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Abstract

What are the main findings?

Lidar-derived Digital Elevation Models and Infrared high-definition imagery from satellites constitute the most efficient tools for identifying aguadas (ponds) in regions covered by dense forest.

Three hundred fifty aguadas were identified in the Calakmul Biosphere Reserve.

What is the implication of the main finding?

Lidar-derived Digital Elevation Model and Infrared high-definition satellite images and supplementary sources can be combined to enhance the identification of minor water bodies in densely wooded regions.

Ponds in the Calakmul Biosphere Reserve exceed the number that has previously documented, and their identification is highly relevant for conservation and archeological studies.

This study explores the detection and classification of aguadas (ponds) in the Bajo El Laberinto region, in the Calakmul Biosphere Reserve, Campeche, Mexico, using remote sensing techniques. Lidar-derived digital elevation models (DEMs), orthophotos and satellite imagery from multiple sources were employed to identify and characterize these water reservoirs, which played a crucial role in ancient Maya water management and continued to be vital for contemporary wildlife. By comparing different visualization techniques and imagery sources, the study demonstrates that while lidar data provides superior topographic detail, satellite imagery—particularly with nominal 3 m, or finer, spatial resolution with a near-infrared band—offers valuable complementary data including present-day hydrological and vegetative characteristics. In this study, 350 aguadas were identified in the broader region. The shapes, canopy cover, and topographic positions of these aguadas were documented, and the anthropogenic origin of most features was emphasized. The paper’s conclusion states that combining various remote sensing datasets enhances the identification and understanding of aguadas, providing insights into ancient Mayan adaptive strategies and contributing to ongoing archaeological and ecological research.

Details

1009240
Location
Company / organization
Title
Detection of Aguadas (Ponds) Through Remote Sensing in the Bajo El Laberinto Region, Calakmul, Campeche, Mexico
Author
Flores Colin Alberto G. 1   VIAFID ORCID Logo  ; Dunning, Nicholas P 2   VIAFID ORCID Logo  ; Anaya Hernández Armando 1 ; Carr, Christopher 2 ; Kupprat Felix 3 ; Reese-Taylor, Kathryn 4 ; Hinojosa-Garro Demián 5 

 Laboratorio de Geomática, Centro de Estudios de Desarrollo Sustentable y Aprovechamiento de la Vida Silvestre (CEDESU), Universidad Autónoma de Campeche, Campeche 24079, Mexico; [email protected] 
 Department of Geography and GIS, University of Cincinnati, Cincinnati, OH 45221, USA; [email protected] (N.P.D.); [email protected] (C.C.) 
 Instituto de Investigaciones Antropológicas, Universidad Nacional Autónoma de Mexico, Ciudad de Mexico 04510, Mexico; [email protected] 
 Department of Anthropology, University of Calgary, Calgary, AB T2N 1N4, Canada; [email protected] 
 Laboratorio de Ecología Acuática y Monitoreo Ambiental, Centro de Estudios de Desarrollo Sustentable y Aprovechamiento de la Vida Silvestre (CEDESU), Universidad Autónoma de Campeche, Campeche 24079, Mexico; [email protected] 
Publication title
Volume
17
Issue
19
First page
3299
Number of pages
35
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-09-25
Milestone dates
2025-04-24 (Received); 2025-09-13 (Accepted)
Publication history
 
 
   First posting date
25 Sep 2025
ProQuest document ID
3261089399
Document URL
https://www.proquest.com/scholarly-journals/detection-aguadas-ponds-through-remote-sensing/docview/3261089399/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-10-16
Database
ProQuest One Academic