Monitoring vegetation water content of grasslands and forest plantations to assess forest fire risk with satellite time-series

2006 ◽  
Vol 234 ◽  
pp. S25
Author(s):  
J. Verbesselt ◽  
S. Van der Linden ◽  
S. Lhermitte ◽  
I. Jonckheere ◽  
J. van Aardt ◽  
...  
Keyword(s):  
Time Series ◽  
Water Content ◽  
Forest Fire ◽  
Fire Risk ◽  
Forest Plantations ◽  
Vegetation Water Content ◽  
Vegetation Water ◽  
Forest Fire Risk

Application of Normalized Difference Water Index for Vegetation Water Content Monitoring from MODIS Data

2011 ◽  
Vol 204-210 ◽  
pp. 2128-2132
Author(s):  
Yi Ding ◽  
Hui Li Gong
Keyword(s):  
Water Content ◽  
Forest Fire ◽  
Fire Risk ◽  
Ignition Point ◽  
Water Index ◽  
Vegetation Water Content ◽  
Normalized Difference Water Index ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Vegetation Water

The needs for vegetation water content monitoring originates from forest fire assessment: Firstly, the vegetation water content affects the forest ignition point; secondly, it affects the spread rate if the forest is on fire. Based on the above reasons, the inversion of vegetation water content in Da Hinggan Ling region of China was studied, using the Normalized Difference Water Index from MODIS (Moderate resolution Imaging Spectroradiometer) data, the relationship between the water content of vegetation and forest fire risk was preliminary analyzed.

scholarly journals Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences

2019 ◽  
Vol 8 (3) ◽  
pp. 143 ◽  
Author(s):  
Masoud Abdollahi ◽  
Ashraf Dewan ◽  
Quazi Hassan
Keyword(s):  
Water Content ◽  
Forest Fire ◽  
Distinct Pattern ◽  
Water Index ◽  
Vegetation Water Content ◽  
Normalized Difference Water Index ◽  
Annual Dynamic ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Vegetation Water

In this study, our aim was to model forest fire occurrences caused by lightning using the variable of vegetation water content over six fire-dominant forested natural subregions in Northern Alberta, Canada. We used eight-day composites of surface reflectance data at 500-m spatial resolution, along with historical lightning-caused fire occurrences during the 2005–2016 period, derived from a Moderate Resolution Imaging Spectroradiometer. First, we calculated the normalized difference water index (NDWI) as an indicator of vegetation/fuel water content over the six natural subregions of interest. Then, we generated the subregion-specific annual dynamic median NDWI during the 2005–2012 period, which was assembled into a distinct pattern every year. We plotted the historical lightning-caused fires onto the generated patterns, and used the concept of cumulative frequency to model lightning-caused fire occurrences. Then, we applied this concept to model the cumulative frequencies of lightning-caused fires using the median NDWI values in each natural subregion. By finding the best subregion-specific function (i.e., R2 values over 0.98 for each subregion), we evaluated their performance using an independent subregion-specific lightning-caused fire dataset acquired during the 2013–2016 period. Our analyses revealed strong relationships (i.e., R2 values in the range of 0.92 to 0.98) between the observed and modeled cumulative frequencies of lightning-caused fires at the natural subregion level throughout the validation years. Finally, our results demonstrate the applicability of the proposed method in modeling lightning-caused fire occurrences over forested regions.

Forest fire risk estimation from time series analisys of NOAA NDVI data

2004 ◽  
Author(s):  
Andrea Gabban ◽  
Giorgio Liberta ◽  
Jesus San-Miguel-Ayanz ◽  
Paulo Barbosa
Keyword(s):  
Time Series ◽  
Forest Fire ◽  
Risk Estimation ◽  
Fire Risk ◽  
Forest Fire Risk

Derivating forest structure from Sentinel-1 time series to assist forest fire risk assessments

2021 ◽  
Author(s):  
Moritz Bruggisser ◽  
Wouter Dorigo ◽  
Alena Dostálová ◽  
Markus Hollaus ◽  
Claudio Navacchi ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Fire ◽  
Forest Structure ◽  
Fire Risk ◽  
Risk Assessments ◽  
Structure Information ◽  
Fractional Cover ◽  
Stand Height ◽  
Structure Metrics ◽  
Forest Fire Risk

<p>The assessment of forest fire risk has recently gained interest in countries of Central Europe and the alpine region since the occurrence of forest fires is expected to increase with a changing climate. Information on forest fuel structure, which is related to forest structure, is a key component in such assessments. Forest structure information can be derived from airborne laser scanning (ALS) data, whose value for the derivation of respective metrics at a high accuracy level has been demonstrated in numerous studies over the last years.</p><p>Yet, the temporal resolution of ALS data is low as flight missions are typically carried out in time intervals of five to ten years in Central Europe. ALS-derived forest structure descriptors for fire risk assessments, therefore, are often outdated. Open access earth observation data offer the potential to fill these information gaps. Data provided by synthetic aperture radar (SAR) sensors, in particular, are of interest in this context since this technology has a known sensitivity to the vegetation structure and acquires data independent of weather or daylight conditions.</p><p>In our study, we investigate the potential to derive forest structure descriptors from time series of Sentinel-1 (S-1) SAR data for a deciduous forest site in the Eastern part of Austria. We focus on forest stand height and fractional cover, which is a measure for forest density, as both of these components impact forest fire propagation and ignition. The two structure metrics are estimated using a random forest (RF) model, which takes a total of 36 predictors as input, which we compute from the S-1 time series. The model is trained using ALS-derived structure metrics acquired during the same year as the S-1 data.</p><p>We estimated stand height with a root mean square error (RMSE) of 4.76 m and a bias of 0.09 m at 100 m resolution, while the RMSE for the fractional cover estimation is 0.08 with a bias of zero at the same resolution. The spatial comparison of the structure predictions with the ALS reference further shows that the general structure is well reproduced. Yet, fine scale variations cannot be completely reproduced by the S1-derived structure products, and the height of tall stands and very dense canopy parts are underestimated. Due to the high correlation of the predicted values to the reference (Pearson’s R of 0.88 and 0.94 for the stand height and the fractional cover, respectively), we consider S-1 time series in combination with ALS data with low temporal resolution and machine learning techniques to be a reliable data source and workflow for regularly (e.g. < yearly) updating ALS structure information in an operational way.</p>

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