Understanding the dynamic of a tropical forest located in Southern Mexico using remotely sensed data

Alejandro Monsivais-Huertero, Jose Carlos Jimenez-Escalona, Jose Mauricio Galeana-Pizana, Aura Citlalli Torres-Gomez, Ramata Magagi, Kalifa Goita, Enrique Zempoaltecatl-Ramirez, Enrique Constantino-Recillas, Jesus Daniel Juarez-Vazquez, Juan Carlos Hernandez-Sanchez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Forested areas have been the ecosystem more impacted by anthropogenic activities as agriculture, livestock, etc. Particularly, tropical forests host a large diversity of flora and fauna. Southern Mexico, Guatemala, and Belize own the second larger area worldwide of tropical forests after the Amazonas. Despite the importance of this ecosystem, there is still a lack of information, and then, an understanding of the local dynamics. This paper aims at reporting on two field campaigns conducted at Calakmul, Southern, Mexico, to characterize soil and vegetation during rainy and dry seasons. Simultaneously to the field campaigns, Radarsat-2 images were acquired. Significant changes are observed between the rainy and dry seasons; primarily, because of the loss of water content during the dry season for both soil and vegetation.

Original languageEnglish
Title of host publication2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3826-3829
Number of pages4
ISBN (Electronic)9781509033324
DOIs
StatePublished - 1 Nov 2016
Event36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China
Duration: 10 Jul 201615 Jul 2016

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2016-November

Conference

Conference36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Country/TerritoryChina
CityBeijing
Period10/07/1615/07/16

Keywords

  • Radarsat-2 images
  • Tropical forest
  • ancillary data

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