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17 results on '"Gormus, Esra Tunc"'

1. Preliminary Results of Canopy Fuel Load Estimation Using Mobile Laser Scanning in Turkish Red Pine Stands

Author

Coskuner, Kadir Alperen, Vatandaslar, Can, Ozturk, Murat, Harman, Ismet, Karahalil, Uzay, Berber, Tolga, Gormus, Esra Tunc, Bilgili, Ertugrul, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Dobrinkova, Nina, editor, and Fidanova, Stefka, editor

Published
2025
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3. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Author

Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, and Schlerf, Martin

Published
2022
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4. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Author

Universidad de Alicante. Departamento de Matemática Aplicada, Kooistra, Lammert, Berger, Katja, Brede, Benjamin, Graf, Lukas Valentin, Aasen, Helge, Roujean, Jean-Louis, Machwitz, Miriam, Schlerf, Martin, Atzberger, Clement, Prikaziuk, Egor, Ganeva, Dessislava, Tomelleri, Enrico, Croft, Holly, Reyes-Muñoz, Pablo, Garcia Millan, Virginia, Darvishzadeh, Roshanak, Koren, Gerbrand, Herrmann, Ittai, Rozenstein, Offer, Belda, Santiago, Rautiainen, Miina, Karlsen, Stein Rune, Silva, Cláudio F., Cerasoli, Sofia, Pierre, Jon, Kayıkçı, Emine Tanır, Halabuk, Andrej, Gormus, Esra Tunc, Fluit, Frank, Cai, Zhanzhang, Kycko, Marlena, Udelhoven, Thomas, Verrelst, Jochem, Universidad de Alicante. Departamento de Matemática Aplicada, Kooistra, Lammert, Berger, Katja, Brede, Benjamin, Graf, Lukas Valentin, Aasen, Helge, Roujean, Jean-Louis, Machwitz, Miriam, Schlerf, Martin, Atzberger, Clement, Prikaziuk, Egor, Ganeva, Dessislava, Tomelleri, Enrico, Croft, Holly, Reyes-Muñoz, Pablo, Garcia Millan, Virginia, Darvishzadeh, Roshanak, Koren, Gerbrand, Herrmann, Ittai, Rozenstein, Offer, Belda, Santiago, Rautiainen, Miina, Karlsen, Stein Rune, Silva, Cláudio F., Cerasoli, Sofia, Pierre, Jon, Kayıkçı, Emine Tanır, Halabuk, Andrej, Gormus, Esra Tunc, Fluit, Frank, Cai, Zhanzhang, Kycko, Marlena, Udelhoven, Thomas, and Verrelst, Jochem

Abstract

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-com

Published
2024

5. Improved classification of remote sensing imagery using image fusion techniques

Author

Gormus, Esra Tunc

Subjects
621.3678
Abstract

Remote sensing is a quick and inexpensive way of gathering information about the Earth. It enables one to constantly get updated information from satellite images for real-time local and global mapping of environmental changes. Current classification methods used for extracting relevant knowledge from this huge information pool are not very efficient because of the limited training samples and high dimensionality of the images. Information fusion is often used in order to improve the classification accuracy prior or after performing classification. However, these techniques cannot always successfully overcome the aforementioned issues. Therefore, in this thesis, new methods are introduced in order to increase the classification accuracy of remotely sensed data by means of information fusion techniques. This thesis is structured in three parts. In the first part, a novel pixel based image fusion technique is introduced to fuse optical and SAR image data in order to increase classification accuracy. Fused images obtained via conventional fusion methods may not contain enough information for subsequent processing such as classification or feature extraction. The proposed method aims to keep the maximum contextual and spatial information from the source data by exploiting the relationship between spatial domain cumulants and wavelet domain cumulants. The novelty of the method consists in integrating the relationship between spatial and wavelet domain cumulants of the source images into an image fusion process as well as in employing these wavelet cumulants for optimisation of weights in a Cauchy convolution based image fusion scheme. In the second part, a novel feature based image fusion method is proposed in order to increase the classification accuracy of hyperspectral images. An application of Empirical Mode Decomposition (EMD) to wavelet based dimensionality reduction is presented with an aim to generate the smallest set I of features that leads to better classification accuracy compared to single tech! niques. Useful spectral information for hyperspectral image classi6cation can be oj:>tained by applying the Wavelet Transform (WT) to each hyperspectral signature. As EMD has the ability to describe short term spatial changes in frequencies, it helps to get a better understanding of the spatial information of the signal. In order to take advantage of both spectral and spatial information, a novel dimensionality reduction method is introduced, which relies on using the wavelet transform of EMD features. This leads to better class separability and hence to better classification.

Published
2013

7. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Author

Global Ecohydrology and Sustainability, Environmental Sciences, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, Schlerf, Martin, Global Ecohydrology and Sustainability, Environmental Sciences, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, and Schlerf, Martin

Published
2022

8. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain:A review

Author

Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, Schlerf, Martin, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, and Schlerf, Martin

Abstract

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analys

Published
2022

9. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Author

Berger, K, Machwitz, M, Kycko, M, Kefauver, S, Van Wittenberghe, S, Gerhards, M, Verrelst, J, Atzberger, C, van der Tol, C, Damm, A, Rascher, U, Herrmann, I, Paz, V, Fahrner, S, Pieruschka, R, Prikaziuk, E, Buchaillot, M, Halabuk, A, Celesti, M, Koren, G, Gormus, E, Rossini, M, Foerster, M, Siegmann, B, Abdelbaki, A, Tagliabue, G, Hank, T, Darvishzadeh, R, Aasen, H, Garcia, M, Pôças, I, Bandopadhyay, S, Sulis, M, Tomelleri, E, Rozenstein, O, Filchev, L, Stancile, G, Schlerf, M, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, Schlerf, Martin, Berger, K, Machwitz, M, Kycko, M, Kefauver, S, Van Wittenberghe, S, Gerhards, M, Verrelst, J, Atzberger, C, van der Tol, C, Damm, A, Rascher, U, Herrmann, I, Paz, V, Fahrner, S, Pieruschka, R, Prikaziuk, E, Buchaillot, M, Halabuk, A, Celesti, M, Koren, G, Gormus, E, Rossini, M, Foerster, M, Siegmann, B, Abdelbaki, A, Tagliabue, G, Hank, T, Darvishzadeh, R, Aasen, H, Garcia, M, Pôças, I, Bandopadhyay, S, Sulis, M, Tomelleri, E, Rozenstein, O, Filchev, L, Stancile, G, Schlerf, M, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, and Schlerf, Martin

Abstract

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analys

Published
2022

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