@article{fdi:010082809,
  title = {{M}onitoring loss of tropical forest cover from {S}entinel-1 time-series : a {C}u{S}um-based approach},
  author = {{Y}gorra, {B}. and {F}rappart, {F}. and {W}igneron, {J}. {P}. and {M}oisy, {C}. and {C}atry, {T}hibault and {B}aup, {F}. and {H}amunyela, {E}. and {R}iazanoff, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. {E}fforts are ongoing to reduce tropical deforestation rates. {E}arth observations are increasingly used to monitor deforestation over the whole equatorial area. {C}hange detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. {F}or instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. {R}ecently, detection methods applied to {S}ynthetic {A}perture {R}adar ({SAR}) have been developed to address the limitations related to cloud cover. {I}n this study, we present an application of a recently developed change detection method for monitoring forest cover loss from {SAR} time-series data in tropical zone. {T}he method is based on the {C}umulative {S}um algorithm ({C}u{S}um) combined with a bootstrap analysis. {T}he method was applied to time-series of {S}entinel-1 ground range detected ({GRD}) dual polarization ({VV}, {VH}) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the {D}emocratic {R}epublic of {C}ongo during the 2018-2020 period. {A} cross-threshold recombination was then conducted on the computed maps. {E}valuated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. {O}ur results show that more than 60% of forest disturbances were detected before the {P}lanet{S}cope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.},
  keywords = {{D}eforestation ; {R}emote sensing ; {S}entinel-1 ; {C}umulative sum algorithm ; {T}ropical forest ; {C}hange detection ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{I}nternational {J}ournal of {A}pplied {E}arth {O}bservation and {G}eoinformation},
  volume = {103},
  numero = {},
  pages = {102532 [19 ]},
  ISSN = {1569-8432},
  year = {2021},
  DOI = {10.1016/j.jag.2021.102532},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082809},
}