RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery

Chhabra, Aakash; Rüdiger, Christoph; Yebra, Marta; Jagdhuber, Thomas; Hilton, James

doi:10.3390/rs14133132

Cited by 9 publications

(4 citation statements)

References 73 publications

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“…In this study, the plots captured did not show variability in time since fire, and hence this attribute was not used in the models. Satellite-based products have been shown to provide valuable information describing both the effect and recovery of Australian forests from disturbances [78][79][80][81]. The integration of such information to a modelling framework utilising plot-based TLS fuel properties will likely create greater understanding of fuel properties across a landscape as well as help in describing the dynamics of those properties.…”

Section: Discussionmentioning

confidence: 99%

Up-Scaling Fuel Hazard Metrics Derived from Terrestrial Laser Scanning Using a Machine Learning Model

et al. 2023

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The characterisation of fuel distribution across heterogeneous landscapes is important for wildfire mitigation, validating fuel models, and evaluating fuel treatment outcomes. However, efficient fuel mapping at a landscape scale is challenging. Fuel hazard metrics were obtained using Terrestrial Laser Scanning (TLS) and the current operational approach (visual fuel assessment) for seven sites across south-eastern Australia. These point-based metrics were then up-scaled to a continuous fuel map, an area relevant to fire management using random forest modelling, with predictor variables derived from Airborne Laser Scanning (ALS), Sentinel 2A images, and climate and soil data. The model trained and validated with TLS observations (R2 = 0.51 for near-surface fuel cover and 0.31 for elevated fuel cover) was found to have higher predictive power than the model trained with visual fuel assessments (R2 = −0.1 for the cover of both fuel layers). Models for height derived from TLS observations exhibited low-to-moderate performance for the near-surface (R2 = 0.23) and canopy layers (R2 = 0.25). The results from this study provide practical guidance for the selection of training data sources and can be utilised by fire managers to accurately generate fuel maps across an area relevant to operational fire management decisions.

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Section: Discussionmentioning

confidence: 99%

Up-Scaling Fuel Hazard Metrics Derived from Terrestrial Laser Scanning Using a Machine Learning Model

et al. 2023

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“…A total of 11 RVIs have been proposed by researchers so far, namely, quad polarization RVI (QRVI), 53,74,75 radar forest degradation index, 76 two purely vegetation-based RVIs (called RVII and RVIII), 77 dual-polarization SAR vegetation index, 50 polarimetric RVI (PRVI), 78 generalized RVI, 54 compact-pol RVI, 51 dual PRVI, 52 radar vegetation structural perpendicular index, 79 and RVI for Sentinel-1 (RVI4S1). 80 From the above RVIs, three classical representative RVIs were selected, namely, the DRVI, RVI4S1, and PRVI for the Sentinel-1 GRD dual polarization data provided by the GEE platform…”

Section: Rvis For Rice Yield Predictionmentioning

confidence: 99%

Rice yield prediction using radar vegetation indices from Sentinel-1 data and multiscale one-dimensional convolutional long- and short-term memory network model

Sun,

Zhang,

et al. 2024

J. Appl. Rem. Sens.

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Reliable rice yield information is critical for global food security. Optical vegetation indices (OVIs) are important parameters for rice yield estimation using remote sensing. Studies have shown that radar vegetation indices (RVIs) are correlated with OVIs. However, research on the implementation of RVIs in rice yield prediction is still in its early stages. In addition, existing deep learning yield prediction models ignore the contribution of temporal features at each time step to the predicted yield and lack the extraction of higher-level features. To address the above issues, this study proposed a rice yield prediction workflow using RVIs and a multiscale one-dimensional convolutional long-and short-term memory network (MultiscaleConv1d-LSTM, MC-LSTM). Sentinel-1 vertical emission and horizontal reception of polarization vertical emission and vertical reception of polarization data and county-level rice yield statistics covering Guangdong Province, China, from 2017 to 2021 were used. The experimental results show that the performance of the RVIs is comparable to that of the OVIs. The proposed MC-LSTM model can effectively improve the accuracy of rice yield prediction. For early rice yield prediction based on RVIs, the optimal accuracy of MC-LSTM [coefficient of determination R 2 of 0.67, unbiased root mean square error (ubRMSE) of 217.77 kg∕ha] was significantly better than that of the LSTM model (R 2 of 0.61, ubRMSE of 229.52 kg∕ha). For late rice yield prediction based on RVIs, the optimal accuracy of MC-LSTM (R 2 of 0.61 and ubRMSE of 456.54 kg∕ha) was significantly better than that of the LSTM model (R 2 of 0.55 and ubRMSE of 486.76 kg∕ha). The above results show that the proposed method has excellent application prospects in crop yield prediction. This work can provide a new feasible scheme for syntheticaperture radar data to serve agricultural monitoring and improve the efficiency of rice yield monitoring in a large area.

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“…Further than passive remote sensing active synthetic aperture radar (SAR) has been used for post-fire vegetation recovery assessments [30,[251][252][253][254]. Laurin et al [30] used Cosmo-SkyMed data in a Mediterranean protected area covered by maquis to detect the burnt area extension and to conduct a mid-term assessment of vegetation regrowth.…”

Section: Post-fire Vegetation Recovery Monitoringmentioning

confidence: 99%

Remote Sensing and GIS Applications in Wildfires

Zagalikis

2023

Geographic Information Systems - Data Science Approach

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Wildfires are closely associated with human activities and global climate change, but they also affect human health, safety, and the eco-environment. The ability of understanding wildfire dynamics is important for managing the effects of wildfires on infrastructures and natural environments. Geospatial technologies (remote sensing and GIS) provide a means to study wildfires at multiple temporal and spatial scales using an efficient and quantitative method. This chapter presents an overview of the applications of geospatial technologies in wildfire management. Applications related to pre-fire conditions management (fire hazard mapping, fire risk mapping, fuel mapping), monitoring fire conditions (fire detection, detection of hot-spots, fire thermal parameters, etc.) and post-fire condition management (burnt area mapping, burn severity, soil erosion assessments, post-fire vegetation recovery assessments and monitoring) are discussed. Emphasis is given to the roles of multispectral sensors, lidar and evolving UAV/drone technologies in mapping, processing, combining and monitoring various environmental characteristics related to wildfires. Current and previous researches are presented, and future research trends are discussed. It is wildly accepted that geospatial technologies provide a low-cost, multi-temporal means for conducting local, regional and global-scale wildfire research, and assessments.

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RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery

Cited by 9 publications

References 73 publications

Up-Scaling Fuel Hazard Metrics Derived from Terrestrial Laser Scanning Using a Machine Learning Model

Up-Scaling Fuel Hazard Metrics Derived from Terrestrial Laser Scanning Using a Machine Learning Model

Rice yield prediction using radar vegetation indices from Sentinel-1 data and multiscale one-dimensional convolutional long- and short-term memory network model

Remote Sensing and GIS Applications in Wildfires

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