Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
From Clusters to Communities: Enhancing Wetland Vegetation Mapping Using Unsupervised and Supervised Synergy
by
Ling, Joanne E.
, Powell, Megan
, Wen, Li
, Ryan, Shawn
in
Aquatic ecosystems
/ Biodiversity
/ Classification
/ Conservation
/ Correlation coefficient
/ Correlation coefficients
/ Ecosystems
/ Floodplains
/ Floods
/ Functional groups
/ Habitats
/ Hydrology
/ inland floodplains
/ K-means clustering
/ Lidar
/ Machine learning
/ Mapping
/ Monitoring
/ Morphology
/ Optical radar
/ Plant communities
/ Protection and preservation
/ random forest
/ Remote sensing
/ Sentinel-1 and Sentinel-2
/ Surveys
/ Vegetation
/ Vegetation mapping
/ Vegetation surveys
/ Water conservation
/ Water management
/ wetland vegetation mapping
/ Wetlands
2025
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
From Clusters to Communities: Enhancing Wetland Vegetation Mapping Using Unsupervised and Supervised Synergy
by
Ling, Joanne E.
, Powell, Megan
, Wen, Li
, Ryan, Shawn
in
Aquatic ecosystems
/ Biodiversity
/ Classification
/ Conservation
/ Correlation coefficient
/ Correlation coefficients
/ Ecosystems
/ Floodplains
/ Floods
/ Functional groups
/ Habitats
/ Hydrology
/ inland floodplains
/ K-means clustering
/ Lidar
/ Machine learning
/ Mapping
/ Monitoring
/ Morphology
/ Optical radar
/ Plant communities
/ Protection and preservation
/ random forest
/ Remote sensing
/ Sentinel-1 and Sentinel-2
/ Surveys
/ Vegetation
/ Vegetation mapping
/ Vegetation surveys
/ Water conservation
/ Water management
/ wetland vegetation mapping
/ Wetlands
2025
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
From Clusters to Communities: Enhancing Wetland Vegetation Mapping Using Unsupervised and Supervised Synergy
by
Ling, Joanne E.
, Powell, Megan
, Wen, Li
, Ryan, Shawn
in
Aquatic ecosystems
/ Biodiversity
/ Classification
/ Conservation
/ Correlation coefficient
/ Correlation coefficients
/ Ecosystems
/ Floodplains
/ Floods
/ Functional groups
/ Habitats
/ Hydrology
/ inland floodplains
/ K-means clustering
/ Lidar
/ Machine learning
/ Mapping
/ Monitoring
/ Morphology
/ Optical radar
/ Plant communities
/ Protection and preservation
/ random forest
/ Remote sensing
/ Sentinel-1 and Sentinel-2
/ Surveys
/ Vegetation
/ Vegetation mapping
/ Vegetation surveys
/ Water conservation
/ Water management
/ wetland vegetation mapping
/ Wetlands
2025
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
From Clusters to Communities: Enhancing Wetland Vegetation Mapping Using Unsupervised and Supervised Synergy
Journal Article
From Clusters to Communities: Enhancing Wetland Vegetation Mapping Using Unsupervised and Supervised Synergy
2025
Request Book From Autostore
and Choose the Collection Method
Overview
High thematic resolution vegetation mapping is essential for monitoring wetland ecosystems, supporting conservation, and guiding water management. However, producing accurate, fine-scale vegetation maps in large, heterogeneous floodplain wetlands remains challenging due to complex hydrology, spectral similarity among vegetation types, and the high cost of extensive field surveys. This study addresses these challenges by developing a scalable vegetation classification framework that integrates cluster-guided sample selection, Random Forest modelling, and multi-source remote-sensing data. The approach combines multi-temporal Sentinel-1 SAR, Sentinel-2 optical imagery, and hydro-morphological predictors derived from LiDAR and hydrologically enforced SRTM DEMs. Applied to the Great Cumbung Swamp, a structurally and hydrologically complex terminal wetland in the lower Lachlan River floodplain of Australia, the framework produced vegetation maps at three hierarchical levels: formations (9 classes), functional groups (14 classes), and plant community types (PCTs; 23 classes). The PCT-level classification achieved an overall accuracy of 93.2%, a kappa coefficient of 0.91, and a Matthews correlation coefficient (MCC) of 0.89, with broader classification levels exceeding 95% accuracy. These results demonstrate that, through targeted sample selection and integration of spectral, structural, and terrain-derived data, high-accuracy, high-resolution wetland vegetation mapping is achievable with reduced field data requirements. The hierarchical structure further enables broader vegetation categories to be efficiently derived from detailed PCT outputs, providing a practical, transferable tool for wetland monitoring, habitat assessment, and conservation planning.
This website uses cookies to ensure you get the best experience on our website.