Assessing the performance of object‐oriented LiDAR predictors for forest bird habitat suitability modeling
Abstract Habitat suitability models (HSMs) are widely used to plan actions for species of conservation interest. Models that will be turned into conservation actions need predictors that are both ecologically pertinent and fit managers’ conceptual view of ecosystems. Remote sensing technologies such...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-03-01
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| Series: | Remote Sensing in Ecology and Conservation |
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| Online Access: | https://doi.org/10.1002/rse2.117 |
| _version_ | 1828519902988730368 |
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| author | Anouk Glad Björn Reineking Marc Montadert Alexandra Depraz Jean‐Matthieu Monnet |
| author_facet | Anouk Glad Björn Reineking Marc Montadert Alexandra Depraz Jean‐Matthieu Monnet |
| author_sort | Anouk Glad |
| collection | DOAJ |
| description | Abstract Habitat suitability models (HSMs) are widely used to plan actions for species of conservation interest. Models that will be turned into conservation actions need predictors that are both ecologically pertinent and fit managers’ conceptual view of ecosystems. Remote sensing technologies such as light detection and ranging (LiDAR) can describe landscapes at high resolution over large spatial areas and have already given promising results for modeling forest species distributions. The point‐cloud (PC) area‐based LiDAR variables are often used as environmental variables in HSMs and have more recently been complemented by object‐oriented (OO) metrics. However, the efficiency of each type of variable to capture structural information on forest bird habitat has not yet been compared. We tested two hypotheses: (1) the use of OO variables in HSMs will give similar performance as PC area‐based models; and (2) OO variables will improve model robustness to LiDAR datasets acquired at different times for the same area. Using the case of a locally endangered forest bird, the capercaillie (Tetrao urogallus), model performance and predictions were compared between the two variable types. Models using OO variables showed slightly lower discriminatory performance than PC area‐based models (average ΔAUC = −0.032 and −0.01 for females and males, respectively). OO‐based models were as robust (absolute difference in Spearman rank correlation of predictions ≤ 0.21) or more robust than PC area‐based models. In sum, LiDAR‐derived PC area‐based metrics and OO metrics showed similar performance for modeling the distribution of the capercaillie. We encourage the further exploration of OO metrics for creating reliable HSMs, and in particular testing whether they might help improve the scientist–stakeholder interface through better interpretability. |
| first_indexed | 2024-12-11T19:21:20Z |
| format | Article |
| id | doaj.art-b92db96e214548c984cd373d821818e1 |
| institution | Directory Open Access Journal |
| issn | 2056-3485 |
| language | English |
| last_indexed | 2024-12-11T19:21:20Z |
| publishDate | 2020-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Remote Sensing in Ecology and Conservation |
| spelling | doaj.art-b92db96e214548c984cd373d821818e12022-12-22T00:53:31ZengWileyRemote Sensing in Ecology and Conservation2056-34852020-03-016151910.1002/rse2.117Assessing the performance of object‐oriented LiDAR predictors for forest bird habitat suitability modelingAnouk Glad0Björn Reineking1Marc Montadert2Alexandra Depraz3Jean‐Matthieu Monnet4University Grenoble Alpes Irstea LESSEM Grenoble 38000 FranceUniversity Grenoble Alpes Irstea LESSEM Grenoble 38000 FranceONCFS 90 impasse Les Daudes Sévrier 74320 FranceGroupe Tétras Jura 9 impasse du tacon Les Bouchoux 39370 FranceUniversity Grenoble Alpes Irstea LESSEM Grenoble 38000 FranceAbstract Habitat suitability models (HSMs) are widely used to plan actions for species of conservation interest. Models that will be turned into conservation actions need predictors that are both ecologically pertinent and fit managers’ conceptual view of ecosystems. Remote sensing technologies such as light detection and ranging (LiDAR) can describe landscapes at high resolution over large spatial areas and have already given promising results for modeling forest species distributions. The point‐cloud (PC) area‐based LiDAR variables are often used as environmental variables in HSMs and have more recently been complemented by object‐oriented (OO) metrics. However, the efficiency of each type of variable to capture structural information on forest bird habitat has not yet been compared. We tested two hypotheses: (1) the use of OO variables in HSMs will give similar performance as PC area‐based models; and (2) OO variables will improve model robustness to LiDAR datasets acquired at different times for the same area. Using the case of a locally endangered forest bird, the capercaillie (Tetrao urogallus), model performance and predictions were compared between the two variable types. Models using OO variables showed slightly lower discriminatory performance than PC area‐based models (average ΔAUC = −0.032 and −0.01 for females and males, respectively). OO‐based models were as robust (absolute difference in Spearman rank correlation of predictions ≤ 0.21) or more robust than PC area‐based models. In sum, LiDAR‐derived PC area‐based metrics and OO metrics showed similar performance for modeling the distribution of the capercaillie. We encourage the further exploration of OO metrics for creating reliable HSMs, and in particular testing whether they might help improve the scientist–stakeholder interface through better interpretability.https://doi.org/10.1002/rse2.117Habitat suitability modelsLiDARobject‐oriented metricspoint‐cloud area‐based metrics |
| spellingShingle | Anouk Glad Björn Reineking Marc Montadert Alexandra Depraz Jean‐Matthieu Monnet Assessing the performance of object‐oriented LiDAR predictors for forest bird habitat suitability modeling Remote Sensing in Ecology and Conservation Habitat suitability models LiDAR object‐oriented metrics point‐cloud area‐based metrics |
| title | Assessing the performance of object‐oriented LiDAR predictors for forest bird habitat suitability modeling |
| title_full | Assessing the performance of object‐oriented LiDAR predictors for forest bird habitat suitability modeling |
| title_fullStr | Assessing the performance of object‐oriented LiDAR predictors for forest bird habitat suitability modeling |
| title_full_unstemmed | Assessing the performance of object‐oriented LiDAR predictors for forest bird habitat suitability modeling |
| title_short | Assessing the performance of object‐oriented LiDAR predictors for forest bird habitat suitability modeling |
| title_sort | assessing the performance of object oriented lidar predictors for forest bird habitat suitability modeling |
| topic | Habitat suitability models LiDAR object‐oriented metrics point‐cloud area‐based metrics |
| url | https://doi.org/10.1002/rse2.117 |
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