Print Email Facebook Twitter Three-Dimensional Mapping of Habitats Using Remote-Sensing Data and Machine-Learning Algorithms Title Three-Dimensional Mapping of Habitats Using Remote-Sensing Data and Machine-Learning Algorithms Author Amani, Meisam (Henan Polytechnic University; WSP Global Inc.) Foroughnia, Fatemeh (TU Delft Geo-engineering) Moghimi, Armin (Leibniz University Hannover) Mahdavi, Sahel (WSP Global Inc.) Jin, Shuanggen (Chinese Academy of Sciences; Henan Polytechnic University) Date 2023 Abstract 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. Subject habitat mappingremote sensingsatellite imageryLiDAR3D mapping To reference this document use: http://resolver.tudelft.nl/uuid:5f1280f0-2190-493c-a057-3611b0bb3058 DOI https://doi.org/10.3390/rs15174135 ISSN 2072-4292 Source Remote Sensing, 15 (17) Part of collection Institutional Repository Document type journal article Rights © 2023 Meisam Amani, Fatemeh Foroughnia, Armin Moghimi, Sahel Mahdavi, Shuanggen Jin Files PDF remotesensing_15_04135.pdf 3.82 MB Close viewer /islandora/object/uuid:5f1280f0-2190-493c-a057-3611b0bb3058/datastream/OBJ/view