22 results on '"Prošek, Jiří"'
Search Results
2. Effects of environmental conditions on ICESat-2 terrain and canopy heights retrievals in Central European mountains
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Moudrý, Vítězslav, Gdulová, Kateřina, Gábor, Lukáš, Šárovcová, Eliška, Barták, Vojtěch, Leroy, François, Špatenková, Olga, Rocchini, Duccio, and Prošek, Jiří
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- 2022
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3. Choosing the Optimal Global Digital Elevation Model for Stream Network Delineation: Beyond Vertical Accuracy.
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Marešová, Jana, Bašta, Petr, Gdulová, Kateřina, Barták, Vojtěch, Kozhoridze, Giorgi, Šmída, Jiri, Markonis, Yannis, Rocchini, Duccio, Prošek, Jiří, Pracná, Petra, and Moudrý, Vítězslav
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DIGITAL elevation models ,NETWORK performance ,CONTRAST effect ,SPATIAL resolution ,LAND cover ,LIDAR - Abstract
Satellite‐derived global digital elevation models (DEMs) are essential for providing the topographic information needed in a wide range of hydrological applications. However, their use is limited by spatial resolution and vertical bias due to sensor limitations in observing bare terrain. Significant efforts have been made to improve the resolution of global DEMs (e.g., TanDEM‐X) and create bare‐earth DEMs (e.g., FABDEM, MERIT, CEDTM). We evaluated the vertical accuracy of bare‐earth and global DEMs in Central European mountains and submontane regions, and assessed how DEM resolution, vegetation offset removal, land cover, and terrain slope affect stream network delineation. Using lidar‐derived DTM and national stream networks as references, we found that: (a) bare‐earth DEMs outperform global DEMs across all land cover types. RMSEs increased with increasing slope for all DEMs in non‐forest areas. In forests, however, the negative effect of the slope was outweighed by the vegetation offset even for bare‐earth DTMs; (b) the accuracy of derived stream networks was affected by terrain slope and land cover more than by the vertical accuracy of DEMs. Stream network delineation performed poorly in non‐forest areas and relatively well in forests. Increasing slope improved the streams delineation performance; (c) using DEMs with higher resolution (e.g., 12 m TanDEM‐X) improved stream network delineation, but increasing resolution also increased the need for effective vegetation bias removal. Our results indicate that vertical accuracy alone does not reflect how well DEMs perform in stream network delineation. This underscores the need to include stream network performance in DEM quality rankings. Plain Language Summary: We evaluated the accuracy of different types of digital elevation models (DEMs) and derived stream networks in Central European mountains. Our focus was on satellite‐derived global DEMs, including bare‐earth DEMs (i.e., DEMs with vegetation and building offsets removed). Using lidar DTM and national stream networks as references, we found that bare‐earth DEMs consistently outperformed other DEMs across all evaluated land cover types. We observed that slope and land cover have contrasting effects on the accuracy of DEMs and delineated stream networks. As slope increases, DEMs accuracy decreases, while for delineated stream networks, the opposite trend is observed. Where land cover is concerned, DEMs' vertical accuracies (i.e., how well they represent the bare‐earth terrain) are lowest in forests, whereas the accuracy of stream network is lowest in non‐forest areas. Furthermore, we demonstrated that slope and land cover type considerably affect the accuracy of stream network delineation, more so than the vertical accuracy of the DEMs. Additionally, we found that using DEMs with higher resolution can improve stream network delineation, but increasing resolution also increases the need for effective vegetation bias removal. Therefore, removing vegetation and buildings offset from Tandem‐X DEM at a 12 m resolution would represent a major next step forward. Key Points: Differences between digital elevation models (DEMs) (i.e., their absolute vertical accuracy) have a lower effect on stream delineation than terrain slope and landcoverThe use of higher‐resolution DEMs to derive stream networks increases the importance of removing vegetation and building biasVertical accuracy alone does not reflect DEMs' performance in stream delineation, emphasizing the need to include this in quality evaluation [ABSTRACT FROM AUTHOR]
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- 2024
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4. Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter.
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Moudrý, Vítězslav, Bazzichetto, Manuele, Remelgado, Ruben, Devillers, Rodolphe, Lenoir, Jonathan, Mateo, Rubén G., Lembrechts, Jonas J., Sillero, Neftalí, Lecours, Vincent, Cord, Anna F., Barták, Vojtěch, Balej, Petr, Rocchini, Duccio, Torresani, Michele, Arenas‐Castro, Salvador, Man, Matěj, Prajzlerová, Dominika, Gdulová, Kateřina, Prošek, Jiří, and Marchetto, Elisa
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SPECIES distribution ,ECOLOGICAL niche ,SAMPLE size (Statistics) ,ECOLOGICAL models ,SPATIAL filters - Abstract
Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Effects of urbanization on taxonomic, functional and phylogenetic avian diversity in Europe
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Morelli, Federico, Benedetti, Yanina, Ibáñez-Álamo, Juan Diego, Tryjanowski, Piotr, Jokimäki, Jukka, Kaisanlahti-Jokimäki, Marja-Liisa, Suhonen, Jukka, Díaz, Mario, Møller, Anders Pape, Moravec, David, Prosek, Jiri, Bussière, Raphaël, Mägi, Marko, Kominos, Theodoros, Galanaki, Antonia, Bukas, Nikos, Marko, Gabor, Pruscini, Fabio, Tonelli, Mattia, Jerzak, Leszek, Ciebiera, Olaf, and Reif, Jiri
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- 2021
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6. Temperature buffering in temperate forests: Comparing microclimate models based on ground measurements with active and passive remote sensing
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Kašpar, Vít, Hederová, Lucia, Macek, Martin, Müllerová, Jana, Prošek, Jiří, Surový, Peter, Wild, Jan, and Kopecký, Martin
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- 2021
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7. How to Find Accurate Terrain and Canopy Height GEDI Footprints in Temperate Forests and Grasslands?
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Moudrý, Vítězslav, Prošek, Jiří, Marselis, Suzanne, Marešová, Jana, Šárovcová, Eliška, Gdulová, Kateřina, Kozhoridze, Giorgi, Torresani, Michele, Rocchini, Duccio, Eltner, Anette, Liu, Xiao, Potůčková, Markéta, Šedová, Adéla, Crespo‐Peremarch, Pablo, Torralba, Jesús, Ruiz, Luis A., Perrone, Michela, Špatenková, Olga, and Wild, Jan
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ECOSYSTEM dynamics , *ECOLOGICAL disturbances , *TEMPERATE forests , *TERRAIN mapping , *LAND cover - Abstract
Filtering approaches on Global Ecosystem Dynamics Investigation (GEDI) data differ considerably across existing studies and it is yet unclear which method is the most effective. We conducted an in‐depth analysis of GEDI's vertical accuracy in mapping terrain and canopy heights across three study sites in temperate forests and grasslands in Spain, California, and New Zealand. We started with unfiltered data (2,081,108 footprints) and describe a workflow for data filtering using Level 2A parameters and for geolocation error mitigation. We found that retaining observations with at least one detected mode eliminates noise more effectively than sensitivity. The accuracy of terrain and canopy height observations depended considerably on the number of modes, beam sensitivity, landcover, and terrain slope. In dense forests, a minimum sensitivity of 0.9 was required, while in areas with sparse vegetation, sensitivity of 0.5 sufficed. Sensitivity greater than 0.9 resulted in an overestimation of canopy height in grasslands, especially on steep slopes, where high sensitivity led to the detection of multiple modes. We suggest excluding observations with more than five modes in grasslands. We found that the most effective strategy for filtering low‐quality observations was to combine the quality flag and difference from TanDEM‐X, striking an optimal balance between eliminating poor‐quality data and preserving a maximum number of high‐quality observations. Positional shifts improved the accuracy of GEDI terrain estimates but not of vegetation height estimates. Our findings guide users to an easy way of processing of GEDI footprints, enabling the use of the most accurate data and leading to more reliable applications. Plain Language Summary: The Global Ecosystem Dynamics Investigation (GEDI) collected terrain and canopy observations using laser altimetry. The quality of terrain and canopy observations is influenced by acquisition conditions and land (cover) characteristics. Consequently, a considerable amount of GEDI observations is discarded as noise, and further filtering is necessary to retain only high‐quality observations. Our objective was to assess how environmental and acquisition characteristics influence the accuracy of terrain and canopy height of GEDI observations. Although the main objective of the GEDI mission was to map forests, we also focused on grasslands. GEDI serves not only as an essential source of information on canopy height but also provides accurate terrain observations. Furthermore, it is important to know that GEDI does not overestimate the height of low vegetation as this can result in an overestimation of carbon storage. We distinguished four steps in the GEDI data processing: (a) removal of noise observations, (b) removal of low‐quality data, (c) effect of additional acquisition characteristics, and (d) mitigation of geolocation error. We found that the accuracy of terrain and canopy height observations depended considerably on the number of detected modes, beam sensitivity, landcover, and terrain slope. Key Points: Terrain is crucial for estimates of canopy height, however only 20%–30% of footprints have an absolute error of terrain estimates <3 mThe quality of terrain and canopy height estimates depends on the interplay of number of modes, sensitivity, land cover, and terrain slopeNoise and low‐quality footprints can be successfully removed using number of modes, sensitivity, quality flag and difference from TanDEM‐X [ABSTRACT FROM AUTHOR]
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- 2024
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8. Comparison of three global canopy height maps and their applicability to biodiversity modeling: Accuracy issues revealed.
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Moudrý, Vítězslav, Gábor, Lukáš, Marselis, Suzanne, Pracná, Petra, Barták, Vojtěch, Prošek, Jiří, Navrátilová, Barbora, Novotný, Jan, Potůčková, Markéta, Gdulová, Kateřina, Crespo‐Peremarch, Pablo, Komárek, Jan, Malavasi, Marco, Rocchini, Duccio, Ruiz, Luis A., Torralba, Jesús, Torresani, Michele, Cazzolla Gatti, Roberto, and Wild, Jan
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STANDARD deviations ,AIRBORNE lasers ,FOREST mapping ,FOREST canopies ,TREE height - Abstract
Global mapping of forest height is an extremely important task for estimating habitat quality and modeling biodiversity. Recently, three global canopy height maps have been released, the global forest canopy height map (GFCH), the high‐resolution canopy height model of the Earth (HRCH), and the global map of tree canopy height (GMTCH). Here, we assessed their accuracy and usability for biodiversity modeling. We examined their accuracy by comparing them with the reference canopy height models derived from airborne laser scanning (ALS). Our results show considerable differences between the evaluated maps. The root mean square error ranged between 10 and 18 m for GFCH, 9–11 m for HRCH, and 10–17 m for GMTCH, respectively. GFCH and GMTCH consistently underestimated the height of all canopies regardless of their height, while HRCH tended to overestimate the height of low canopies and underestimate tall canopies. Biodiversity models using predicted global canopy height maps as input data are sufficient for estimating simple relationships between species occurrence and canopy height, but their use leads to a considerable decrease in the discrimination ability of the models and to mischaracterization of species niches where derived indices (e.g., canopy height heterogeneity) are concerned. We showed that canopy height heterogeneity is considerably underestimated in the evaluated global canopy height maps. We urge that for temperate areas rich in ALS data, activities should concentrate on harmonizing ALS canopy height maps rather than relying on modeled global products. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Integration of hyperspectral and LiDAR data for mapping small water bodies
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Prošek, Jiří, Gdulová, Kateřina, Barták, Vojtěch, Vojar, Jiří, Solský, Milič, Rocchini, Duccio, and Moudrý, Vítězslav
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- 2020
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10. UAV for mapping shrubland vegetation: Does fusion of spectral and vertical information derived from a single sensor increase the classification accuracy?
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Prošek, Jiří and Šímová, Petra
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- 2019
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11. The potential of Unmanned Aerial Systems: A tool towards precision classification of hard-to-distinguish vegetation types?
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Komárek, Jan, Klouček, Tomáš, and Prošek, Jiří
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- 2018
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12. Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection
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Moudrý, Vítězslav, Keil, Petr, Gábor, Lukáš, Lecours, Vincent, Zarzo-Arias, Alejandra, Barták, Vojtěch, Malavasi, Marco, Rocchini, Duccio, Torresani, Michele, Gdulová, Kateřina, Grattarola, Florencia, Leroy, François, Marchetto, Elisa, Thouverai, Elisa, Prošek, Jiří, Wild, Jan, Šímová, Petra, Moudrý, Vítězslav, Keil, Petr, Gábor, Lukáš, Lecours, Vincent, Zarzo-Arias, Alejandra, Barták, Vojtěch, Malavasi, Marco, Rocchini, Duccio, Torresani, Michele, Gdulová, Kateřina, Grattarola, Florencia, Leroy, François, Marchetto, Elisa, Thouverai, Elisa, Prošek, Jiří, Wild, Jan, and Šímová, Petra
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There is a lack of guidance on the choice of the spatial grain of predictor and response variables in species distribution models (SDM). This review summarizes the current state of the art with regard to the following points: (i) the effects of changing the resolution of predictor and response variables on model performance; (ii) the effect of conducting multi-grain versus single-grain analysis on model performance; and (iii) the role of land cover type and spatial autocorrelation in selecting the appropriate grain size. In the reviewed literature, we found that coarsening the resolution of the response variable typically leads to declining model performance. Therefore, we recommend aiming for finer resolutions unless there is a reason to do otherwise (e.g. expert knowledge of the ecological scale). We also found that so far, the improvements in model performance reported for multi-grain models have been relatively low and that useful predictions can be generated even from single-scale models. In addition, the use of high-resolution predictors improves model performance; however, there is only limited evidence on whether this applies to models with coarser-resolution response variables (e.g. 100 km2 and coarser). Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance. Thus, assessing the spatial autocorrelation of the predictors at multiple grains can provide insights into the impacts of coarsening their resolution on model performance. Overall, we observed a lack of studies examining the simultaneous manipulation of the resolution of predictor and response variables. We stress the need to explicitly report the resol
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- 2023
13. Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection
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Moudrý, Vítězslav, primary, Keil, Petr, additional, Cord, Anna F, additional, Gábor, Lukáš, additional, Lecours, Vincent, additional, Zarzo-Arias, Alejandra, additional, Barták, Vojtěch, additional, Malavasi, Marco, additional, Rocchini, Duccio, additional, Torresani, Michele, additional, Gdulová, Kateřina, additional, Grattarola, Florencia, additional, Leroy, François, additional, Marchetto, Elisa, additional, Thouverai, Elisa, additional, Prošek, Jiří, additional, Wild, Jan, additional, and Šímová, Petra, additional
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- 2023
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14. Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward
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Moudrý, Vítězslav, primary, Cord, Anna F., additional, Gábor, Lukáš, additional, Laurin, Gaia Vaglio, additional, Barták, Vojtěch, additional, Gdulová, Kateřina, additional, Malavasi, Marco, additional, Rocchini, Duccio, additional, Stereńczak, Krzysztof, additional, Prošek, Jiří, additional, Klápště, Petr, additional, and Wild, Jan, additional
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- 2022
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15. Applicability of Data Acquisition Characteristics to the Identification of Local Artefacts in Global Digital Elevation Models: Comparison of the Copernicus and TanDEM-X DEMs
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Marešová, Jana, primary, Gdulová, Kateřina, additional, Pracná, Petra, additional, Moravec, David, additional, Gábor, Lukáš, additional, Prošek, Jiří, additional, Barták, Vojtěch, additional, and Moudrý, Vítězslav, additional
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- 2021
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16. Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non-forest ecosystems
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Cunliffe, Andrew M., Anderson, Karen, Boschetti, Fabio, Brazier, Richard E., Graham, Hugh A., Myers‐Smith, Isla H., Astor, Thomas, Boer, Matthias M., Calvo, Leonor G., Clark, Patrick E., Cramer, Michael D., Encinas‐Lara, Miguel S., Escarzaga, Stephen M., Fernández‐Guisuraga, José M., Fisher, Adrian G., Gdulová, Kateřina, Gillespie, Breahna M., Griebel, Anne, Hanan, Niall P., Hanggito, Muhammad S., Haselberger, Stefan, Havrilla, Caroline A., Heilman, Phil, Ji, Wenjie, Karl, Jason W., Kirchhoff, Mario, Kraushaar, Sabine, Lyons, Mitchell B., Marzolff, Irene, Mauritz, Marguerite E., McIntire, Cameron D., Metzen, Daniel, Méndez‐Barroso, Luis A., Power, Simon C., Prošek, Jiří, Sanz‐Ablanedo, Enoc, Sauer, Katherine J., Schulze‐Brüninghoff, Damian, Šímová, Petra, Sitch, Stephen, Smit, Julian L., Steele, Caiti M., Suárez‐Seoane, Susana, Vargas, Sergio A., Villarreal, Miguel, Visser, Fleur, Wachendorf, Michael, Wirnsberger, Hannes, Wojcikiewicz, Robert, Ecologia, Facultad de Ciencias Biologicas y Ambientales, Sankey, Temuulen, and Carter, A
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Canopy ,Technology ,010504 meteorology & atmospheric sciences ,UAV ,0211 other engineering and technologies ,Canopy height model ,3308 Ingeniería y Tecnología del Medio Ambiente ,02 engineering and technology ,Ingeniería forestal ,01 natural sciences ,Ecosystem services ,Structure-from-motion photogrammetry ,QH301 ,Fine spatial resolution remote sensing ,structure‐from‐motion photogrammetry ,Forest ecology ,Unoccupied Aerial Vehicle Data Quantify Aboveground Biomass ,Ecosystem ,Computers in Earth Sciences ,QH540-549.5 ,Ecology, Evolution, Behavior and Systematics ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Nature and Landscape Conservation ,Biomass (ecology) ,GB ,Ecology ,GA ,Elevation ,Vegetation ,Ecología. Medio ambiente ,Drone ,Plant height ,Photogrammetry ,Environmental science ,Physical geography - Abstract
EU Horizon 2020 grant No. 776681 (PHUSICOS)..., Cunliffe, A.M., Anderson, K., Boschetti, F., Brazier, R.E., Graham, H.A., Myers-Smith, I.H., Astor, T., Boer, M.M., Calvo, L.G., Clark, P.E., Cramer, M.D., Encinas-Lara, M.S., Escarzaga, S.M., Fernández-Guisuraga, J.M., Fisher, A.G., Gdulová, K., Gillespie, B.M., Griebel, A., Hanan, N.P., Hanggito, M.S., Haselberger, S., Havrilla, C.A., Heilman, P., Ji, W., Karl, J.W., Kirchhoff, M., Kraushaar, S., Lyons, M.B., Marzolff, I., Mauritz, M.E., McIntire, C.D., Metzen, D., Méndez-Barroso, L.A., Power, S.C., Prošek, J., Sanz-Ablanedo, E., Sauer, K.J., Schulze-Brüninghoff, D., Šímová, P., Sitch, S., Smit, J.L., Steele, C.M., Suárez-Seoane, S., Vargas, S.A., Villarreal, M., Visser, F., Wachendorf, M., Wirnsberger, H., Wojcikiewicz, R.
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- 2021
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17. Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems
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Cunliffe, Andrew M., primary, Anderson, Karen, additional, Boschetti, Fabio, additional, Brazier, Richard E., additional, Graham, Hugh A., additional, Myers‐Smith, Isla H., additional, Astor, Thomas, additional, Boer, Matthias M., additional, Calvo, Leonor G., additional, Clark, Patrick E., additional, Cramer, Michael D., additional, Encinas‐Lara, Miguel S., additional, Escarzaga, Stephen M., additional, Fernández‐Guisuraga, José M., additional, Fisher, Adrian G., additional, Gdulová, Kateřina, additional, Gillespie, Breahna M., additional, Griebel, Anne, additional, Hanan, Niall P., additional, Hanggito, Muhammad S., additional, Haselberger, Stefan, additional, Havrilla, Caroline A., additional, Heilman, Phil, additional, Ji, Wenjie, additional, Karl, Jason W., additional, Kirchhoff, Mario, additional, Kraushaar, Sabine, additional, Lyons, Mitchell B., additional, Marzolff, Irene, additional, Mauritz, Marguerite E., additional, McIntire, Cameron D., additional, Metzen, Daniel, additional, Méndez‐Barroso, Luis A., additional, Power, Simon C., additional, Prošek, Jiří, additional, Sanz‐Ablanedo, Enoc, additional, Sauer, Katherine J., additional, Schulze‐Brüninghoff, Damian, additional, Šímová, Petra, additional, Sitch, Stephen, additional, Smit, Julian L., additional, Steele, Caiti M., additional, Suárez‐Seoane, Susana, additional, Vargas, Sergio A., additional, Villarreal, Miguel, additional, Visser, Fleur, additional, Wachendorf, Michael, additional, Wirnsberger, Hannes, additional, and Wojcikiewicz, Robert, additional
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- 2021
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18. Comparison of a commercial and home-assembled fixed-wing UAV for terrain mapping of a post-mining site under leaf-off conditions
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Moudrý, Vítězslav, primary, Urban, Rudolf, additional, Štroner, Martin, additional, Komárek, Jan, additional, Brouček, Josef, additional, and Prošek, Jiří, additional
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- 2018
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19. Comparison of a commercial and home-assembled fixed-wing UAV for terrain mapping of a post-mining site under leaf-off conditions.
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Moudrý, Vítězslav, Urban, Rudolf, Štroner, Martin, Komárek, Jan, Brouček, Josef, and Prošek, Jiří
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DRONE aircraft ,TERRAIN mapping ,DIGITAL elevation models ,GROUND vegetation cover ,ENVIRONMENTAL mapping - Abstract
Unmanned aerial vehicle (UAV) platforms are rapidly becoming popular in many research and industry sectors. Due to their relatively low purchase price and the fact they can be used to monitor areas that are difficult or even unsafe to access, they have been increasingly used in land surveying and mapping of smaller areas. Numerous UAV platforms equipped with various cameras are increasingly available on the market, differing in their suitability for environmental mapping. Surveyors therefore face a question whether to buy or assemble their own UAV. The objective of this study is to assess the performance of two fixed-wing UAV systems for land survey and mapping applications. In particular, we: (1) compared a commercial eBee platform equipped with a Sony Cybershot DSC-WX220 camera with zoom lens and a home assembled EasyStar II equipped with Nikon Coolpix A with a lens of fixed focal length to find out if a home-assembled solution can compete with specialized commercial platform; (2) investigated the utilization of UAV images acquired under leaf-off conditions for digital terrain model (DTM) generation with respect to vegetation cover (steppes and forests); (3) assessed whether an increase in the image quantity can compensate for a lower quality of images; and (4) compared the DTM derived from UAV imagery with the official Czech Republic airborne laser scanning (ALS)-derived DTM. One flight with Easystar II and two perpendicular flights with eBee were performed. From these three flights, four point clouds were derived (one from each flight, and one resulting from a combination of two eBee flights), supplemented with four ground filtered point clouds. The accuracy of point clouds and DTM was assessed through a comparison with a conventional GNSS survey. We successfully identified the bare ground during the leaf-off period in the deciduous forest using images from both platforms. Point densities of point clouds acquired with Easystar II exceeded the densities of those acquired with eBee even after combining images from two eBee flights. Root mean square error of all derived point clouds ranged between 0.11 and 0.19 m, exceeding the accuracy of a nationwide ALS-derived DTM in both forest and open steppe areas. The most accurate point cloud was acquired using Easystar II. This is likely due to a combined effect of the quality of onboard cameras, camera settings and environmental conditions during the flight. For users who prefer to have greater control over their options rather than being dependent on the commercially available kit solution, home-assembled kits utilizing drones capable of carrying any camera available on the market may be an advantage. [ABSTRACT FROM AUTHOR]
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- 2019
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20. Péče o zákazníky jako součást marketingového mixu
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Prošek, Jiří and Prošek, Jiří
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Tato diplomová práce má za cíl zmapovat historický vývoj péče o zákazníky, definuje péči o zákazníky jako součást strategií přežití podniku, vysvětlit souvislosti a přechody od transaction marketing k relations marketing. Představuje vztahy se zákazníky jako součást systému řízení kvality a definuje vztahy mezi systémem řízení vztahů se zákazníky a prvky marketingového mixu. V analytické části se zabývá procesy a metodami měření spokojenosti zákazníků v sektoru B2B. Projektová část vyhodnocuje informace získané analýzou a určuje výstupy z šetření spokojenosti včetně určení nápravných opatření pro odstranění nekorektních jevů., This thesis aims to chart the historical development of customer care, care of the customer defines as part of the firm's survival strategies, explain the context and the shift from transaction marketing to marketing relations. It represents the relationships with customers as part of the quality management system and defines the relationship between customer relationship management and marketing mix elements. The analytical part deals with the processes and methods of measuring customer satisfaction in the B2B sector. Design of the analysis assesses the information received and determines the outcomes of the investigation including the determination of the satisfaction of remedial measures to eliminate incorrect phenomena, Ústav marketingových komunikací, obhájeno
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- 2010
21. Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems
- Author
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Ecologia, Cunliffe, Andrew M., Anderson, Karen, Boschetti, Fabio, Brazier, Richard E., Graham, Hugh A., Myers‐Smith, Isla H., Astor, Thomas, Boer, Matthias M., Calvo Galván, María Leonor, Clark, Patrick E., Cramer, Michael D., Encinas‐Lara, Miguel S., Escarzaga, Stephen M., Fernández Guisuraga, José Manuel, Fisher, Adrian G., Gdulová, Kateřina, Gillespie, Breahna M., Griebel, Anne, Hanan, Niall P., Hanggito, Muhammad S., Haselberger, Stefan, Havrilla, Caroline A., Heilman, Phil, Ji, Wenjie, Karl, Jason W., Kirchhoff, Mario, Kraushaar, Sabine, Lyons, Mitchell B., Marzolff, Irene, Mauritz, Marguerite E., McIntire, Cameron D., Metzen, Daniel, Méndez‐Barroso, Luis A., Power, Simon C., Prošek, Jiří, Sanz Ablanedo, Enoc, Sauer, Katherine J., Schulze‐Brüninghoff, Damian, Šímová, Petra, Sitch, Stephen, Smit, Julian L., Steele, Caiti M., Suárez Seoane, Susana, Vargas, Sergio A., Villarreal, Miguel, Visser, Fleur, Wachendorf, Michael, Wirnsberger, Hannes, Wojcikiewicz, Robert, Ecologia, Cunliffe, Andrew M., Anderson, Karen, Boschetti, Fabio, Brazier, Richard E., Graham, Hugh A., Myers‐Smith, Isla H., Astor, Thomas, Boer, Matthias M., Calvo Galván, María Leonor, Clark, Patrick E., Cramer, Michael D., Encinas‐Lara, Miguel S., Escarzaga, Stephen M., Fernández Guisuraga, José Manuel, Fisher, Adrian G., Gdulová, Kateřina, Gillespie, Breahna M., Griebel, Anne, Hanan, Niall P., Hanggito, Muhammad S., Haselberger, Stefan, Havrilla, Caroline A., Heilman, Phil, Ji, Wenjie, Karl, Jason W., Kirchhoff, Mario, Kraushaar, Sabine, Lyons, Mitchell B., Marzolff, Irene, Mauritz, Marguerite E., McIntire, Cameron D., Metzen, Daniel, Méndez‐Barroso, Luis A., Power, Simon C., Prošek, Jiří, Sanz Ablanedo, Enoc, Sauer, Katherine J., Schulze‐Brüninghoff, Damian, Šímová, Petra, Sitch, Stephen, Smit, Julian L., Steele, Caiti M., Suárez Seoane, Susana, Vargas, Sergio A., Villarreal, Miguel, Visser, Fleur, Wachendorf, Michael, Wirnsberger, Hannes, and Wojcikiewicz, Robert
- Abstract
Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation model
22. Integration of hyperspectral and LiDAR data for mapping small water bodies
- Author
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Milič Solský, Jiří Prošek, Kateřina Gdulová, Vítězslav Moudrý, Jiří Vojar, Duccio Rocchini, Vojtěch Barták, Prošek, Jiří, Gdulová, Kateřina, Barták, Vojtěch, Vojar, Jiří, Solský, Milič, Rocchini, Duccio, and Moudrý, Vítězslav
- Subjects
Global and Planetary Change ,LiDAR ,010504 meteorology & atmospheric sciences ,Pixel ,Continuous monitoring ,0211 other engineering and technologies ,Hyperspectral imaging ,02 engineering and technology ,Management, Monitoring, Policy and Law ,Classification ,01 natural sciences ,Mining ,Support vector machine ,Water resources ,Tree (data structure) ,Identification (information) ,Lidar ,Fusion: Hyperspectral ,Environmental science ,Computers in Earth Sciences ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Earth-Surface Processes ,Remote sensing - Abstract
Inland water bodies are globally threatened by environmental degradation and climate change. On the other hand, new water bodies can be designed during landscape restoration (e.g. after coal mining). Effective management of new water resources requires continuous monitoring; in situ surveys are, however, extremely time-demanding. Remote sensing has been widely used for identifying water bodies. However, the use of optical imagery is constrained by accuracy problems related to the difficulty in distinguishing water features from other surfaces with low albedo, such as tree shadows. This is especially true when mapping water bodies of different sizes. To address these problems, we evaluated the potential of integrating hyperspectral data with LiDAR (hereinafter “integrative approach”). The study area consisted of several spoil heaps containing heterogeneous water bodies with a high variability of shape and size. We utilized object-based classification (Support Vector Machine) based on: (i) hyperspectral data; (ii) LiDAR variables; (iii) integration of both datasets. Besides, we classified hyperspectral data using pixel-based approaches (K-mean, spectral angle mapper). Individual approaches (hyperspectral data, LiDAR data and integrative approach) resulted in 2–22.4 % underestimation of the water surface area (i.e, omission error) and 0.4–1.5 % overestimation (i.e., commission error).The integrative approach yielded an improved discrimination of open water surface compared to other approaches (omission error of 2 % and commission error of 0.4 %). We also evaluated the success of detecting individual ponds; the integrative approach was the only one capable of detecting the water bodies with both omission and commission errors below 10 %. Finally, the assessment of misclassification reasons showed a successful elimination of shadows in the integrative approach. Our findings demonstrate that the integration of hyperspectral and LiDAR data can greatly improve the identification of small water bodies and can be applied in practice to support mapping of restoration process.
- Published
- 2020
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