@article{HalbgewachsWegmanndaPonte2022,
  author    = {Halbgewachs, Magdalena and Wegmann, Martin and da Ponte, Emmanuel},
  title     = {A spectral mixture analysis and landscape metrics based framework for monitoring spatiotemporal forest cover changes: a case study in Mato Grosso, Brazil},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {8},
  issn      = {2072-4292},
  doi       = {10.3390/rs14081907},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-270644},
  year      = {2022},
  abstract  = {An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8\% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3\% of the number of patches and a decrease of the mean patch area of 86.1\% for the selected time period, resulting in altered habitats for flora and fauna.},
  language  = {en}
}
@article{PhilippWegmannKuebertFlock2021,
  author    = {Philipp, Marius and Wegmann, Martin and K{\"u}bert-Flock, Carina},
  title     = {Quantifying the Response of German Forests to Drought Events via Satellite Imagery},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {9},
  issn      = {2072-4292},
  doi       = {10.3390/rs13091845},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-239575},
  year      = {2021},
  abstract  = {Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data.},
  language  = {en}
}