@article{CoelhoHeimFoersteretal.2017,
  author    = {Coelho, Christine and Heim, Birgit and F{\"o}rster, Saskia and Brosinsky, Arlena and de Araujo, Jose Carlos},
  title     = {In Situ and Satellite Observation of CDOM and Chlorophyll-a Dynamics in Small Water Surface Reservoirs in the Brazilian Semiarid Region},
  series = {Water},
  volume    = {9},
  journal   = {Water},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2073-4441},
  doi       = {10.3390/w9120913},
  pages     = {22},
  year      = {2017},
  abstract  = {We analyzed chlorophyll-a and Colored Dissolved Organic Matter (CDOM) dynamics from field measurements and assessed the potential of multispectral satellite data for retrieving water-quality parameters in three small surface reservoirs in the Brazilian semiarid region. More specifically, this work is comprised of: (i) analysis of Chl-a and trophic dynamics; (ii) characterization of CDOM; (iii) estimation of Chl-a and CDOM from OLI/Landsat-8 and RapidEye imagery. The monitoring lasted 20 months within a multi-year drought, which contributed to water-quality deterioration. Chl-a and trophic state analysis showed a highly eutrophic status for the perennial reservoir during the entire study period, while the non-perennial reservoirs ranged from oligotrophic to eutrophic, with changes associated with the first events of the rainy season. CDOM characterization suggests that the perennial reservoir is mostly influenced by autochthonous sources, while allochthonous sources dominate the non-perennial ones. Spectral-group classification assigned the perennial reservoir as a CDOM-moderate and highly eutrophic reservoir, whereas the non-perennial ones were assigned as CDOM-rich and oligotrophic-dystrophic reservoirs. The remote sensing initiative was partially successful: the Chl-a was best modelled using RapidEye for the perennial one; whereas CDOM performed best with Landsat-8 for non-perennial reservoirs. This investigation showed potential for retrieving water quality parameters in dry areas with small reservoirs.},
  language  = {en}
}
@article{JavharChenBaoetal.2019,
  author    = {Javhar, Aminov and Chen, Xi and Bao, Anming and Jamshed, Aminov and Yunus, Mamadjanov and Jovid, Aminov and Latipa, Tuerhanjiang},
  title     = {Comparison of Multi-Resolution Optical Landsat-8, Sentinel-2 and Radar Sentinel-1 Data for Automatic Lineament Extraction},
  series = {Remote sensing},
  volume    = {11},
  journal   = {Remote sensing},
  number    = {7},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs11070778},
  pages     = {29},
  year      = {2019},
  abstract  = {Lineament mapping, which is an important part of any structural geological investigation, is made more efficient and easier by the availability of optical as well as radar remote sensing data, such as Landsat and Sentinel with medium and high spatial resolutions. However, the results from these multi-resolution data vary due to their difference in spatial resolution and sensitivity to soil occupation. The accuracy and quality of extracted lineaments depend strongly on the spatial resolution of the imagery. Therefore, the aim of this study was to compare the optical Landsat-8, Sentinel-2A, and radar Sentinel-1A satellite data for automatic lineament extraction. The framework of automatic approach includes defining the optimal parameters for automatic lineament extraction with a combination of edge detection and line-linking algorithms and determining suitable bands from optical data suited for lineament mapping in the study area. For the result validation, the extracted lineaments are compared against the manually obtained lineaments through the application of directional filtering and edge enhancement as well as to the lineaments digitized from the existing geological maps of the study area. In addition, a digital elevation model (DEM) has been utilized for an accuracy assessment followed by the field verification. The obtained results show that the best correlation between automatically extracted lineaments, manual interpretation, and the preexisting lineament map is achieved from the radar Sentinel-1A images. The tests indicate that the radar data used in this study, with 5872 and 5865 lineaments extracted from VH and VV polarizations respectively, is more efficient for structural lineament mapping than the Landsat-8 and Sentinel-2A optical imagery, from which 2338 and 4745 lineaments were extracted respectively.},
  language  = {en}
}