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For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC contents at a field scale, Earth Observation (EO) can be a valuable data source for area-wide mapping. The extraction of exposed soils from EO data is challenging due to temporal or permanent vegetation cover, the influence of soil moisture or the condition of the soil surface. Compositing techniques of multitemporal satellite images provide an alternative to retrieve exposed soils and to produce a data source. The repeatable soil composites, containing averaged exposed soil areas over several years, are relatively independent from seasonal soil moisture and surface conditions and provide a new EO-based data source that can be used to estimate SOC contents over large geographical areas with a high spatial resolution. Here, we applied the Soil Composite Mapping Processor (SCMaP) to the Landsat archive between 1984 and 2014 of images covering Bavaria, Germany. Compared to existing SOC modeling approaches based on single scenes, the 30-year SCMaP soil reflectance composite (SRC) with a spatial resolution of 30 m is used. The SRC spectral information is correlated with point soil data using different machine learning algorithms to estimate the SOC contents in cropland topsoils of Bavaria. We developed a pre-processing technique to address the issue of combining point information with EO pixels for the purpose of modeling. We applied different modeling methods often used in EO soil studies to choose the best SOC prediction model. Based on the model accuracies and performances, the Random Forest (RF) showed the best capabilities to predict the SOC contents in Bavaria (R² = 0.67, RMSE = 1.24%, RPD = 1.77, CCC = 0.78). We further validated the model results with an independent dataset. The comparison between the measured and predicted SOC contents showed a mean difference of 0.11% SOC using the best RF model. The SCMaP SRC is a promising approach to predict the spatial SOC distribution over large geographical extents with a high spatial resolution (30 m).
Simone Zepp; Uta Heiden; Martin Bachmann; Martin Wiesmeier; Michael Steininger; Bas van Wesemael. Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites. Remote Sensing 2021, 13, 3141 .
AMA StyleSimone Zepp, Uta Heiden, Martin Bachmann, Martin Wiesmeier, Michael Steininger, Bas van Wesemael. Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites. Remote Sensing. 2021; 13 (16):3141.
Chicago/Turabian StyleSimone Zepp; Uta Heiden; Martin Bachmann; Martin Wiesmeier; Michael Steininger; Bas van Wesemael. 2021. "Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites." Remote Sensing 13, no. 16: 3141.
Effects of climate change-induced events on forest ecosystem dynamics of composition, function and structure call for increased long-term, interdisciplinary and integrated research on biodiversity indicators, in particular within strictly protected areas with extensive non-intervention zones. The long-established concept of forest supersites generally relies on long-term funds from national agencies and goes beyond the logistic and financial capabilities of state- or region-wide protected area administrations, universities and research institutes. We introduce the concept of data pools as a smaller-scale, user-driven and reasonable alternative to co-develop remote sensing and forest ecosystem science to validated products, biodiversity indicators and management plans. We demonstrate this concept with the Bohemian Forest Ecosystem Data Pool, which has been established as an interdisciplinary, international data pool within the strictly-protected Bavarian Forest and Šumava National Parks and currently comprises 10 active partner. We demonstrate how the structure and impact of the data pool differs from comparable cases. We assessed the international influence and visibility of the data pool with the help of a systematic literature search and a brief analysis of the results. Results primarily suggest an increase in the impact and visibility of published material during the lifespan of the data pool, with highest visibilities achieved by research conducted on leaf traits, vegetation phenology, and 3D-based forest inventory. We conclude that the data pool results in an efficient contribution to the concept of global biodiversity observatory by evolving towards a training platform, functioning as a pool of data and algorithms, directly communicating with management for implementation and providing test fields for feasibility studies on earth observation missions.
Hooman Latifi; Stefanie Holzwarth; Andrew Skidmore; Josef Brůna; Jaroslav Červenka; Roshanak Darvishzadeh; Martin Hais; Uta Heiden; Lucie Homolová; Peter Krzystek; Thomas Schneider; Martin Starý; Tiejun Wang; Jörg Müller; Marco Heurich. A laboratory for conceiving Essential Biodiversity Variables (EBVs)—The ‘Data pool initiative for the Bohemian Forest Ecosystem’. Methods in Ecology and Evolution 2021, 1 .
AMA StyleHooman Latifi, Stefanie Holzwarth, Andrew Skidmore, Josef Brůna, Jaroslav Červenka, Roshanak Darvishzadeh, Martin Hais, Uta Heiden, Lucie Homolová, Peter Krzystek, Thomas Schneider, Martin Starý, Tiejun Wang, Jörg Müller, Marco Heurich. A laboratory for conceiving Essential Biodiversity Variables (EBVs)—The ‘Data pool initiative for the Bohemian Forest Ecosystem’. Methods in Ecology and Evolution. 2021; ():1.
Chicago/Turabian StyleHooman Latifi; Stefanie Holzwarth; Andrew Skidmore; Josef Brůna; Jaroslav Červenka; Roshanak Darvishzadeh; Martin Hais; Uta Heiden; Lucie Homolová; Peter Krzystek; Thomas Schneider; Martin Starý; Tiejun Wang; Jörg Müller; Marco Heurich. 2021. "A laboratory for conceiving Essential Biodiversity Variables (EBVs)—The ‘Data pool initiative for the Bohemian Forest Ecosystem’." Methods in Ecology and Evolution , no. : 1.
Andrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Claudia Roeoesli; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. Author Correction: Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution 2021, 1 -1.
AMA StyleAndrew K. Skidmore, Nicholas C. Coops, Elnaz Neinavaz, Abebe Ali, Michael E. Schaepman, Marc Paganini, W. Daniel Kissling, Petteri Vihervaara, Roshanak Darvishzadeh, Hannes Feilhauer, Miguel Fernandez, Néstor Fernández, Noel Gorelick, Ilse Geijzendorffer, Uta Heiden, Marco Heurich, Donald Hobern, Stefanie Holzwarth, Frank E. Muller-Karger, Ruben Van De Kerchove, Angela Lausch, Pedro J. Leitão, Marcelle C. Lock, Caspar A. Mücher, Brian O’Connor, Duccio Rocchini, Claudia Roeoesli, Woody Turner, Jan Kees Vis, Tiejun Wang, Martin Wegmann, Vladimir Wingate. Author Correction: Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution. 2021; ():1-1.
Chicago/Turabian StyleAndrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Claudia Roeoesli; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. 2021. "Author Correction: Priority list of biodiversity metrics to observe from space." Nature Ecology & Evolution , no. : 1-1.
Andrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. Author Correction: Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution 2021, 1 -1.
AMA StyleAndrew K. Skidmore, Nicholas C. Coops, Elnaz Neinavaz, Abebe Ali, Michael E. Schaepman, Marc Paganini, W. Daniel Kissling, Petteri Vihervaara, Roshanak Darvishzadeh, Hannes Feilhauer, Miguel Fernandez, Néstor Fernández, Noel Gorelick, Ilse Geijzendorffer, Uta Heiden, Marco Heurich, Donald Hobern, Stefanie Holzwarth, Frank E. Muller-Karger, Ruben Van De Kerchove, Angela Lausch, Pedro J. Leitão, Marcelle C. Lock, Caspar A. Mücher, Brian O’Connor, Duccio Rocchini, Woody Turner, Jan Kees Vis, Tiejun Wang, Martin Wegmann, Vladimir Wingate. Author Correction: Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution. 2021; ():1-1.
Chicago/Turabian StyleAndrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. 2021. "Author Correction: Priority list of biodiversity metrics to observe from space." Nature Ecology & Evolution , no. : 1-1.
Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales. Remote sensing of geospatial biodiversity patterns is an important complement to field observations. This priority list suggests how remote sensing observations can be better integrated into the essential biodiversity variables.
Andrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Claudia Roeoesli; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution 2021, 5, 896 -906.
AMA StyleAndrew K. Skidmore, Nicholas C. Coops, Elnaz Neinavaz, Abebe Ali, Michael E. Schaepman, Marc Paganini, W. Daniel Kissling, Petteri Vihervaara, Roshanak Darvishzadeh, Hannes Feilhauer, Miguel Fernandez, Néstor Fernández, Noel Gorelick, Ilse Geijzendorffer, Uta Heiden, Marco Heurich, Donald Hobern, Stefanie Holzwarth, Frank E. Muller-Karger, Ruben Van De Kerchove, Angela Lausch, Pedro J. Leitão, Marcelle C. Lock, Caspar A. Mücher, Brian O’Connor, Duccio Rocchini, Claudia Roeoesli, Woody Turner, Jan Kees Vis, Tiejun Wang, Martin Wegmann, Vladimir Wingate. Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution. 2021; 5 (7):896-906.
Chicago/Turabian StyleAndrew K. Skidmore; Nicholas C. Coops; Elnaz Neinavaz; Abebe Ali; Michael E. Schaepman; Marc Paganini; W. Daniel Kissling; Petteri Vihervaara; Roshanak Darvishzadeh; Hannes Feilhauer; Miguel Fernandez; Néstor Fernández; Noel Gorelick; Ilse Geijzendorffer; Uta Heiden; Marco Heurich; Donald Hobern; Stefanie Holzwarth; Frank E. Muller-Karger; Ruben Van De Kerchove; Angela Lausch; Pedro J. Leitão; Marcelle C. Lock; Caspar A. Mücher; Brian O’Connor; Duccio Rocchini; Claudia Roeoesli; Woody Turner; Jan Kees Vis; Tiejun Wang; Martin Wegmann; Vladimir Wingate. 2021. "Priority list of biodiversity metrics to observe from space." Nature Ecology & Evolution 5, no. 7: 896-906.
Pilot studies have demonstrated the potential of remote sensing for soil organic carbon (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non-photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building image composites. These composites tend to minimize the disturbing effects by applying sets of criteria. Here, we aim to develop a robust method that allows selecting Sentinel-2 (S-2) pixels with minimal influence of the following disturbing factors: crop residues, surface roughness and soil moisture. We selected all S-2 cloud-free images covering the Belgian Loam Belt from January 2019 to December 2020 (in total 36 images). We then built nine exposed soil composites based on four sets of criteria: (1) lowest Normalized Burn Ratio (NBR2), (2) Normalized Difference Vegetation Index (NDVI) < 0.25, (3–5) NDVI < 0.25 and NBR2 < threshold, (6) the ‘greening-up’ period of a crop and (7–9) the ‘greening-up’ period of a crop and NBR2 < threshold. The ‘greening-up’ period was selected based on the NDVI timeline, where ‘greening-up’ is considered as the last date of acquisition where the soil is exposed (NDVI < 0.25) before the crop develops (NDVI > 0.25). We then built a partial least square regression (PLSR) model with 10-fold cross-validation to estimate the SOC content based on 137 georeferenced calibration samples on the nine composites. We obtained non-satisfactory results (R2 < 0.30, RMSE > 2.50 g C kg–1, and RPD < 1.4, n > 68) for all composites except for the composite in the ‘greening-up’ stage with a NBR2 < 0.07 (R2 = 0.54 ± 0.12, RPD = 1.68 ± 0.45 and RMSE = 2.09 ± 0.39 g C kg–1, n = 49). Hence, the ‘greening-up’ method combined with a strict NBR2 threshold allows selecting the purest exposed soil pixels suitable for SOC prediction. The limit of this method might be its coverage of the total cropland area, which in a two-year period reached 62%, compared to 95% coverage if only the NDVI threshold is applied.
Klara Dvorakova; Uta Heiden; Bas van Wesemael. Sentinel-2 Exposed Soil Composite for Soil Organic Carbon Prediction. Remote Sensing 2021, 13, 1791 .
AMA StyleKlara Dvorakova, Uta Heiden, Bas van Wesemael. Sentinel-2 Exposed Soil Composite for Soil Organic Carbon Prediction. Remote Sensing. 2021; 13 (9):1791.
Chicago/Turabian StyleKlara Dvorakova; Uta Heiden; Bas van Wesemael. 2021. "Sentinel-2 Exposed Soil Composite for Soil Organic Carbon Prediction." Remote Sensing 13, no. 9: 1791.
Mapping a specific tree species at individual tree level across landscapes using remote sensing is challenging, especially in forests where co-occurring tree species exhibit similar characteristics. In Central European mixed forests, silver fir and Norway spruce have been identified as a pair of coniferous tree species with similar spectral and structural characteristics, typically leading to a major misclassification error in mapping studies. Here, we aimed to accurately map individual silver fir trees in a spruce-dominated natural forest in the Bavarian Forest National Park using integrated airborne hyperspectral and LiDAR data. To accomplish this goal, we extracted a set of relevant spectral and structural features from the hyperspectral and LiDAR data and used them to build machine learning classification models. Specifically, we compared the performance of three one-class classification algorithms (i.e. one-class support vector machine, biased support vector machine, and maximum entropy) for mapping individual silver fir trees. Our results showed that the biased support vector machine classifier yielded the highest mapping accuracy, with the area under the curve for positive and unlabeled samples (puAUC) achieving 0.95 (kappa 0.90). We found that the intensity value of 95th percentile of normalized tree height and the percentage of first returns above 2 m high were the most influential structural features, capturing the main morphological difference between silver fir and Norway spruce at the top tree crown. We also found that the wavebands at 700.1 nm, 714.5 nm, and 1201.6 nm were the most robust spectral bands, which are strongly affected by chlorophyll and foliar water content. Our study suggests that discovering links between spectral and structural features captured by different remotely sensed data and species-specific traits can significantly improve the mapping accuracy of a focal species at the individual tree level.
Yifang Shi; Tiejun Wang; Andrew K. Skidmore; Stefanie Holzwarth; Uta Heiden; Marco Heurich. Mapping individual silver fir trees using hyperspectral and LiDAR data in a Central European mixed forest. International Journal of Applied Earth Observation and Geoinformation 2021, 98, 102311 .
AMA StyleYifang Shi, Tiejun Wang, Andrew K. Skidmore, Stefanie Holzwarth, Uta Heiden, Marco Heurich. Mapping individual silver fir trees using hyperspectral and LiDAR data in a Central European mixed forest. International Journal of Applied Earth Observation and Geoinformation. 2021; 98 ():102311.
Chicago/Turabian StyleYifang Shi; Tiejun Wang; Andrew K. Skidmore; Stefanie Holzwarth; Uta Heiden; Marco Heurich. 2021. "Mapping individual silver fir trees using hyperspectral and LiDAR data in a Central European mixed forest." International Journal of Applied Earth Observation and Geoinformation 98, no. : 102311.
Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.
Abebe Mohammed Ali; Roshanak Darvishzadeh; Andrew Skidmore; Marco Heurich; Marc Paganini; Uta Heiden; Sander Mücher. Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes. Remote Sensing 2020, 12, 1 .
AMA StyleAbebe Mohammed Ali, Roshanak Darvishzadeh, Andrew Skidmore, Marco Heurich, Marc Paganini, Uta Heiden, Sander Mücher. Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes. Remote Sensing. 2020; 12 (11):1.
Chicago/Turabian StyleAbebe Mohammed Ali; Roshanak Darvishzadeh; Andrew Skidmore; Marco Heurich; Marc Paganini; Uta Heiden; Sander Mücher. 2020. "Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes." Remote Sensing 12, no. 11: 1.
Imaging spectrometry from aerial or spaceborne platforms, also known as hyperspectral remote sensing, provides dense sampled and fine structured spectral information for each image pixel, allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The ground sample distance is 30 m with 1024 pixels across track. In this article, a detailed review is given on the applicability of DESIS data based on the specifics of the instrument, the characteristics of the ISS orbit, and the methods applied to generate products. The various DESIS data products available for users are described with the focus on specific processing steps. The results of the data quality and product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements. The limitations of the DESIS data products are also subject to a critical examination.
Kevin Alonso; Martin Bachmann; Kara Burch; Emiliano Carmona; Daniele Cerra; Raquel De Los Reyes; Daniele Dietrich; Uta Heiden; Andreas Hölderlin; Jack Ickes; Uwe Knodt; David Krutz; Heath Lester; Rupert Müller; Mary Pagnutti; Peter Reinartz; Rudolf Richter; Robert Ryan; Ilse Sebastian; Mirco Tegler. Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS). Sensors 2019, 19, 4471 .
AMA StyleKevin Alonso, Martin Bachmann, Kara Burch, Emiliano Carmona, Daniele Cerra, Raquel De Los Reyes, Daniele Dietrich, Uta Heiden, Andreas Hölderlin, Jack Ickes, Uwe Knodt, David Krutz, Heath Lester, Rupert Müller, Mary Pagnutti, Peter Reinartz, Rudolf Richter, Robert Ryan, Ilse Sebastian, Mirco Tegler. Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS). Sensors. 2019; 19 (20):4471.
Chicago/Turabian StyleKevin Alonso; Martin Bachmann; Kara Burch; Emiliano Carmona; Daniele Cerra; Raquel De Los Reyes; Daniele Dietrich; Uta Heiden; Andreas Hölderlin; Jack Ickes; Uwe Knodt; David Krutz; Heath Lester; Rupert Müller; Mary Pagnutti; Peter Reinartz; Rudolf Richter; Robert Ryan; Ilse Sebastian; Mirco Tegler. 2019. "Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)." Sensors 19, no. 20: 4471.
This paper reviews in detail the contributions of hyperspectral imaging to the topic of urban remote sensing. Hyperspectral imaging is traditionally connected to the spectral characterization of surface materials. Moreover, urban areas are characterized by a very complex geometrical structure, which requires either very high spatial resolution or complex unmixing procedures based on linear and non-linear mixing models. Non-linear unmixing and material mapping using both spectral and spatial features are therefore two important topics when using hyperspectral imaging to monitor human settlements and infrastructures. Finally, even when no specific material and or urban element is sought, the mixture of artificial (as opposed to natural) materials in human settlements can be used to delineate their extents, with excellent results with respect to those obtained by multispectral optical sensors with the same spatial resolution.
Andrea Marinoni; Uta Heiden; Paolo Gamba. Human settlement and infrastructure monitoring with hyperspectral imaging. 2019 Joint Urban Remote Sensing Event (JURSE) 2019, 1 -4.
AMA StyleAndrea Marinoni, Uta Heiden, Paolo Gamba. Human settlement and infrastructure monitoring with hyperspectral imaging. 2019 Joint Urban Remote Sensing Event (JURSE). 2019; ():1-4.
Chicago/Turabian StyleAndrea Marinoni; Uta Heiden; Paolo Gamba. 2019. "Human settlement and infrastructure monitoring with hyperspectral imaging." 2019 Joint Urban Remote Sensing Event (JURSE) , no. : 1-4.
To understand processes in urban environments, such as urban energy fluxes or surface temperature patterns, it is important to map urban surface materials. Airborne imaging spectroscopy data have been successfully used to identify urban surface materials mainly based on unmixing algorithms. Upcoming spaceborne Imaging Spectrometers (IS), such as the Environmental Mapping and Analysis Program (EnMAP), will reduce the time and cost-critical limitations of airborne systems for Earth Observation (EO). However, the spatial resolution of all operated and planned IS in space will not be higher than 20 to 30 m and, thus, the detection of pure Endmember (EM) candidates in urban areas, a requirement for spectral unmixing, is very limited. Gradient analysis could be an alternative method for retrieving urban surface material compositions in pixels from spaceborne IS. The gradient concept is well known in ecology to identify plant species assemblages formed by similar environmental conditions but has never been tested for urban materials. However, urban areas also contain neighbourhoods with similar physical, compositional and structural characteristics. Based on this assumption, this study investigated (1) whether cover fractions of surface materials change gradually in urban areas and (2) whether these gradients can be adequately mapped and interpreted using imaging spectroscopy data (e.g. EnMAP) with 30 m spatial resolution. Similarities of material compositions were analysed on the basis of 153 systematically distributed samples on a detailed surface material map using Detrended Correspondence Analysis (DCA). Determined gradient scores for the first two gradients were regressed against the corresponding mean reflectance of simulated EnMAP spectra using Partial Least Square regression models. Results show strong correlations with R2 = 0.85 and R2 = 0.71 and an RMSE of 0.24 and 0.21 for the first and second axis, respectively. The subsequent mapping of the first gradient reveals patterns that correspond to the transition from predominantly vegetation classes to the dominance of artificial materials. Patterns resulting from the second gradient are associated with surface material compositions that are related to finer structural differences in urban structures. The composite gradient map shows patterns of common surface material compositions that can be related to urban land use classes such as Urban Structure Types (UST). By linking the knowledge of typical material compositions with urban structures, gradient analysis seems to be a powerful tool to map characteristic material compositions in 30 m imaging spectroscopy data of urban areas.
Marianne Jilge; Uta Heiden; Carsten Neumann; Hannes Feilhauer. Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data. Remote Sensing of Environment 2019, 223, 179 -193.
AMA StyleMarianne Jilge, Uta Heiden, Carsten Neumann, Hannes Feilhauer. Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data. Remote Sensing of Environment. 2019; 223 ():179-193.
Chicago/Turabian StyleMarianne Jilge; Uta Heiden; Carsten Neumann; Hannes Feilhauer. 2019. "Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data." Remote Sensing of Environment 223, no. : 179-193.
Plant functional traits have been extensively used to describe, rank and discriminate species according to their variability between species in classical plant taxonomy. However, the utility of plant functional traits for tree species classification from remote sensing data in natural forests has not been clearly established. In this study, we integrated three selected plant functional traits (i.e. equivalent water thickness (Cw), leaf mass per area (Cm) and leaf chlorophyll (Cab)) retrieved from hyperspectral data with hyperspectral derived spectral features and airborne LiDAR derived metrics for mapping five tree species in a natural forest in Germany. Our results showed that when plant functional traits were combined with spectral features and LiDAR metrics, an overall accuracy of 83.7% was obtained, which was statistically significantly higher than using LiDAR (65.1%) or hyperspectral (69.3%) data alone. The results of our study demonstrate that plant functional traits retrieved from hyperspectral data using radiative transfer models can be used in conjunction with hyperspectral features and LiDAR metrics to further improve individual tree species classification in a mixed temperate forest.
Yifang Shi; Andrew K. Skidmore; Tiejun Wang; Stefanie Holzwarth; Uta Heiden; Nicole Pinnel; Xi Zhu; Marco Heurich. Tree species classification using plant functional traits from LiDAR and hyperspectral data. International Journal of Applied Earth Observation and Geoinformation 2018, 73, 207 -219.
AMA StyleYifang Shi, Andrew K. Skidmore, Tiejun Wang, Stefanie Holzwarth, Uta Heiden, Nicole Pinnel, Xi Zhu, Marco Heurich. Tree species classification using plant functional traits from LiDAR and hyperspectral data. International Journal of Applied Earth Observation and Geoinformation. 2018; 73 ():207-219.
Chicago/Turabian StyleYifang Shi; Andrew K. Skidmore; Tiejun Wang; Stefanie Holzwarth; Uta Heiden; Nicole Pinnel; Xi Zhu; Marco Heurich. 2018. "Tree species classification using plant functional traits from LiDAR and hyperspectral data." International Journal of Applied Earth Observation and Geoinformation 73, no. : 207-219.
Calibration and validation determine the quality and integrity of the data provided by sensors and have enormous downstream impacts on the accuracy and reliability of the products generated by these sensors. With the imminent launch of the next generation of spaceborne imaging spectroscopy sensors, the IEEE Geoscience and Remote Sensing Society's (GRSS's) Geoscience Spaceborne Imaging Spectroscopy Technical Committee (GSIS TC) initiated a calibration and validation initiative.
Cindy Ong; Kurt Thome; Uta Heiden; Jeff Czapla-Myers; Andreas Mueller; Kurtis Thome. Reflectance-Based Imaging Spectrometer Error Budget Field Practicum at the Railroad Valley Test Site, Nevada [Technical Committees]. IEEE Geoscience and Remote Sensing Magazine 2018, 6, 111 -115.
AMA StyleCindy Ong, Kurt Thome, Uta Heiden, Jeff Czapla-Myers, Andreas Mueller, Kurtis Thome. Reflectance-Based Imaging Spectrometer Error Budget Field Practicum at the Railroad Valley Test Site, Nevada [Technical Committees]. IEEE Geoscience and Remote Sensing Magazine. 2018; 6 (3):111-115.
Chicago/Turabian StyleCindy Ong; Kurt Thome; Uta Heiden; Jeff Czapla-Myers; Andreas Mueller; Kurtis Thome. 2018. "Reflectance-Based Imaging Spectrometer Error Budget Field Practicum at the Railroad Valley Test Site, Nevada [Technical Committees]." IEEE Geoscience and Remote Sensing Magazine 6, no. 3: 111-115.
Forthcoming spaceborne imaging spectrometers will provide novel opportunities for mapping urban composition globally. To move from case studies for single cities towards comparative and more operational analyses, generalized models that may be transferred throughout space are desired. In this study, we investigated how single regression models can be spatially generalized for vegetation-impervious-soil (VIS) mapping across multiple cities. The combination of support vector regression (SVR) with synthetically mixed training data generated from spectral libraries was used for fraction mapping. We developed three local models based on separate spectral libraries from Berlin (Germany), Brussels (Belgium), and Santa Barbara (U.S.), and a generalized model based on a combined multi-site spectral library. To examine the performance and transferability of the generalized model compared to local models, we first applied all model variants to simulated Environmental Mapping and Analysis Program (EnMAP) data from the three cities that were represented in the models, i.e., known sites. Next, we transferred the models to two unknown sites not represented in the models, San Francisco Bay Area (U.S.) and Munich (Germany). In the first mapping constellation, results demonstrated that the generalized model was capable of accurately mapping VIS fractions across all three known sites. Average mean absolute errors (AV-MAEs) were 8.5, 12.2, and 11.0% for Berlin, Brussels, and Santa Barbara. The performance of the generalized model was very similar to the local models, with ∆AV-MAEs falling within a range of ±0.7%. A detailed assessment of fraction maps and class-wise accuracies confirmed that modeling errors related to remaining limitations of urban mapping based on optical remote sensing data rather than to the choice between a local or generalized model. For the second mapping constellation, the generalized model proved to be useful for mapping vegetation and impervious fractions in the unknown sites. MAEs for both cover types were 5.4 and 10.9% for the San Francisco Bay Area, and 6.3 and 15.4% for Munich. In contrast, the three local models were only found to have similar accuracies as the generalized model for one of the two sites or for individual VIS categories. Despite the enhanced transferability of the generalized model to the unknown sites, deficiencies remained for accurate soil mapping. MAEs were 22.4 and 12.3%, and high over - and underestimations were observed at the low and high end of the fraction range. These shortcomings indicated possible limitations of the spectral libraries to account for the spectral characteristics of soils in the unknown sites. Overall, we conclude that the combination of SVR and synthetically mixed training data generated from multi-site libraries constitutes a flexible modeling approach for generalized urban mapping across multiple cities.
Akpona Okujeni; Frank Canters; Sam D. Cooper; Jeroen Degerickx; Uta Heiden; Patrick Hostert; Frederik Priem; Dar A. Roberts; Ben Somers; Sebastian van der Linden. Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities. Remote Sensing of Environment 2018, 216, 482 -496.
AMA StyleAkpona Okujeni, Frank Canters, Sam D. Cooper, Jeroen Degerickx, Uta Heiden, Patrick Hostert, Frederik Priem, Dar A. Roberts, Ben Somers, Sebastian van der Linden. Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities. Remote Sensing of Environment. 2018; 216 ():482-496.
Chicago/Turabian StyleAkpona Okujeni; Frank Canters; Sam D. Cooper; Jeroen Degerickx; Uta Heiden; Patrick Hostert; Frederik Priem; Dar A. Roberts; Ben Somers; Sebastian van der Linden. 2018. "Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities." Remote Sensing of Environment 216, no. : 482-496.
Future spaceborne imaging spectroscopy data will offer new possibilities for mapping ecosystems globally, including urban environments. The high spectral information content of such data is expected to improve accuracies and thematic detail of maps on urban composition and urban environmental condition. This way, urgently needed information for environmental models will be provided, for example, for microclimate or hydrological models. The diverse vertical structures, highly frequent spatial change and a great variety of materials cause challenges for urban environmental mapping with Earth observation data, especially at the 30 m spatial resolution of data from future spaceborne imaging spectrometers. This paper gives an overview of the state-of-the-art in urban imaging spectroscopy considering decreasing spatial resolution, the related user requirements and existing knowledge gaps, as well as expected future directions for the work with new data sets.
S. Van Der Linden; A. Okujeni; F. Canters; Jeroen Degerickx; Uta Heiden; P. Hostert; Frederik Priem; Ben Somers; F. Thiel. Imaging Spectroscopy of Urban Environments. Surveys in Geophysics 2018, 40, 471 -488.
AMA StyleS. Van Der Linden, A. Okujeni, F. Canters, Jeroen Degerickx, Uta Heiden, P. Hostert, Frederik Priem, Ben Somers, F. Thiel. Imaging Spectroscopy of Urban Environments. Surveys in Geophysics. 2018; 40 (3):471-488.
Chicago/Turabian StyleS. Van Der Linden; A. Okujeni; F. Canters; Jeroen Degerickx; Uta Heiden; P. Hostert; Frederik Priem; Ben Somers; F. Thiel. 2018. "Imaging Spectroscopy of Urban Environments." Surveys in Geophysics 40, no. 3: 471-488.
Ground reflectance was acquired at the Railroad Valley Playa calibration site in Nevada USA using different methods of collection. The data was collected near the time and date of Landsat 8 OLI and Sentinel-2 satellite overpasses so an inter-comparison could be made with the reflectance products to determine which method was more suitable for vicarious calibration. The field spectrometers and reference panels were characterized before the field campaign. A continuous acquisition method was compared to stop and measure collections. Both acquisition methods were collected along an 80 m east-west transect as well as for a series of north-south transects over an 80 × 320 m area, with the stop and measure method being performed at random sampling locations. The measurements were performed using two field spectrometers by three teams of two people to compare the repeatability. The aim of the field campaign was to determine the variability due to the operator and the method of collection.
Ian Lau; Cindy Ong; Kurt J. Thome; Brian Wenny; Andreas Mueller; Uta Heiden; Jeff Czapla-Myers; Stuart Biggar; Nikolaus Anderson; Lorcan McGonigle; William Thomas; Carolina Barrientos; Yuki Itoh. Intercomparison of Field Methods for Acquiring Ground Reflectance at Railroad Valley Playa for Spectral Calibration of Satellite Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 186 -188.
AMA StyleIan Lau, Cindy Ong, Kurt J. Thome, Brian Wenny, Andreas Mueller, Uta Heiden, Jeff Czapla-Myers, Stuart Biggar, Nikolaus Anderson, Lorcan McGonigle, William Thomas, Carolina Barrientos, Yuki Itoh. Intercomparison of Field Methods for Acquiring Ground Reflectance at Railroad Valley Playa for Spectral Calibration of Satellite Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():186-188.
Chicago/Turabian StyleIan Lau; Cindy Ong; Kurt J. Thome; Brian Wenny; Andreas Mueller; Uta Heiden; Jeff Czapla-Myers; Stuart Biggar; Nikolaus Anderson; Lorcan McGonigle; William Thomas; Carolina Barrientos; Yuki Itoh. 2018. "Intercomparison of Field Methods for Acquiring Ground Reflectance at Railroad Valley Playa for Spectral Calibration of Satellite Data." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 186-188.
EnMAP (Environmental Mapping and Analysis Program, www.enmap.org) is a German, Earth observing, imaging spectroscopy, spaceborne mission planned for launch in 2020. This work reflects the status of the EnMAP Ground Segment, currently procuring its facilities and elements for later testing and integrating them. The Ground Segment's Design Model is discussed as well as its constituents are introduced. It further discusses the recent changes to be respected by the design covering the topics, how low quality data is handled within the Ground Segment, how the files aboard the satellite are deleted to ensure a maximum data security, and how the moon could serve as further calibration source during the EnMAP mission.
Martin Habermeyer; Andreas Ohndorf; Gintautas Palubinskas; Tobias Storch; Steffen Zimmermann; Martin Bachmann; Emiliano Carmona; Heiko Damerow; Sabine Engelbrecht; Thomas Fruth; Uta Heiden; Klaus-Dieter Missling; Helmut Miihle. Status Report of the Enmap Ground Segment: Presentation of the Design and the Changes Recently Accomplished. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 171 -174.
AMA StyleMartin Habermeyer, Andreas Ohndorf, Gintautas Palubinskas, Tobias Storch, Steffen Zimmermann, Martin Bachmann, Emiliano Carmona, Heiko Damerow, Sabine Engelbrecht, Thomas Fruth, Uta Heiden, Klaus-Dieter Missling, Helmut Miihle. Status Report of the Enmap Ground Segment: Presentation of the Design and the Changes Recently Accomplished. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():171-174.
Chicago/Turabian StyleMartin Habermeyer; Andreas Ohndorf; Gintautas Palubinskas; Tobias Storch; Steffen Zimmermann; Martin Bachmann; Emiliano Carmona; Heiko Damerow; Sabine Engelbrecht; Thomas Fruth; Uta Heiden; Klaus-Dieter Missling; Helmut Miihle. 2018. "Status Report of the Enmap Ground Segment: Presentation of the Design and the Changes Recently Accomplished." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 171-174.
The German Aerospace Center (DLR) and Teledyne Brown Engineering (TBE), located in Huntsville, Alabama, USA, cooperate to develop and operate the new space-based hyperspectral sensor DLR Earth Sensing Imaging Spectrometer (DESIS). While TBE provides the Multi-User platform MUSES and infrastructure for operation of the DESIS instrument on the ISS, DLR is responsible for providing the instrument and the processing software as well as instrument in-flight calibration and product quality operations. MUSES has been already launched and installed on the International Space Station ISS in early 2017 and DESIS will follow mid of 2018. We present here an overview of the DESIS instrument, the on-ground data processing, the in-flight calibration and product quality investigations.
R. Muller; M. Bachmann; K. Alonso; E. Carmona; D. Cerra; R. De Los Reyes; B. Gerasch; H. Krawczyk; V. Ziel; Uta Heiden; D. Krutz. Processing, Validation And Quality Control Of Spaceborne Imaging Spectroscopy Data From Desis Mission on the Iss. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 189 -191.
AMA StyleR. Muller, M. Bachmann, K. Alonso, E. Carmona, D. Cerra, R. De Los Reyes, B. Gerasch, H. Krawczyk, V. Ziel, Uta Heiden, D. Krutz. Processing, Validation And Quality Control Of Spaceborne Imaging Spectroscopy Data From Desis Mission on the Iss. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():189-191.
Chicago/Turabian StyleR. Muller; M. Bachmann; K. Alonso; E. Carmona; D. Cerra; R. De Los Reyes; B. Gerasch; H. Krawczyk; V. Ziel; Uta Heiden; D. Krutz. 2018. "Processing, Validation And Quality Control Of Spaceborne Imaging Spectroscopy Data From Desis Mission on the Iss." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 189-191.
Soil information with high spatial and temporal resolution is crucial to assess potential soil degradation and to achieve sustainable productivity and ultimately food security. The spatial resolution of existing soil maps can commonly be too coarse to account for local soil variations and owing to the cost and resource needs required to update information these maps lack temporal information. With improved computational processing capabilities, increased data storage and most recently, the increasing amount of freely available data (e.g. Landsat, Sentinel-2A/B), remote sensing imagery can be integrated into existing soil mapping approaches to increase temporal and spatial resolution of soil information. Satellite multi-temporal data allows for generating cloud-free, radiometrically and phenologically consistent pixel based image composites of regional scale. Such data sets are of particular use for extracting soil information in areas of intermediate climate where soils are rarely exposed. The Soil Composite Mapping Processor (SCMaP) is a new approach designed to make use of per-pixel compositing to overcome the issue of limited soil exposure. The objective of this paper is to demonstrate the automated processors ability to handle large image databases to build multispectral reflectance composite base data layers that can support large scale top soil analyses. The functionality of the 5SCMaP6 is demonstrated using Landsat imagery over Germany from 1984 to 2014 applied over 5 year periods. Three primary product levels are generated that will allow for a long term assessment and distribution of soils that include the distribution of exposed soils, a statistical information related to soil use and intensity and the generation of exposed soil reflectance image composites. The resulting composite maps provide useful value-added information on soils with the exposed soil reflectance composites showing high spatial coverage that correlate well with existing soil maps and the underlying geological structural regions
Derek Rogge; Agnes Bauer; Julian Zeidler; Andreas Mueller; Thomas Esch; Uta Heiden. Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984–2014). Remote Sensing of Environment 2018, 205, 1 -17.
AMA StyleDerek Rogge, Agnes Bauer, Julian Zeidler, Andreas Mueller, Thomas Esch, Uta Heiden. Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984–2014). Remote Sensing of Environment. 2018; 205 ():1-17.
Chicago/Turabian StyleDerek Rogge; Agnes Bauer; Julian Zeidler; Andreas Mueller; Thomas Esch; Uta Heiden. 2018. "Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984–2014)." Remote Sensing of Environment 205, no. : 1-17.
High resolution imaging spectroscopy data have been recognised as a valuable data resource for augmenting detailed material inventories that serve as input for various urban applications. Image-specific urban spectral libraries are successfully used in urban imaging spectroscopy studies. However, the regional- and sensor-specific transferability of such libraries is limited due to the wide range of different surface materials. With the developed methodology, incomplete urban spectral libraries can be utilised by assuming that unknown surface material spectra are dissimilar to the known spectra in a basic spectral library (BSL). The similarity measure SID-SCA (Spectral Information Divergence-Spectral Correlation Angle) is applied to detect image-specific unknown urban surfaces while avoiding spectral mixtures. These detected unknown materials are categorised into distinct and identifiable material classes based on their spectral and spatial metrics. Experimental results demonstrate a successful redetection of material classes that had been previously erased in order to simulate an incomplete BSL. Additionally, completely new materials e.g., solar panels were identified in the data. It is further shown that the level of incompleteness of the BSL and the defined dissimilarity threshold are decisive for the detection of unknown material classes and the degree of spectral intra-class variability. A detailed accuracy assessment of the pre-classification results, aiming to separate natural and artificial materials, demonstrates spectral confusions between spectrally similar materials utilizing SID-SCA. However, most spectral confusions occur between natural or artificial materials which are not affecting the overall aim. The dissimilarity analysis overcomes the limitations of working with incomplete urban spectral libraries and enables the generation of image-specific training databases.
Marianne Jilge; Uta Heiden; Martin Habermeyer; André Mende; Carsten Juergens. Detecting Unknown Artificial Urban Surface Materials Based on Spectral Dissimilarity Analysis. Sensors 2017, 17, 1826 .
AMA StyleMarianne Jilge, Uta Heiden, Martin Habermeyer, André Mende, Carsten Juergens. Detecting Unknown Artificial Urban Surface Materials Based on Spectral Dissimilarity Analysis. Sensors. 2017; 17 (8):1826.
Chicago/Turabian StyleMarianne Jilge; Uta Heiden; Martin Habermeyer; André Mende; Carsten Juergens. 2017. "Detecting Unknown Artificial Urban Surface Materials Based on Spectral Dissimilarity Analysis." Sensors 17, no. 8: 1826.