@article{MTMT:33901615, title = {Trends and population estimate of the threatened Buff-breasted Sandpiper Calidris subruficollis wintering in coastal grasslands of southern Brazil}, url = {https://m2.mtmt.hu/api/publication/33901615}, author = {Faria, Fernando A. and Dias, Rafael A. and Bencke, Glayson A. and Bugoni, Leandro and Senner, Nathan R. and Almeida, Juliana B. and Nunes, Guilherme Tavares and Goncalves, Maycon S. S. and Lyons, James E.}, doi = {10.1017/S0959270923000138}, journal-iso = {BIRD CONSERV INT}, journal = {BIRD CONSERVATION INTERNATIONAL}, volume = {33}, unique-id = {33901615}, issn = {0959-2709}, abstract = {Information about population sizes, trends, and habitat use is key for species conservation and management. The Buff-breasted Sandpiper Calidris subruficollis (BBSA) is a long-distance migratory shorebird that breeds in the Arctic and migrates to south-eastern South America, wintering in the grasslands of southern Brazil, Uruguay, and Argentina. Most studies of Nearctic migratory species occur in the Northern Hemisphere, but monitoring these species at non-breeding areas is crucial for conservation during this phase of the annual cycle. Our first objective was to estimate trends of BBSA at four key areas in southern Brazil during the non-breeding season. We surveyed for BBSA and measured vegetation height in most years from 2008/09 to 2019/20. We used hierarchical distance sampling models in which BBSA abundance and density were modelled as a function of vegetation height and corrected for detectability. Next, we used on-the-ground surveys combined with satellite imagery and habitat classification models to estimate BBSA population size in 2019/20 at two major non-breeding areas. We found that abundance and density were negatively affected by increasing vegetation height. Abundance fluctuated five- to eight-fold over the study period, with peaks in the middle of the study (2014/15). We estimated the BBSA wintering population size as 1,201 (95% credible interval [CI]: 637-1,946) birds in Torotama Island and 2,232 (95% CI: 1,199-3,584) in Lagoa do Peixe National Park during the 2019/20 austral summer. Although no pronounced trend was detected, BBSA abundance fluctuated greatly from year to year. Our results demonstrate that only two of the four key areas hold high densities of BBSA and highlight the positive effect of short grass on BBSA numbers. Short-grass coastal habitats used by BBSA are strongly influenced by livestock grazing and climate, and are expected to shrink in size with future development and climatic changes.}, keywords = {CONSERVATION; SHOREBIRDS; Hierarchical Distance Sampling; Calidris subruficollis; Coastal rangelands}, year = {2023}, eissn = {1474-0001} } @article{MTMT:34292151, title = {Demonstration of a Modular Prototype End-to-End Simulator for Aquatic Remote Sensing Applications}, url = {https://m2.mtmt.hu/api/publication/34292151}, author = {Matthews, Mark W. and Dekker, Arnold and Price, Ian and Drayson, Nathan and Pease, Joshua and Antoine, David and Anstee, Janet and Sharp, Robert and Woodgate, William and Phinn, Stuart and Gensemer, Stephen}, doi = {10.3390/s23187824}, journal-iso = {SENSORS-BASEL}, journal = {SENSORS}, volume = {23}, unique-id = {34292151}, abstract = {This study introduces a prototype end-to-end Simulator software tool for simulating two-dimensional satellite multispectral imagery for a variety of satellite instrument models in aquatic environments. Using case studies, the impact of variable sensor configurations on the performance of value-added products for challenging applications, such as coral reefs and cyanobacterial algal blooms, is assessed. This demonstrates how decisions regarding satellite sensor design, driven by cost constraints, directly influence the quality of value-added remote sensing products. Furthermore, the Simulator is used to identify situations where retrieval algorithms require further parameterization before application to unsimulated satellite data, where error sources cannot always be identified or isolated. The application of the Simulator can verify whether a given instrument design meets the performance requirements of end-users before build and launch, critically allowing for the justification of the cost and specifications for planned and future sensors. It is hoped that the Simulator will enable engineers and scientists to understand important design trade-offs in phase 0/A studies easily, quickly, reliably, and accurately in future Earth observation satellites and systems.}, keywords = {DESIGN; remote sensing; OPTICS; OPTICAL SENSORS; bathymetry; SATELLITE; CYANOBACTERIAL BLOOMS; Cubesat; Coral reefs; smallsat; end-to-end simulator}, year = {2023}, eissn = {1424-8220}, orcid-numbers = {Antoine, David/0000-0002-9082-2395; Anstee, Janet/0000-0002-1681-9630; Woodgate, William/0000-0002-5298-4828; Phinn, Stuart/0000-0002-2605-6104} } @article{MTMT:32328742, title = {An alternative approach to delineate wetland influence zone of a tropical intertidal mudflat using geo-information technology}, url = {https://m2.mtmt.hu/api/publication/32328742}, author = {Datta, Debajit and Roy, Asit Kumar and Kundu, Arnab and Dutta, Dipanwita and Neogy, Sohini}, doi = {10.1016/j.ecss.2021.107308}, journal-iso = {ESTUAR COAST SHELF S}, journal = {ESTUARINE COASTAL AND SHELF SCIENCE}, volume = {253}, unique-id = {32328742}, issn = {0272-7714}, abstract = {Mudflats are important tropical coastal wetlands having high carbon sequestration and biodiversity potentials. The actual reach of a mudflat environment spreads far beyond its perennial wetland areas in a coastal site. Accurate demarcation of the extent of these buffer areas is imperative for sustainable wetland management. This study tried to delineate the wetland influence zone (WIZ) of an intertidal mudflat in the Medinipur coastal plain, India as a functional extension around the considerably smaller yet hydrous cum muddy depressions. Normalized Difference Water Index (NDWI), derived from Sentinel-2 MultiSpectral Instrument (MSI) datasets, and Temperature Vegetation Dryness Index (TVDI), derived from the coupled use of Sentinel-2 MSI and Landsat-8 datasets, had been analyzed and merged to develop nine characteristic zones. Among these, eight zones (Zone I to Zone VIII) were identified as part of the WIZ and the rest one (NDWI: -0.15, TVDI 0.7) was considered as a supra-tidal zone, devoid of the mudflat features. Although the overall extent of WIZ (Minimum: 339.20 ha, Maximum: 350.22 ha) did not change much from 2016 to 2020, the inter-zonal spatial arrangements of vegetation and physiography transformed continuously. The gradual disappearance of the major tidal creek from the south-western parts and increasing human footprints throughout the mudflat had expedited this transformation. Accordingly, periodic assessments incorporating microwave and optical datasets along with high precision insitu measurements of soil moisture was suggested for the efficient monitoring of these fragile tropical ecosystems.}, keywords = {Soil moisture; human interference; vegetation health; geospatial data; Bio-tidal accretion; Surface inundation}, year = {2021}, eissn = {1096-0015}, orcid-numbers = {Dutta, Dipanwita/0000-0002-2211-7248} } @article{MTMT:32328739, title = {Breeding habitats, phenology and size of a resident population of Two-banded Plover (Charadrius falklandicus) at the northern edge of its distributionPalavras-chave}, url = {https://m2.mtmt.hu/api/publication/32328739}, author = {Faria, Fernando A. and Repenning, Marcio and Tavares Nunes, Guilherme and Senner, Nathan R. and Bugoni, Leandro}, doi = {10.1111/aec.13074}, journal-iso = {AUSTRAL ECOL}, journal = {AUSTRAL ECOLOGY}, unique-id = {32328739}, issn = {1442-9985}, abstract = {The central-peripheral hypothesis states that the demographic performance of a species decreases from the centre to the edge of its range. Peripheral populations are often smaller and tend to occur under different and suboptimal conditions from those of core populations. Peripheral populations can also coexist during part of their annual cycle with populations from the core of the species' range. Studies on peripheral populations are thus valuable for broadly understanding ecological and evolutionary processes. The Two-banded Plover (TWBP, Charadrius falklandicus, Charadriidae) is an endemic South American shorebird that breeds in Argentine and Chilean Patagonia and migrates northward during the Austral winter. There are breeding records, however, from Lagoa do Peixe National Park in southern Brazil. In this study, we (i) mapped TWBP nests, (ii) characterised their reproductive biology and nesting habitats, (iii) colour-marked birds and evaluated their seasonal occurrence patterns and (iv) estimated the size of the Brazilian population by combining supervised habitat classification analyses and generalised additive models. We estimated that the Brazilian population has 55 (95% CI: 44.1-66.6) breeding pairs and found that the length of their breeding season was roughly 5 months, spanning the Austral spring and summer. The population's nesting habitat differed, and their apparent reproductive success was lower than that of core populations. Unlike more southerly populations, the results of our mark-resighting efforts demonstrate that the Brazilian population is sedentary. Taken together, these results indicate that the Brazilian TWBP population seems geographically isolated from the species' southernmost core populations, resulting in a heteropatric distribution. Furthermore, differences in nesting habitat and year-round residency indicate that this peripheral population is ecologically distinct. The marked behavioural and ecological differences combined with the small population at the northern edge of the TWBP distribution support the central-peripheral hypothesis in a Neotropical system. Abstract in Portuguese is available with online material}, keywords = {ABUNDANCE; SHOREBIRDS; peripheral population; Population estimates; central-peripheral hypothesis; heteropatric distribution}, year = {2021}, eissn = {1442-9993}, orcid-numbers = {Bugoni, Leandro/0000-0003-0689-7026} } @article{MTMT:32009379, title = {Developing a spatially explicit modelling and evaluation framework for integrated carbon sequestration and biodiversity conservation: Application in southern Finland}, url = {https://m2.mtmt.hu/api/publication/32009379}, author = {Forsius, M. and Kujala, H. and Minunno, F. and Holmberg, M. and Leikola, N. and Mikkonen, N. and Autio, I. and Paunu, V.-V. and Tanhuanpää, T. and Hurskainen, P. and Mäyrä, J. and Kivinen, S. and Keski-Saari, S. and Kosenius, A.-K. and Kuusela, S. and Virkkala, R. and Viinikka, A. and Vihervaara, P. and Akujärvi, A. and Bäck, J. and Karvosenoja, N. and Kumpula, T. and Kuzmin, A. and Mäkelä, A. and Moilanen, A. and Ollikainen, M. and Pekkonen, M. and Peltoniemi, M. and Poikolainen, L. and Rankinen, K. and Rasilo, T. and Tuominen, S. and Valkama, J. and Vanhala, P. and Heikkinen, R.K.}, doi = {10.1016/j.scitotenv.2021.145847}, journal-iso = {SCI TOTAL ENVIRON}, journal = {SCIENCE OF THE TOTAL ENVIRONMENT}, volume = {775}, unique-id = {32009379}, issn = {0048-9697}, year = {2021}, eissn = {1879-1026} } @article{MTMT:32328740, title = {Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery}, url = {https://m2.mtmt.hu/api/publication/32328740}, author = {Jamali, Ali and Mahdianpari, Masoud and Brisco, Brian and Granger, Jean and Mohammadimanesh, Fariba and Salehi, Bahram}, doi = {10.3390/rs13112046}, journal-iso = {REMOTE SENS-BASEL}, journal = {REMOTE SENSING}, volume = {13}, unique-id = {32328740}, abstract = {Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current use of machine learning algorithms to classify complex environments, specifically in remote sensing. In this paper, we explore the potential and possible limitations to be overcome regarding the use of ensemble deep learning techniques for complex wetland classification and discusses the potential and limitation of various solo convolutional neural networks (CNNs), including DenseNet, GoogLeNet, ShuffleNet, MobileNet, Xception, Inception-ResNet, ResNet18, and ResNet101 in three different study areas located in Newfoundland and Labrador, Canada (i.e., Avalon, Gros Morne, and Grand Falls). Moreover, to improve the classification accuracies of wetland classes of bog, fen, marsh, swamp, and shallow water, the results of the three best CNNs in each study area is fused using three supervised classifiers of random forest (RF), bagged tree (BTree), Bayesian optimized tree (BOT), and one unsupervised majority voting classifier. The results suggest that the ensemble models, in particular BTree, have a valuable role to play in the classification of wetland classes of bog, fen, marsh, swamp, and shallow water. The ensemble CNNs show an improvement of 9.63-19.04% in terms of mean producer's accuracy compared to the solo CNNs, to recognize wetland classes in three different study areas. This research indicates a promising potential for integrating ensemble-based learning and deep learning for operational large area land cover, particularly complex wetland type classification.}, keywords = {Ensemble learning; Convolutional neural network; Deep learning; Satellite image classification; wetland mapping}, year = {2021}, eissn = {2072-4292}, orcid-numbers = {Jamali, Ali/0000-0002-6073-5493; Mahdianpari, Masoud/0000-0002-7234-959X; Brisco, Brian/0000-0001-8439-362X; Salehi, Bahram/0000-0002-7742-5475} } @article{MTMT:32328737, title = {Wetland Mapping Using Multi-Spectral Satellite Imagery and Deep Convolutional Neural Networks: A Case Study in Newfoundland and Labrador, Canada}, url = {https://m2.mtmt.hu/api/publication/32328737}, author = {Jamali, Ali and Mahdianpari, Masoud and Brisco, Brian and Granger, Jean and Mohammadimanesh, Fariba and Salehi, Bahram}, doi = {10.1080/07038992.2021.1901562}, journal-iso = {CAN J REMOTE SENS}, journal = {CANADIAN JOURNAL OF REMOTE SENSING}, volume = {47}, unique-id = {32328737}, issn = {0703-8992}, abstract = {Due to the advent of powerful parallel processing tools, including modern Graphics Processing Units (GPU), new deep learning algorithms, such as Convolutional Neural Networks (CNNs), have significantly altered the state-of-the-art algorithms in satellite classification of complex environments. Recent studies have demonstrated that the generic feature maps extracted from CNNs are incredibly effective in wetland classification. The main drawback of very deep CNNs is described as structurally complex, causing the need for extensive training data. To address deep Convolutional Neural Network's limitations, a timely and computationally efficient CNN architecture is proposed in this paper. The results of the proposed model were compared to other well-known CNNs (i.e., GoogleNet and SqueezeNet) and several machine learning algorithms, including Random Forest, Gaussian Naive Bayes, and the Bayesian Optimized Tree. Results showed while significantly reduced the training time, the proposed deep learning method outperformed GoogleNet and SqueezeNet by about 12.71% and 12.2% in terms of mean overall accuracy, respectively. The classification results shown that the accuracy of wetland classes (fen, marsh, swamp, and shallow water) were significantly improved by applying the proposed CNN method.}, year = {2021}, eissn = {1712-7971}, pages = {243-260}, orcid-numbers = {Jamali, Ali/0000-0002-6073-5493; Mahdianpari, Masoud/0000-0002-7234-959X; Brisco, Brian/0000-0001-8439-362X; Mohammadimanesh, Fariba/0000-0002-9472-2324; Salehi, Bahram/0000-0002-7742-5475} } @article{MTMT:32328744, title = {Information Extraction and Population Estimates of Settlements from Historic Corona Satellite Imagery in the 1960s}, url = {https://m2.mtmt.hu/api/publication/32328744}, author = {Stratoulias, Dimitris and Grekousis, George}, doi = {10.3390/s21072423}, journal-iso = {SENSORS-BASEL}, journal = {SENSORS}, volume = {21}, unique-id = {32328744}, abstract = {The Corona satellite program was a historic reconnaissance mission which provided high spatial resolution panchromatic images during the Cold War era. Nevertheless, and despite the historic uniqueness and importance of the dataset, efforts to extract tangible information from this dataset have primarily focused on visual interpretation. More sophisticated approaches have been either hampered or unrealized, often justified by the primitive quality of this early satellite product. In the current study we attempt to showcase the usability of Corona imagery outside the context of visual interpretation. Using a 1968 Corona image acquired over the city municipality of Plovdiv, Bulgaria, we reconstruct a panchromatic 1.8 m spatial resolution georegistered image with a relative displacement Root Mean Square Error (RMSE) of 6.616 (for x dimension) and 1.886 (for y dimension) and employ segmentation and texture analysis to discern agricultural parcels and settlements' footprints. Population statistics of this past era are retrieved from national census and related to settlements' footprints. An exponential relationship between the two variables was identified by applying a semi-log regression. The high adjusted R-2 value found (76.54%) indicates that Corona images offer a unique opportunity for population data analysis of the past. Overall, we showcase that the Corona images' usability extends beyond the visual interpretation, and features of interest extracted through image analysis can be subsequently used for further geographical and historical research.}, keywords = {Feature extraction; texture; remote sensing; AGRICULTURE; Segmentation; Human population; SETTLEMENTS; Field delineation; historical GIS; Corona mission}, year = {2021}, eissn = {1424-8220}, orcid-numbers = {Stratoulias, Dimitris/0000-0002-3133-9432} } @article{MTMT:31100847, title = {Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification}, url = {https://m2.mtmt.hu/api/publication/31100847}, author = {Macintyre, Paul and van Niekerk, Adriaan and Mucina, Ladislav}, doi = {10.1016/j.jag.2019.101980}, journal-iso = {INT J APPL EARTH OBS}, journal = {INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION}, volume = {85}, unique-id = {31100847}, issn = {1569-8432}, abstract = {Vegetation maps are essential tools for the conservation and management of landscapes as they contain essential information for informing conservation decisions. Traditionally, maps have been created using field-based approaches which, due to limitations in costs and time, restrict the size of the area for which they can be created and frequency at which they can be updated. With the increasing availability of satellite sensors providing multi-spectral imagery with high temporal frequency, new methods for efficient and accurate vegetation mapping have been developed. The objective of this study was to investigate to what extent multi-seasonal Sentinel-2 imagery can assist in mapping complex compositional classifications at fine spatial scales. We deliberately chose a challenging case study, namely a visually and structurally homogenous scrub vegetation (known as kwongan) of Western Australia. The classification scheme consists of 24 target classes and a random 60/40 split was used for model building and validation. We compared several multi-temporal (seasonal) feature sets, consisting of numerous combinations of spectral bands, vegetation indices as well as principal component and tasselled cap transformations, as input to four machine learning classifiers (Support Vector Machines; SVM, Nearest Neighbour; NN, Random Forests; RF, and Classification Trees; CT) to separate target classes. The results show that a multi-temporal feature set combining autumn and spring images sufficiently captured the phenological differences between the classes and produced the best results, with SVM (74%) and NN (72%) classifiers returning statistically superior results compared to RF (65%) and CT (50%). The SWIR spectral bands captured during spring, the greenness indices captured during spring and the tasselled cap transformations derived from the autumn image emerged as most informative, which suggests that ecological factors (e.g. shared species, patch dynamics) occurring at a sub-pixel level likely had the biggest impact on class confusion. However, despite these challenges, the results are auspicious and suggest that seasonal Sentinel-2 imagery has the potential to predict compositional vegetation classes with high accuracy. Further work is needed to determine whether these results are replicable in other vegetation types and regions.}, keywords = {Phenology; Mapping; Vegetation classification; Multispectral; Tasselled cap}, year = {2020}, eissn = {1872-826X} } @article{MTMT:31769388, title = {Suitability of resampled multispectral datasets for mapping flowering plants in the Kenyan savannah}, url = {https://m2.mtmt.hu/api/publication/31769388}, author = {Makori, David Masereti and Abdel-Rahman, Elfatih M. and Landmann, Tobias and Mutanga, Onisimo and Odindi, John and Nguku, Evelyn and Tonnang, Henry E. and Raina, Suresh}, doi = {10.1371/journal.pone.0232313}, journal-iso = {PLOS ONE}, journal = {PLOS ONE}, volume = {15}, unique-id = {31769388}, issn = {1932-6203}, abstract = {Pollination services and honeybee health in general are important in the African savannahs particularly to farmers who often rely on honeybee products as a supplementary source of income. Therefore, it is imperative to understand the floral cycle, abundance and spatial distribution of melliferous plants in the African savannah landscapes. Furthermore, placement of apiaries in the landscapes could benefit from information on spatiotemporal patterns of flowering plants, by optimising honeybees' foraging behaviours, which could improve apiary productivity. This study sought to assess the suitability of simulated multispectral data for mapping melliferous (flowering) plants in the African savannahs. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) spatial and spectral resolutions, and a 10-cm ultra-high spatial resolution aerial imagery coinciding with onset and peak flowering periods were used in this study. Ground reference data was collected at the time of imagery capture. The advanced machine learning random forest (RF) classifier was used to map the flowering plants at a landscape scale and a classification accuracy validated using 30% independent test samples. The results showed that 93.33%, 69.43%, 67.52% and 82.18% accuracies could be achieved using WorldView-2, RapidEye, Spot-6 and Sentinel-2 data sets respectively, at the peak flowering period. Our study provides a basis for the development of operational and cost-effective approaches for mapping flowering plants in an African semiarid agroecological landscape. Specifically, such mapping approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale.}, year = {2020}, eissn = {1932-6203} } @article{MTMT:31180467, title = {Photophysiology and Spectroscopy of Sun and Shade Leaves of Phragmites australis and the Effect on Patches of Different Densities}, url = {https://m2.mtmt.hu/api/publication/31180467}, author = {Stratoulias, Dimitris and Tóth, Viktor R.}, doi = {10.3390/rs12010200}, journal-iso = {REMOTE SENS-BASEL}, journal = {REMOTE SENSING}, volume = {12}, unique-id = {31180467}, year = {2020}, eissn = {2072-4292} } @article{MTMT:31438603, title = {Impact of upstream landslide on perialpine lake ecosystem: An assessment using multi-temporal satellite data}, url = {https://m2.mtmt.hu/api/publication/31438603}, author = {Villa, Paolo and Bresciani, Mariano and Bolpagni, Rossano and Braga, Federica and Bellingeri, Dario and Giardino, Claudia}, doi = {10.1016/j.scitotenv.2020.137627}, journal-iso = {SCI TOTAL ENVIRON}, journal = {SCIENCE OF THE TOTAL ENVIRONMENT}, volume = {720}, unique-id = {31438603}, issn = {0048-9697}, abstract = {Monitoring freshwater and wetland systems and their response to stressors of natural or anthropogenic origin is critical for ecosystem conservation.A multi-temporal set of 87 images, acquired by Sentinel-2 satellites over three years (2016-2018), provided quantitative information for assessing the temporal evolution of key ecosystem variables in the perialpine Lake Mezzola (northern Italy), which has suffered from the impacts of a massive landslide that took place upstream of the lake basin in summer 2017.Sentinel-2 derived products revealed an increase in lake turbidity triggered by the landslide that amounted to twice the average values scored in the years preceding and following the event. Hotspots of turbidity within the lake were in particular highlighted. Moreover, both submerged and riparian vegetation showed harmful impacts due to sediment deposition. A partial loss of submerged macrophyte cover was found, with delayed growth and a possible community shift in favor of species adapted to inorganic substrates. Satellite-derived seasonal dynamics showed that exceptional sediment load can overwrite climatic factors in controlling phenology of riparian reed beds, resulting in two consecutive years with shorter than normal growing season and roughly 20% drop in productivity, according to spectral proxies. Compared to 2016, senescence came earlier by around 20 days on average in 2017 season, and green-up was delayed by up to 50 days (20 days, on average) in 2018, following the landslide.The approach presented could be easily implemented for continuous monitoring of similar ecosystems subject to external pressures with periods of high sediment loads. (C) 2020 Elsevier B.V. All rights reserved.}, keywords = {Phenology; TURBIDITY; SUBMERGED MACROPHYTES; Sentinel-2; helophytes; Lake Mezzola}, year = {2020}, eissn = {1879-1026}, orcid-numbers = {Braga, Federica/0000-0002-4131-9080; Giardino, Claudia/0000-0002-3937-4988} } @article{MTMT:31294514, title = {High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms}, url = {https://m2.mtmt.hu/api/publication/31294514}, author = {Zhou, Tao and Geng, Yajun and Chen, Jie and Pan, Jianjun and Haase, Dagmar and Lausch, Angela}, doi = {10.1016/j.scitotenv.2020.138244}, journal-iso = {SCI TOTAL ENVIRON}, journal = {SCIENCE OF THE TOTAL ENVIRONMENT}, volume = {729}, unique-id = {31294514}, issn = {0048-9697}, year = {2020}, eissn = {1879-1026}, orcid-numbers = {Zhou, Tao/0000-0003-1216-0709} } @article{MTMT:31101102, title = {Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition}, url = {https://m2.mtmt.hu/api/publication/31101102}, author = {Henrys, Peter A. and Jarvis, Susan G.}, doi = {10.1002/ece3.5376}, journal-iso = {ECOL EVOL}, journal = {ECOLOGY AND EVOLUTION}, volume = {9}, unique-id = {31101102}, issn = {2045-7758}, abstract = {The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground-based field survey. Financial and logistical constraints mean that on-the-ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km(2) (Z(i)) is unobserved, but both ground survey and remote sensing can be used to estimate Z(i). The model allows the relationship between remote sensing data and Z(i) to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model-based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007.}, keywords = {remote sensing; Great Britain; peatland; Data integration; field survey; Bayesian model calibration}, year = {2019}, eissn = {2045-7758}, pages = {8104-8112} } @article{MTMT:31101103, title = {Multi-sensor mapping of honey bee habitats and fragmentation in agro-ecological landscapes in Eastern Kenya}, url = {https://m2.mtmt.hu/api/publication/31101103}, author = {Ochungo, Pamela and Veldtman, Ruan and Abdel-Rahman, Elfatih M. and Raina, Suresh and Muli, Eliud and Landmann, Tobias}, doi = {10.1080/10106049.2019.1629645}, journal-iso = {GEOCAR INT}, journal = {GEOCARTO INTERNATIONAL}, unique-id = {31101103}, issn = {1010-6049}, abstract = {Extensive land transformation leads to habitat loss, which directly affects and fragments species habitats. Such land transformations can adversely affect fodder availability for bees and thus colony strength with consequences for rural communities that use bee keeping as a livelihood option. Quantification of the landscape structure is thus critical if the linkages between the landscape and honey bee colony health are to be well understood. In this study, a random forest algorithm was used on dual-polarized multi-season Sentinel-1A (S1) synthetic aperture radar (SAR) and single season Sentinel-2A (S2) optical imagery to map honey bee habitats and their degree of fragmentation in a heterogeneous agro-ecological landscape in eastern Kenya. The dry season S2 optical imagery was fused with the S1 data and class-wise mapping accuracies (with and without radar) were compared. Relevant fragmentation indices representing patch sizes, isolation and configuration were thereafter generated using the fused imagery. The fused imagery recorded an overall accuracy of 86% with a kappa of 0.83 versus the SAR imagery only, which had an overall accuracy of 76% with a kappa of 0.68. However, the S1 imagery had slightly higher user's and producer's accuracies for under-represented but important honey bee habitat classes, that is, natural grasslands and hedges. The variable importance analysis using the fused imagery showed that the short-wave infrared and the red-edge waveband regions were highly relevant for the classification model. Our mapping approach showed that fusing data generated from S1 and S2 with improved spectral resolution, could be effectively used for the spatially explicit mapping of honey bee habitats and their degree of fragmentation in semi-arid African agro-ecological landscapes.}, keywords = {Image fusion; Kenya; random forest; Honey bees; Landscape structure; Sentinels 1 and 2}, year = {2019}, eissn = {1752-0762} } @article{MTMT:31101100, title = {Identification of most spectrally distinguishable phenological stage of invasive Phramites australis in Lake Erie wetlands (Canada) for accurate mapping using multispectral satellite imagery}, url = {https://m2.mtmt.hu/api/publication/31101100}, author = {Rupasinghe, Prabha Amali and Chow-Fraser, Patricia}, doi = {10.1007/s11273-019-09675-2}, journal-iso = {WETLANDS ECOL MANAG}, journal = {WETLANDS ECOLOGY AND MANAGEMENT}, volume = {27}, unique-id = {31101100}, issn = {0923-4861}, abstract = {Phragmites australis (Cav.) Trin. ex Steudel subspecies australis is one of the worst plant invaders in wetlands of North America. Remote sensing is the most cost-effective method to track its spread given its widespread distribution and rapid colonization rate. We hypothesize that the morphological and/or physiological features associated with different phenological states of Phragmites can influence their reflectance signal and thus affect mapping accuracies. We tested this hypothesis by comparing classification accuracies of cloud-free images acquired by Landsat 7, Landsat 8, and Sentinel 2 at roughly monthly intervals over a calendar year for two wetlands in southern Ontario. We used the Support Vector Machines classification and employed field observations and image acquired from unmanned aerial vehicle (8 cm) to perform accuracy assessments. The highest Phragmites producer's, user's, and overall accuracy (96.00, 91.11, and 88.56% respectively) were provided by images acquired in late summer and fall period. During this period, green, Near Infrared, and Short-Wave Infrared bands generated more unique reflectance signals for Phragmites. Both Normalized Difference Vegetation Index and Normalized Difference Water Index showed significant difference between Phragmites and the most confused classes (cattail; Typha latifolia L., and meadow marsh) during the late summer and fall period. Since meadow marsh separated out best from Phragmites and cattail in the February image, we used it to mask the meadow marsh in the July image to reduce confusion. The unique reflectance signal of Phragmites in late summer and fall is likely due to prolonged greenness of Phragmites when compared to other wetland vegetation, large, distinct inflorescence, and the water content of Phragmites during this period.}, keywords = {WETLANDS; Phragmites; SVM classification; Multispectral images}, year = {2019}, eissn = {1572-9834}, pages = {513-538}, orcid-numbers = {Chow-Fraser, Patricia/0000-0002-0416-7196} } @article{MTMT:31101104, title = {Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles}, url = {https://m2.mtmt.hu/api/publication/31101104}, author = {Zhang, Aizhu and Sun, Genyun and Ma, Ping and Jia, Xiuping and Ren, Jinchang and Huang, Hui and Zhang, Xuming}, doi = {10.3390/rs11080952}, journal-iso = {REMOTE SENS-BASEL}, journal = {REMOTE SENSING}, volume = {11}, unique-id = {31101104}, abstract = {Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.}, keywords = {Image classification; Gravitational Search Algorithm; Multilayer perceptron; coastal wetland; morphological attribute profiles}, year = {2019}, eissn = {2072-4292}, orcid-numbers = {Jia, Xiuping/0000-0001-9916-6382; Ren, Jinchang/0000-0001-6116-3194; Huang, Hui/0000-0001-5503-9090} } @article{MTMT:30436872, title = {Forest mapping and species composition using supervised per pixel classification of Sentinel-2 imagery}, url = {https://m2.mtmt.hu/api/publication/30436872}, author = {Bolyn, Corentin and Michez, Adrien and Gaucher, Peter and Lejeune, Philippe and Bonnet, Stephanie}, journal-iso = {BIOTECHNOL AGRON SOC}, journal = {BIOTECHNOLOGIE AGRONOMIE SOCIETE ET ENVIRONNEMENT}, volume = {22}, unique-id = {30436872}, issn = {1370-6233}, abstract = {Description of the subject. Understanding the current situation and evolution of forests is essential for a sustainable management plan that maintains forests' ecological and socio-economic functions. Remote sensing is a helpful tool in developing this knowledge. Objectives. This paper investigates the new opportunities offered by using Sentinel-2 (S2) imagery for forest mapping in Belgian Ardenne ecoregion. The first classification objective was to create a forest map at the regional scale. The second objective was the discrimination of 11 forest classes (Fagus sylvatica L., Betula sp., Quercus sp., other broad-leaved stands, Pseudotsuga menziesii (Mirb.) Franco, Larix sp., Pinus sylvestris L., Picea abies (L.) H. Karst., young needle-leaved stands, other needle-leaved stands, and recent clear-cuts). Method. Two S2 scenes were used and a series of spectral indices were computed for each. We applied supervised pixel-based classifications with a Random Forest classifier. The classification models were processed with a pure S2 dataset and with additional 3D data to compare obtained precisions. Results. 3D data slightly improved the precision of each objective, but the overall improvement in accuracy was only significant for objective 1. The produced forest map had an overall accuracy of 93.3%. However, the model testing tree species discrimination was also encouraging, with an overall accuracy of 88.9%. Conclusions. Because of the simple analyses done in this study, results need to be interpreted with caution. However, this paper confirms the great potential of S2 imagery, particularly SWIR and red-edge bands, which are the most important S2 bands in our study.}, keywords = {INDEX; remote sensing; chlorophyll content; RED; LAND; SATELLITES; SATELLITE DATA; agronomy; REFLECTANCE; Biotechnology & Applied Microbiology; Tree species; Belgian Ardenne ecoregion; per-pixel classification; AEROSOL OPTICAL-THICKNESS; ATMOSPHERIC CORRECTION; GLOBAL VEGETATION}, year = {2018}, eissn = {1780-4507}, pages = {172-187} } @CONFERENCE{MTMT:30432730, title = {Object-based habitat mapping of reedbeds using country-wide airborne laser scanning point clouds}, url = {https://m2.mtmt.hu/api/publication/30432730}, author = {Koma, Zsófia and Seijmonsbergen, Arie C and Meijer, Christiaan and Bouten, Willem and Kissling, W Daniel}, booktitle = {GEOBIA 2018}, unique-id = {30432730}, year = {2018}, pages = {&} } @article{MTMT:27269734, title = {A New Approach to Design Autonomous Wireless Sensor Node Based on RF Energy Harvesting System}, url = {https://m2.mtmt.hu/api/publication/27269734}, author = {Mouapi, Alex and Hakem, Nadir}, doi = {10.3390/s18010133}, journal-iso = {SENSORS-BASEL}, journal = {SENSORS}, volume = {18}, unique-id = {27269734}, year = {2018}, eissn = {1424-8220} } @mastersthesis{MTMT:30436906, title = {Identification of Tree Species Using Airborne Hyperspectral DataA Case study of Ngangao Forest in Taita Hills, Keny}, url = {https://m2.mtmt.hu/api/publication/30436906}, author = {Nthuni, Samuel Mwenje}, publisher = {University of Nairobi}, unique-id = {30436906}, year = {2018} } @article{MTMT:27269733, title = {The contribution of Earth observation technologies to the reporting obligations of the Habitats Directive and Natura 2000 network in a protected wetland}, url = {https://m2.mtmt.hu/api/publication/27269733}, author = {Regos, Adrian and Dominguez, Jesus}, doi = {10.7717/peerj.4540}, journal-iso = {PEERJ}, journal = {PEERJ}, volume = {6}, unique-id = {27269733}, issn = {2167-8359}, year = {2018}, eissn = {2167-8359} } @article{MTMT:30436869, title = {Remote Sensing of Floodpath Lakes and Wetlands: A Challenging Frontier in the Monitoring of Changing Environments}, url = {https://m2.mtmt.hu/api/publication/30436869}, author = {Wang, Yeqiao and Yesou, Herve}, doi = {10.3390/rs10121955}, journal-iso = {REMOTE SENS-BASEL}, journal = {REMOTE SENSING}, volume = {10}, unique-id = {30436869}, abstract = {Monitoring of changing lake and wetland environments has long been among the primary focus of scientific investigation, technology innovation, management practice, and decision-making analysis. Floodpath lakes and wetlands are the lakes and associated wetlands affected by seasonal variations of water level and water surface area. Floodpath lakes and wetlands are, in particular, sensitive to natural and anthropogenic impacts, such as climate change, human-induced intervention on hydrological regimes, and land use and land cover change. Rapid developments of remote sensing science and technologies, provide immense opportunities and capacities to improve our understanding of the changing lake and wetland environments. This special issue on Remote Sensing of Floodpath Lakes and Wetlands comprise featured articles reporting the latest innovative research and reflects the advancement in remote sensing applications on the theme topic. In this editorial paper, we review research developments using state-of-the-art remote sensing technologies for monitoring dynamics of floodpath lakes and wetlands; discuss challenges of remote sensing in inventory, monitoring, management, and governance of floodpath lakes and wetlands; and summarize the highlights of the articles published in this special issue.}, keywords = {DISSOLVED ORGANIC-MATTER; Tibetan Plateau; floodpath lakes and wetlands; Landsat-LakeTime; Sentinel-1 SAR; Sentinel-2 MSI; Sentinel-3 OLCI; TanDEM-X; Poyang and Dongting lakes; Barguzin Valley Lake Baikal; WATER-LEVEL CHANGES; SATELLITE RADAR ALTIMETRY; HIGH-SPATIAL-RESOLUTION; POYANG LAKE; DISCHARGE ESTIMATION; AMAZON FLOODPLAIN; inundation area}, year = {2018}, eissn = {2072-4292} } @article{MTMT:30436870, title = {Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data}, url = {https://m2.mtmt.hu/api/publication/30436870}, author = {Wessel, Mathias and Brandmeier, Melanie and Tiede, Dirk}, doi = {10.3390/rs10091419}, journal-iso = {REMOTE SENS-BASEL}, journal = {REMOTE SENSING}, volume = {10}, unique-id = {30436870}, abstract = {We use freely available Sentinel-2 data and forest inventory data to evaluate the potential of different machine-learning approaches to classify tree species in two forest regions in Bavaria, Germany. Atmospheric correction was applied to the level 1C data, resulting in true surface reflectance or bottom of atmosphere (BOA) output. We developed a semiautomatic workflow for the classification of deciduous (mainly spruce trees), beech and oak trees by evaluating different classification algorithms (object- and pixel-based) in an architecture optimized for distributed processing. A hierarchical approach was used to evaluate different band combinations and algorithms (Support Vector Machines (SVM) and Random Forest (RF)) for the separation of broad-leaved vs. coniferous trees. The Ebersberger forest was the main project region and the Freisinger forest was used in a transferability study. Accuracy assessment and training of the algorithms was based on inventory data, validation was conducted using an independent dataset. A confusion matrix, with User's and Producer's Accuracies, as well as Overall Accuracies, was created for all analyses. In total, we tested 16 different classification setups for coniferous vs. broad-leaved trees, achieving the best performance of 97% for an object-based multitemporal SVM approach using only band 8 from three scenes (May, August and September). For the separation of beech and oak trees we evaluated 54 different setups, the best result achieved an accuracy of 91% for an object-based, SVM, multitemporal approach using bands 8, 2 and 3 of the May scene for segmentation and all principal components of the August scene for classification. The transferability of the model was tested for the Freisinger forest and showed similar results. This project points out that Sentinel-2 had only marginally worse results than comparable commercial high-resolution satellite sensors and is well-suited for forest analysis on a tree-stand level.}, keywords = {GIS; SATELLITE DATA; pine; Support vector machines; Support vector machines; Image classification; random forest; random forest; REMOTELY-SENSED DATA; VEGETATION INDEXES; forest classification; LIDAR DATA}, year = {2018}, eissn = {2072-4292}, orcid-numbers = {Tiede, Dirk/0000-0002-5473-3344} } @article{MTMT:30436871, title = {Assessing Vegetation Response to Soil Moisture Fluctuation under Extreme Drought Using Sentinel-2}, url = {https://m2.mtmt.hu/api/publication/30436871}, author = {West, Harry and Quinn, Nevil and Horswell, Michael and White, Paul}, doi = {10.3390/w10070838}, journal-iso = {WATER-SUI}, journal = {WATER}, volume = {10}, unique-id = {30436871}, abstract = {The aim of this study was to determine the extent to which Sentinel-2 Normalised Difference Vegetation Index (NDVI) reflects soil moisture conditions, and whether this product offers an improvement over Landsat-8. Based on drought exposure, cloud-free imagery availability, and measured soil moisture, five sites in the Southwestern United States were selected. These sites, normally dry to arid, were in various states of drought. A secondary focus was therefore the performance of the NDVI under extreme conditions. Following supervised classification, the NDVI values for one-kilometre radius areas were calculated. Sentinel-2 NDVI variants using Spectral Bands 8 (10 m spatial resolution), 5, 6, 7, and 8A (20 m spatial resolution) were calculated. Landsat-8 NDVI was calculated at 30 m spatial resolution. Pearson correlation analysis was undertaken for NDVI against moisture at various depths. To assess the difference in correlation strength, a principal component analysis was performed on the combination of all bands and the combination of the new red-edge bands. Performance of the red-edge NDVI against the standard near infrared (NIR) was then evaluated using a Steiger comparison. No significant correlations between Landsat-8 NDVI and soil moisture were found. Significant correlations at depths of less than 30 cm were present between Sentinel-2 NDVI and soil moisture at three sites. The remaining two sites were characterised by low vegetation cover, suggesting a cover threshold of approximately 30-40% is required for a correlation to be present. At all sites of significant positive moisture to NDVI correlation, the linear combination of the red-edge bands produced stronger correlations than the poorer spectral but higher spatial resolution band. NDVI calculated using the higher spectral resolution bands may therefore be of greater use in this context than the higher spatial resolution option. Results suggest potential for the application of Sentinel-2 NDVI in soil moisture monitoring, even in extreme environments. To the best of our knowledge, this paper represents the first study of this kind using Sentinel-2.}, keywords = {PLANTS; VARIABILITY; Africa; Soil moisture; GROUNDWATER; INDEXES; NDVI; REMOTELY-SENSED DATA; Sentinel-2; Normalised Difference Vegetation Index (NDVI); Landsat-8; extreme climates; RED-EDGE BANDS; LANDSAT 8}, year = {2018}, eissn = {2073-4441} } @article{MTMT:27520907, title = {Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis}, url = {https://m2.mtmt.hu/api/publication/27520907}, author = {Zhou, Guanhua and Ma, Zhongqi and Sathyendranath, Shubha and Platt, Trevor and Jiang, Cheng and Sun, Kang}, doi = {10.3390/rs10060837}, journal-iso = {REMOTE SENS-BASEL}, journal = {REMOTE SENSING}, volume = {10}, unique-id = {27520907}, year = {2018}, eissn = {2072-4292} } @article{MTMT:26711782, title = {Atmospheric correction issues for retrieving total suspended matter concentrations in inland waters using OLI/Landsat-8 image}, url = {https://m2.mtmt.hu/api/publication/26711782}, author = {Bernardo, Nariane and Watanabe, Fernanda and Rodrigues, Thanan and Alcantara, Enner}, doi = {10.1016/j.asr.2017.02.017}, journal-iso = {ADV SPACE RES}, journal = {ADVANCES IN SPACE RESEARCH}, volume = {59}, unique-id = {26711782}, issn = {0273-1177}, year = {2017}, eissn = {1879-1948}, pages = {2335-2348} } @article{MTMT:27269735, title = {Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning}, url = {https://m2.mtmt.hu/api/publication/27269735}, author = {Chatziantoniou, Andromachi and Petropoulos, George P and Psomiadis, Emmanouil}, doi = {10.3390/rs9121259}, journal-iso = {REMOTE SENS-BASEL}, journal = {REMOTE SENSING}, volume = {9}, unique-id = {27269735}, year = {2017}, eissn = {2072-4292} } @article{MTMT:27269736, title = {Evaluation of Green-LiDAR Data for Mapping Extent, Density and Height of Aquatic Reed Beds at Lake Chiemsee, BavariaGermany}, url = {https://m2.mtmt.hu/api/publication/27269736}, author = {Meneses, Nicolas Corti and Baier, Simon and Geist, Juergen and Schneider, Thomas}, doi = {10.3390/rs9121308}, journal-iso = {REMOTE SENS-BASEL}, journal = {REMOTE SENSING}, volume = {9}, unique-id = {27269736}, year = {2017}, eissn = {2072-4292}, orcid-numbers = {Geist, Juergen/0000-0001-7698-3443; Schneider, Thomas/0000-0003-1126-2818} } @article{MTMT:30436910, title = {Testing the Potential Application of Simulated Multispectral Data in Discriminating Tree Species in Taita Hills}, url = {https://m2.mtmt.hu/api/publication/30436910}, author = {Nthuni, Samuel and Karanja, Faith and Pellikka, Petri Kauko Emil and Siljander, Mika}, doi = {10.12691/jgg-5-5-3}, journal = {Journal of Geosciences and Geomatics}, volume = {5}, unique-id = {30436910}, issn = {2373-6690}, year = {2017}, pages = {243-250} } @article{MTMT:26888944, title = {The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations}, url = {https://m2.mtmt.hu/api/publication/26888944}, author = {Sadeghi, Morteza and Babaeian, Ebrahim and Tuller, Markus and Jones, Scott B}, doi = {10.1016/j.rse.2017.05.041}, journal-iso = {REMOTE SENS ENVIRON}, journal = {REMOTE SENSING OF ENVIRONMENT}, volume = {198}, unique-id = {26888944}, issn = {0034-4257}, year = {2017}, eissn = {1879-0704}, pages = {52-68} } @article{MTMT:26711781, title = {Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species}, url = {https://m2.mtmt.hu/api/publication/26711781}, author = {Shoko, C and Mutanga, O}, doi = {10.1016/j.isprsjprs.2017.04.016}, journal-iso = {ISPRS J PHOTOGRAMM}, journal = {ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING}, volume = {129}, unique-id = {26711781}, issn = {0924-2716}, year = {2017}, eissn = {1872-8235}, pages = {32-40} } @article{MTMT:26888943, title = {Seasonal discrimination of C3 and C4 grasses functional types: An evaluation of the prospects of varying spectral configurations of new generation sensors}, url = {https://m2.mtmt.hu/api/publication/26888943}, author = {Shoko, Cletah and Mutanga, Onisimo}, doi = {10.1016/j.jag.2017.05.015}, journal-iso = {INT J APPL EARTH OBS}, journal = {INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION}, volume = {62}, unique-id = {26888943}, issn = {1569-8432}, year = {2017}, eissn = {1872-826X}, pages = {47-55} } @article{MTMT:25606106, title = {First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe}, url = {https://m2.mtmt.hu/api/publication/25606106}, author = {Immitzer, Markus and Vuolo, Francesco and Atzberger, Clement}, doi = {10.3390/rs8030166}, journal-iso = {REMOTE SENS-BASEL}, journal = {REMOTE SENSING}, volume = {8}, unique-id = {25606106}, year = {2016}, eissn = {2072-4292}, pages = {166-193} } @CONFERENCE{MTMT:26066796, title = {Suitability of Sentinel-2 Data for Tree Species Classification in Central Europe}, url = {https://m2.mtmt.hu/api/publication/26066796}, author = {Immitzer, Markus and Vuolo, Francesco and Einzmann, Kathrin and Ng, Wai-Tim and Böck, Sebastian and Atzberger, Clement}, booktitle = {Proceedings of Living Planet Symposium 2016}, unique-id = {26066796}, year = {2016}, pages = {16} } @article{MTMT:26066760, title = {Verwendung von multispektralen Sentinel-2 Daten für die Baumartenklassifikation und Vergleich mit anderen Satellitensensoren}, url = {https://m2.mtmt.hu/api/publication/26066760}, author = {IMMITZER, MARKUS and VUOLO, FRANCESCO and EINZMANN, KATHRIN and NG, WAITIM}, journal-iso = {DGPF}, journal = {DEUTSCHEN GESELLSCHAFT FUR PHOTOGRAMMETRIE UND FERNERKUNDUNG}, volume = {25}, unique-id = {26066760}, issn = {0942-2870}, year = {2016}, pages = {417-427} } @article{MTMT:25581118, title = {Spectral Similarity Assessment Based on a Spectrum Reflectance-Absorption Index and Simplified Curve Patterns for Hyperspectral Remote Sensing}, url = {https://m2.mtmt.hu/api/publication/25581118}, author = {Ma, Dan and Liu, Jun and Huang, Junyi and Li, Huali and Liu, Ping and Chen, Huijuan and Qian, Jing}, doi = {10.3390/s16020152}, journal-iso = {SENSORS-BASEL}, journal = {SENSORS}, volume = {16}, unique-id = {25581118}, year = {2016}, eissn = {1424-8220} } @article{MTMT:25581117, title = {The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: a simulation study}, url = {https://m2.mtmt.hu/api/publication/25581117}, author = {Majasalmi, Titta and Rautiainen, Miina}, doi = {10.1080/2150704X.2016.1149251}, journal-iso = {REMOTE SENS LETT}, journal = {REMOTE SENSING LETTERS}, volume = {7}, unique-id = {25581117}, issn = {2150-704X}, year = {2016}, eissn = {2150-7058}, pages = {427-436} } @article{MTMT:26045169, title = {Remote Sensing of Grassland Biophysical Parameters in the Context of the Sentinel-2 Satellite Mission}, url = {https://m2.mtmt.hu/api/publication/26045169}, author = {Sakowska, Karolina and Juszczak, Radoslaw and Gianelle, Damiano}, doi = {10.1155/2016/4612809}, journal-iso = {J SENSORS}, journal = {JOURNAL OF SENSORS}, volume = {2016}, unique-id = {26045169}, issn = {1687-725X}, year = {2016}, eissn = {1687-7268} } @article{MTMT:26066831, title = {Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices}, url = {https://m2.mtmt.hu/api/publication/26066831}, author = {Sibanda, M and Mutanga, O and Rouget, M}, doi = {10.1080/15481603.2016.1221576}, journal-iso = {GISCI REMOTE SENS}, journal = {GISCIENCE AND REMOTE SENSING}, volume = {53}, unique-id = {26066831}, issn = {1548-1603}, year = {2016}, eissn = {1943-7226}, pages = {614-633} } @article{MTMT:26397262, title = {Sentinel-2A MSI and Landsat 8 OLI Provide Data Continuity for Geological Remote Sensing}, url = {https://m2.mtmt.hu/api/publication/26397262}, author = {van der Werff, Harald and van der Meer, Freek}, doi = {10.3390/rs8110883}, journal-iso = {REMOTE SENS-BASEL}, journal = {REMOTE SENSING}, volume = {8}, unique-id = {26397262}, year = {2016}, eissn = {2072-4292}, orcid-numbers = {van der Werff, Harald/0000-0002-2871-3913} }