Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.ecolind.2017.10.029 |
| Document | accepted manuscript |
| Title (Primary) | Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing |
| Author | Wellmann, T.; Haase, D.; Knapp, S.; Salbach, C.; Selsam, P.; Lausch, A. |
| Source Titel | Ecological Indicators |
| Year | 2018 |
| Department | CLE; BZF |
| Volume | 85 |
| Page From | 190 |
| Page To | 203 |
| Language | englisch |
| Keywords | Spectral traits (ST); Spectral trait variations (STV); Urban land-use-intensity (U-LUI); Human-use-intensity; Remote sensing; Hemeroby; NDVI; GLCM |
| UFZ wide themes | RU1; |
| 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. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=19856 |
| Wellmann, T., Haase, D., Knapp, S., Salbach, C., Selsam, P., Lausch, A. (2018): Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing Ecol. Indic. 85 , 190 - 203 10.1016/j.ecolind.2017.10.029 |
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