Estimating lichen α- and β-diversity using satellite data at different spatial resolutions
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[1] N. Fenton,et al. Small but visible: Predicting rare bryophyte distribution and richness patterns using remote sensing-based ensembles of small models , 2022, PloS one.
[2] M. Kukwa,et al. How sensitive are epiphytic and epixylic cryptogams as indicators of forest naturalness? Testing bryophyte and lichen predictive power in stands under different management regimes in the Białowieża forest , 2021 .
[3] Flavio Marzialetti,et al. Measuring Alpha and Beta Diversity by Field and Remote-Sensing Data: A Challenge for Coastal Dunes Biodiversity Monitoring , 2021, Remote. Sens..
[4] N. Fenton,et al. No place to hide: Rare plant detection through remote sensing , 2021, Diversity and Distributions.
[5] E. Tyystjärvi,et al. Chlorophyll does not reflect green light – how to correct a misconception , 2020, Journal of Biological Education.
[6] Philip A. Townsend,et al. Remote Sensing of Plant Biodiversity , 2020 .
[7] Ewa Grabska,et al. Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series , 2020, Remote. Sens..
[8] Ke Xu,et al. DeepMask: an algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network , 2019, ArXiv.
[9] A. Huth,et al. Inferring plant functional diversity from space: the potential of Sentinel-2 , 2019, Remote Sensing of Environment.
[10] J. Lendemer,et al. Lichen conservation in North America: a review of current practices and research in Canada and the United States , 2019, Biodiversity and Conservation.
[11] Andres Kuusk,et al. Reflectance Properties of Hemiboreal Mixed Forest Canopies with Focus on Red Edge and Near Infrared Spectral Regions , 2019, Remote. Sens..
[12] Shruti K. Mishra,et al. Microclimatic variations and their effects on photosynthetic efficiencies and lichen species distribution along elevational gradients in Garhwal Himalayas , 2019, Biodiversity and Conservation.
[13] Y. Wiersma,et al. Out with OLD growth, in with ecological contin NEW ity: new perspectives on forest conservation , 2019, Frontiers in Ecology and the Environment.
[14] A. Beaudoin,et al. Digital mapping of paludification in soils under black spruce forests of eastern Canada , 2018, Geoderma Regional.
[15] S. Mérmoz,et al. Remote sensing of &bgr;‐diversity: Evidence from plant communities in a semi‐natural system , 2018, Applied Vegetation Science.
[16] Damaris Zurell,et al. Outstanding Challenges in the Transferability of Ecological Models. , 2018, Trends in ecology & evolution.
[17] B. Brisco,et al. Spectral analysis of wetlands using multi-source optical satellite imagery , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[18] Miska Luoto,et al. The importance of snow in species distribution models of arctic vegetation , 2018 .
[19] Ruben Van De Kerchove,et al. Transferability of species distribution models for the detection of an invasive alien bryophyte using imaging spectroscopy data , 2018, International Journal of Applied Earth Observation and Geoinformation.
[20] Michael Dixon,et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .
[21] D. Wardle,et al. How lichens impact on terrestrial community and ecosystem properties , 2017, Biological reviews of the Cambridge Philosophical Society.
[22] Robert Lücking,et al. Fungal Diversity Revisited: 2.2 to 3.8 Million Species , 2017, Microbiology spectrum.
[23] Tom Carlberg. Keys to Lichens of North America: Revised and Expanded , 2017, The Bryologist.
[24] Fabian Ewald Fassnacht,et al. The spectral variability hypothesis does not hold across landscapes , 2017 .
[25] M. Parisien,et al. Spatial and temporal dimensions of fire activity in the fire‐prone eastern Canadian taiga , 2017, Global change biology.
[26] Jonah L Keim,et al. Estimating plant abundance using inflated beta distributions: Applied learnings from a lichen–caribou ecosystem , 2016, Ecology and evolution.
[27] R. Lücking,et al. The 2016 classification of lichenized fungi in the Ascomycota and Basidiomycota – Approaching one thousand genera , 2016, The Bryologist.
[28] M. Rautiainen,et al. Structural factors driving boreal forest albedo in Finland , 2016 .
[29] Duccio Rocchini,et al. Will remote sensing shape the next generation of species distribution models? , 2015 .
[30] Michael Förster,et al. Remote sensing for mapping natural habitats and their conservation status - New opportunities and challenges , 2015, Int. J. Appl. Earth Obs. Geoinformation.
[31] M. Parisien,et al. Resistance of the boreal forest to high burn rates , 2014, Proceedings of the National Academy of Sciences.
[32] N. Pettorelli,et al. Satellite remote sensing for applied ecologists: opportunities and challenges , 2014 .
[33] W. Elbert,et al. Estimating impacts of lichens and bryophytes on global biogeochemical cycles , 2014 .
[34] Andri Baltensweiler,et al. High‐resolution remote sensing data improves models of species richness , 2013 .
[35] C. Peng,et al. Monitoring and estimating drought-induced impacts on forest structure, growth, function, and ecosystem services using remote-sensing data: recent progress and future challenges , 2013 .
[36] M. Andreae,et al. Contribution of cryptogamic covers to the global cycles of carbon and nitrogen , 2012 .
[37] Julian D. Olden,et al. Assessing transferability of ecological models: an underappreciated aspect of statistical validation , 2012 .
[38] P. Škaloud,et al. Do photobionts influence the ecology of lichens? A case study of environmental preferences in symbiotic green alga Asterochloris (Trebouxiophyceae) , 2011, Molecular ecology.
[39] C. Marshall,et al. Has the Earth’s sixth mass extinction already arrived? , 2011, Nature.
[40] Markus Neteler,et al. Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges , 2010, Ecol. Informatics.
[41] Hannes Feilhauer,et al. Mapping continuous fields of forest alpha and beta diversity , 2009 .
[42] Jianting Zhang,et al. Is spectral distance a proxy of beta diversity at different taxonomic ranks? A test using quantile regression , 2009, Ecol. Informatics.
[43] Eva Ivits,et al. Prediction of lichen diversity in an UNESCO biosphere reserve – correlation of high resolution remote sensing data with field samples , 2007 .
[44] Youngwook Kim,et al. 2-band enhanced vegetation index without a blue band and its application to AVHRR data , 2007, SPIE Optical Engineering + Applications.
[45] Anne-Béatrice Dufour,et al. The ade4 Package: Implementing the Duality Diagram for Ecologists , 2007 .
[46] J. Elith,et al. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment , 2007 .
[47] R. Hall,et al. Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume , 2006 .
[48] Duccio Rocchini,et al. Maximizing plant species inventory efficiency by means of remotely sensed spectral distances , 2005 .
[49] D. Vitt,et al. The ones we left behind: Comparing plot sampling and floristic habitat sampling for estimating bryophyte diversity , 2005 .
[50] Thomas R. Crow,et al. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA , 2004 .
[51] Lars T. Waser,et al. Prediction of biodiversity - regression of lichen species richness on remote sensing data , 2004 .
[52] J. Kerr,et al. From space to species: ecological applications for remote sensing , 2003 .
[53] M. Fladeland,et al. Remote sensing for biodiversity science and conservation , 2003 .
[54] Kenneth J. Ranson,et al. Disturbance recognition in the boreal forest using radar and Landsat-7 , 2003 .
[55] M. Hunter,et al. Enlisting Taxonomists to Survey Poorly Known Taxa for Biodiversity Conservation: a Lichen Case Study , 2002 .
[56] V. Ahmadjian. Lichens are more important than you think , 1995 .
[57] L. Tibell. Crustose lichens as indicators of forest continuity in boreal coniferous forests , 1992 .
[58] G. Guyot,et al. Physical measurements and signatures in remote sensing , 1992 .
[59] R. Whittaker. Evolution and measurement of species diversity , 1972 .
[60] R. Whittaker. Vegetation of the Siskiyou Mountains, Oregon and California , 1960 .
[61] P. Legendre. Numerical Ecology , 2019, Encyclopedia of Ecology.
[62] O. Mutanga,et al. Transferability of species distribution models for the detection of an invasive alien bryophyte using imaging spectroscopy data. , 2018 .
[63] D. Boscolo,et al. Influence of landscape structure on Euglossini composition in open vegetation environments , 2017 .
[64] D. Edwards,et al. How Should Beta-Diversity Inform Biodiversity Conservation? , 2016, Trends in ecology & evolution.
[65] Duccio Rocchini,et al. Advancing species diversity estimate by remotely sensed proxies: A conceptual review , 2015, Ecol. Informatics.
[66] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[67] Ian C. Marschner,et al. glm2: Fitting Generalized Linear Models with Convergence Problems , 2011, R J..
[68] M. Lakatos. Lichens and Bryophytes: Habitats and Species , 2011 .
[69] A. Thell,et al. Nordic Lichen Flora Volume 4 – Parmeliaceae , 2011 .
[70] Kate S. He,et al. Linking variability in species composition and MODIS NDVI based on beta diversity measurements , 2009 .
[71] P. Legendre,et al. vegan : Community Ecology Package. R package version 1.8-5 , 2007 .
[72] M. Seaward. The use of lichens for environmental impact assessment , 2004 .
[73] T. Sharkey,et al. Chloroplast to Leaf , 2004 .
[74] R. D. Boertje,et al. Seasonal Diets of the Denali Caribou Herd, Alaska , 1984 .