Mapping Chestnut Stands Using Bi-Temporal VHR Data
This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the pu...
| Main Authors: | , , , , |
|---|---|
| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
MDPI
2020
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/7480 |
| _version_ | 1789406688668811264 |
|---|---|
| author | Marchetti, Francesca Waske, Björn Arbelo, Manuel Moreno Ruiz, José Andrés Alonso Benito, Alfonso |
| author_facet | Marchetti, Francesca Waske, Björn Arbelo, Manuel Moreno Ruiz, José Andrés Alonso Benito, Alfonso |
| author_sort | Marchetti, Francesca |
| collection | DSpace |
| description | This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7480 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-74802023-04-12T19:26:21Z Mapping Chestnut Stands Using Bi-Temporal VHR Data Marchetti, Francesca Waske, Björn Arbelo, Manuel Moreno Ruiz, José Andrés Alonso Benito, Alfonso WorldView bi-temporal image extended morphological profiles random forest Canary Islands This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife. 2020-01-17T07:32:11Z 2020-01-17T07:32:11Z 2019-10-31 info:eu-repo/semantics/article 2072-4292 http://hdl.handle.net/10835/7480 en https://www.mdpi.com/2072-4292/11/21/2560 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | WorldView bi-temporal image extended morphological profiles random forest Canary Islands Marchetti, Francesca Waske, Björn Arbelo, Manuel Moreno Ruiz, José Andrés Alonso Benito, Alfonso Mapping Chestnut Stands Using Bi-Temporal VHR Data |
| title | Mapping Chestnut Stands Using Bi-Temporal VHR Data |
| title_full | Mapping Chestnut Stands Using Bi-Temporal VHR Data |
| title_fullStr | Mapping Chestnut Stands Using Bi-Temporal VHR Data |
| title_full_unstemmed | Mapping Chestnut Stands Using Bi-Temporal VHR Data |
| title_short | Mapping Chestnut Stands Using Bi-Temporal VHR Data |
| title_sort | mapping chestnut stands using bi-temporal vhr data |
| topic | WorldView bi-temporal image extended morphological profiles random forest Canary Islands |
| url | http://hdl.handle.net/10835/7480 |
| work_keys_str_mv | AT marchettifrancesca mappingchestnutstandsusingbitemporalvhrdata AT waskebjorn mappingchestnutstandsusingbitemporalvhrdata AT arbelomanuel mappingchestnutstandsusingbitemporalvhrdata AT morenoruizjoseandres mappingchestnutstandsusingbitemporalvhrdata AT alonsobenitoalfonso mappingchestnutstandsusingbitemporalvhrdata |