Data

Annual woody vegetation and canopy cover grids for Tasmania

Commonwealth Scientific and Industrial Research Organisation
Stewart, Stephen
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/0px1-t065&rft.title=Annual woody vegetation and canopy cover grids for Tasmania&rft.identifier=10.25919/0px1-t065&rft.publisher=Commonwealth Scientific and Industrial Research Organisation (CSIRO)&rft.description=This collection provides annual woody vegetation (> 10 % canopy cover, > 2 m height) and canopy cover (0 – 100%) grids for Tasmania with a spatial resolution of 10 m. This dataset was developed to improve the availability of information suitable for farm-scale analyses of tree cover using publicly available, non-commercial remote sensing data. It enables fine-scale analyses of woody vegetation and canopy cover trends in natural and modified ecosystems across Tasmania between 2017 and 2023.All modelling was performed in Google Earth Engine using the random forest algorithm in classification and regression mode for woody vegetation and canopy cover, respectively. The model was trained on 44,009 points derived from airborne lidar data acquired between 24/Jan/2019 and 20/Apr/2019 across Tasmania. Woody vegetation and canopy cover models used Sentinel 1 Synthetic Aperture Radar (SAR) and Sentinel 2 MultiSpectral Instrument (MSI) imagery and derived features (e.g., vegetation indices, temporal variability, spatial texture) as model covariates. All imagery was acquired during late summer (01/Jan/2019 to 31/Mar/2019) to enhance the separability of trees from crops and grasses. This same period (01/Jan to 31/Mar) is used for modelling all subsequent years. Independent validation on 18,867 points yielded the following results: Woody vegetation: Overall accuracy = 0.94, Kappa = 0.87, Sensitivity = 0.94, Specificity = 0.94. Canopy cover: R² = 0.83, Lin's Concordance Coefficient (CCC) = 0.90, MAE = 0.09, Bias = 0.00, Mean = 0.27. Note that in a small number of cases, valid SAR pixels were not available (e.g., regions of active shadow and layover) and therefore a backup algorithm was used to fill no data gaps where possible. This backup algorithm uses only Sentinel 2 MSI imagery. The specific model used for each pixel is given in the 'source' image folder provided in this data collection (S1S2 = Sentinel 1 SAR and Sentinel 2 MSI; S2 = Sentinel 2 MSI only). The backup algorithm yielded the following results during independent validation: Woody vegetation: Overall accuracy = 0.93, Kappa = 0.86, Sensitivity = 0.93, Specificity = 0.93. Canopy cover: R² = 0.81, Lin's Concordance Coefficient (CCC) = 0.90, MAE = 0.09, Bias = 0.00, Mean = 0.27.&rft.creator=Stewart, Stephen &rft.date=2023&rft.edition=v5&rft.coverage=northlimit=-39.1623; southlimit=-43.7979; westlimit=143.7510; eastLimit=148.5634; projection=WGS84&rft_rights=All Rights (including copyright) CSIRO 2021.&rft_rights=Creative Commons Attribution-Noncommercial https://creativecommons.org/licenses/by-nc/4.0/&rft_subject=Lidar&rft_subject=Sentinel 1&rft_subject=Sentinel 2&rft_subject=Woody vegetation&rft_subject=Canopy cover&rft_subject=Natural capital&rft_subject=Agroforestry&rft_subject=Forestry sciences&rft_subject=AGRICULTURAL, VETERINARY AND FOOD SCIENCES&rft_subject=Geospatial information systems and geospatial data modelling&rft_subject=Geomatic engineering&rft_subject=ENGINEERING&rft_subject=Forest ecosystems&rft_subject=Photogrammetry and remote sensing&rft.type=dataset&rft.language=English Access the data
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https://creativecommons.org/licenses/by-nc/4.0/

All Rights (including copyright) CSIRO 2021.

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

Brief description

This collection provides annual woody vegetation (> 10 % canopy cover, > 2 m height) and canopy cover (0 – 100%) grids for Tasmania with a spatial resolution of 10 m. This dataset was developed to improve the availability of information suitable for farm-scale analyses of tree cover using publicly available, non-commercial remote sensing data. It enables fine-scale analyses of woody vegetation and canopy cover trends in natural and modified ecosystems across Tasmania between 2017 and 2023.

Lineage

All modelling was performed in Google Earth Engine using the random forest algorithm in classification and regression mode for woody vegetation and canopy cover, respectively. The model was trained on 44,009 points derived from airborne lidar data acquired between 24/Jan/2019 and 20/Apr/2019 across Tasmania. Woody vegetation and canopy cover models used Sentinel 1 Synthetic Aperture Radar (SAR) and Sentinel 2 MultiSpectral Instrument (MSI) imagery and derived features (e.g., vegetation indices, temporal variability, spatial texture) as model covariates. All imagery was acquired during late summer (01/Jan/2019 to 31/Mar/2019) to enhance the separability of trees from crops and grasses. This same period (01/Jan to 31/Mar) is used for modelling all subsequent years.

Independent validation on 18,867 points yielded the following results:
Woody vegetation: Overall accuracy = 0.94, Kappa = 0.87, Sensitivity = 0.94, Specificity = 0.94.
Canopy cover: R² = 0.83, Lin's Concordance Coefficient (CCC) = 0.90, MAE = 0.09, Bias = 0.00, Mean = 0.27.

Note that in a small number of cases, valid SAR pixels were not available (e.g., regions of active shadow and layover) and therefore a backup algorithm was used to fill no data gaps where possible. This backup algorithm uses only Sentinel 2 MSI imagery. The specific model used for each pixel is given in the 'source' image folder provided in this data collection (S1S2 = Sentinel 1 SAR and Sentinel 2 MSI; S2 = Sentinel 2 MSI only).

The backup algorithm yielded the following results during independent validation:
Woody vegetation: Overall accuracy = 0.93, Kappa = 0.86, Sensitivity = 0.93, Specificity = 0.93.
Canopy cover: R² = 0.81, Lin's Concordance Coefficient (CCC) = 0.90, MAE = 0.09, Bias = 0.00, Mean = 0.27.

Data time period: 2019-01-01

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148.5634,-39.1623 148.5634,-43.7979 143.751,-43.7979 143.751,-39.1623 148.5634,-39.1623

146.1572,-41.4801

Subjects
Agricultural, Veterinary and Food Sciences | Agroforestry | Canopy cover | Engineering | Forest Ecosystems | Forestry Sciences | Geomatic Engineering | Geospatial Information Systems and Geospatial Data Modelling | Lidar | Natural capital | Photogrammetry and Remote Sensing | Sentinel 1 | Sentinel 2 | Woody vegetation |

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