Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies
暂无分享,去创建一个
Stéphane Joost | Kevin Leempoel | Estelle Rochat | Christian Parisod | Michel Kasser | Annie S. Guillaume | Aude Rogivue | Felix Gugerli
[1] R. Devillers,et al. Comparing Selections of Environmental Variables for Ecological Studies: A Focus on Terrain Attributes , 2016, PloS one.
[2] Miroslav Dudík,et al. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .
[3] K. Cook. An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection , 2017 .
[4] Trevor Hastie,et al. A statistical explanation of MaxEnt for ecologists , 2011 .
[5] J. Monteith,et al. Boundary Layer Climates. , 1979 .
[6] Michael Höhle,et al. Accuracy assessment of digital elevation models by means of robust statistical methods , 2009 .
[7] Reamonn Fealy,et al. Are fine resolution digital elevation models always the best choice in digital soil mapping , 2013 .
[8] P. Orozco‐terWengel,et al. Simple rules for an efficient use of Geographic Information Systems in molecular ecology , 2017, bioRxiv.
[9] F. Gugerli,et al. Population genomic footprints of selection and associations with climate in natural populations of Arabidopsis halleri from the Alps , 2013, Molecular ecology.
[10] H. H. Birks,et al. Stay or go – how topographic complexity influences alpine plant population and community responses to climate change , 2017 .
[11] D. Ackerly,et al. Microclimate and demography interact to shape stable population dynamics across the range of an alpine plant. , 2018, The New phytologist.
[12] Bharat Lohani,et al. Airborne LiDAR Technology: A Review of Data Collection and Processing Systems , 2017 .
[13] N. Zimmermann,et al. Predictive mapping of alpine grasslands in Switzerland: Species versus community approach , 1999 .
[14] M. Uysal,et al. An Experimental Analysis of Digital Elevation Models Generated with Lidar Data and UAV Photogrammetry , 2018, Journal of the Indian Society of Remote Sensing.
[15] K. Anderson,et al. The problem of scale in predicting biological responses to climate , 2020, Global change biology.
[16] S. Joost,et al. Nested Species Distribution Models of Chlamydiales in Ixodes ricinus (Tick) Hosts in Switzerland , 2020, Applied and Environmental Microbiology.
[17] Vítězslav Moudrý,et al. Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure: Prospects for monitoring hazards and restoration success , 2019, Applied Geography.
[18] Xiaoye Liu,et al. Airborne LiDAR for DEM generation: some critical issues , 2008 .
[19] T. Sankey,et al. The effects of topographic surveying technique and data resolution on the detection and interpretation of geomorphic change , 2019, Geomorphology.
[20] Joachim Höhle,et al. The EuroSDR Test "Checking and Improving of Digital Terrain Models" , 2006 .
[21] Christian Bernhofer,et al. GIS‐based regionalisation of radiation, temperature and coupling measures in complex terrain for low mountain ranges , 2005 .
[22] Robert P. Anderson,et al. Opening the black box: an open-source release of Maxent , 2017 .
[23] Craig J. Brown,et al. Influence of artefacts in marine digital terrain models on habitat maps and species distribution models: a multiscale assessment , 2017 .
[24] Michaël Kalbermatten,et al. Multiscale analysis of high resolution digital elevation models using the wavelet transform , 2010 .
[25] Antoine Guisan,et al. What we use is not what we know: environmental predictors in plant distribution models , 2016 .
[26] G. Walther,et al. Trends in the upward shift of alpine plants , 2005 .
[27] M. Fortin,et al. Considering spatial and temporal scale in landscape‐genetic studies of gene flow , 2010, Molecular ecology.
[28] J. M. Sappington,et al. Quantifying Landscape Ruggedness for Animal Habitat Analysis: A Case Study Using Bighorn Sheep in the Mojave Desert , 2007 .
[29] Bo Wu,et al. Review of geometric fusion of remote sensing imagery and laser scanning data , 2015 .
[30] G. Grabherr,et al. Effects of climate change on the alpine and nival vegetation of the Alps , 2014 .
[31] Hanna Tuomisto,et al. DISSECTING THE SPATIAL STRUCTURE OF ECOLOGICAL DATA AT MULTIPLE SCALES , 2004 .
[32] J. Elith,et al. Sensitivity of predictive species distribution models to change in grain size , 2007 .
[33] Stéphane Joost,et al. Very high resolution digital elevation models : Do they improve models of plant species distribution? , 2006 .
[34] Christian Ginzler,et al. Accuracy assessment of airborne photogrammetrically derived high-resolution digital elevation models in a high mountain environment , 2014 .
[35] Sandra M. Guzmán,et al. Selection of optimal scales for soil depth prediction on headwater hillslopes: A modeling approach , 2018 .
[36] Kevin Leempoel,et al. Very high‐resolution digital elevation models: are multi‐scale derived variables ecologically relevant? , 2015 .
[37] Jürgen Böhner,et al. Land-Surface Parameters Specific to Topo-Climatology , 2009 .
[38] Airborne LiDAR for DEM generation: some critical issues , 2008 .
[39] J. Böhner,et al. Spatial Prediction of Soil Attributes Using Terrain Analysis and Climate Regionalisation , 2006 .
[40] Emmanuel P. Baltsavias,et al. Airborne laser scanning: basic relations and formulas , 1999 .
[41] Ottar Michelsen,et al. Continent-wide response of mountain vegetation to climate change , 2012 .
[42] Dimitri Van De Ville,et al. Multiscale analysis of geomorphological and geological features in high resolution digital elevation models using the wavelet transform , 2012 .
[43] C. Thorne,et al. Quantitative analysis of land surface topography , 1987 .
[44] D. Inouye. Effects of climate change on alpine plants and their pollinators , 2020, Annals of the New York Academy of Sciences.
[45] Aurélie Coulon,et al. Identifying future research needs in landscape genetics: where to from here? , 2009, Landscape Ecology.
[46] M. Hutchinson,et al. Digital terrain analysis. , 2008 .
[47] C. Körner,et al. Topographically controlled thermal‐habitat differentiation buffers alpine plant diversity against climate warming , 2011 .
[48] B. J,et al. Soil regionalisation by means of terrain analysis and process parameterisation , 2002 .
[49] S. Joost,et al. Nested species distribution models of Chlamydiales in tick host Ixodes ricinus in Switzerland , 2020, bioRxiv.
[50] Ross Purves,et al. Terrestrial laser scanning improves digital elevation models and topsoil pH modelling in regions with complex topography and dense vegetation , 2017, Environ. Model. Softw..
[51] M. Guglielmin,et al. Unexpected impacts of climate change on alpine vegetation , 2007 .
[52] David I. Warton,et al. Topoclimate versus macroclimate: how does climate mapping methodology affect species distribution models and climate change projections? , 2014 .
[53] Emmanuel P. Baltsavias,et al. A comparison between photogrammetry and laser scanning , 1999 .
[54] Sung Yong Shin,et al. Scattered Data Interpolation with Multilevel B-Splines , 1997, IEEE Trans. Vis. Comput. Graph..
[55] Robert P. Anderson,et al. Maximum entropy modeling of species geographic distributions , 2006 .
[56] A. Guisan,et al. Assessing alpine plant vulnerability to climate change: a modeling perspective , 2000 .
[57] C. Randin,et al. Very high resolution environmental predictors in species distribution models , 2014 .
[58] I. Woodhouse,et al. Structure from Motion (SfM) Photogrammetry with Drone Data: A Low Cost Method for Monitoring Greenhouse Gas Emissions from Forests in Developing Countries , 2017 .
[59] S. Joost,et al. Regional investigation of spatial-temporal variability of soil magnesium - a case study from Switzerland , 2020 .
[60] Bingbo Gao,et al. State-of-the-Art: DTM Generation Using Airborne LIDAR Data , 2017, Sensors.
[61] Craig J. Brown,et al. Towards a framework for terrain attribute selection in environmental studies , 2017, Environ. Model. Softw..