Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia
Wetlands are one of the most biologically productive ecosystems. Wetland ecosystem services, ranging from provision of food security to climate change mitigation, are enormous, far outweighing those of dryland ecosystems per hectare. However, land use change and water regulation infrastructure have...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/15/3032 |
| _version_ | 1797525177508036608 |
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| author | Debbie A. Chamberlain Stuart R. Phinn Hugh P. Possingham |
| author_facet | Debbie A. Chamberlain Stuart R. Phinn Hugh P. Possingham |
| author_sort | Debbie A. Chamberlain |
| collection | DOAJ |
| description | Wetlands are one of the most biologically productive ecosystems. Wetland ecosystem services, ranging from provision of food security to climate change mitigation, are enormous, far outweighing those of dryland ecosystems per hectare. However, land use change and water regulation infrastructure have reduced connectivity in many river systems and with floodplain and estuarine wetlands. Mangrove forests are critical communities for carbon uptake and storage, pollution control and detoxification, and regulation of natural hazards. Although the clearing of mangroves in Australia is strictly regulated, Great Barrier Reef catchments have suffered landscape modifications and hydrological alterations that can kill mangroves. We used remote sensing datasets to investigate land cover change and both intra- and inter-annual seasonality in mangrove forests in a large estuarine region of Central Queensland, Australia, which encompasses a national park and Ramsar Wetland, and is adjacent to the Great Barrier Reef World Heritage site. We built a time series using spectral, auxiliary, and phenology variables with Landsat surface reflectance products, accessed in Google Earth Engine. Two land cover classes were generated (mangrove versus non-mangrove) in a Random Forest classification. Mangroves decreased by 1480 hectares (−2.31%) from 2009 to 2019. The overall classification accuracies and Kappa coefficient for 2008–2010 and 2018–2020 land cover maps were 95% and 95%, respectively. Using an NDVI-based time series we examined intra- and inter-annual seasonality with linear and harmonic regression models, and second with TIMESAT metrics of mangrove forests in three sections of our study region. Our findings suggest a relationship between mangrove growth phenology along with precipitation anomalies and severe tropical cyclone occurrence over the time series. The detection of responses to extreme events is important to improve understanding of the connections between climate, extreme weather events, and biodiversity in estuarine and mangrove ecosystems. |
| first_indexed | 2024-03-10T09:09:05Z |
| format | Article |
| id | doaj.art-54bc6107ff8a4ab2a23d933a71aeb4ee |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:09:05Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-54bc6107ff8a4ab2a23d933a71aeb4ee2023-11-22T06:07:51ZengMDPI AGRemote Sensing2072-42922021-08-011315303210.3390/rs13153032Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, AustraliaDebbie A. Chamberlain0Stuart R. Phinn1Hugh P. Possingham2Centre for Biodiversity and Conservation Science, School of Biological Sciences, The University of Queensland, St. Lucia, QLD 4072, AustraliaRemote Sensing Research Centre, School of Earth and Environmental Sciences, The University of Queensland, St. Lucia, QLD 4072, AustraliaCentre for Biodiversity and Conservation Science, School of Biological Sciences, The University of Queensland, St. Lucia, QLD 4072, AustraliaWetlands are one of the most biologically productive ecosystems. Wetland ecosystem services, ranging from provision of food security to climate change mitigation, are enormous, far outweighing those of dryland ecosystems per hectare. However, land use change and water regulation infrastructure have reduced connectivity in many river systems and with floodplain and estuarine wetlands. Mangrove forests are critical communities for carbon uptake and storage, pollution control and detoxification, and regulation of natural hazards. Although the clearing of mangroves in Australia is strictly regulated, Great Barrier Reef catchments have suffered landscape modifications and hydrological alterations that can kill mangroves. We used remote sensing datasets to investigate land cover change and both intra- and inter-annual seasonality in mangrove forests in a large estuarine region of Central Queensland, Australia, which encompasses a national park and Ramsar Wetland, and is adjacent to the Great Barrier Reef World Heritage site. We built a time series using spectral, auxiliary, and phenology variables with Landsat surface reflectance products, accessed in Google Earth Engine. Two land cover classes were generated (mangrove versus non-mangrove) in a Random Forest classification. Mangroves decreased by 1480 hectares (−2.31%) from 2009 to 2019. The overall classification accuracies and Kappa coefficient for 2008–2010 and 2018–2020 land cover maps were 95% and 95%, respectively. Using an NDVI-based time series we examined intra- and inter-annual seasonality with linear and harmonic regression models, and second with TIMESAT metrics of mangrove forests in three sections of our study region. Our findings suggest a relationship between mangrove growth phenology along with precipitation anomalies and severe tropical cyclone occurrence over the time series. The detection of responses to extreme events is important to improve understanding of the connections between climate, extreme weather events, and biodiversity in estuarine and mangrove ecosystems.https://www.mdpi.com/2072-4292/13/15/3032Landsatmangrove foreststime seriesGoogle Earth Enginerandom forestsphenology |
| spellingShingle | Debbie A. Chamberlain Stuart R. Phinn Hugh P. Possingham Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia Remote Sensing Landsat mangrove forests time series Google Earth Engine random forests phenology |
| title | Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia |
| title_full | Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia |
| title_fullStr | Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia |
| title_full_unstemmed | Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia |
| title_short | Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia |
| title_sort | mangrove forest cover and phenology with landsat dense time series in central queensland australia |
| topic | Landsat mangrove forests time series Google Earth Engine random forests phenology |
| url | https://www.mdpi.com/2072-4292/13/15/3032 |
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