An Ongoing Blended Long-Term Vegetation Health Product for Monitoring Global Food Security

Yang, Wen‐Jei; Kogan, Felix; Guo, Wei

doi:10.3390/agronomy10121936

Cited by 9 publications

(2 citation statements)

References 70 publications

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“…However, this study focuses on the two incidents of ENSO on La Niña in late 2017 and early 2018 and EL Niño in 2015/2016. Most past studies have used AVHRR data in studies on vegetative health products (Wenze et al, 2020). AVHRR data has expired in 2017 and will be replaced by VIIRS which started operations in 2013.…”

Section: Methodsmentioning

confidence: 99%

Spatial-Temporal Variability of Vegetation Health During El Niño 2015/2016 and La Niña 2017/2018: A Case Study of Sarawak

Kemarau¹,

Eboy²

2022

MJSSH

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The El Niño incident in 2015/2016 resulted in a 30% decrease in Gross Domestic Product (GDP) mainly from the agricultural sector. This is due to the El Niño incident which contributed to the prolonged drought for 3 months. This prolonged drought disaster has caused the deterioration of plant health due to a decrease in the amount of rainfall. This study examines and analyzes the spatial-temporal dynamics to identify and delineate the vegetation stress zones in Sarawak. In achieving the objectives of this study Vegetation Health Index (VHI) has been used in studying the changes in monthly space due to changes in Oceanic Niño Index (ONI). This study only focuses on the two occurrences of El Niño in 2015/2016 and La Niña in 2017 and 2018 because the Visible Infrared Imaging Radiometer Suite (VIIRS) data only started operations in 2013 until now. The results of this study offer the discovery of the effect of La Niña on plant health compared to previous studies that only focus on the effects of El Niño (Drought) on plant health. Indirectly, this study can provide information on which areas of the district are severely affected by the occurrence of La Niña and El Niño on plant health.

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Section: Methodsmentioning

confidence: 99%

Spatial-Temporal Variability of Vegetation Health During El Niño 2015/2016 and La Niña 2017/2018: A Case Study of Sarawak

Kemarau¹,

Eboy²

2022

MJSSH

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“…Several static potential covariates were explored (Figures 2d -2f). To represent fuel availability, smoothed rasters of NDVI were retrieved from the Blended Vegetation Health Product (Kogan 2001;Yang, Kogan, and Guo 2020). Maximum-NDVI was chosen to capture the peak amount of above ground live biomass (Xu et al 2012).…”

Section: Data Preparationmentioning

confidence: 99%

Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)

Jiménez-Ruano¹,

Jolly²,

Freeborn³

et al. 2022

Advances in Forest Fire Research 2022

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Spatial wildfire ignition predictions are needed to ensure efficient and effective wildfire response but robust methods for modelling wildfire occurrence have not been fully evaluated. Here we leverage high resolution, static spatial data to predict the ignition locations of human and naturally-caused wildfires across the state of Montana (USA). We leveraged a 25-year historical wildfire dataset (1992-2017) and four high resolution spatial variables that capture fuel availability, topography, geographic location and human transport infrastructure. We combined these data to train spatial logistic regression Generalized Additive Models (GAM) designed for big datasets (BAM) for both human and natural ignitions and we tested the efficacy of incremental changes in model complexity. Results showed that the best human and natural-caused ignition models were highly capable of distinguishing locations with and without new wildfire occurrences statewide (AUC = 0.89 and 0.84 respectively). Natural-caused ignitions were strongly influenced by slope and location, while human-caused fires were best predicted by distance to roads as well as terrain and fuel availability. Finally, these spatial fire occurrence models can be combined with temporally-variant data to predict the spatial and temporal distribution of wildfires across the state with the view that these methods can be used to develop predictive models at larger scales.

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Food Security and Advanced Imaging Radiometer ML Models

Vuppalapati¹

2022

International Series in Operations Research &Amp; Management Science

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An Ongoing Blended Long-Term Vegetation Health Product for Monitoring Global Food Security

Cited by 9 publications

References 70 publications

Spatial-Temporal Variability of Vegetation Health During El Niño 2015/2016 and La Niña 2017/2018: A Case Study of Sarawak

Spatial-Temporal Variability of Vegetation Health During El Niño 2015/2016 and La Niña 2017/2018: A Case Study of Sarawak

Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)

Food Security and Advanced Imaging Radiometer ML Models

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