@article{fdi:010089668,
  title = {{R}adiometric landscape: a new conceptual framework and operational approach for landscape characterisation and mapping},
  author = {{L}emettais, {L}ouise and {A}lleaume, {S}. and {L}uque, {S}. and {L}aques, {A}nne-{E}lisabeth and {A}lim, {Y}. and {D}emagistri, {L}aurent and {B}{\'e}gu{\'e}, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{L}andscape mapping has the potential to address some of the most pressing research issues of our time, including climate change, sustainable development, and human well-being. {I}n this paper, we propose an original method that lays the foundations for landscape mapping and overcomes some of the major limitations of existing biophysical methods. {B}ased on the assumption that the primary components of the landscape can be extracted directly from the radiometric information of satellite image time series, this paper presents a new approach to landscape characterization and mapping based solely on remote sensing data. {T}he approach relies on a conceptual model, which links the description, characteristics, structure and functions of the landscape to a set of {R}emote {S}ensing-based {E}ssential {L}andscape {V}ariables ({RS}-{ELV}s). {T}he {RS}-{ELV}s are then processed according to geographic object-based image analysis ({GEOBIA}) approach to produce a radiometric landscape map. {T}he model and the remote sensing data processing chain are tested on a case study in central {M}adagascar (about 13 000 km2) composed of contrasting landscapes resulting from different climatic conditions and agricultural practices. {T}he {RS}-{ELV}s are extracted from {MODIS} image time series for the temporal and spectral variables, and from {MODIS} and {S}entinel-2 images for the texture variables. {T}he parameterization of the segmentation and clustering algorithms is determined by statistical optimization. {T}he final result is a radiometric landscape map in six classes. {T}he landscape classes are then characterized using an independent set of remote sensing variables, a global land cover map and ground observations. {T}he approach successfully identifies and delineates the gradient and major landscape types of the complex region of central {M}adagascar, confirming our initial hypothesis. {T}he production of such radiometric landscape maps opens the way for integrated territorial development, including the planning and protection of the living environment and human well-being, and the implementation of sectoral policies.},
  keywords = {{R}emote sensing ; {MODIS} ; {S}entinel-2 ; essential variables ; satellite image ; time series ; {M}adagascar ; {MADAGASCAR}},
  booktitle = {},
  journal = {{G}eo-{S}patial {I}nformation {S}cience},
  volume = {[{E}arly access]},
  numero = {},
  pages = {[23 p.]},
  ISSN = {1009-5020},
  year = {2024},
  DOI = {10.1080/10095020.2024.2314558},
  URL = {https://www.documentation.ird.fr/hor/fdi:010089668},
}