Faculty of Mathematics and Natural Sciences - Earth Observation Lab

Faculty of Mathematics and Natural Sciences | Geography Department | Earth Observation Lab | News | Archive | Congratulations to two doctoral researchers of the lab

Congratulations to two doctoral researchers of the lab

The papers' of Cornelius Senf and Hannes Müller have been accepted for publication with Remote Sensing of Environment.

Corleius Senf

"Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery"

The paper is resulting from a short employment in the EnMAP team.

We could show that multitemporal analysis can enhance landcover classifications of spatially and temporally complex landscapes, with southern Portugal serving as test case. If Landsat data is unavailable (e.g. due to clouds, data access to ESA archive), STARFM can be used to supplement existing time series. However, using original Landsat data was always superior to using STARFM.

 

Hannes Müller

"Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape"

We basically investigated the potential to reliably separate cropland, pasture, natural savanna vegetation, and other relevant land cover classes employing Landsat-derived spectral-temporal variability metrics for a savanna landscape in the Brazilian Cerrado. In order to better understand the surplus value and limitations of spectral-temporal variability metrics for classification purposes, we analyzed four datasets of different temporal depth, using 344 Landsat scenes across four footprints between 2009 and 2012. Our results showed a reliable separation between cropland, pasture, and natural savanna vegetation achieving an adjusted overall accuracy of 93%. A similar accuracy and spatial consistency of LULC classification could not be achieved based on spectral information alone, indicating the high additional value of temporal information for identifying LULC classes in the complex land use systems of savanna landscapes.