Seasonally-decomposed Sentinel-1 backscatter time-series are useful indicators of peatland wildfire vulnerability

Millard, Koreen; Darling, S.; Pelletier, Nicolas; Schultz, S.

doi:10.1016/j.rse.2022.113329

Cited by 8 publications

(6 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, implementing this method necessitates substantial computing resources for generating spatial estimates. Nevertheless, the study highlights the potential of using seasonally decomposed Sentinel-1 backscatter time series to indicate peatland wildfire vulnerability, contributing to peatland fire occurrence [79].…”

Section: Monitoring Restoration Efforts and Successmentioning

confidence: 96%

“…It is crucial to highlight that SAR cannot currently be directly utilised for comparing soil moisture across different sites. This limitation stems from the inconsistent relationship between SAR backscatter and soil moisture, which is influenced by factors including vegetation structure and surface topography [76,77] [79]. They emphasise the potential of SAR backscatter data as a nearly real-time tool for predicting fire vulnerability over extensive geographic areas.…”

Section: Monitoring Restoration Efforts and Successmentioning

confidence: 99%

“…They emphasise the potential of SAR backscatter data as a nearly real-time tool for predicting fire vulnerability over extensive geographic areas. However, they also acknowledge certain limitations [79]. The method's effectiveness relies on the continuous acquisition of SAR data, making it susceptible to disruptions resulting in an incomplete time series dataset.…”

Section: Monitoring Restoration Efforts and Successmentioning

confidence: 99%

See 2 more Smart Citations

Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects

Abdelmajeed,

Juszczak

2024

Remote Sensing

View full text Add to dashboard Cite

This systematic literature review (SLR) provides a comprehensive overview of remote sensing (RS) applications in northern peatlands from 2017 to 2022, utilising various platforms, including in situ, UAV, airborne, and satellite technologies. It addresses the challenges and limitations presented by the sophisticated nature of northern peatland ecosystems. This SLR reveals an in-creased focus on mapping, monitoring, and hydrology but identifies noticeable gaps in peatland degradation research. Despite the benefits of remote sensing, such as extensive spatial coverage and consistent monitoring, challenges persist, including high costs, underexplored areas, and limitations in hyperspectral data application. Fusing remote sensing data with on-site research offers new insights for regional peatland studies. However, challenges arise from issues like the cost of high-resolution data, coverage limitations, and inadequate field validation data in remote areas. This review suggests refining methodologies, validating with high-resolution data, and addressing these limitations for future research.

show abstract

Section: Monitoring Restoration Efforts and Successmentioning

confidence: 96%

Section: Monitoring Restoration Efforts and Successmentioning

confidence: 99%

Section: Monitoring Restoration Efforts and Successmentioning

confidence: 99%

See 1 more Smart Citation

Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects

Abdelmajeed,

Juszczak

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…However, for the prediction of peatland fires in tropical areas, soil parameters need to be considered, namely, groundwater level [28], soil temperature [29], soil humidity [30], etc. In addition, according to the actual theoretical situation, atmospheric pressure and solar radiation should also be considered factors in predicting peatland fires [31].…”

Section: Neural Network For Predicting Fwimentioning

confidence: 99%

Prediction of Peatlands Forest Fires in Malaysia Using Machine Learning

Sali

Noordin

et al. 2023

Forests

View full text Add to dashboard Cite

The occurrence of fires in tropical peatlands poses significant threats to their ecosystems. An Internet of Things (IoT) system was developed to measure and collect fire risk factors in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, to address this issue. In this paper, neural networks with different layers were employed to predict peatland forests’ Fire Weather Index (FWI). The neural network models used two sets of input parameters, consisting of four and nine fire factors. The predicted FWI values were compared with actual values obtained from the Malaysian meteorological department. The findings revealed that the five-layer neural network outperformed others in both the four-input and nine-input models. Specifically, the nine-input neural network achieved a mean square error (MSE) of 1.116 and a correlation of 0.890, surpassing the performance of the four-input neural network with the MSE of 1.537 and the correlation of 0.852. These results hold significant research and practical implications for precise peatland fire prevention, control, and the formulation of preventive measures.

show abstract

“…Climate models that make use of surface water data could improve inputs for simulating water in the biogeochemical pathways involved in climate, such as evaporation. Monitoring peatlands via high-resolution surface water maps could provide an indicator of peat drying [33], which may help to quantify both carbon dioxide exchange and methane release. Water resource management and emergency management could be improved by spatially and temporally specific surface water data, enhancing current capabilities of predicting water supply and flood risk.…”

Section: Surface Water Mapping With Remote Sensingmentioning

confidence: 99%

Investigating the Use of Sentinel-1 for Improved Mapping of Small Peatland Water Bodies: Towards Wildfire Susceptibility Monitoring in Canada’s Boreal Forest

et al. 2023

View full text Add to dashboard Cite

Peatlands provide vital ecosystem and carbon services, and Canada is home to a significant peatland carbon stock. Global climate warming trends are expected to lead to increased carbon release from peatlands, as a consequence of drought and wildfire. Monitoring hydrologic regimes is a key in understanding the impacts of warming, including monitoring changes in small and temporally variable water bodies in peatlands. Global surface water mapping has been implemented, but the spatial and temporal scales of the resulting data products prevent the effective monitoring of peatland water bodies, which are small and prone to rapid hydrologic changes. One hurdle in the quest to improve remote-sensing-derived global surface water map quality is the omission of small and temporally variable water bodies. This research investigated the reasons for small peatland water body omission as a preparatory step for surface water mapping, using Sentinel-1 SAR data and image classification methods. It was found that Sentinel-1 backscatter signatures for small peatland water bodies differ from large water bodies, due in part to differing physical characteristics such as waves and emergent vegetation, and limitations in detectable feature sizes as a result of SAR image processing and resolution. The characterization of small peatland water body backscatter provides a theoretical basis for the development of SAR-based surface water mapping methods with high accuracy for our purposes of wildfire susceptibility monitoring in peatlands. This study discusses the implications of small peatland water body omission from surface water maps on carbon, climate, and hydrologic models.

show abstract

Seasonally-decomposed Sentinel-1 backscatter time-series are useful indicators of peatland wildfire vulnerability

Cited by 8 publications

References 72 publications

Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects

Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects

Prediction of Peatlands Forest Fires in Malaysia Using Machine Learning

Investigating the Use of Sentinel-1 for Improved Mapping of Small Peatland Water Bodies: Towards Wildfire Susceptibility Monitoring in Canada’s Boreal Forest

Contact Info

Product

Resources

About