Automatic detection of vegetation cover changes in urban-rural interface areas
| Main Author: | |
|---|---|
| Publication Date: | 2022 |
| Other Authors: | , , |
| Format: | Article |
| Language: | eng |
| Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Download full: | http://hdl.handle.net/10451/52022 |
Summary: | The present work started from the need to streamline the process of monitoring changes in vegetation in the in urban-rural interface fuel management bands, defined by Portuguese legislation as areas where the existing biomass must be totally or partially removed. The model developed uses a time series of Sentinel 2 satellite images to search for changes in the vegetation cover in a 100 m buffer around built-up areas. The use of satellite data allows analysing large areas and speeds up the task of identifying the places where fuel management took place and the places where there is a need to carry out such management. The objective of the proposed method is to give a script in Python language that can verify the cleanliness of vegetation in the fuel management ranges through multi-temporal analysis of satellite images. • The paper presents a step-by-step procedure for a Sentinel 2 time series vegetation index analysis. • Automated routine to detection of spatiotemporal vegetation changes based on statistical parameters. • Used Python language to do geoprocessing analysis. |
| id |
RCAP_dd9dd5a1af882c0e2fd291a05ca0afb1 |
|---|---|
| oai_identifier_str |
oai:repositorio.ulisboa.pt:10451/52022 |
| network_acronym_str |
RCAP |
| network_name_str |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| repository_id_str |
https://opendoar.ac.uk/repository/7160 |
| spelling |
Automatic detection of vegetation cover changes in urban-rural interface areasSentinel 2Time SeriesVegetation indexVegetation changePythonThe present work started from the need to streamline the process of monitoring changes in vegetation in the in urban-rural interface fuel management bands, defined by Portuguese legislation as areas where the existing biomass must be totally or partially removed. The model developed uses a time series of Sentinel 2 satellite images to search for changes in the vegetation cover in a 100 m buffer around built-up areas. The use of satellite data allows analysing large areas and speeds up the task of identifying the places where fuel management took place and the places where there is a need to carry out such management. The objective of the proposed method is to give a script in Python language that can verify the cleanliness of vegetation in the fuel management ranges through multi-temporal analysis of satellite images. • The paper presents a step-by-step procedure for a Sentinel 2 time series vegetation index analysis. • Automated routine to detection of spatiotemporal vegetation changes based on statistical parameters. • Used Python language to do geoprocessing analysis.ElsevierRepositório da Universidade de LisboaBarbosa, BrunoRocha, JorgeCosta, HugoCaetano, Mário2022-03-28T15:33:33Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/52022engBarbosa, B., Rocha, J., Costa, H., & Caetano, M. (2022). Automatic detection of vegetation cover changes in urban-rural interface areas. MethodsX, 9, 101643. https://doi.org/10.1016/j.mex.2022.1016432215-016110.1016/j.mex.2022.101643info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-17T14:43:02Zoai:repositorio.ulisboa.pt:10451/52022Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T03:22:56.637295Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
| dc.title.none.fl_str_mv |
Automatic detection of vegetation cover changes in urban-rural interface areas |
| title |
Automatic detection of vegetation cover changes in urban-rural interface areas |
| spellingShingle |
Automatic detection of vegetation cover changes in urban-rural interface areas Barbosa, Bruno Sentinel 2 Time Series Vegetation index Vegetation change Python |
| title_short |
Automatic detection of vegetation cover changes in urban-rural interface areas |
| title_full |
Automatic detection of vegetation cover changes in urban-rural interface areas |
| title_fullStr |
Automatic detection of vegetation cover changes in urban-rural interface areas |
| title_full_unstemmed |
Automatic detection of vegetation cover changes in urban-rural interface areas |
| title_sort |
Automatic detection of vegetation cover changes in urban-rural interface areas |
| author |
Barbosa, Bruno |
| author_facet |
Barbosa, Bruno Rocha, Jorge Costa, Hugo Caetano, Mário |
| author_role |
author |
| author2 |
Rocha, Jorge Costa, Hugo Caetano, Mário |
| author2_role |
author author author |
| dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
| dc.contributor.author.fl_str_mv |
Barbosa, Bruno Rocha, Jorge Costa, Hugo Caetano, Mário |
| dc.subject.por.fl_str_mv |
Sentinel 2 Time Series Vegetation index Vegetation change Python |
| topic |
Sentinel 2 Time Series Vegetation index Vegetation change Python |
| description |
The present work started from the need to streamline the process of monitoring changes in vegetation in the in urban-rural interface fuel management bands, defined by Portuguese legislation as areas where the existing biomass must be totally or partially removed. The model developed uses a time series of Sentinel 2 satellite images to search for changes in the vegetation cover in a 100 m buffer around built-up areas. The use of satellite data allows analysing large areas and speeds up the task of identifying the places where fuel management took place and the places where there is a need to carry out such management. The objective of the proposed method is to give a script in Python language that can verify the cleanliness of vegetation in the fuel management ranges through multi-temporal analysis of satellite images. • The paper presents a step-by-step procedure for a Sentinel 2 time series vegetation index analysis. • Automated routine to detection of spatiotemporal vegetation changes based on statistical parameters. • Used Python language to do geoprocessing analysis. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-03-28T15:33:33Z 2022 2022-01-01T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/52022 |
| url |
http://hdl.handle.net/10451/52022 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
Barbosa, B., Rocha, J., Costa, H., & Caetano, M. (2022). Automatic detection of vegetation cover changes in urban-rural interface areas. MethodsX, 9, 101643. https://doi.org/10.1016/j.mex.2022.101643 2215-0161 10.1016/j.mex.2022.101643 |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Elsevier |
| publisher.none.fl_str_mv |
Elsevier |
| dc.source.none.fl_str_mv |
reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia instacron:RCAAP |
| instname_str |
FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
| instacron_str |
RCAAP |
| institution |
RCAAP |
| reponame_str |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| collection |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| repository.name.fl_str_mv |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
| repository.mail.fl_str_mv |
info@rcaap.pt |
| _version_ |
1833601681030381568 |