@article{fdi:010069490,
  title = {{E}stimating babassu palm density using automatic palm tree detection with very high spatial resolution satellite images},
  author = {{M}oreira dos {S}antos, {A}. and {M}itja, {D}anielle and {D}ela{\^i}tre, {E}ric and {D}emagistri, {L}aurent and {S}ouza {M}iranda, {I}. de and {L}ibourel, {T}. and {P}etit, {M}ichel},
  editor = {},
  language = {{ENG}},
  abstract = {{H}igh spatial resolution images as well as image processing and object detection algorithms are recent technologies that aid the study of biodiversity and commercial plantations of forest species. {T}his paper seeks to contribute knowledge regarding the use of these technologies by studying randomly dispersed native palm tree. {H}ere, we analyze the automatic detection of large circular crown ({LCC}) palm tree using a high spatial resolution panchromatic {G}eo{E}ye image (0.50 m) taken on the area of a community of small agricultural farms in the {B}razilian {A}mazon. {W}e also propose auxiliary methods to estimate the density of the {LCC} palm tree {A}ttalea speciosa (babassu) based on the detection results. {W}e used the "{C}ompt-palm" algorithm based on the detection of palm tree shadows in open areas via mathematical morphology techniques and the spatial information was validated using field methods (i.e. structural census and georeferencing). {T}he algorithm recognized individuals in life stages 5 and 6, and the extraction percentage, branching factor and quality percentage factors were used to evaluate its performance. {A} principal components analysis showed that the structure of the studied species differs from other species. {A}pproximately 96% of the babassu individuals in stage 6 were detected. {T}hese individuals had significantly smaller stipes than the undetected ones. {I}n turn, 60% of the stage 5 babassu individuals were detected, showing significantly a different total height and a different number of leaves from the undetected ones. {O}ur calculations regarding resource availability indicate that 6870 ha contained 25,015 adult babassu palm tree, with an annual potential productivity of 27.4 t of almond oil. {T}he detection of {LCC} palm tree and the implementation of auxiliary field methods to estimate babassu density is an important first step to monitor this industry resource that is extremely important to the {B}razilian economy and thousands of families over a large scale.},
  keywords = {{S}hadow detection ; {M}athematical morphology ; {D}ensity estimate ; {R}emote sensing ; {B}razilian {A}mazon ; {BRESIL} ; {AMAZONIE}},
  booktitle = {},
  journal = {{J}ournal of {E}nvironmental {M}anagement},
  volume = {193},
  numero = {},
  pages = {40--51},
  ISSN = {0301-4797},
  year = {2017},
  DOI = {10.1016/j.jenvman.2017.02.004},
  URL = {https://www.documentation.ird.fr/hor/fdi:010069490},
}