@article{fdi:010096873,
  title = {{M}apping small-sized logging disturbances in tropical forests using {S}entinel-1 time series and an extensive ground truth dataset},
  author = {{M}ercier, {A}. and {B}etbeder, {J}. and {M}ortier, {F}. and {B}arbier, {N}icolas and {P}loton, {P}ierre and {C}ornu, {G}. and {C}outeron, {P}ierre},
  editor = {},
  language = {{ENG}},
  abstract = {{D}eforestation and forest degradation are the main threats to biodiversity and carbon stocks in tropical forests. {A}dvances in optical and {SAR} satellite sensors have enabled the development of real-time monitoring of deforestation on a global scale. {SAR} is particularly appealing in tropical areas due to its insensitivity to cloud cover. {H}owever, the automatic detection of small disturbed areas (such as individual tree felling gaps) remains a major challenge. {T}hanks to a unique dataset consisting of 23,759 locations of individual tree felling gaps and multi-date drone lidar acquisitions, we evaluated the potential of {S}entinel-1 dense time series for monitoring small-sized forest disturbances substantially smaller than 0.1 ha on both {FSC}-certified and artisanal logging sites in the {C}ongo {B}asin. {W}e designed a new method for forest monitoring using the fused-lasso technique optimized to detect abrupt changes of at least 0.02 ha in {S}entinel-1 time series using the fused-lasso technique ({F}used-{L}asso {C}hange {D}etection, {FLCD}). {W}e assessed our new method along with the {C}umulative {S}um ({C}u{S}um) that also proved promising for detecting small impacts, referring for the first time to precise disturbance dates over large areas. {B}oth approaches reached similar rates of confirmed felling gaps that were similarly increasing with gap size, and similar rates of unconfirmed detected gaps. {T}he {FLCD} method estimates the dates of tree felling more accurately in {FSC}-certified areas (-2 days difference for {FLCD} and -19 for {C}u{S}um on average). {T}he effective resolution of the {S}-1 images limits detection for the smallest gaps, yet the approach can help detect and monitor degradation fronts. {F}used lasso regression is relevant for modeling the temporal trajectories of the radar signal, which will allow taking advantage of both the increasing availability of {UAV}-borne data and the lengthening of the {S}-1 image series.},
  keywords = {artisanal and certified logging ; {C}ongo basin ; fused-lasso ; {S}entinel-1 ; {SAR} time series ; tree-felling gap detection ; tropical forest degradation ; {CONGO} ; {REPUBLIQUE} {DEMOCRATIQUE} {DU} {CONGO} ; {CAMEROUN} ; {CONGO} {BASSIN} ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{F}rontiers in {R}emote {S}ensing},
  volume = {7},
  numero = {},
  pages = {1659305 [18 p.]},
  year = {2026},
  DOI = {10.3389/frsen.2026.1659305},
  URL = {https://www.documentation.ird.fr/hor/fdi:010096873},
}