Vegetation Canopy Water Content Estimation Using GVMI and EWT Model from MODIS Data

Liu, Min; Huang, Fang; Zhang, Hannv; Wang, Ping

doi:10.1109/cisp.2009.5301008

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…GVMI is derived from Systeme Probatoire d'Observation de la Terre (SPOT) Vegetation data [32] to estimate vegetation moisture. It is built on combination of near-infrared and short-wave infrared regions of the electromagnetic spectrum.…”

Section: Landscape Factorsmentioning

confidence: 99%

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Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?

Lan

Guo

et al. 2021

Forests

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Understanding the drivers of wildfire occurrence is of great value for fire prevention and management, but due to the variation in research methods, data sources, and data resolution of those studies, it is challenging to conduct a large-scale comprehensive comparative qualitative analysis on the topic. China has diverse vegetation types and topography, and has undergone rapid economic and social development, but experiences a high frequency of wildfires, making it one of the ideal locations for wildfire research. We applied the Random Forests modelling approach to explore the main types of wildfire drivers (climate factors, landscape factors and human factors) in three high wildfire density regions (Northeast (NE), Southwest (SW), and Southeast (SE)) of China. The results indicate that climate factors were the main driver of wildfire occurrence in the three regions. Precipitation and temperature significantly impacted the fire occurrence in the three regions due to the direct influence on the moisture content of forest fuel. However, wind speed had important influence on fire occurrence in the SE and SW. The explanation power of the landscape and human factors varied significantly between regions. Human factors explained 40% of the fire occurrence in the SE but only explained less than 10% of the fire occurrence in the NE and SW. The density of roads was identified as the most important human factor driving fires in all three regions, but railway density had more explanation power on fire occurrence in the SE than in the other regions. The landscape factors showed nearly no influence on fire occurrence in the NE but explained 46.4% and 20.6% in the SE and SW regions, respectively. Amongst landscape factors, elevation had the highest average explanation power on fire occurrence in the three regions, particularly in the SW. In conclusion, this study provides useful insights into targeted fire prediction and prevention, which should be more precise and effective under climate change and socio-economic development.

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Section: Landscape Factorsmentioning

confidence: 99%

“…The batch processing tool (MODIS Reprojection Tool, MRT) was used to splice and project the downloaded MODIS images and the images were resampled into 1 km. A detailed statement about the calculation of GVMI can be found in Liu et al 2009.…”

Section: Landscape Factorsmentioning

confidence: 99%

Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?

Lan

Guo

et al. 2021

Forests

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“…With its high temporal resolution, the use of Terra-MODerate Resolution Imaging Spectroradiometer (Terra-MODIS) data has also been already explored for crop water content monitoring (Chen et al, 2005;Trombetti et al, 2008), for semi-arid ecosystems monitoring (Fensholt & Sandholt, 2003;Bajgiran et al, 2009) or for forest water content monitoring (Dasgupta, 2007;Liu et al, 2009;Qu & Ambrose, 2009). Walker et al (2012) recently demonstrated the feasibility of a combined use of Terra-MODIS images (with high temporal resolution) and Landsat images (high spatial resolution) in order to monitor dryland forest phenology.…”

Section: Introductionmentioning

confidence: 99%

Testing the Relevance of Daily MODIS Data to Monitor Mediterranean Shrubland Canopy Water Content with Temporal Cross-Correlation Analyses

Delenne

Bailly

Deshayes

2013

International Journal of Agricultural and Environmental Information Systems

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International audienceDrought alert systems for forest fire prevention often rely on vegetation water content (VWC) monitoring which is a key parameter in forest fire hazard. In southern France, VWC is up to now monitored through regular field surveys. Thanks to the theoretical sensitivity of shortwave infrared reflectance to VWC, MODIS satellite data are potentially able to monitor VWC depending on plant species VWC magnitude. In this paper, a specific statistical approach based on temporal cross-correlations is developed in order to test the correlation between two MODIS water indices and VWC measurements coming from field surveys. This test aims in assessing the ability of daily MODIS data to monitor Mediterranean shrubland canopy water content and detect any delay effect between MODIS and field survey temporal series. Statistical tests are carried out for 29 sites containing 18 dominant shrubland Mediterranean species. 67 % and 54 % of significant correlation were found using respectively the NDII and NDWI indices from MODIS data. Correlation were found low (0.5 maximum) with a dominant negative delay effect, i.e. with a MODIS signal that reacts a few days after the field VWC. Test results show that, even if deeper pre-processing of MODIS data may be required, site soil, site vegetation cover and heterogeneity at MODIS pixel scale, as well as species VWC sensitivity make correlation between field VWC and MODIS water indices non univoque and highly variable. It stems from this study that many obstacles are still to overcome, for an accurate monitoring of Mediterranean shrubland canopy water content using MODIS daily data

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Vegetation Canopy Water Content Estimation Using GVMI and EWT Model from MODIS Data

Cited by 2 publications

References 6 publications

Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?

Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?

Testing the Relevance of Daily MODIS Data to Monitor Mediterranean Shrubland Canopy Water Content with Temporal Cross-Correlation Analyses

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