ESTIMATION OF CORK PRODUCTION USINGAERIAL IMAGERY1
ABSTRACT Inventory and prediction of cork harvest over time and space is important to forest managers who must plan and organize harvest logistics (transport, storage, etc.). Common field inventory methods including the stem density, diameter and height structure are costly and generally point (plot...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sociedade de Investigações Florestais
2015-10-01
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| Series: | Revista Árvore |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622015000500853&lng=en&tlng=en |
| _version_ | 1819297243446902784 |
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| author | Peter Surovy Nuno de Almeida Ribeiro João Santos Pereira Atsushi Yoshimoto |
| author_facet | Peter Surovy Nuno de Almeida Ribeiro João Santos Pereira Atsushi Yoshimoto |
| author_sort | Peter Surovy |
| collection | DOAJ |
| description | ABSTRACT Inventory and prediction of cork harvest over time and space is important to forest managers who must plan and organize harvest logistics (transport, storage, etc.). Common field inventory methods including the stem density, diameter and height structure are costly and generally point (plot) based. Furthermore, the irregular horizontal structure of cork oak stands makes it difficult, if not impossible, to interpolate between points. We propose a new method to estimate cork production using digital multispectral aerial imagery. We study the spectral response of individual trees in visible and near infrared spectra and then correlate that response with cork production prior to harvest. We use ground measurements of individual trees production to evaluate the model’s predictive capacity. We propose 14 candidate variables to predict cork production based on crown size in combination with different NDVI index derivates. We use Akaike Information Criteria to choose the best among them. The best model is composed of combinations of different NDVI derivates that include red, green, and blue channels. The proposed model is 15% more accurate than a model that includes only a crown projection without any spectral information. |
| first_indexed | 2024-12-24T05:10:54Z |
| format | Article |
| id | doaj.art-29943b158ddb4ed29023a77dc6b5acfc |
| institution | Directory Open Access Journal |
| issn | 1806-9088 |
| language | English |
| last_indexed | 2024-12-24T05:10:54Z |
| publishDate | 2015-10-01 |
| publisher | Sociedade de Investigações Florestais |
| record_format | Article |
| series | Revista Árvore |
| spelling | doaj.art-29943b158ddb4ed29023a77dc6b5acfc2022-12-21T17:13:41ZengSociedade de Investigações FlorestaisRevista Árvore1806-90882015-10-0139585386110.1590/0100-67622015000500008S0100-67622015000500853ESTIMATION OF CORK PRODUCTION USINGAERIAL IMAGERY1Peter SurovyNuno de Almeida RibeiroJoão Santos PereiraAtsushi YoshimotoABSTRACT Inventory and prediction of cork harvest over time and space is important to forest managers who must plan and organize harvest logistics (transport, storage, etc.). Common field inventory methods including the stem density, diameter and height structure are costly and generally point (plot) based. Furthermore, the irregular horizontal structure of cork oak stands makes it difficult, if not impossible, to interpolate between points. We propose a new method to estimate cork production using digital multispectral aerial imagery. We study the spectral response of individual trees in visible and near infrared spectra and then correlate that response with cork production prior to harvest. We use ground measurements of individual trees production to evaluate the model’s predictive capacity. We propose 14 candidate variables to predict cork production based on crown size in combination with different NDVI index derivates. We use Akaike Information Criteria to choose the best among them. The best model is composed of combinations of different NDVI derivates that include red, green, and blue channels. The proposed model is 15% more accurate than a model that includes only a crown projection without any spectral information.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622015000500853&lng=en&tlng=enNDVISensoriamento remotoCritério de Informação de Akaike |
| spellingShingle | Peter Surovy Nuno de Almeida Ribeiro João Santos Pereira Atsushi Yoshimoto ESTIMATION OF CORK PRODUCTION USINGAERIAL IMAGERY1 Revista Árvore NDVI Sensoriamento remoto Critério de Informação de Akaike |
| title | ESTIMATION OF CORK PRODUCTION USINGAERIAL IMAGERY1 |
| title_full | ESTIMATION OF CORK PRODUCTION USINGAERIAL IMAGERY1 |
| title_fullStr | ESTIMATION OF CORK PRODUCTION USINGAERIAL IMAGERY1 |
| title_full_unstemmed | ESTIMATION OF CORK PRODUCTION USINGAERIAL IMAGERY1 |
| title_short | ESTIMATION OF CORK PRODUCTION USINGAERIAL IMAGERY1 |
| title_sort | estimation of cork production usingaerial imagery1 |
| topic | NDVI Sensoriamento remoto Critério de Informação de Akaike |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622015000500853&lng=en&tlng=en |
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