Three-Dimensional Mapping of Habitats Using Remote-Sensing Data and Machine-Learning Algorithms

Meisam Amani, Fatemeh Foroughnia, Armin Moghimi, Sahel Mahdavi, Shuanggen Jin

Research output: Contribution to journalArticleScientificpeer-review

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Progress toward habitat protection goals can effectively be performed using satellite imagery and machine-learning (ML) models at various spatial and temporal scales. In this regard, habitat types and landscape structures can be discriminated against using remote-sensing (RS) datasets. However, most existing research in three-dimensional (3D) habitat mapping primarily relies on same/cross-sensor features like features derived from multibeam Light Detection And Ranging (LiDAR), hydrographic LiDAR, and aerial images, often overlooking the potential benefits of considering multi-sensor data integration. To address this gap, this study introduced a novel approach to creating 3D habitat maps by using high-resolution multispectral images and a LiDAR-derived Digital Surface Model (DSM) coupled with an object-based Random Forest (RF) algorithm. LiDAR-derived products were also used to improve the accuracy of the habitat classification, especially for the habitat classes with similar spectral characteristics but different heights. Two study areas in the United Kingdom (UK) were chosen to explore the accuracy of the developed models. The overall accuracies for the two mentioned study areas were high (91% and 82%), which is indicative of the high potential of the developed RS method for 3D habitat mapping. Overall, it was observed that a combination of high-resolution multispectral imagery and LiDAR data could help the separation of different habitat types and provide reliable 3D information.
Original languageEnglish
Article number4135
Number of pages12
JournalRemote Sensing
Issue number17
Publication statusPublished - 2023


  • habitat mapping
  • remote sensing
  • satellite imagery
  • LiDAR
  • 3D mapping


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