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.

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

scholarly journals Vegetation Water Content Prediction: Towards More Relevant Explicatory Waveband Variables

2017 ◽  
Author(s):  
Eric Ariel L. Salas
Keyword(s):  
Water Content ◽  
Field Data ◽  
Water Status ◽  
Individual Performance ◽  
Absorption Feature ◽  
Water Index ◽  
Vegetation Water Content ◽  
Vegetation Water ◽  
The Individual ◽  
Canopy Water

Although the water absorption feature (WAF) at 970 nm is not very well-defined, it may be used alongside other indices to estimate the canopy water content.  The individual performance of a number of existing vegetation water content (VWC) indices against the WAF is assessed using linear regression model.  We developed a new Combined Vegetation Water Index (CVWI) by merging indices to boost the weak absorption feature. CVWI showed a promise in assessing the vegetation water status derived from the 970 nm absorption wavelength.  CVWI was able to differentiate two groups of dataset when regressed against the absorption feature.  CVWI could be seen as an easy and robust method for vegetation water content studies using hyperspectral field data.

Optical Remote Sensing of Vegetation Water Content

2018 ◽  
pp. 183-200
Author(s):  
Colombo Roberto ◽  
Busetto Lorenzo ◽  
Meroni Michele ◽  
Rossini Micol ◽  
Panigada Cinzia
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Optical Remote Sensing ◽  
Vegetation Water Content ◽  
Vegetation Water
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