible to the observed quality of the maps. The second objective focuses on the link between in situ phenological events (leaf growth at the beginning of the season, or leaf loss and coloration at the end of the season) and what can be observed by remote sensing. In addition to the ability to detect these events, it will be studied whether what allows the algorithms to identify tree species from each other is related to species-specific behaviors
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Petitjean, François. "Dynamic time warping : apports théoriques pour l'analyse de données temporelles : application à la classification de séries temporelles d'images satellites." Thesis, Strasbourg, 2012. http://www.theses.fr/2012STRAD023.

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Les séries temporelles d’images satellites (STIS) sont des données cruciales pour l’observation de la terre. Les séries temporelles actuelles sont soit des séries à haute résolution temporelle (Spot-Végétation, MODIS), soit des séries à haute résolution spatiale (Landsat). Dans les années à venir, les séries temporelles d’images satellites à hautes résolutions spatiale et temporelle vont être produites par le programme Sentinel de l’ESA. Afin de traiter efficacement ces immenses quantités de données qui vont être produites (par exemple, Sentinel-2 couvrira la surface de la terre tous les cinq jours, avec des résolutions spatiales allant de 10m à 60m et disposera de 13 bandes spectrales), de nouvelles méthodes ont besoin d’être développées. Cette thèse se focalise sur la comparaison des profils d’évolution radiométrique, et plus précisément la mesure de similarité « Dynamic Time Warping », qui constitue un outil permettant d’exploiter la structuration temporelle des séries d’images satellites
Satellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions, which aim at providing a coverage of the Earth every few days with high spatial resolution (ESA’s Sentinel program). In the case of optical imagery, it will be possible to produce land use and cover change maps with detailed nomenclatures. However, due to meteorological phenomena, such as clouds, these time series will become irregular in terms of temporal sampling. In order to consistently handle the huge amount of information that will be produced (for instance, Sentinel-2 will cover the entire Earth’s surface every five days, with 10m to 60m spatial resolution and 13 spectral bands), new methods have to be developed. This Ph.D. thesis focuses on the “Dynamic Time Warping” similarity measure, which is able to take the most of the temporal structure of the data, in order to provide an efficient and relevant analysis of the remotely observed phenomena
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Shen, Meicheng. "Statistical Estimation of Vegetation Production in the Northern High Latitude Region based on Satellite Image Time Series." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563552594966495.

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Books on the topic "Time series of satellite images"

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Nunes Kehl, Thiago, Viviane Todt, Maurício Roberto Veronez, and Silvio Cesar Cazella. Real time deforestation detection using ANN and Satellite images. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15741-2.

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Computing brain activity maps from fMRI time-series images. Cambridge: Cambridge University Press, 2007.

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Remsberg, Ellis E. Time series comparisons of satellite and rocketsonde temperatures in 1978-79. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.

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Joel, Katz Eli, and United States. National Aeronautics and Space Administration., eds. A comparison of coincidental time series of the ocean surface height by satellite altimeter, mooring, and inverted echo sounder: Final technical report. Palisades, NY: Lamont-Doherty Earth Observatory of Columbia University, 1994.

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Comiso, Josefino C. Polar microwave brightness temperatures from Nimbus-7 SMMR: Time series of daily and monthly maps from 1978 to 1987. Washington, D.C: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1989.

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Jay, Zwally H., and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Polar microwave brightness temperatures from Nimbus-7 SMMR: Time series of daily and monthly maps from 1978 to 1987. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1989.

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T, DeLand Matthew, Hilsenrath Ernest, and United States. National Aeronautics and Space Administration., eds. Analysis of solar spectral irradiance measurements from the SBUV/2-series and the SSBUV instruments: Semi-annual report ... 1 March 1996 to 31 August 1996. [Washington, DC: National Aeronautics and Space Administration, 1996.

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T, DeLand Matthew, Hilsenrath Ernest, and United States. National Aeronautics and Space Administration., eds. Analysis of solar spectral irradiance measurements from the SBUV/2-series and the SSBUV instruments: Semi-annual report, period of performance: 1 March 1997 to 31 August 1997; contract number: NASW-4864. [Washington, DC: National Aeronautics and Space Administration, 1997.

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T, DeLand Matthew, Hilsenrath Ernest, and United States. National Aeronautics and Space Administration., eds. Analysis of solar spectral irradiance measurements from the SBUV/2-series and the SSBUV instruments: Semi-annual report, period of performance: 1 March 1997 to 31 August 1997; contract number: NASW-4864. [Washington, DC: National Aeronautics and Space Administration, 1997.

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T, DeLand Matthew, Hilsenrath Ernest, and United States. National Aeronautics and Space Administration., eds. Analysis of solar spectral irradiance measurements from the SBUV/2-series and the SSBUV instruments: Semi-annual report, period of performance: 31 August 1996 to 28 February 1997, contract number-- NASW-4864. [Washington, DC: National Aeronautics and Space Administration, 1997.

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Book chapters on the topic "Time series of satellite images"

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Al-Obed, Meshari, Sief Uddin, and Ashraf Ramadhan. "Dust Storm Satellite Images." In Atlas of Fallen Dust in Kuwait, 1–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66977-5_1.

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Abstract DATA of Aerosol Robotic Network (Aeronet) stations and Ozone Monitoring Instrument (OMI) were obtained to get valuable and reliable information about the occurrence of dust events. In addition to Total Ozone Mapping Spectrometer (TOMS) provide informative and long dust events record. To analyze the dust time series, monthly, annual and seasonal linear trends are applied to the dust time series. This is achieved by summing the total number of dusty hours for each month and then the total number of dusty days for the month is calculated. Dust trend analysis includes; annual, winter, spring, summer and autumn with the rate of change. Dust frequency of seasons in days/season before and after sorting in a descending manner from 1984 to 2013. Satelliteimagesuse for PM2.5 Estimation and concentrations Remote sensing-based measurements Calibration of Field and Laboratory Equipment. Particle concentrations in different size ranges and the total suspended particulate matter in the air in Kuwait. Dust deposition rates were monitored and analyzed in Kuwait at the northern ArabianGulf to estimate quantities of fallen dust within major eight dust trajectories in the ArabianGulf. Kuwait is surrounded by five major sources of dust rather than intermediate dust source areas that are listed. Satelliteimages from 2000 to 2010 were used to identify major dust trajectories within seven major deserts in the world.
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Arya, K. V., and Suggula Jagadeesh. "Time Series Forecasting of Soil Moisture Using Satellite Images." In Communications in Computer and Information Science, 385–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07005-1_33.

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Garnot, Vivien Sainte Fare, and Loic Landrieu. "Lightweight Temporal Self-attention for Classifying Satellite Images Time Series." In Advanced Analytics and Learning on Temporal Data, 171–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65742-0_12.

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Sanchez, Eduardo H., Mathieu Serrurier, and Mathias Ortner. "Learning Disentangled Representations of Satellite Image Time Series." In Machine Learning and Knowledge Discovery in Databases, 306–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46133-1_19.

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Gomes da Silva, Paula, Anne-Laure Beck, Jara Martinez Sanchez, Raúl Medina Santanmaria, Martin Jones, and Amine Taji. "Advances on coastal erosion assessment from satellite earth observations: exploring the use of Sentinel products along with very high resolution sensors." In Proceedings e report, 412–21. Florence: Firenze University Press, 2020. http://dx.doi.org/10.36253/978-88-5518-147-1.41.

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This work proposes the use of automatic co-registered satellite images to obtain large, high frequency and highly accurate shorelines time series. High resolution images are used to co-register Landsat and Sentinel-2 images. 90% of the co-registered images presented vertical and horizontal shift lower than 3 m. Satellite derived shorelines presented errors lower than mission’s precision. A discussion is presented on the applicability of those shorelines through an application to Tordera Delta (Spain).
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Honda, Rie, and Osamu Konishi. "Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB." In Principles of Data Mining and Knowledge Discovery, 204–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44794-6_17.

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Chakroun, Hedia. "Drought Assessment in Tunisia by Time-Series Satellite Images: An Ecohydrologic Approach." In Springer Water, 233–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63668-5_12.

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da Silva Adeu, Rodrigo de Sales, Karine Reis Ferreira, Pedro R. Andrade, and Lorena Santos. "Assessing Satellite Image Time Series Clustering Using Growing SOM." In Computational Science and Its Applications – ICCSA 2020, 270–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58814-4_19.

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Petitjean, François, Pierre Gançarski, Florent Masseglia, and Germain Forestier. "Analysing Satellite Image Time Series by Means of Pattern Mining." In Intelligent Data Engineering and Automated Learning – IDEAL 2010, 45–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15381-5_6.

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Nguyen, Tuan, Nicolas Méger, Christophe Rigotti, Catherine Pothier, and Rémi Andreoli. "SITS-P2miner: Pattern-Based Mining of Satellite Image Time Series." In Machine Learning and Knowledge Discovery in Databases, 63–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46131-1_14.

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Conference papers on the topic "Time series of satellite images"

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Keswani, Mahesh, Sanket Mahale, Rahul Kanwal, and Shalu Chopra. "Land Cover Classification from Time Series Satellite Images." In 2021 2nd International Conference for Emerging Technology (INCET). IEEE, 2021. http://dx.doi.org/10.1109/incet51464.2021.9456315.

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Petitjean, Francois, Jordi Inglada, and Pierre Gancarskv. "Clustering of satellite image time series under Time Warping." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005050.

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RÖDER, A., T. KÜMMERLE, and J. HILL. "EXTENDING TIME-SERIES OF SATELLITE IMAGES BY RADIOMETRIC INTERCALIBRATION." In Proceedings of the Second International Workshop on the Multitemp 2003. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702630_0003.

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Radoi, Anamaria, and Mihai Datcu. "Spatio-temporal characterization in satellite image time series." In 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp). IEEE, 2015. http://dx.doi.org/10.1109/multi-temp.2015.7245805.

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Giros, A. "Comparison of Partitions of Two Images for Satellite Image Time Series Segmentation." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.670.

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North, Heather, D. Pairman, S. E. Belliss, and J. Cuff. "Classifying agricultural land uses with time series of satellite images." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6352319.

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Lafabregue, Baptiste, Anne Puissant, Jonathan Weber, and Germain Forestier. "Deep Clustering Methods Study Applied to Satellite Images Time Series." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884322.

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Lodge, Felicity, Nicolas Meger, Christophe Rigotti, Catherine Pothier, and Marie-Pierre Doin. "Iterative summarization of satellite image time series." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6946703.

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Griparis, Andreea, Anamaria Rădoi, Daniela Faur, and Mihai Datcu. "Visual Exploration of Satellite Image Time Series." In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023. http://dx.doi.org/10.1109/igarss52108.2023.10282850.

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Tuna, Caglayan, Francois Merciol, and Sebastien Lefevre. "Attribute Profiles For Satellite Image Time Series." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8898493.

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Reports on the topic "Time series of satellite images"

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Harris, Brian, Kathleen Harris, Navid Jafari, Jasmine Bekkaye, Elizabeth Murray, and Safra Altman. Selection of a time series of beneficial use wetland creation sites in the Sabine National Wildlife Refuge for use in restoration trajectory development. Engineer Research and Development Center (U.S.), September 2023. http://dx.doi.org/10.21079/11681/47579.

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The development of regional restoration trajectories of marsh creation and nourishment projects is key to improved design, management, and implementation of adaptive management principles. Synthesizing information from multiple marsh creation projects constructed at various times but with consistent site characteristics and borrow material sources, helps elucidate restoration success in a specific region. Specifically, this technical note (TN) documents the process of determining a suitable study area, construction methods, and the current state of establishing sites in the Louisiana Gulf Coast that could be used for restoration trajectory development. This investigation compiled information from the construction phases, Landset 8 satellite imagery, and the most recent digital elevation model (DEM) to investigate elevation and vegetation establishment within these sites.
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Deschamps, Henschel, and Robert. PR-420-123712-R01 Lateral Ground Movement Detection Capabilities Derived from Synthetic Aperture Radar. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), November 2014. http://dx.doi.org/10.55274/r0010831.

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The objective of this research was to quantify long-term ground deformation at the Belridge Oil Field, in the San Joaquin Valley (SJV), California using operational Interferometric Synthetic Aperture Radar (InSAR) monitoring techniques. A high spatial and temporal resolution, millimeter-precision time-series of ground deformation measurements was produced for the entire oil field from 2000 to 2012 using imagery from multiple satellites and beam modes. Trihedral Corner Reflectors (CRs) with co-located Global Navigation Satellite System (GNSS) units were used to validate the wide-area measurements along a section of Southern California Gas Company (SoCalGas) Line 7056. The GNSS measurements were also used to validate the precision of the InSAR measurements, and to determine what component of the overall motion was lateral motion. Deformation profiles over Lines 1203 were analyzed to identify periods of rapid deformation related to known pipeline incidents. Finally, we also investigated the use Multiple Aperture Interferometry (MAI) for measuring horizontal motion in the alongtrack (north-south) direction. The result is a detailed, seamless, long-term, validated time-series of ground change observations that could prove useful for further analysis of reservoir changes. Combined with injection and production data, the results may be used to extend an understanding of the geomechanics of Enhanced Oil Recovery (EOR) fields. This work reinforces the operational capability of InSAR for monitoring both EOR reservoir dynamics and deformation over buried pipelines.
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Swanson, David. Stability of ice wedges in Alaska's Arctic National Parks, 1951-2019. National Park Service, May 2022. http://dx.doi.org/10.36967/nrr-2293324.

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Ice-wedge polygons are a striking and widespread feature of the arctic landscape. Ice wedges are vulnerable to thaw because they are nearly pure ice bodies near the surface, with little insulating overlying material. Ice-wedge polygon monitoring is a part of the permafrost monitoring program for the National Park Services Arctic Inventory and Monitoring Network (ARCN, the five National Park units in northern Alaska). The present report is a re-analysis of ice-wedge condition in three study areas, based on satellite images taken in 2019 and 2020 (sampling episode 3). Results are compared to previous analyses based on aerial photographs from 1950-51 (episode 1) and satellite images from 2006-2009 (episode 2). Significant ice-wedge degradation occurred between sampling episodes 1 and 2 in one of the study areas (in Kobuk Valley National Park, KOVA). Sampling episode 3 revealed relatively minor changes from episode 2 in all three areas. This is somewhat surprising given the record warm temperatures that occurred between sampling episodes 2 and 3. Apparently the recent warming did not cross any thresholds that would trigger immediate and widespread visible changes in ice wedges, or insufficient time has elapsed since the recent onset of warmer temperatures in 2014. However, the effects of previous ice-wedge degradation continued to be manifested in new drainage channels that formed by linkage of pits from previous ice-wedge degradation. The Noatak National Preserve (NOAT) study area was affected by wildfires in 1977 and 2010, and comparison of burned to unburned areas in subsequent sampling episodes failed to show significant new ice-wedge degradation brought about by these fires.
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Swanson, David, and Celia Hampton-Miller. Drained lakes in Bering Land Bridge National Preserve: Vegetation succession and impacts on loon habitat. National Park Service, January 2023. http://dx.doi.org/10.36967/2296593.

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The northern coastal plain of Bering Land Bridge National Preserve (BELA) lost lakes at an alarming rate over the first two decades of this century, including four lakes over 100 ha in size in 2018-2019 alone. To understand the effects of these lake drainages, we sampled vegetation of these lakes in 2019 (a reconnaissance visit) and 2021 (for the installation of permanent vegetation monitoring plots). We used these data to summarize the changes that occurred in the first three years after drainage, and to create vegetation maps from 3-m resolution satellite images coinciding with the visit dates. We used time series of these satellite images to study the rate of drainage and vegetation colonization on the lakes. We analyzed our existing data from older drained lake basins (estimated to be more than 200 years since drainage) and reviewed the literature on vegetation change in drained lakes to understand the vegetation changes that are likely in the future. Finally, we used a model of lake occupancy by loons developed by Mizel et al. (2021) to predict the effect of the 2018-2019 lake drainages on available loon habitat, using both our detailed maps of the four sampled drained lakes, and also data on all drained lakes over most of northern BELA derived from Landsat satellite images. Our results show that the four study lakes drained early in the summer, before the end of June, in 2018 (3 lakes) and 2019 (one lake). A combination of record warm weather and heavy snowfall made 2018 and 2019 especially favorable for lake drainage: thaw subsidence probably enlarged existing drainage outlet channels from the lakes, and large amounts of spring snowmelt runoff deepened the outlet channels by thermal erosion (the combination of thaw and erosion). Drainage exposed moist loamy sediment on the lake bottoms that was rapidly colonized by plants. Substantial vegetation cover developed by late summer in the same year as lake drainage in one lake, in the first post-drainage summer in a second lake, and during the 2nd year after drainage in the remaining two lakes. The first vegetation communities to develop consisted of just one or two dominant species, notably Eleocharis acicularis (spike rush), Equisetum arvense (horsetail), and/or Tephroseris palustris (mastodon flower). Other important early species were Arctophila fulva (pendant grass) and Rorippa palustris (yellow cress). By year 3, the communities had become more diverse, with significant cover by taller wetland graminoid species, including A. fulva, Eriophorum scheuchzeri, and Carex aquatilis. Frozen soil was observed in most locations on the lakes in July of 2021, suggesting that permafrost was forming on the lake bottoms. Comparison of the three-year trends in vegetation change with data from older lake basins suggest that ultimately most lake basins will develop wet tundra communities dominated by Carex aquatilis and mosses, with various low shrub species on acid, peat-dominated soils and permafrost; however, this process should take several centuries. The loon habitat model suggests that drainage essentially eliminated the potential habitat for Yellow-billed Loons on the four study lakes, because the residuals ponds were too small for Yellow-billed Loons to take flight from. A total of 17 lakes drained in northern BELA in 2018-2019. As a result, the potential Yellow-billed Loon nesting habitat in northern BELA probably decreased by approximately 2%, while habitat for Pacific Loons decreased less, by about 0.6%. Habitat for the more abundant Red-throated Loons probably increased slightly as a result of lake drainage, because of their ability to use the small residual ponds created by lake drainage.
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Thompson, Anna, Michael Loso, Sydney Mooneyham, Brandon Tober, Christopher Larsen, and John Holt. Surficial geology and proglacial lake change at S?t? Tlein (Malaspina Glacier), Wrangell-St. Elias National Park and Preserve, Alaska. National Park Service, 2024. http://dx.doi.org/10.36967/2301689.

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S?t? Tlein (Tlingit for ?Big Glacier?) is the traditional name for what has recently been called Malaspina Glacier, the largest glacier in Alaska. The piedmont terminal lobe of S?t? Tlein is protected from the adjacent Pacific Ocean by a narrow, vegetated foreland dotted with proglacial lakes. Ice of the piedmont lobe is largely covered with debris and vegetation. These lakes and sedimentary deposits impact rates of melt and calving and therefore impact ongoing evolution of the glacier itself. To document these features, we present 1) a new surficial geology map for the foreland and piedmont lobe of S?t? Tlein (an area of 3477 km2) at a scale of 1:24,000, and 2) a detailed time-series of proglacial lake extents. The surficial geology is referenced to a 2012 IFSAR Digital Terrain Model with 5-m resolution, supplemented with additional satellite images, maps, and digital elevation models. We visited the foreland in 2021 to ground-truth portions of the mapped area. Lake outlines were digitized from Landsat imagery, focusing on lakes adjacent to the central ?Seward Lobe? of S?t? Tlein. A majority of the mapping area is occupied by glacier ice, a sizable fraction of which is covered by supraglacial debris of varying thicknesses. Off glacier, in the foreland, glacial outwash is the most common mapping unit, followed by moraines of varying ages and finally by marine beaches, bars, and lagoons. Perhaps surprisingly, given significant changes in the glacier itself over the last half-century, these deposits have not changed dramatically since a similar map was produced by Plafker and Miller in 1958. The most significant changes we found are related to lake development. Other than Malaspina Lake, the largest and most persistent lake in the foreland, proglacial lakes were uncommon in the foreland in 1958. Our mapping shows that lake numbers on the Seward Lobe increased from 5 to more than 200 between 1972 and 2020. Most of the new thermokarst lakes are small, compared to Malaspina Lake, but may be having strong impacts on the future evolution of S?t? Tlein. One of these new lakes, Sitkagi Lagoon, is ice-walled and receives input from the Pacific Ocean, portending the possible initiation of catastrophic tidewater glacier retreat.
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Jääskeläinen, Emmihenna. Construction of reliable albedo time series. Finnish Meteorological Institute, September 2023. http://dx.doi.org/10.35614/isbn.9789523361782.

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A reliable satellite-based black-sky albedo time series is a crucial part of detecting changes in the climate. This thesis studies the solutions to several uncertainties impairing the quality of the black-sky albedo time series. These solutions include creating a long dynamic aerosol optical depth time series for enhancing the removal of atmospheric effects, a method to fill missing data to improve spatial and temporal coverage, and creating a function to correctly model the diurnal variation of melting snow albedo. Mathematical methods are the center pieces of the solutions found in this thesis. Creating a melting snow albedo function and the construction of an aerosol optical depth time series lean on a linear regression approach, whereas the process to fill missing values is based on gradient boosting, a machine learning method that is in turn based on decision trees. These methods reflect the basic nature of these problems as well as the need to take into account the large amounts of satellite-based data and computational resources available.
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Melrose, Rachel, Jeff Kingwell, Leo Lymburner, and Rohan Coghlan. Murray-Darling Basin vegetation monitoring project : using time series Landsat Satellite data for the assessment of vegetation control. Geoscience Australia, 2013. http://dx.doi.org/10.11636/record.2013.037.

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Salazar, Lina, Ana Claudia Palacios, Michael Selvaraj, and Frank Montenegro. Using Satellite Images to Measure Crop Productivity: Long-Term Impact Assessment of a Randomized Technology Adoption Program in the Dominican Republic. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003604.

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This study combines three rounds of surveys with remote sensing to measure long-term impacts of a randomized irrigation program in the Dominican Republic. Specifically, Landsat 7 and Landsat 8 satellite images are used to measure the causal effects of the program on agricultural productivity, measured through vegetation indices (NDVI and OSAVI). To this end, 377 plots were analyzed (129 treated and 248 controls) for the period from 2011 to 2019. Following a Differencein-Differences (DD) and Event study methodology, the results confirmed that program beneficiaries have higher vegetation indices, and therefore experienced a higher productivity throughout the post-treatment period. Also, there is some evidence of spillover effects to neighboring farmers. Furthermore, the Event Study model shows that productivity impacts are obtained in the third year after the adoption takes place. These findings suggest that adoption of irrigation technologies can be a long and complex process that requires time to generate productivity impacts. In a more general sense, this study reveals the great potential that exists in combining field data with remote sensing information to assess long-term impacts of agricultural programs on agricultural productivity.
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Rosinska, Olena. Образи батьків у молодіжних серіалах: наратив протистояння. Ivan Franko National University of Lviv, March 2023. http://dx.doi.org/10.30970/vjo.2023.52-53.11748.

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The subject of the research in the publication is the method of parents-children conflicts construction and the typology of parents’ images in teen serials of Ukraine and Poland. For analysis such series as “School” (Ukraine, 2017), “First sparrows” (Ukraine (2020), “Sex, Insta and ZNO” (Ukraine, 2021), “Sexify” (Poland, 2021) have been chosen; that allows drawing parallels between these media products made at different time, specify the methods of reflecting the conflicts between parents and children, peculiarities in constructing the parents’ images in each of the series, typology of the images due to psychological problems actualized in the series. The purpose of the research is to specify media narratives in representing the parents-children conflict and images formation based on the material of teen series. The purpose of the research can be reached due to the application of content analysis as a system research technique for objective description of the available content of communication in media material; such methods of analysis as comparison, synthesis, narrative analysis. Due to the use of the above methods, the following results have been reached: summarized the typology of conflicts in the series specified outlining those storylines and characters related to these conflicts, in particular, the conflict of opinions, values and behavior; determined and systemized typological images of parents in the series being researched – aggressive parents, parents imposing their own vision of the future on a child, making them implement parents’ own dreams and comply with the stereotypes topical for them; asocial parents, parents who cannot cope with their own lives, etc.: write the narrative strategies of formation of these kinds of images. Thus, the research outlines particular media psychological problems related to the narratives in teen series made in Ukraine and Poland. The perspective of the research is the engagement of larger volume of media materials of the thematic group, determination of new problematic areas to deepen media psychological context. Key words: teen series, narrative, typology of images, conflict.
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Temple, Dorota S., Jason S. Polly, Meghan Hegarty-Craver, James I. Rineer, Daniel Lapidus, Kemen Austin, Katherine P. Woodward, and Robert H. Beach III. The View From Above: Satellites Inform Decision-Making for Food Security. RTI Press, August 2019. http://dx.doi.org/10.3768/rtipress.2019.rb.0021.1908.

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Despite notable progress in reducing global poverty and hunger in recent decades, about one out of nine people in the world suffers from hunger and malnutrition. Stakeholders charged with making decisions pertaining to agricultural production, development priorities, and policies at a region-to-country scale require quantitative and up-to-date information on the types of crops being cultivated, the acreage under cultivation, and crop yields. However, many low- and middle-income countries lack the infrastructure and resources for frequent and extensive agricultural field surveys to obtain this information. Technology supports a change of paradigm. Traditional methods of obtaining agricultural information through field surveys are increasingly being augmented by images of the Earth acquired through sensors placed on satellites. The continued improvement in the resolution of satellite images, the establishment of open-access infrastructure for processing of the images, and the recent revolutionary progress in artificial intelligence make it feasible to obtain the information at low cost and in near-to-real time. In this brief, we discuss the use of satellite images to provide information about agricultural production in low-income countries, and we comment on research challenges and opportunities. We highlight the near-term potential of the methodology in the context of Rwanda, a country in sub-Saharan Africa whose government has recognized early the value of information technology in its strategic planning for food security and sustainability.
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