Tropical Forest Ecology Monitoring—New Techniques and Future Implications

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Ecology and Management".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 196

Special Issue Editors


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Guest Editor
Department of Forest Sciences, Federal University of Sergipe, Av. Marechal Rondon, s/n, São Cristóvão 49100-000, SE, Brazil
Interests: species diversity; forest restoration; spectral diversity; aerial biomass; digital aerial photogrammetry
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Agricultural Engineering, Federal University of Sergipe, São Cristóvão 49100-000, Brazil
Interests: forest; REDD+; digital aerial photogrammetry (DAP); uas; drone; structure from motion (SfM); plot-level forest variables; AGB; forest inventory

E-Mail Website
Guest Editor
Department of Forest Sciences, Federal University of Sergipe, Av. Marechal Rondon, s/n, São Cristóvão 49100-000, SE, Brazil
Interests: species diversity; aerial biomass; digital aerial photogrammetry; light detection and ranging; remote sensing; forest management

Special Issue Information

Dear Colleagues,

Tropical forests are home to the greatest diversity of forest species in the world. Monitoring the ecology and diversity of forest species in tropical forests has been a challenge. Traditional methods based on data collected manually in the field are time-consuming and expensive. In recent years, some technologies that use remote sensing, whether by passive sensors with satellite images or unmanned aerial vehicles, or by active sensors with light detection and ranging, have been used to monitor the structure and diversity of forest species. Combined with these technologies based on remote sensing, artificial intelligence techniques such as neural networks have been developed and can be used to monitor the ecology of tropical forests.

This Special Issue aims to provide selected contributions on advances and new techniques that have future implications for monitoring the ecology of tropical forests.

Potential topics include, but are not limited to, the following:

- Light detection and ranging;

- Multi- and hyperspectral imaging;

- Artificial intelligence;

- Functional diversity;

- Carbon stock and aerial biomass;

- Species diversity;

- Forest management.

Prof. Dr. Milton Marques Fernandes
Dr. André Quintão de Almeida
Dr. Márcia Rodrigues de Moura Fernandes
Guest Editors

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Keywords

  • remote sensing
  • remotely operated vehicles
  • tropical forests
  • species diversity
  • forest structure
  • forest restoration
  • climate change
  • artificial intelligence
  • forest management

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Published Papers (1 paper)

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Research

21 pages, 3178 KiB  
Article
Using DAP-RPA Point Cloud-Derived Metrics to Monitor Restored Tropical Forests in Brazil
by Milton Marques Fernandes, Milena Viviane Vieira de Almeida, Marcelo Brandão José, Italo Costa Costa, Diego Campana Loureiro, Márcia Rodrigues de Moura Fernandes, Gilson Fernandes da Silva, Lucas Berenger Santana and André Quintão de Almeida
Forests 2025, 16(7), 1092; https://doi.org/10.3390/f16071092 - 1 Jul 2025
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Abstract
Monitoring forest structure, diversity, and biomass in restoration areas is both expensive and time-consuming. Metrics derived from digital aerial photogrammetry (DAP) may offer a cost-effective and efficient alternative for monitoring forest restoration. The main objective of this study was to use metrics derived [...] Read more.
Monitoring forest structure, diversity, and biomass in restoration areas is both expensive and time-consuming. Metrics derived from digital aerial photogrammetry (DAP) may offer a cost-effective and efficient alternative for monitoring forest restoration. The main objective of this study was to use metrics derived from digital aerial photogrammetry (DAP) point clouds obtained by remotely piloted aircraft (RPA) to estimate aboveground biomass (AGB), species diversity, and structural variables for monitoring restored secondary tropical forest areas. The study was conducted in three active and one passive forest restoration systems located in a secondary forest in Sergipe state, Brazil. A total of 2507 tree individuals from 36 plots (0.0625 ha each) were identified, and their total height (ht) and diameter at breast height (dbh) were measured in the field. Concomitantly with the field inventory, the plots were mapped using an RPA, and traditional height-based point cloud metrics and Fourier transform-derived metrics were extracted for each plot. Regression models were developed to calculate AGB, Shannon diversity index (H′), ht, dbh, and basal area (ba). Furthermore, multivariate statistical analyses were used to characterize AGB and H′ in the different restoration systems. All fitted models selected Fourier transform-based metrics. The AGB estimates showed satisfactory accuracy (R2 = 0.88; RMSE = 31.2%). The models for H′ and ba also performed well, with R2 values of 0.90 and 0.67 and RMSEs of 24.8% and 20.1%, respectively. Estimates of structural variables (dbh and ht) showed high accuracy, with RMSE values close to 10%. Metrics derived from the Fourier transform were essential for estimating AGB, species diversity, and forest structure. The DAP-RPA-derived metrics used in this study demonstrate potential for monitoring and characterizing AGB and species richness in restored tropical forest systems. Full article
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