Data

Fractional tree canopy cover for Murray-Darling Basin wetlands

Commonwealth Scientific and Industrial Research Organisation
Gao, Steve ; Doody, Tanya
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/9w38-8268&rft.title=Fractional tree canopy cover for Murray-Darling Basin wetlands&rft.identifier=https://doi.org/10.25919/9w38-8268&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=This dataset contains yearly fractional tree canopy cover (FTCC, %) data at 20 meter spatial resolution from 2016 to 2022 for Murray-Darling Basin wetlands. A machine learning model was created to predict FTCC using optical and radar satellite. This model was subjected to validation using tree canopy cover data extracted from LiDAR data. The model achieved a remarkable 85% explanatory capacity regarding FTCC variations, with an associated error rate of 8%.\n\nThe fine-scale FTCC will be of value to catchment management concerns such as altered catchment water yields related to bushfires.\nLineage: A novel method was developed to derive tree fractional canopy cover (FTCC) at 20 m resolution in semi-arid and arid floodplain areas. The method employs LiDAR as a canopy area field measurement proxy (10 m resolution). Sentinel-1 and Sentinel-2 (radar and multispectral imagery) were used in Random Forest analysis, undertaken to develop a predictive FTCC model trained using LiDAR for two regions in the Murray–Darling Basin. \n\nDetailed method refers to Gao, S. (2020). Fine scale mapping of fractional tree canopy cover to support river basin management. Hydrological Processes (https://doi.org/10.1002/hyp.14156).&rft.creator=Gao, Steve &rft.creator=Doody, Tanya &rft.date=2023&rft.edition=v1&rft.relation=https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.14156&rft.coverage=westlimit=138.78; southlimit=-37.22; eastlimit=150.85642166666665; northlimit=-25.27120972222222; projection=WGS84&rft_rights=Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence https://creativecommons.org/licenses/by-nc-sa/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2023.&rft_subject=Murray-Darling Basin&rft_subject=Remote sensing&rft_subject=Sentinel-2&rft_subject=Machine learning&rft_subject=Photogrammetry and remote sensing&rft_subject=Geomatic engineering&rft_subject=ENGINEERING&rft_subject=Forestry management and environment&rft_subject=Forestry sciences&rft_subject=AGRICULTURAL, VETERINARY AND FOOD SCIENCES&rft_subject=Forest ecosystems&rft.type=dataset&rft.language=English Access the data
Cite Saved to MyRDA Save to MyRDA

Licence & Rights:

Non-Commercial Licence view details
CC-BY-NC-SA

Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence
https://creativecommons.org/licenses/by-nc-sa/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2023.

Access:

Open view details

Accessible for free

Contact Information



Brief description

This dataset contains yearly fractional tree canopy cover (FTCC, %) data at 20 meter spatial resolution from 2016 to 2022 for Murray-Darling Basin wetlands. A machine learning model was created to predict FTCC using optical and radar satellite. This model was subjected to validation using tree canopy cover data extracted from LiDAR data. The model achieved a remarkable 85% explanatory capacity regarding FTCC variations, with an associated error rate of 8%.

The fine-scale FTCC will be of value to catchment management concerns such as altered catchment water yields related to bushfires.
Lineage: A novel method was developed to derive tree fractional canopy cover (FTCC) at 20 m resolution in semi-arid and arid floodplain areas. The method employs LiDAR as a canopy area field measurement proxy (10 m resolution). Sentinel-1 and Sentinel-2 (radar and multispectral imagery) were used in Random Forest analysis, undertaken to develop a predictive FTCC model trained using LiDAR for two regions in the Murray–Darling Basin.

Detailed method refers to Gao, S. (2020). Fine scale mapping of fractional tree canopy cover to support river basin management. Hydrological Processes (https://doi.org/10.1002/hyp.14156).

Available: 2023-09-14

Data time period: 2016-01-01 to 2022-12-31

This dataset is part of a larger collection

Click to explore relationships graph
Help

Related Publications

  • Associated with Fine scale mapping of fractional tree canopy cover to support river basin management
    doi : 10.1002/HYP.14156

    Management of water, regionally, nationally and globally will continue to be a priority and complex undertaking. In riverine systems, biotic components like flora and fauna play critical roles in filtering water so it is available for human use and consumption. Preservation of ecosystems and associated ecosystem functions is therefore vital. In highly regulated large river basins, natural ecosystems are often supported through provision of environmental flows. Flow delivery, however, should be underpinned by rigorous monitoring to identify and prioritise biotic water requirements. Currently, large-scale monitoring solutions are scaled from remote sensing data via measurement of field evapotranspiration for woody tree vegetation species. However, as there is generally a mismatch between field data collection area and remote sensing pixel size, new methods are required to proportion tree evapotranspiration based on tree fractional canopy area per pixel. We present a novel method to derive tree fractional canopy cover (FTCC) at 20 m resolution in semi-arid and arid floodplain areas. The method employs LiDAR as a canopy area field measurement proxy (10 m resolution). We used Sentinel-1 and Sentinel-2 (radar and multispectral imagery) in a Random Forest analysis, undertaken to develop a predictive FTCC model trained using LiDAR for two regions in the Murray–Darling Basin. A predictor model combining the results of both regions was able to explain between 71%–85% of FTCC variation when compared to LiDAR FTCC when output in 10% increments. Development of this method underpins the advancement of woody vegetation monitoring to inform environmental flow management in the Murray–Darling Basin. The method and fine scale outputs will also be of value to other catchment management concerns such as altered catchment water yields related to bushfires and as such has application to water management worldwide.

150.8564,-25.2712 150.8564,-37.22 138.78,-37.22 138.78,-25.2712 150.8564,-25.2712

144.8182,-31.2456

Subjects
Agricultural, Veterinary and Food Sciences | Engineering | Forest Ecosystems | Forestry Management and Environment | Forestry Sciences | Geomatic Engineering | Machine learning | Murray-Darling Basin | Photogrammetry and Remote Sensing | Remote sensing | Sentinel-2 |

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Identifiers
Similar data you may be interested in: