@article{fdi:010090974,
  title = {{A}utomated landform classification and mapping using a combined textural-morphometric approach : the {C}ongo basin and surroundings},
  author = {{V}iennois, {G}. and {B}{\'e}tard, {F}. and {F}reycon, {V}. and {B}arbier, {N}icolas and {C}outeron, {P}ierre},
  editor = {},
  language = {{ENG}},
  abstract = {{A}n automatic method of landform mapping applicable to large continental areas is presented, based on 30-meter {SRTM} ({S}huttle {R}adar {T}opography {M}ission) data and combining texture analysis using {F}ourier 2{D} periodograms ({FOTO} method) with a set of morphometric variables. {T}his integrated strategy was applied to the whole {C}ongo {B}asin and adjacent regions in {C}entral {A}frica, where landscapes and landforms mapping remains heterogeneous and partial with existing expert maps differing in aims and scales. {T}hrough the {FOTO} method, a principal component analysis ({PCA}) on obtained {F}ourier r-spectra yielded six textural features, which were further combined with seven morphometric criteria into a global {PCA}. {A} k-means classification from these output results provided an automatic mapping of 12 landform classes (at a final resolution of 900 m) which were successfully interpreted in terms of geomorphological meaning together with some hydrological and soil attributes. {F}inally, comparison of our landform map with existing, independent geomorphological sheets revealed a good spatial congruence. {O}verall, our method proved effective to depict landform assemblages at regional or continental scales based on complementary textural information and morphometric parameters. {A}s such, it could serve as a sound basis for further predicting and mapping soil units at the landscape scale, given the close soil-landform imbrications and interactions at the catena level. {I}t could serve as well as a predictive variable for biodiversity measures and biomass estimates, especially in the humid tropics where environmental data are lacking whilst ecological modelling is urgently needed to support land planning and forest management.},
  keywords = {{CONGO} {BASSIN}},
  booktitle = {},
  journal = {{J}ournal of {G}eomorphology},
  volume = {1},
  numero = {1},
  pages = {79--102},
  ISSN = {2628-6017},
  year = {2022},
  DOI = {10.1127/jgeomorphology/2022/0752},
  URL = {https://www.documentation.ird.fr/hor/fdi:010090974},
}