2020
Lausch, Angela; Schaepman, Michael E.; Skidmore, Andrew K.; Truckenbrodt, Sina C.; Hacker, Jörg M.; Baade, Jussi; Bannehr, Lutz; Borg, Erik; Bumberger, Jan; Dietrich, Peter; Gläßer, Cornelia; Haase, Dagmar; Heurich, Marco; Jagdhuber, Thomas; Jany, Sven; Krönert, Rudolf; Möller, Markus; Mollenhauer, Hannes; Montzka, Carsten; Pause, Marion; Rogass, Christian; Salepci, Nesrin; Schmullius, Christiane; Schrodt, Franziska; Schütze, Claudia; Schweitzer, Christian; Selsam, Peter; Spengler, Daniel; Vohland, Michael; Volk, Martin; Weber, Ute; Wellmann, Thilo; Werban, Ulrike; Zacharias, Steffen; Thiel, Christian
Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces Journal Article
In: Remote Sensing, vol. 12, no. 22, pp. 3690, 2020.
Abstract | Links | BibTeX | Tags: Earth observation, Geodiversity, Geomorphology, Monitoring, Remote Sensing, Spectral traits, Traits
@article{Lausch_2020,
title = {Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces},
author = {Angela Lausch and Michael E. Schaepman and Andrew K. Skidmore and Sina C. Truckenbrodt and Jörg M. Hacker and Jussi Baade and Lutz Bannehr and Erik Borg and Jan Bumberger and Peter Dietrich and Cornelia Gläßer and Dagmar Haase and Marco Heurich and Thomas Jagdhuber and Sven Jany and Rudolf Krönert and Markus Möller and Hannes Mollenhauer and Carsten Montzka and Marion Pause and Christian Rogass and Nesrin Salepci and Christiane Schmullius and Franziska Schrodt and Claudia Schütze and Christian Schweitzer and Peter Selsam and Daniel Spengler and Michael Vohland and Martin Volk and Ute Weber and Thilo Wellmann and Ulrike Werban and Steffen Zacharias and Christian Thiel},
url = {https://doi.org/10.3390%2Frs12223690},
doi = {10.3390/rs12223690},
year = {2020},
date = {2020-11-01},
urldate = {2020-11-01},
journal = {Remote Sensing},
volume = {12},
number = {22},
pages = {3690},
publisher = {MDPI AG},
abstract = {The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.},
keywords = {Earth observation, Geodiversity, Geomorphology, Monitoring, Remote Sensing, Spectral traits, Traits},
pubstate = {published},
tppubtype = {article}
}
Wellmann, Thilo; Lausch, Angela; Scheuer, Sebastian; Haase, Dagmar
In: Ecological Indicators, vol. 111, pp. 106029, 2020.
Abstract | Links | BibTeX | Tags: Leipzig, Machine learning, Random forest, RapidEye, Remote Sensing, Species Distribution Models, Spectral trait variations, Spectral traits, Urban birds
@article{wellmann2020earth,
title = {Earth observation based indication for avian species distribution models using the spectral trait concept and machine learning in an urban setting},
author = {Thilo Wellmann and Angela Lausch and Sebastian Scheuer and Dagmar Haase},
url = {https://thilowellmann.de/wp/wp-content/uploads/2020/01/WellmannEtAl_BreedngbirdsEO_SDM_Leipzig_AcceptedManuscript.pdf},
doi = {10.1016/j.ecolind.2019.106029},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Ecological Indicators},
volume = {111},
pages = {106029},
publisher = {Elsevier},
abstract = {Birds respond strongly to vegetation structure and composition, yet typical species distribution models (SDMs) that incorporate Earth observation (EO) data use discrete land-use/cover data to model habitat suitability. Since this neglects factors of internal spatial composition and heterogeneity of EO data, we suggest a novel scheme deriving continuous indicators of vegetation heterogeneity from high-resolution EO data.
The deployed concepts encompass vegetation fractions for determining vegetation density and spectral traits for the quantification of vegetation heterogeneity. Both indicators are derived from RapidEye data, thus featuring a continuous spatial resolution of 6.5 m. Using these indicators as predictors, we model breeding bird habitats using a random forest (RF) classifier for the city of Leipzig, Germany using a single EO image.
SDMs are trained for the breeding sites of 44 urban bird species, featuring medium to very high accuracies (59–90%). Analysing similarities between the models regarding variable importance of single predictors allows species groups to be determined based on their preferences and dependencies regarding the amount of vegetation and its spatial and structural heterogeneity. When combining the SDMs, models of urban bird species richness can be derived.
The combination of high-resolution EO data paired with the RF machine learning technique creates very detailed insights into the ecology of the urban avifauna, opening up opportunities of optimising greenspace management schemes or urban development in densifying cities concerning overall bird species richness or single species under threat of local extinction.},
keywords = {Leipzig, Machine learning, Random forest, RapidEye, Remote Sensing, Species Distribution Models, Spectral trait variations, Spectral traits, Urban birds},
pubstate = {published},
tppubtype = {article}
}
The deployed concepts encompass vegetation fractions for determining vegetation density and spectral traits for the quantification of vegetation heterogeneity. Both indicators are derived from RapidEye data, thus featuring a continuous spatial resolution of 6.5 m. Using these indicators as predictors, we model breeding bird habitats using a random forest (RF) classifier for the city of Leipzig, Germany using a single EO image.
SDMs are trained for the breeding sites of 44 urban bird species, featuring medium to very high accuracies (59–90%). Analysing similarities between the models regarding variable importance of single predictors allows species groups to be determined based on their preferences and dependencies regarding the amount of vegetation and its spatial and structural heterogeneity. When combining the SDMs, models of urban bird species richness can be derived.
The combination of high-resolution EO data paired with the RF machine learning technique creates very detailed insights into the ecology of the urban avifauna, opening up opportunities of optimising greenspace management schemes or urban development in densifying cities concerning overall bird species richness or single species under threat of local extinction.
2018
Wellmann, Thilo; Haase, Dagmar; Knapp, Sonja; Salbach, Christoph; Selsam, Peter; Lausch, Angela
Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing Journal Article
In: Ecological Indicators, vol. 85, pp. 190–203, 2018.
Abstract | Links | BibTeX | Tags: GLCM, Hemeroby, Human-use-intensity, NDVI, RapidEye, Remote Sensing, Spectral trait variations, Spectral traits, Urban land-use-intensity
@article{wellmann2018urban,
title = {Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing},
author = {Thilo Wellmann and Dagmar Haase and Sonja Knapp and Christoph Salbach and Peter Selsam and Angela Lausch},
url = {https://thilowellmann.de/wp/wp-content/uploads/2022/03/Wellmann_etal_Manuscript_U-LUI_2018.pdf},
doi = {10.1016/j.ecolind.2017.10.029},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Ecological Indicators},
volume = {85},
pages = {190--203},
publisher = {Elsevier},
abstract = {By adding attributes of space and time to the spectral traits (ST) concept we developed a completely new way of quantifying and assessing land use intensity and the hemeroby of urban landscapes. Calculating spectral traits variations (STV) from remote sensing data and regressing STV against hemeroby, we show how to estimate human land use intensity and the degree of hemeroby for large spatial areas with a dense temporal resolution for an urban case study. We found a linear statistical significant relationship (p=0.01) between the annual amplitude in spectral trait variations and the degree of hemeroby. It was thereof possible to separate the different types of land use cover according to their degree of hemeroby and land use intensity, respectively. Moreover, since the concept of plant traits is a functional framework in which each trait can be assigned to one or more ecosystem functions, the assessment of STV is a promising step towards assessing the diversity of spectral traits in an ecosystem as a proxy of functional diversity.},
keywords = {GLCM, Hemeroby, Human-use-intensity, NDVI, RapidEye, Remote Sensing, Spectral trait variations, Spectral traits, Urban land-use-intensity},
pubstate = {published},
tppubtype = {article}
}