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Habitat Fragmentation

Habitat Fragmentation

Habitat fragmentation describes the division of a more extensive, continuous habitat into smaller, discontinuous areas. Human activities such as land conversion, as well as slower geological processes leading to changes in the physical environment, are the two leading causes of habitat fragmentation. The fragmentation process typically follows the same steps: perforation, dissection, fragmentation, shrinkage, and attrition.

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Related Experiment Video

Updated: Jan 16, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

Species habitat modeling based on image semantic segmentation.

Lingjun Wang^1,2, Haofeng Tan³, Peng Luo^4,5

¹School of Resource and Environmental Sciences, Wuhan University, No.129, Luoyu Road, Wuhan, 430079, Hubei, China.

Scientific Reports

|September 30, 2025

Summary

This study introduces a new habitat modeling framework using kernel density analysis and deep learning. The method improves habitat mapping accuracy for species like the Sandpiper family, aiding biodiversity conservation.

Keywords:

Deep learning Kernel density analysis Semantic segmentation Species distribution models

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Last Updated: Jan 16, 2026

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Published on: August 13, 2014

Area of Science:

Ecological modeling
Biodiversity conservation
Geospatial analysis

Background:

Traditional habitat modeling (lasagna model) overlooks surrounding environmental influences.
Accurate habitat mapping is vital for species reproduction and ecological preservation.
Existing methods often struggle with presence-only data.

Purpose of the Study:

To propose an integrated habitat modeling framework incorporating surrounding environmental conditions.
To evaluate the effectiveness of kernel density analysis and semantic segmentation for habitat mapping.
To compare deep learning approaches with traditional methods like MaxEnt.

Main Methods:

Kernel density analysis to expand presence-only data into presence-absence data.
Semantic segmentation using Segformer for habitat mapping.
Comparative analysis with the traditional MaxEnt model.

Main Results:

Kernel density analysis effectively converts presence-only data for habitat modeling.
Segformer achieved higher accuracy (AUC 0.76) than MaxEnt (AUC 0.69) for Sandpiper family habitat mapping.
Case studies revealed both the strengths and limitations of the proposed framework.

Conclusions:

Deep learning methods show significant potential for advancing habitat modeling.
The proposed framework offers a more comprehensive approach to biodiversity assessment and conservation planning.
Further research is needed to refine the method for diverse species and environments.