Wildlife species distribution modeling with unmanned aircraft

Aquí tienes el texto con la ortografía y el estilo académico pulidos (no cambié el significado, solo mejoré fluidez y correcciones menores): The use of drones and open-source software in Species Distribution Models (SDMs) represents an innovative approach to the study of wildlife. This article reviews the current state of the art regarding their integration, identifying trends, challenges, and opportunities. The methodology followed the PRISMA guidelines and included a literature search in databases such as Scopus, Web of Science, and Google Scholar. Articles published within the last decade were selected if they addressed the use of drones in wildlife monitoring and the application of open-source tools in spatial data analysis. The results show a significant increase in the use of drones to collect precise geospatial data, improving the identification of habitats and species distribution patterns. Tools such as QGIS, R, and MaxEnt enable data processing without licensing costs, promoting accessibility and scientific reproducibility. However, challenges remain in terms of methodological standardization, the integration of heterogeneous data sources, and limited detection capabilities for certain species. Variability in image quality and environmental conditions also affects the accuracy of the results. In conclusion, the combination of drones and open-source software offers clear benefits: it enhances efficiency, improves model accuracy, and reduces costs. Nevertheless, greater standardization and technical validation are required to optimize its application in ecology and conservation.

• Publication year

  2026

• Venue

  INNOVATION & DEVELOPMENT IN ENGINEERING AND APPLIED SCIENCES

• Publication date

  2026-01-30

• Fields of study

  Not labeled

• Identifiers
  DOI 10.53358/ideas.v8i1.1177
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Modelling current and future potential distribution of medicinal orchids in Darjeeling eastern Himalaya

  2024influential reference
• Advancements and Applications of Drone-Integrated Geographic Information System Technology - A Review

  2023cited by this paper
• Vulnerability and risk assessment mapping of Bhitarkanika national park, Odisha, India using machine-based embedded decision support system

  2023influential reference
• Effectiveness of UAV‐based DTM and satellite‐based DEMs for local‐level flood modeling in Jamuna floodplain

  2023influential reference
• A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs

  2023cited by this paper
• Exploring the opportunities and risks of aerial monitoring for biodiversity conservation

  2023cited by this paper
• Predicting extinctions with species distribution models

  2023cited by this paper
• Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys

  2023cited by this paper
• A Scoping Review of Species Distribution Modeling Methods for Tick Vectors

  2022cited by this paper
• Protecting endangered megafauna through AI analysis of drone images in a low-connectivity setting: a case study from Namibia

  2022influential reference
• UAV-Based Forest Health Monitoring: A Systematic Review

  2022cited by this paper
• Transferability of an individual- and trait-based forest dynamics model: A test case across the tropics

  2022influential reference
• Approaching wildlife with drones: using scientific literature to identify factors to consider for minimising disturbance

  2021cited by this paper
• Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment

  2021cited by this paper
• A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

  2021influential reference
• A quantitative review of abundance-based species distribution models

  2021influential reference
• Integrating Multi-Sensors Data for Species Distribution Mapping Using Deep Learning and Envelope Models

  2021cited by this paper
• Species distribution modelling of the Southern Ocean benthos: a review on methods, cautions and solutions

  2021cited by this paper
• Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management

  2021cited by this paper
• Sentinel-2 Data for Land Cover/Use Mapping: A Review

  2020cited by this paper
• Modelling species presence-only data with random forests

  2020influential reference
• Predictive Modelling of Current and Future Potential Distribution of the Spectacled Bear (Tremarctos ornatus) in Amazonas, Northeast Peru

  2020cited by this paper
• Hierarchical species distribution models in support of vegetation conservation at the landscape scale

  2019cited by this paper
• Drones for Conservation in Protected Areas: Present and Future

  2019influential reference
• Realized ecological forecast through an interactive Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models

  2019cited by this paper
• Comparison of four UAV georeferencing methods for environmental monitoring purposes focusing on the combined use with airborne and satellite remote sensing platforms

  2019influential reference
• A comparision between satellite based and drone based remote sensing technology to achieve sustainable development: a review

  2017cited by this paper
• What do we gain from simplicity versus complexity in species distribution models

  2014cited by this paper
• A statistical explanation of MaxEnt for ecologists

  2011cited by this paper
• Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

  2010influential reference
• Maximum entropy modeling of species geographic distributions

  2006influential reference
• Modelling and analysis of the atmospheric nitrogen deposition in North Carolina

  2006influential reference
• Generalized linear and generalized additive models in studies of species distributions: setting the scene

  2002cited by this paper

• No citing papers are available for this paper.