Moisture Content Vegetation Seasonal Variability Based on a Multiscale Remote Sensing Approach
Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensi...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4434 |
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| author | Filippe L. M. Santos Gonçalo Rodrigues Miguel Potes Flavio T. Couto Maria João Costa Susana Dias Maria José Monteiro Nuno de Almeida Ribeiro Rui Salgado |
| author_facet | Filippe L. M. Santos Gonçalo Rodrigues Miguel Potes Flavio T. Couto Maria João Costa Susana Dias Maria José Monteiro Nuno de Almeida Ribeiro Rui Salgado |
| author_sort | Filippe L. M. Santos |
| collection | DOAJ |
| description | Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the <i>Cistus ladanifer</i> species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R<sup>2</sup> = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R<sup>2</sup> = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques. |
| format | Article |
| id | doaj-art-7d410a679bf5447ab757e901afca4739 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-7d410a679bf5447ab757e901afca47392025-08-20T02:50:36ZengMDPI AGRemote Sensing2072-42922024-11-011623443410.3390/rs16234434Moisture Content Vegetation Seasonal Variability Based on a Multiscale Remote Sensing ApproachFilippe L. M. Santos0Gonçalo Rodrigues1Miguel Potes2Flavio T. Couto3Maria João Costa4Susana Dias5Maria José Monteiro6Nuno de Almeida Ribeiro7Rui Salgado8Instituto de Ciências da Terra (ICT), Instituto de Investigação e Formação Avançada (IIFA), Universidade de Évora, Rua Romão Ramalho, 59, 7000-671 Évora, PortugalInstituto de Ciências da Terra (ICT), Instituto de Investigação e Formação Avançada (IIFA), Universidade de Évora, Rua Romão Ramalho, 59, 7000-671 Évora, PortugalInstituto de Ciências da Terra (ICT), Instituto de Investigação e Formação Avançada (IIFA), Universidade de Évora, Rua Romão Ramalho, 59, 7000-671 Évora, PortugalInstituto de Ciências da Terra (ICT), Instituto de Investigação e Formação Avançada (IIFA), Universidade de Évora, Rua Romão Ramalho, 59, 7000-671 Évora, PortugalInstituto de Ciências da Terra (ICT), Instituto de Investigação e Formação Avançada (IIFA), Universidade de Évora, Rua Romão Ramalho, 59, 7000-671 Évora, PortugalVALORIZA—Research Centre for Endogenous Resource Valorization, Instituto Politécnico de Portalegre, 7300-110 Portalegre, PortugalInstituto Português do Mar e da Atmosfera I.P., Rua C do Aeroporto, 1749-077 Lisboa, PortugalInstituto de Ciências da Terra (ICT), Instituto de Investigação e Formação Avançada (IIFA), Universidade de Évora, Rua Romão Ramalho, 59, 7000-671 Évora, PortugalInstituto de Ciências da Terra (ICT), Instituto de Investigação e Formação Avançada (IIFA), Universidade de Évora, Rua Romão Ramalho, 59, 7000-671 Évora, PortugalWater content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the <i>Cistus ladanifer</i> species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R<sup>2</sup> = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R<sup>2</sup> = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques.https://www.mdpi.com/2072-4292/16/23/4434LFMCremote sensingUAVSentinel-2FieldspecRandom Forest |
| spellingShingle | Filippe L. M. Santos Gonçalo Rodrigues Miguel Potes Flavio T. Couto Maria João Costa Susana Dias Maria José Monteiro Nuno de Almeida Ribeiro Rui Salgado Moisture Content Vegetation Seasonal Variability Based on a Multiscale Remote Sensing Approach Remote Sensing LFMC remote sensing UAV Sentinel-2 Fieldspec Random Forest |
| title | Moisture Content Vegetation Seasonal Variability Based on a Multiscale Remote Sensing Approach |
| title_full | Moisture Content Vegetation Seasonal Variability Based on a Multiscale Remote Sensing Approach |
| title_fullStr | Moisture Content Vegetation Seasonal Variability Based on a Multiscale Remote Sensing Approach |
| title_full_unstemmed | Moisture Content Vegetation Seasonal Variability Based on a Multiscale Remote Sensing Approach |
| title_short | Moisture Content Vegetation Seasonal Variability Based on a Multiscale Remote Sensing Approach |
| title_sort | moisture content vegetation seasonal variability based on a multiscale remote sensing approach |
| topic | LFMC remote sensing UAV Sentinel-2 Fieldspec Random Forest |
| url | https://www.mdpi.com/2072-4292/16/23/4434 |
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