Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response
The ability of deep convolutional neural networks (deep learning) to learn complex visual characteristics offers a new method to classify tree species using lower-cost data such as regional aerial RGB imagery. In this study, we use 10 cm resolution imagery and 4600 trees to develop a deep learning m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/9/1789 |
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| author | Grant D. Pearse Michael S. Watt Julia Soewarto Alan Y. S. Tan |
| author_facet | Grant D. Pearse Michael S. Watt Julia Soewarto Alan Y. S. Tan |
| author_sort | Grant D. Pearse |
| collection | DOAJ |
| description | The ability of deep convolutional neural networks (deep learning) to learn complex visual characteristics offers a new method to classify tree species using lower-cost data such as regional aerial RGB imagery. In this study, we use 10 cm resolution imagery and 4600 trees to develop a deep learning model to identify <i>Metrosideros</i> <i>excelsa</i> (pōhutukawa)—a culturally important New Zealand tree that displays distinctive red flowers during summer and is under threat from the invasive pathogen <i>Austropuccinia psidii</i> (myrtle rust). Our objectives were to compare the accuracy of deep learning models that could learn the distinctive visual characteristics of the canopies with tree-based models (XGBoost) that used spectral and textural metrics. We tested whether the phenology of pōhutukawa could be used to enhance classification by using multitemporal aerial imagery that showed the same trees with and without widespread flowering. The XGBoost model achieved an accuracy of 86.7% on the dataset with strong phenology (flowering). Without phenology, the accuracy fell to 79.4% and the model relied on the blueish hue and texture of the canopies. The deep learning model achieved 97.4% accuracy with 96.5% sensitivity and 98.3% specificity when leveraging phenology—even though the intensity of flowering varied substantially. Without strong phenology, the accuracy of the deep learning model remained high at 92.7% with sensitivity of 91.2% and specificity of 94.3% despite significant variation in the appearance of non-flowering pōhutukawa. Pooling time-series imagery did not enhance either approach. The accuracy of XGBoost and deep learning models were, respectively, 83.2% and 95.2%, which were of intermediate precision between the separate models. |
| first_indexed | 2024-03-10T11:42:14Z |
| format | Article |
| id | doaj.art-42b8e24e83d94a1ea31fd04436aa0bc4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:42:14Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-42b8e24e83d94a1ea31fd04436aa0bc42023-11-21T18:23:33ZengMDPI AGRemote Sensing2072-42922021-05-01139178910.3390/rs13091789Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity ResponseGrant D. Pearse0Michael S. Watt1Julia Soewarto2Alan Y. S. Tan3Scion, Private Bag 3020, Rotorua 3046, New ZealandScion, 10 Kyle Street, Christchurch 8011, New ZealandScion, Private Bag 3020, Rotorua 3046, New ZealandScion, Private Bag 3020, Rotorua 3046, New ZealandThe ability of deep convolutional neural networks (deep learning) to learn complex visual characteristics offers a new method to classify tree species using lower-cost data such as regional aerial RGB imagery. In this study, we use 10 cm resolution imagery and 4600 trees to develop a deep learning model to identify <i>Metrosideros</i> <i>excelsa</i> (pōhutukawa)—a culturally important New Zealand tree that displays distinctive red flowers during summer and is under threat from the invasive pathogen <i>Austropuccinia psidii</i> (myrtle rust). Our objectives were to compare the accuracy of deep learning models that could learn the distinctive visual characteristics of the canopies with tree-based models (XGBoost) that used spectral and textural metrics. We tested whether the phenology of pōhutukawa could be used to enhance classification by using multitemporal aerial imagery that showed the same trees with and without widespread flowering. The XGBoost model achieved an accuracy of 86.7% on the dataset with strong phenology (flowering). Without phenology, the accuracy fell to 79.4% and the model relied on the blueish hue and texture of the canopies. The deep learning model achieved 97.4% accuracy with 96.5% sensitivity and 98.3% specificity when leveraging phenology—even though the intensity of flowering varied substantially. Without strong phenology, the accuracy of the deep learning model remained high at 92.7% with sensitivity of 91.2% and specificity of 94.3% despite significant variation in the appearance of non-flowering pōhutukawa. Pooling time-series imagery did not enhance either approach. The accuracy of XGBoost and deep learning models were, respectively, 83.2% and 95.2%, which were of intermediate precision between the separate models.https://www.mdpi.com/2072-4292/13/9/1789tree speciesclassificationdeep learningconvolutional networksbiosecurityforest pathology |
| spellingShingle | Grant D. Pearse Michael S. Watt Julia Soewarto Alan Y. S. Tan Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response Remote Sensing tree species classification deep learning convolutional networks biosecurity forest pathology |
| title | Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response |
| title_full | Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response |
| title_fullStr | Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response |
| title_full_unstemmed | Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response |
| title_short | Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response |
| title_sort | deep learning and phenology enhance large scale tree species classification in aerial imagery during a biosecurity response |
| topic | tree species classification deep learning convolutional networks biosecurity forest pathology |
| url | https://www.mdpi.com/2072-4292/13/9/1789 |
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