@article{fdi:010085211,
  title = {{M}ultiscale diagnosis of mangrove status in data-poor context using very high spatial resolution satellite images : a case study in {P}ichavaram mangrove forest, {T}amil {N}adu, {I}ndia},
  author = {{G}hosh, {S}. and {P}roisy, {C}hristophe and {M}uthusankar, {G}. and {H}assenruck, {C}. and {H}elfer, {V}. and {M}athevet, {R}. and {A}ndrieu, {J}. and {B}alachandran, {N}. and {N}arendran, {R}.},
  editor = {},
  language = {{ENG}},
  abstract = {{H}ighlighting spatiotemporal changes occurring within mangrove habitats at the finest possible scale could contribute fundamental knowledge and data for local sustainable management. {T}his study presents the current situation of the {P}ichavaram mangrove area, a coastal region of {S}outheast {I}ndia prone to both cyclones and reduced freshwater inflow. {B}ased on the supervised classification and visual inspection of very high spatial resolution ({VHSR}) satellite images provided with a pixel size of <4 m, we generated time-series maps to analyze the changes that occurred in both the natural and planted mangroves between 2003 and 2019. {W}e achieved a high mapping accuracy (>85%), which confirmed the potential of classification techniques applied to {VHSR} images in capturing changes in mangroves on a very fine scale. {O}ur diagnosis reveals variable expansion rates in plantations made by the local authorities. {W}e also report an ongoing mangrove dieback and confirm progressive shoreline erosion along the coastline. {D}espite a lack of field data, {VHSR} images allowed for the multiscale diagnosis of the ecosystem situation, thus constituting the first fine-scale assessment of the fragile {P}ichavaram mangrove area upon which the coastal community is dependent.},
  keywords = {remote sensing-based monitoring ; plantation ; restoration ; change ; detection ; dieback ; {B}ay of {B}engal ; {INDE} ; {TAMIL} {NADU} ; {PICHAVARAM} ; {BENGALE} {GOLFE}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {14},
  numero = {10},
  pages = {2317 [23 ]},
  year = {2022},
  DOI = {10.3390/rs14102317},
  URL = {https://www.documentation.ird.fr/hor/fdi:010085211},
}