TY - JOUR AU - Enterkine, Josh AU - Caughlin, T. Trevor AU - Dashti, Hamid AU - Glenn, Nancy F. TI - Applied soft classes and fuzzy confusion in a patchwork semi-arid ecosystem: Stitching together classification techniques to preserve ecologically-meaningful information JF - REMOTE SENSING OF ENVIRONMENT J2 - REMOTE SENS ENVIRON VL - 300 PY - 2024 PG - 11 SN - 0034-4257 DO - 10.1016/j.rse.2023.113853 UR - https://m2.mtmt.hu/api/publication/34651168 ID - 34651168 LA - English DB - MTMT ER - TY - JOUR AU - Torresani, M. AU - Rocchini, D. AU - Ceola, G. AU - de, Vries J.P.R. AU - Feilhauer, H. AU - Moudrý, V. AU - Bartholomeus, H. AU - Perrone, M. AU - Anderle, M. AU - Gamper, H.A. AU - Chieffallo, L. AU - Guatelli, E. AU - Gatti, R.C. AU - Kleijn, D. TI - Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 14 PY - 2024 IS - 1 SN - 2045-2322 DO - 10.1038/s41598-023-50308-9 UR - https://m2.mtmt.hu/api/publication/34562370 ID - 34562370 N1 - Faculty of Agricultural, Environmental and Food Sciences, Free University of Bolzano/Bozen, Piazza Universitá/Universitätsplatz 1, Bolzano/Bozen, 39100, Italy BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, Bologna, 40126, Italy Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcka 129, Praha - Suchdol, 16500, Czech Republic Plant Ecology and Nature Conservation Group, Wageningen University, Droevendaalsesteeg 3a, Wageningen, 6708PB, Netherlands Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University, Leipzig, Germany German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany Department of Remote Sensing, Helmholtz-Centre for Environmental Research - UFZ, Permoserstr. 15, Leipzig, 04318, Germany Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, P.O. Box 47, Wageningen, 6700 AA, Netherlands Eurac Research, Inst. for Alpine Environment, Bolzano, Italy Department of Environmental Science and Policy, University of Milan, Milan, Italy Visual art, FEIMC, Bolzano, Italy Export Date: 6 February 2024 Correspondence Address: Rocchini, D.; Department of Spatial Sciences, Kamýcka 129, Czech Republic; email: duccio.rocchini@unibo.it LA - English DB - MTMT ER - TY - JOUR AU - Rocchini, Duccio AU - Nowosad, Jakub AU - D'Introno, Rossella AU - Chieffallo, Ludovico AU - Bacaro, Giovanni AU - Gatti, Roberto Cazzolla AU - Foody, Giles M. AU - Furrer, Reinhard AU - Gabor, Lukas AU - Malavasi, Marco AU - Marcantonio, Matteo AU - Marchetto, Elisa AU - Moudry, Vitezslav AU - Ricotta, Carlo AU - Simova, Petra AU - Torresani, Michele AU - Thouverai, Elisa TI - Scientific maps should reach everyone: The cblindplot R package to let colour blind people visualise spatial patterns JF - ECOLOGICAL INFORMATICS J2 - ECOL INFORM VL - 76 PY - 2023 PG - 7 SN - 1574-9541 DO - 10.1016/j.ecoinf.2023.102045 UR - https://m2.mtmt.hu/api/publication/33954213 ID - 33954213 AB - Maps represent powerful tools to show the spatial variation of a variable in a straightforward manner. A crucial aspect in map rendering for its interpretation by users is the gamut of colours used for displaying data. One part of this problem is linked to the proportion of the human population that is colour blind and, therefore, highly sensitive to colour palette selection. The aim of this paper is to present the cblindplot R package and its founding function -cblind.plot() -which enables colour blind people to just enter an image in a coding workflow, simply set their colour blind deficiency type, and immediately get as output a colour blind friendly plot. We will first describe in detail colour blind problems, and then show a step by step example of the function being proposed. While examples exist to provide colour blind people with proper colour palettes, in such cases (i) the workflow include a separate import of the image and the application of a set of colour ramp palettes and (ii) albeit being well documented, there are many steps to be done before plotting an image with a colour blind friendly ramp palette. The function described in this paper, on the contrary, allows to (i) automatically call the image inside the function without any initial import step and (ii) explicitly refer to the colour blind deficiency type being experienced, to further automatically apply the proper colour ramp palette. LA - English DB - MTMT ER - TY - JOUR AU - Torresani, M. AU - Kleijn, D. AU - de, Vries J.P.R. AU - Bartholomeus, H. AU - Chieffallo, L. AU - Cazzolla, Gatti R. AU - Moudrý, V. AU - Da, Re D. AU - Tomelleri, E. AU - Rocchini, D. TI - A novel approach for surveying flowers as a proxy for bee pollinators using drone images JF - ECOLOGICAL INDICATORS J2 - ECOL INDIC VL - 149 PY - 2023 SN - 1470-160X DO - 10.1016/j.ecolind.2023.110123 UR - https://m2.mtmt.hu/api/publication/33795872 ID - 33795872 N1 - Free University of Bolzano/Bozen, Faculty of Agricultural, Environmental and Food Sciences, Piazza Universitá/ Universitätsplatz 1, Bolzano/Bozen, 39100, Italy BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, Bologna, 40126, Italy Plant Ecology and Nature Conservation Group, Wageningen University, Droevendaalsesteeg 3a, Wageningen, 6708PB, Netherlands Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, P.O. Box 47, AA Wageningen, 6700, Netherlands Czech University of Life Sciences Prague, Faculty of Environmental Sciences, Department of Spatial Sciences, Kamýcka 129, Praha - Suchdol, 16500, Czech Republic Georges Lemaı̂tre Center for Earth and Climate Research, Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium Export Date: 4 May 2023 Correspondence Address: Torresani, M.; Free University of Bolzano/Bozen, Italy LA - English DB - MTMT ER - TY - JOUR AU - van, der Merwe S. AU - Greve, M. AU - Skowno, A.L. AU - Hoffman, M.T. AU - Cramer, M.D. TI - Can vegetation be discretely classified in species-poor environments? Testing plant community concepts for vegetation monitoring on sub-Antarctic Marion Island JF - ECOLOGY AND EVOLUTION J2 - ECOL EVOL VL - 13 PY - 2023 IS - 1 SN - 2045-7758 DO - 10.1002/ece3.9681 UR - https://m2.mtmt.hu/api/publication/33721066 ID - 33721066 N1 - Department of Biological Sciences, University of Cape Town, Cape Town, South Africa Kirstenbosch Research Centre, South African National Biodiversity Institute, Cape Town, South Africa Department of Plant and Soil Sciences, University of Pretoria, Pretoria, South Africa Export Date: 28 March 2023 Correspondence Address: van der Merwe, S.; Department of Biological Sciences, HW Pearson Building, Rondebosch, South Africa; email: stephni.vdm@gmail.com LA - English DB - MTMT ER - TY - JOUR AU - Wan, Yue AU - Zhang, Jingxiong AU - Zhang, Wangle AU - Zhang, Ying AU - Yang, Wenjing AU - Wang, Jianxu AU - Chukwunonso, Okafor Somtoochukwu AU - Nadeeka, Asurapplullige Milani Tharuka TI - Characterizing Uncertainty and Enhancing Utility in Remotely Sensed Land Cover Using Error Matrices Localized in Canonical Correspondence Analysis Ordination Space JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 15 PY - 2023 IS - 5 PG - 23 SN - 2072-4292 DO - 10.3390/rs15051367 UR - https://m2.mtmt.hu/api/publication/33954215 ID - 33954215 AB - In response to uncertainty in remotely sensed land cover products, there is continuing research on accuracy assessment and analysis. Given reference sample data, accuracy indicators are commonly estimated based on error matrices, from which areal extents of different cover types are also estimated. There are merits to explore the ways utilities of land cover products may be further enhanced beyond map face values and conventional area estimation. This paper presents an integrative method (CCAErrMat) for uncertainty characterization and utility enhancement. This works through reference-map cover type co-occurrence analyses based on error matrices localized in canonical correspondence analysis (CCA) ordination space rather than in geographic space to overcome the sparsity of reference sample data. The aforementioned co-occurrence analyses facilitate quantification of accuracy indicators, identification of correctly classified and perfectly misclassified pixels, and prediction of reference class probabilities, all at individual pixels. Moreover, these predicted reference class probabilities are used as auxiliary variables to formulate model-assisted area estimation, further enhancing map utilities. Extensions to CCAErrMat are also investigated as a way to bypass the pre-computing of map class occurrence pattern indices as candidate explanatory variables for CCAErrMat, leading to two variant methods: CCACCAErrMat and CNNCCAErrMat. A case study based in Wuhan municipality, central China was undertaken to compare the proposed method against alternative methods, including CCA-separate and CNN-separate. The advantages of CCAErrMat and CCACCAErrMat were confirmed. The proposed method is recommendable for characterizing uncertainty and enhancing utilities in land cover maps by analyzing locally constrained error matrices. The method is also cost-effective in terms of reference sample data, as requirements for them are similar to those for conventional accuracy assessments. LA - English DB - MTMT ER - TY - JOUR AU - Wolff, Franziska AU - Kolari, Tiina H. M. AU - Villoslada, Miguel AU - Tahvanainen, Teemu AU - Korpelainen, Pasi AU - Zamboni, Pedro A. P. AU - Kumpula, Timo TI - RGB vs. Multispectral imagery: Mapping aapa mire plant communities with UAVs JF - ECOLOGICAL INDICATORS J2 - ECOL INDIC VL - 148 PY - 2023 PG - 14 SN - 1470-160X DO - 10.1016/j.ecolind.2023.110140 UR - https://m2.mtmt.hu/api/publication/33954214 ID - 33954214 AB - Plant communities of mires can be linked to important ecological processes, such as carbon storage and gas fluxes. As indicators of ecosystem dynamics, knowledge about their distribution and condition can support ecosystem assessment. Mapping mire vegetation enables monitoring at ecosystem-scale, which can be done with UAVs (Unmanned Aerial Vehicles). Depending on the mounted sensor and the spectral signals recorded, various attributes of plant communities can be retrieved. However, it is uncertain to what extent plant communities can be derived, as mapping vegetation on detailed level remains challenging due to overlapping spectral signatures of plant species. Advancing technology offers the choice between low cost RGB and multispectral sensors as well as a variety of classification methods to overcome these challenges. Therefore, we used K-means unsupervised classification and Random Forest supervised classification with different input variables to map microtopo-graphical patterns and plant communities of two aapa mires as resolved by hierarchical clustering. This extensive approach allowed the assessment of both classifier's strength and weaknesses, as well as the criteria of selecting suitable input data. UAV-RGB and multispectral imagery with associated spectral and topographical indices of both 0.05 m and 0.30 m spatial resolution were used for the K-means method. We assessed the relationship between these generated spectral classes and plant community clusters. The clusters further served as training and validation labels for to classify the high resolution, multispectral UAV-data (0.05 m) using Random Forest. Our study demonstrates that maps reflecting microtopograpical patterns and a wetness gradient can be produced with low-cost RGB imagery and unsupervised classification. Despite this linkage to plant communities, enhanced and detailed maps of plant community distribution can only be achieved with multispectral data and robust machine learning techniques. Random forest classifications showed good overall accuracies (0.59 - 0.82) in mapping microtopographical patterns and plant communities based on hierarchical clustering of vegetation data. While the strength of both classifiers lies in the distinction of bog hummock communities, classification per-formance was weaker between different transition and lawn community types. Casual misclassifications occurred also for communities along the transition of microtopographical patterns. The main obstacle for accurate mapping remains the overlap of spectral signatures from species and spectral noise originating from wetness in mires that lead to misclassification. Future studies addressing plant community and diversity mapping should therefore consider the origin of spectral variation with further sensors. LA - English DB - MTMT ER - TY - JOUR AU - Bessinger, Mariel AU - Luck-Vogel, Melanie AU - Skowno, Andrew AU - Conrad, Ferozah TI - Landsat-8 based coastal ecosystem mapping in South Africa using random forest classification in Google Earth Engine JF - SOUTH AFRICAN JOURNAL OF BOTANY J2 - S AFR J BOT VL - 150 PY - 2022 SP - 928 EP - 939 PG - 12 SN - 0254-6299 DO - 10.1016/j.sajb.2022.08.014 UR - https://m2.mtmt.hu/api/publication/33471672 ID - 33471672 AB - Coastlines worldwide are home to an increasing number of people and are subject to many pressures. This, combined with natural dynamics and hazards, often results in the degradation of coastal and marine ecosys-tems and infrastructure. Therefore, it is necessary to adopt effective management strategies to ensure sus-tainable use of coastal ecosystems, which requires up-to-date data on the extent of coastal ecosystems. This research aimed to create a coastal ecosystem land cover map for South Africa using the random forest algo-rithm to classify Landsat 8 imagery. Processing was done using the Google Earth Engine platform. A total of 522 Landsat 8 images were called to create a median image for classification. The impact of the number of trees, the number of variables per split, and variable selection on overall classification accuracy and Kappa values were evaluated. This was done by increasing the number of trees from 100 to 500 with increments of 100, setting the number of variables per split to three, four or five, and reducing the number of input varia-bles from the original 18 variables, to the 10 most important variables, to the 5 most important variables, based on variable importance scores. Results suggest that the number of input variables used in the model had a greater impact on accuracy than the number of trees used, or the number of variables used per split. The average overall accuracy was 82.28%, with values ranging between 75.33% and 86.70%, while the average Kappa was 0.8068 and values ranged between 0.7310 and 0.8550. The model with the highest overall accu-racy was the model using all input variables, 500 trees, and three variables per split. A major challenge was the misclassification of certain vegetation classes due to the complex successional mosaic they form, causing mixed signals and generally lower classification accuracy. Despite model limitations, results were satisfactory and have shown that coastal land cover classification and monitoring could be aided by the rapid classifica-tion of Landsat 8 imagery in Google Earth Engine using the random forest algorithm. (c) 2022 SAAB. Published by Elsevier B.V. All rights reserved. LA - English DB - MTMT ER - TY - JOUR AU - Braun, Andreas C. TI - Deforestation by Afforestation: Land Use Change in the Coastal Range of Chile JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 14 PY - 2022 IS - 7 PG - 42 SN - 2072-4292 DO - 10.3390/rs14071686 UR - https://m2.mtmt.hu/api/publication/33489858 ID - 33489858 AB - In southern Chile, an establishment of a plantation-based forest industry occurred early in the industrial era. Forest companies claim that plantations were established on eroded lands. However, the plantation industry is under suspicion to have expanded its activities by clearing near-natural forests since the early 1970s. This paper uses a methodologically complex classification approach from own previously published research to elucidate land use dynamics in southern Chile. It uses spatial data (extended morphological profiles) in addition to spectral data from historical Landsat imagery, which are fusioned by kernel composition and then classified in a multiple classifier system (based on support, import and relevance vector machines). In a large study area (similar to 67,000 km(2)), land use change is investigated in a narrow time frame (five-year steps from 1975 to 2010) in a twoway (prospective and retrospective) analysis. The results are discussed synoptically with other results on Chile. Two conclusions can be drawn for the coastal range. Near-natural forests have always been felled primarily in favor of the plantation industry. Vice versa, industrial plantations have always been primarily established on sites, that were formerly forest covered. This refutes the claim that Chilean plantations were established primarily to restore eroded lands; also known as badlands. The article further shows that Chile is not an isolated case of deforestation by afforestation, which has occurred in other countries alike. Based on the findings, it raises the question of the extent to which the Chilean example could be replicated in other countries through afforestation by market economy and climate change mitigation. LA - English DB - MTMT ER - TY - JOUR AU - Costa, Hugo AU - Benevides, Pedro AU - Moreira, Francisco D. AU - Moraes, Daniel AU - Caetano, Mario TI - Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 14 PY - 2022 IS - 8 PG - 21 SN - 2072-4292 DO - 10.3390/rs14081865 UR - https://m2.mtmt.hu/api/publication/33489856 ID - 33489856 AB - Portugal is building a land cover monitoring system to deliver land cover products annually for its mainland territory. This paper presents the methodology developed to produce a prototype relative to 2018 as the first land cover map of the future annual map series (COSsim). A total of thirteen land cover classes are represented, including the most important tree species in Portugal. The mapping approach developed includes two levels of spatial stratification based on landscape dynamics. Strata are analysed independently at the higher level, while nested sublevels can share data and procedures. Multiple stages of analysis are implemented in which subsequent stages improve the outputs of precedent stages. The goal is to adjust mapping to the local landscape and tackle specific problems or divide complex mapping tasks in several parts. Supervised classification of Sentinel-2 time series and post-classification analysis with expert knowledge were performed throughout four stages. The overall accuracy of the map is estimated at 81.3% (+/- 2.1) at the 95% confidence level. Higher thematic accuracy was achieved in southern Portugal, and expert knowledge significantly improved the quality of the map. LA - English DB - MTMT ER - TY - JOUR AU - Fassnacht, Fabian Ewald AU - Mullerova, Jana AU - Conti, Luisa AU - Malavasi, Marco AU - Schmidtlein, Sebastian TI - About the link between biodiversity and spectral variation JF - APPLIED VEGETATION SCIENCE J2 - APP VEGE SCI VL - 25 PY - 2022 IS - 1 PG - 13 SN - 1402-2001 DO - 10.1111/avsc.12643 UR - https://m2.mtmt.hu/api/publication/33489860 ID - 33489860 AB - Aim The spectral variability hypothesis (SVH) suggests a link between spectral variation and plant biodiversity. The underlying assumptions are that higher spectral variation in canopy reflectance (depending on scale) is caused by either (1) variation in habitats or linked vegetation types or plant communities with their specific optical community traits or (2) variation in the species themselves and their specific optical traits. Methods The SVH was examined in several empirical remote-sensing case studies, which often report some correlation between spectral variation and biodiversity-related variables (mostly plant species counts); however, the strength of the observed correlations varies between studies. In contrast, studies focussing on understanding the causal relationship between (plant) species counts and spectral variation remain scarce. Here, we discuss these causal relationships and support our perspectives through simulations and experimental data. Results We reveal that in many situations the spectral variation caused by species or functional traits is subtle in comparison to other factors such as seasonality and physiological status. Moreover, the degree of contrast in reflectance has little to do with the number but rather with the identity of the species or communities involved. Hence, spectral variability should not be expressed based on contrast but rather based on metrics expressing manifoldness. While we describe cases where a certain link between spectral variation and plant species diversity can be expected, we believe that as a scientific hypothesis (which suggests a general validity of this assumed relationship) the SVH is flawed and requires refinement. Conclusions To this end we call for more research examining the drivers of spectral variation in vegetation canopies and their link to plant species diversity and biodiversity in general. Such research will allow critically assessing under which conditions spectral variation is a useful indicator for biodiversity monitoring and how it could be integrated into monitoring networks. LA - English DB - MTMT ER - TY - JOUR AU - Henits, László AU - Szerletics, Ákos AU - Szokol, Dávid AU - Szlovák, Gergely AU - Gojdár, Emese AU - Zlinszky, András TI - Sentinel-2 Enables Nationwide Monitoring of Single Area Payment Scheme and Greening Agricultural Subsidies in Hungary JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 14 PY - 2022 IS - 16 SN - 2072-4292 DO - 10.3390/rs14163917 UR - https://m2.mtmt.hu/api/publication/33061334 ID - 33061334 AB - The verification and monitoring of agricultural subsidy claims requires combined evaluation of several criteria at the scale of over a million cultivation units. Sentinel-2 satellite imagery is a promising data source and paying agencies are encouraged to test their pre-operational use. Here, we present the outcome of the Hungarian agricultural subsidy monitoring pilot: our goal was to propose a solution based on open-source components and evaluate the main strengths and weaknesses for Sentinel-2 in the framework of a complex set of tasks. These include the checking of the basic cultivation of grasslands and arable land and compliance to the criteria of ecological focus areas. The processing of the satellite data was conducted based on random forest for crop classification and the detection of cultivation events was conducted based on NDVI (Normalized Differential Vegetation Index) time series analysis results. The outputs of these processes were combined in a decision tree ruleset to provide the final results. We found that crop classification provided good performance (overall accuracy 88%) for 22 vegetation classes and cultivation detection was also reliable when compared to on-screen visual interpretation. The main limitation was the size of fields, which were frequently small compared to the spatial resolution of the images: more than 4% of the parcels had to be excluded, although these represent less than 3% of the cultivated area of Hungary. Based on these results, we find that operational satellite-based monitoring is feasible for Hungary, and expect further improvements from integration with Sentinel-1 due to additional temporal resolution. LA - English DB - MTMT ER - TY - JOUR AU - Jackson-Bue, Tim AU - Whitton, Timothy A. AU - Roberts, Michael J. AU - Brown, Alice Goward AU - Amir, Hana AU - King, Jonathan AU - Powell, Ben AU - Rowlands, Steven J. AU - Jones, Gerallt Llewelyn AU - Davies, Andrew J. TI - Seabed morphology and bed shear stress predict temperate reef habitats in a high energy marine region JF - ESTUARINE COASTAL AND SHELF SCIENCE J2 - ESTUAR COAST SHELF S VL - 274 PY - 2022 PG - 12 SN - 0272-7714 DO - 10.1016/j.ecss.2022.107934 UR - https://m2.mtmt.hu/api/publication/33489853 ID - 33489853 AB - High energy marine regions host ecologically important habitats like temperate reefs, but are less anthropogenically developed and understudied compared to lower energy waters. In the marine environment direct habitat observation is limited to small spatial scales, and high energy waters present additional logistical challenges and constraints. Semi-automated predictive habitat mapping is a cost-effective tool to map benthic habitats across large extents, but performance is context specific. High resolution environmental data used for predictive mapping are often limited to bathymetry, acoustic backscatter and their derivatives. However, hydrodynamic energy at the seabed is a critical habitat structuring factor and likely an important, yet rarely incorporated, predictor of habitat composition and spatial patterning. Here, we used a machine learning classification approach to map temperate reef substrate and biogenic reef habitat in a tidal energy development area, incorporating bathymetric derivatives at multiple scales and simulated tidally induced seabed shear stress. We mapped reef substrate (four classes: sediment (not reef), stony reef (low resemblance), stony reef (medium - high resemblance) and bedrock reef) with overall balanced accuracy of 71.7%. Our model to predict potential biogenic Sabellaria spinulosa reef performed less well with an overall balanced accuracy of 63.4%. Despite low performance metrics for the target class of potential reef in this model, it still provided insight into the importance of different environmental variables for mapping S. spinulosa biogenic reef habitat. Tidally induced mean bed shear stress was one of the most important predictor variables for both reef substrate and biogenic reef models, with ruggedness calculated at multiple scales from 3 m to 140 m also important for the reef substrate model. We identified previously unresolved relationships between temperate reef spatial distribution, hydrodynamic energy and seabed three-dimensional structure in energetic waters. Our findings contribute to a better understanding of the spatial ecology of high energy marine ecosystems and will inform evidence-based decision making for sustainable development, particularly within the growing tidal energy sector. LA - English DB - MTMT ER - TY - CHAP AU - Kathmann, H. S. AU - van Natijne, A. L. AU - Lindenbergh, R. C. ED - Yilmaz, A ED - Wegner, JD ED - Qin, R ED - Remondino, F ED - Fuse, T ED - Toschi, I TI - PROBABILISTIC VEGETATION TRANSITIONS IN DUNES BY COMBINING SPECTRAL AND LIDAR DATA T2 - 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II PB - Copernicus GmbH CY - Göttingen T3 - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, ISSN 2194-9034 PY - 2022 SP - 1033 EP - 1040 PG - 8 DO - 10.5194/isprs-archives-XLIII-B2-2022-1033-2022 UR - https://m2.mtmt.hu/api/publication/33489849 ID - 33489849 AB - Monitoring the status of the vegetation is required for nature conservation. This monitoring task is time consuming as kilometers of area have to be investigated and classified. To make this task more manageable, remote sensing is used. The acquisition of airplane remote sensing data is dependent on weather conditions and permission to fly in the busy airspace above the Netherlands. These conditions make it difficult to get a new, dedicated acquisition every year. Therefore, alternatives for this dependency on dedicated airplane surveys are needed. One alternative is the use of optical satellite imagery, as this type of data has improved rapidly in the last decade both in terms of resolution and revisit time. For this study, 0.5 m resolution satellite imagery from the Superview satellite is combined with geometric height data from the Dutch national airborne LiDAR elevation data set AHN. Goal is to classify vegetation into three different classes: sand, grass and trees, apply this classification to multiple epochs, and analyze class transition patterns. Three different classification methods were compared: nearest centroid, random forest and neural network. We show that outcomes of all three methods can be interpreted as class probabilities, but also that these probabilities have different properties for each method. The classification is implemented for 11 different epochs on the Meijendel en Berkheide dunal area on the Dutch coast. We show that mixed probabilities (i.e. between two classes) agree well with class transition processes, and conclude that a shallow neural network combined with pure training samples applied on four different bands (RGB + relative DSM height) produces satisfactory results for the analysis of vegetation transitions with accuracies close to 100%. LA - English DB - MTMT ER - TY - JOUR AU - Pacheco-Labrador, J. AU - Migliavacca, M. AU - Ma, X. AU - Mahecha, M. AU - Carvalhais, N. AU - Weber, U. AU - Benavides, R. AU - Bouriaud, O. AU - Barnoaiea, I. AU - Coomes, D.A. AU - Bohn, F.J. AU - Kraemer, G. AU - Heiden, U. AU - Huth, A. AU - Wirth, C. TI - Challenging the link between functional and spectral diversity with radiative transfer modeling and data JF - REMOTE SENSING OF ENVIRONMENT J2 - REMOTE SENS ENVIRON VL - 280 PY - 2022 SN - 0034-4257 DO - 10.1016/j.rse.2022.113170 UR - https://m2.mtmt.hu/api/publication/33107747 ID - 33107747 N1 - Max Planck Institute for Biogeochemistry, Hans Knöll Straße 10, Jena, D-07745, Germany College of Earth and Environmental Science, Lanzhou University, Gansu, Lanzhou, 730000, China International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094, China State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, 100875, China Remote Sensing Center for Earth System Research, Leipzig University, Leipzig, Germany German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, Caparica, 2829-516, Portugal Departamento de Sistemas y Recursos Naturales. E.T.S. Ingeniería de Montes, Forestales y del Medio Natural, Universidad Politécnica de Madrid, C/ José Antonio Novais 10, Madrid, 28040, Spain University ‘Ștefan cel Mare’ of Suceava, Universității 13, Suceava, 720229, Romania Laboratoire d'Inventaire Forestier, IGN, Nancy, France Geomatics Laboratory, Faculty of Forestry, University ‘Ștefan cel Mare’ Suceava, Romania Department of Plant Sciences and the Conservation Research Institute, Downing Street, Cambridge, CB2 3EA, United Kingdom Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany Department of Ecological Modelling, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Oberpfaffenhofen, Wessling, 82234, Germany Institute of Environmental Systems Research, University of Osnabrück, Lower Saxony, Osnabrück, Germany Systematic Botany and Functional Biodiversity, Leipzig University, Leipzig, Germany Export Date: 23 September 2022 CODEN: RSEEA Correspondence Address: Pacheco-Labrador, J.; Max Planck Institute for Biogeochemistry, Hans Knöll Straße 10, Germany; email: jpacheco@bgc-jena.mpg.de LA - English DB - MTMT ER - TY - JOUR AU - Torresani, Michele AU - Masiello, Guido AU - Vendrame, Nadia AU - Gerosa, Giacomo AU - Falocchi, Marco AU - Tomelleri, Enrico AU - Serio, Carmine AU - Rocchini, Duccio AU - Zardi, Dino TI - Correlation Analysis of Evapotranspiration, Emissivity Contrast and Water Deficit Indices: A Case Study in Four Eddy Covariance Sites in Italy with Different Environmental Habitats JF - LAND (BASEL) J2 - LAND-BASEL VL - 11 PY - 2022 IS - 11 PG - 16 SN - 2073-445X DO - 10.3390/land11111903 UR - https://m2.mtmt.hu/api/publication/33489847 ID - 33489847 AB - Evapotranspiration (ET) represents one of the essential processes controlling the exchange of energy by terrestrial vegetation, providing a strong connection between energy and water fluxes. Different methodologies have been developed in order to measure it at different spatial scales, ranging from individual plants to an entire watershed. In the last few years, several methods and approaches based on remotely sensed data have been developed over different ecosystems for the estimation of ET. In the present work, we outline the correlation between ET measured at four eddy covariance (EC) sites in Italy (situated either in forest or in grassland ecosystems) and (1) the emissivity contrast index (ECI) based on emissivity data from thermal infrared spectral channels of the MODIS and ASTER satellite sensors (CAMEL data-set); (2) the water deficit index (WDI), defined as the difference between the surface and dew point temperature modeled by the ECMWF (European Centre for Medium-Range Weather Forecasts) data. The analysis covers a time-series of 1 to 7 years depending on the site. The results showed that both the ECI and WDI correlate to the ET calculated through EC. In the relationship WDI-ET, the coefficient of determination ranges, depending on the study area, between 0.5 and 0.9, whereas it ranges between 0.5 and 0.7 when ET was correlated to the ECI. The slope and the sign of the latter relationship is influenced by the vegetation habitat, the snow cover (particularly in winter months) and the environmental heterogeneity of the area (calculated in this study through the concept of the spectral variation hypothesis using Rao's Q heterogeneity index). LA - English DB - MTMT ER - TY - JOUR AU - von Reumont, Frederik AU - Schabitz, Frank AU - Asrat, Asfawossen TI - Fuzzy model-based reconstruction of paleovegetation in Ethiopia JF - JOURNAL OF MAPS J2 - J MAPS PY - 2022 PG - 7 SN - 1744-5647 DO - 10.1080/17445647.2022.2082332 UR - https://m2.mtmt.hu/api/publication/33489855 ID - 33489855 AB - We introduce a new method to compute plant distribution in Ethiopia under paleoclimatic conditions using fuzzy logic. Using a published map of the potential vegetation for Ethiopia we decipher the boundary conditions for the main vegetation units shown, reflecting modern climatic conditions for temperature and precipitation in this region. Fuzzy logic using these climatic values on a GIS platform then derived the computational map of the potential vegetation. Comparing it with the original map shows a general correspondence of about 90%. By changing the underlying climate parameters, we then used this model for hypothetical paleoclimatic conditions to simulate the vegetational response on these changed climate settings. Finally, vegetational response maps for Ethiopia are presented for two scenarios: (i) a colder and drier condition (such as the Last Glacial Maximum) and (ii) a warmer and wetter condition (such as the last interglacial) than today. LA - English DB - MTMT ER - TY - JOUR AU - Wiser, Susan. K. K. AU - McCarthy, James. K. K. AU - Bellingham, Peter. J. J. AU - Jolly, Ben AU - Meiforth, Jane. J. J. AU - Kaitiaki, Warawara Komiti TI - Integrating plot-based and remotely sensed data to map vegetation types in a New Zealand warm-temperate rainforest JF - APPLIED VEGETATION SCIENCE J2 - APP VEGE SCI VL - 25 PY - 2022 IS - 4 PG - 15 SN - 1402-2001 DO - 10.1111/avsc.12695 UR - https://m2.mtmt.hu/api/publication/33489845 ID - 33489845 N1 - Manaaki Whenua – Landcare Research, Lincoln, New Zealand School of Biological Sciences, University of Auckland, Auckland, New Zealand Manaaki Whenua – Landcare Research, Palmerston North, New Zealand Warawara Komiti Kaitiaki, Kaitaia, New Zealand Export Date: 5 December 2023 CODEN: AVSCF Correspondence Address: Wiser, S.K.; Manaaki Whenua – Landcare Research, PO Box 69040, New Zealand; email: wisers@landcareresearch.co.nz AB - Questions(1) What can be learned by extending a national classification into unsampled forest types? (2) Are both remotely sensed and environmental predictors needed to model and map associations? (3) For mapping, are LiDAR-generated canopy structure parameters or reflectance from spectral imagery more useful? (4) How can we assess uncertainty of a final map? LocationWarawara Forest, Northland, New Zealand. MethodsWe sampled 205 vegetation plots and assigned them to an existing national classification using the fuzzy classification framework of noise clustering. Plots too distinct to be assigned were used to define new associations. We produced spatial models of each association using boosted regression trees. Predictors included 11 environmental, 11 canopy reflectance, and 17 canopy structure variables. We created a composite map by assigning each map pixel to the association with the highest occurrence probability. We evaluated uncertainty by examining locations where no class was predicted with probability above 0.2 and by creating a confusion map based on entropy. ResultsForty-five plots were assigned to six of 79 existing national associations and 147 plots were used to define two new forest associations. Three shrubland types are widespread nationally, whereas two young forest types are northern. Three mature forest types are narrowly distributed nationally, with the new "High-elevation hardwood forest" largely restricted to Warawara Forest. Three associations were mapped using remotely sensed predictors alone, whereas two also required environmental predictors. Overall, canopy reflectance predictors explained more deviance than canopy structure. Examining locations where no association was predicted well and where multiple associations were predicted equally showed areas mapped as younger forests to have greatest uncertainty. ConclusionsIn answering our questions, we present a vegetation classification and map for Warawara Forest that provides a framework to guide the indigenous people's management responses to threats to valued communities and their species. LA - English DB - MTMT ER - TY - JOUR AU - Ji, Chaonan AU - Heiden, Uta AU - Lakes, Tobia AU - Feilhauer, Hannes TI - Are urban material gradients transferable between areas? JF - INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION J2 - INT J APPL EARTH OBS VL - 100 PY - 2021 PG - 10 SN - 1569-8432 DO - 10.1016/j.jag.2021.102332 UR - https://m2.mtmt.hu/api/publication/32329740 ID - 32329740 AB - Urban areas contain a complex mixture of surface materials resulting in mixed pixels that are challenging to handle with conventional mapping approaches. In particular, for spaceborne hyperspectral images (HSIs) with sufficient spectral resolution to differentiate urban surface materials, the spatial resolution of 30 m (e.g. EnMAP HSIs) makes it difficult to find the spectrally pure pixels required for detailed mapping of urban surface materials. Gradient analysis, which is commonly used in ecology to map natural vegetation consisting of a complex mixture of species, is therefore a promising and practical tool for pattern recognition of urban surface material mixtures. However, the gradients are determined in a data-driven manner, so analysis of their spatial transferability is urgently required. We selected two areas?the Ostbahnhof (Ost) area and the Nymphenburg (Nym) area in Munich, Germany?with simulated EnMAP HSIs and material maps, treating the Ost area as the target area and the Nym area as the well-known area. Three gradient analysis approaches were subsequently proposed for pattern recognition in the Ost area for the cases of (i) sufficient samples collected in the Ost area; (ii) some samples in the Ost area; and (iii) no samples in the Ost area. The Ost samples were used to generate an ordination space in case (i), while the Nym samples were used to create the ordination space to support the pattern recognition of the Ost area in cases (ii) and (iii). The Mantel statistical results show that the sample distributions in the two ordination spaces are similar, with high confidence (the Mantel statistics are 0.995 and 0.990, with a significance of 0.001 in 999 free permutations of the Ost and Nym samples). The results of the partial least square regression models and 10-fold cross-validation show a strong relationship (the calculation-validation R2 values on the first gradient among the three approaches are 0.898, 0.892; 0.760, 0.743; and 0.860, 0.836, and those on the second gradient are 0.433, 0.351; 0.698, 0.648; and 0.736, 0.646) between the ordination scores of the samples and their reflectance values. The mapping results of the Ost area from three approaches also show similar patterns (e.g. the distribution of vegetation, artificial materials, water, and ceremony area) and characteristics of urban structures (the intensity of buildings). Therefore, our findings can help assess the transferability of urban material gradients between similar urban areas. LA - English DB - MTMT ER -