Wetland dynamics in northern Australia’s tropical savanna (1987–2024): a multi-index Google Earth Engine approach for long-term monitoring
Wetlands are essential for biodiversity conservation and ecosystem services, yet they remain under-monitored in many parts of the world, including wet-dry tropical savannas. In Northern Australia, increasing development pressures and climate change raise concerns about the long-term conservation of...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25009793 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849233606008897536 |
|---|---|
| author | Kaline de Mello Edimilson Rodrigues dos Santos Junior Erica A. Garcia Anna E. Richards Peter Scott Waugh Jessica Huxley Simon Linke |
| author_facet | Kaline de Mello Edimilson Rodrigues dos Santos Junior Erica A. Garcia Anna E. Richards Peter Scott Waugh Jessica Huxley Simon Linke |
| author_sort | Kaline de Mello |
| collection | DOAJ |
| description | Wetlands are essential for biodiversity conservation and ecosystem services, yet they remain under-monitored in many parts of the world, including wet-dry tropical savannas. In Northern Australia, increasing development pressures and climate change raise concerns about the long-term conservation of the extensive and relatively intact wetlands. This study developed a wetland mapping framework tailored to the Australian tropical savanna, mapping change to wetlands in the Adelaide River Catchment from 1987 to 2024. A multi-index Landsat-based classification approach was implemented in Google Earth Engine, combining spectral indices, topography, and soil properties. To address the strong seasonality of tropical wetlands, the classification included separate wet/dry season processing. Post-classification refinement using hierarchical rules and auxiliary datasets helped resolve confusion among spectrally similar classes. Wetlands were categorized into twelve classes based on hydrological regime and vegetation structure. Validation showed high accuracy (93 % ± 1 %), with class-level accuracy above 80 % for most wetland types, including water bodies, mangroves, salt flats, swamps, marshes, and floodplains. Wetlands occupied a substantial portion of the catchment, covering approximately 40.5 % of the area in 1987 and 37.4 % in 2024, with marshes and floodplain woodlands dominating due to the flat terrain. Despite land use changes, 80 % of wetlands retained their class between 1987 and 2024. However, floodplain woodlands declined by 16,044 ha, often transitioning to other wetlands, non-wetland, or agricultural land, which increased by 29,000 ha. Internal transitions were common among estuarine and floodplain wetlands, reflecting natural dynamics. Dry periods reduced open water areas, while human-made wetlands increased. This is the first long-term assessment of wetland distribution in the region and provides essential spatial data for water management and conservation. The framework offers a transferable method for monitoring wetlands in other tropical savannas under environmental and development pressures. |
| format | Article |
| id | doaj-art-8340c6bb41de4c95b8e75b1a5c81fe9f |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-8340c6bb41de4c95b8e75b1a5c81fe9f2025-08-20T05:04:53ZengElsevierEcological Indicators1470-160X2025-09-0117811404710.1016/j.ecolind.2025.114047Wetland dynamics in northern Australia’s tropical savanna (1987–2024): a multi-index Google Earth Engine approach for long-term monitoringKaline de Mello0Edimilson Rodrigues dos Santos Junior1Erica A. Garcia2Anna E. Richards3Peter Scott Waugh4Jessica Huxley5Simon Linke6Research Institute for the Environment and Livelihoods, Charles Darwin University, Ellengowan Drive, Casuarina, NT 0810, Australia; CSIRO Environment, 564 Vanderlin Drive, Berrimah, NT 0828, Australia; Corresponding author at: Research Institute for the Environment and Livelihoods, Charles Darwin University, Ellengowan Drive, Casuarina, NT 0810, Australia.São Carlos School of Engineering, University of São Paulo, Av. João Dagnone de Melo, 1100, Jardim - Santa Angelina, São Carlos - SP, 13563-120, BrazilResearch Institute for the Environment and Livelihoods, Charles Darwin University, Ellengowan Drive, Casuarina, NT 0810, AustraliaCSIRO Environment, 564 Vanderlin Drive, Berrimah, NT 0828, AustraliaDepartment of Lands, Planning and Environment – Water Resources Division, Northern Territory Government, 25 Chung Wah Terrace, Palmerston, NT 0830, AustraliaDepartment of Lands, Planning and Environment – Water Resources Division, Northern Territory Government, 25 Chung Wah Terrace, Palmerston, NT 0830, AustraliaCSIRO Environment, Dutton Park, Queensland, AustraliaWetlands are essential for biodiversity conservation and ecosystem services, yet they remain under-monitored in many parts of the world, including wet-dry tropical savannas. In Northern Australia, increasing development pressures and climate change raise concerns about the long-term conservation of the extensive and relatively intact wetlands. This study developed a wetland mapping framework tailored to the Australian tropical savanna, mapping change to wetlands in the Adelaide River Catchment from 1987 to 2024. A multi-index Landsat-based classification approach was implemented in Google Earth Engine, combining spectral indices, topography, and soil properties. To address the strong seasonality of tropical wetlands, the classification included separate wet/dry season processing. Post-classification refinement using hierarchical rules and auxiliary datasets helped resolve confusion among spectrally similar classes. Wetlands were categorized into twelve classes based on hydrological regime and vegetation structure. Validation showed high accuracy (93 % ± 1 %), with class-level accuracy above 80 % for most wetland types, including water bodies, mangroves, salt flats, swamps, marshes, and floodplains. Wetlands occupied a substantial portion of the catchment, covering approximately 40.5 % of the area in 1987 and 37.4 % in 2024, with marshes and floodplain woodlands dominating due to the flat terrain. Despite land use changes, 80 % of wetlands retained their class between 1987 and 2024. However, floodplain woodlands declined by 16,044 ha, often transitioning to other wetlands, non-wetland, or agricultural land, which increased by 29,000 ha. Internal transitions were common among estuarine and floodplain wetlands, reflecting natural dynamics. Dry periods reduced open water areas, while human-made wetlands increased. This is the first long-term assessment of wetland distribution in the region and provides essential spatial data for water management and conservation. The framework offers a transferable method for monitoring wetlands in other tropical savannas under environmental and development pressures.http://www.sciencedirect.com/science/article/pii/S1470160X25009793Random forestLandsatSpectral indicesWetland mappingWet-dry tropicsFloodplain |
| spellingShingle | Kaline de Mello Edimilson Rodrigues dos Santos Junior Erica A. Garcia Anna E. Richards Peter Scott Waugh Jessica Huxley Simon Linke Wetland dynamics in northern Australia’s tropical savanna (1987–2024): a multi-index Google Earth Engine approach for long-term monitoring Ecological Indicators Random forest Landsat Spectral indices Wetland mapping Wet-dry tropics Floodplain |
| title | Wetland dynamics in northern Australia’s tropical savanna (1987–2024): a multi-index Google Earth Engine approach for long-term monitoring |
| title_full | Wetland dynamics in northern Australia’s tropical savanna (1987–2024): a multi-index Google Earth Engine approach for long-term monitoring |
| title_fullStr | Wetland dynamics in northern Australia’s tropical savanna (1987–2024): a multi-index Google Earth Engine approach for long-term monitoring |
| title_full_unstemmed | Wetland dynamics in northern Australia’s tropical savanna (1987–2024): a multi-index Google Earth Engine approach for long-term monitoring |
| title_short | Wetland dynamics in northern Australia’s tropical savanna (1987–2024): a multi-index Google Earth Engine approach for long-term monitoring |
| title_sort | wetland dynamics in northern australia s tropical savanna 1987 2024 a multi index google earth engine approach for long term monitoring |
| topic | Random forest Landsat Spectral indices Wetland mapping Wet-dry tropics Floodplain |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25009793 |
| work_keys_str_mv | AT kalinedemello wetlanddynamicsinnorthernaustraliastropicalsavanna19872024amultiindexgoogleearthengineapproachforlongtermmonitoring AT edimilsonrodriguesdossantosjunior wetlanddynamicsinnorthernaustraliastropicalsavanna19872024amultiindexgoogleearthengineapproachforlongtermmonitoring AT ericaagarcia wetlanddynamicsinnorthernaustraliastropicalsavanna19872024amultiindexgoogleearthengineapproachforlongtermmonitoring AT annaerichards wetlanddynamicsinnorthernaustraliastropicalsavanna19872024amultiindexgoogleearthengineapproachforlongtermmonitoring AT peterscottwaugh wetlanddynamicsinnorthernaustraliastropicalsavanna19872024amultiindexgoogleearthengineapproachforlongtermmonitoring AT jessicahuxley wetlanddynamicsinnorthernaustraliastropicalsavanna19872024amultiindexgoogleearthengineapproachforlongtermmonitoring AT simonlinke wetlanddynamicsinnorthernaustraliastropicalsavanna19872024amultiindexgoogleearthengineapproachforlongtermmonitoring |