Time series of chaparral live fuel moisture maps derived from MODIS satellite data

2006 ◽  
Vol 15 (3) ◽  
pp. 347 ◽  
Author(s):  
Douglas Stow ◽  
Madhura Niphadkar ◽  
John Kaiser
Keyword(s):  
Time Series ◽  
San Diego ◽  
Southern California ◽  
San Diego County ◽  
Spatially Explicit ◽  
Fuel Moisture ◽  
Ecological Disturbance ◽  
Moderate Resolution ◽  
Live Fuel Moisture ◽  
Moderate Resolution Imaging Spectroradiometer

Wildfires in chaparral shrublands of southern California are a major hazard and important ecological disturbance agent. Fire managers typically monitor fuel moisture of chaparral shrublands to assess the risk of wildfires, using field-based sampling methods for a few small study areas located sparsely throughout southern California. Remote sensing provides the potential for deriving spatially explicit and temporally frequent data on live fuel moisture (LFM) conditions. The objective of this present study was to explore the potential for monitoring LFM with maps derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data on the National Aeronautics and Space Administration (NASA) Terra Earth-observing satellite. A time series of MODIS surface reflectance data (MOD-09_A1) for San Diego County, California from Fall 2000 through 2003 was used to derive normalized difference indices, which were regressed against LFM data. A high degree of temporal co-variability was found, with three MODIS indices providing similar predictability. Regression relationships were inverted and applied to MODIS images to map LFM interval classes for chaparral areas of San Diego County. The spatial–temporal patterns of LFM maps suggest that, at a minimum, the MODIS can provide spatially explicit information that extends the utility of ground-based measurements of LFM data at a few sites.

Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices

2017 ◽  
Vol 26 (5) ◽  
pp. 384
Author(s):  
L. M. Ellsworth ◽  
A. P. Dale ◽  
C. M. Litton ◽  
T. Miura
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Native Grass ◽  
Tropical Ecosystems ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Live Fuel Moisture ◽  
Moderate Resolution Imaging Spectroradiometer

The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.

scholarly journals Assessment of Ecological Disturbance Caused by Flood and Fire in Assam Forests, India, Using MODIS Time Series Data of 2001-2011

2020 ◽  
Author(s):  
Dibyendu Dutta ◽  
Akanksha Balha ◽  
Prabir Kumar Das ◽  
Pragyan Jain ◽  
Libeesh Lukose ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Fire ◽  
Land Surface ◽  
Vegetation Index ◽  
Time Series Data ◽  
Forest Area ◽  
Series Data ◽  
Ecological Disturbance ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

The forest area of Assam State is known for its rich biodiversity. In the present study, the disturbance regime within the Assam forest area caused by periodic flood and forest fire, was assessed using the Moderate Resolution Imaging Spectroradiometer (MODIS) time-series (2001–2011) data. The MODIS Global Disturbance Index (MGDI) images were generated using MODIS derived Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) images. The temporal intensity of flood and forest fire in sixteen representative forests was analyzed to develop the MGDI based thresholds for detecting the disturbed area. The threshold for the non-instantaneous disturbance, i.e. flood, was found to be 107% whereas it was 111% for instantaneous disturbance, i.e. forest fire. The thresholds were applied on the MGDI images to delineate disturbed caused by flood and fire, separately for each year. The time-series disturbance areas were integrated over the years (2001–2011) to generate the classified disturbance prone maps.

scholarly journals The Southern California Extracorporeal Membrane Oxygenation Consortium during the Coronavirus Disease 2019 Pandemic

2021 ◽  
pp. 1-21
Author(s):  
Mazen Odish ◽  
Cassia Yi ◽  
Juliann Eigner ◽  
Amelia Kenner Brininger ◽  
Kristi L. Koenig ◽  
...  
Keyword(s):  
Extracorporeal Membrane Oxygenation ◽  
San Diego ◽  
Southern California ◽  
Limited Resource ◽  
The United States ◽  
San Diego County ◽  
Clinical Practices ◽  
Membrane Oxygenation ◽  
Effective Manner ◽  
Share Data

Abstract In March 2020, at the onset of the coronavirus disease 2019 (COVID-19) pandemic in the United States, the Southern California Extracorporeal Membrane Oxygenation (ECMO) Consortium was formed. The consortium included physicians and coordinators from the four ECMO centers in San Diego County. Guidelines were created to ensure that ECMO was delivered equitably and in a resource effective manner across the county during the pandemic. A biomedical ethicist reviewed the guidelines to ensure ECMO utilization would provide maximal community benefit of this limited resource. The San Diego County Health and Human Services Agency further incorporated the guidelines into its plans for the allocation of scarce resources. The consortium held weekly video conferences to review countywide ECMO capacity (including census and staffing), share data, and discuss clinical practices and difficult cases. Equipment exchanges between ECMO centers maximized regional capacity. From March 1 to November 30, 2020, consortium participants placed 97 patients on ECMO. No eligible patients were denied ECMO due to lack of resources or capacity. The Southern California ECMO Consortium may serve as a model for other communities seeking to optimize ECMO resources during the current COVID-19 or future pandemics.

scholarly journals Spatiotemporal Mapping and Monitoring of Whiting in the Semi-Enclosed Gulf Using Moderate Resolution Imaging Spectroradiometer (MODIS) Time Series Images and a Generic Ensemble Tree-Based Model

2019 ◽  
Vol 11 (10) ◽  
pp. 1193 ◽  
Author(s):  
Abdallah Shanableh ◽  
Rami Al-Ruzouq ◽  
Mohamed Barakat A. Gibril ◽  
Cristina Flesia ◽  
Saeed AL-Mansoori
Keyword(s):  
Time Series ◽  
Spectral Band ◽  
Classification Model ◽  
Series Data ◽  
Natural Phenomenon ◽  
Adaptive Boosting ◽  
Rule Based ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Whiting events in seas and lakes are a natural phenomenon caused by suspended calcium carbonate (CaCO3) particles. The Arabian Gulf, which is a semi-enclosed sea, is prone to extensive whiting that covers tens of thousands of square kilometres. Despite the extent and frequency of whiting events in the Gulf, studies documenting the whiting phenomenon are lacking. Therefore, the primary objective of this study was to detect, map and document the spatial and temporal distributions of whiting events in the Gulf using daily images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites from 2002 to 2018. A method integrating a geographic object-based image analysis, the correlation-based feature selection technique (CFS), the adaptive boosting decision tree (AdaBoost DT) and the rule-based classification were used in the study to detect, quantify and assess whiting events in the Gulf from the MODIS data. Firstly, a multiresolution segmentation was optimised using unsupervised quality measures. Secondly, a set of spectral bands and indices were investigated using the CFS to select the most relevant feature(s). Thirdly, a generic AdaBoost DT model and a rule-based classification were adopted to classify the MODIS time series data. Finally, the developed classification model was compared with various tree-based classifiers such as random forest, a single DT and gradient boosted DT. Results showed that both the combination of the mean of the green spectral band and the normalised difference index between the green and blue bands (NDGB), or the combination of the NDGB and the colour index for estimating the concentrations of calcium carbonates (CI) of the image objects, were the most significant features for detecting whiting. Moreover, the generic AdaBoost DT classification model outperformed the other tested tree-based classifiers with an overall accuracy of 97.86% and a kappa coefficient of 0.97. The whiting events during the study period (2002–2018) occurred exclusively during the winter season (November to March) and mostly in February. Geographically, the whiting events covered areas ranging from 12,000 km2 to 60,000 km2 and were mainly located along the southwest coast of the Gulf. The duration of most whiting events was 2 to 6 days, with some events extending as long as 8 to 11 days. The study documented the spatiotemporal distribution of whiting events in the Gulf from 2002 to 2018 and presented an effective tool for detecting and motoring whiting events.

scholarly journals SST Comparison of AVHRR and MODIS Time Series in the Western Mediterranean Sea

2020 ◽  
Vol 12 (14) ◽  
pp. 2241
Author(s):  
María José López García
Keyword(s):  
Time Series ◽  
Regional Scale ◽  
Atmospheric Correction ◽  
Western Mediterranean ◽  
Western Mediterranean Sea ◽  
Temporal And Spatial Variability ◽  
Moderate Resolution ◽  
Long Term Trends ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Development And Validation

Sea Surface Temperature (SST) is a key parameter for understanding atmospheric and oceanic processes. Since the late 1980s, infrared satellite images have been used to complement in situ records for studying the temporal and spatial variability of SST. The Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA)’s satellite was the first sensor successfully used to compute SST following the development and validation of the atmospheric correction algorithm known as “split-window”. More recently, the MODerate-resolution Imaging Spectroradiometer (MODIS) on board the National Aeronautics and Space Administration (NASA)’s Terra and Aqua satellites, launched in 1999 and 2002, respectively, also provides SST products which can be combined with AVHRR series to complete the analysis of time series. This paper presents a comparison of the monthly SST data derived from both sensors, AVHRR and MODIS, in a series of ten years (2000–2009) in the Western Mediterranean basins. The results showed a high correlation (R2 = 0.99) between the sensors when averaged values at the regional scale were compared. SST obtained from AVHRR were slightly higher (+0.18 °C ± 0.2 °C, on average) than SST from MODIS. The series were most similar during winter and spring (+0.09 °C ± 0.1 °C for January to May) with a greater difference from June to December (+0.24 °C ± 0.2 °C). The comparative analysis showed that the two sensors can be used jointly to estimate long-term trends at the regional scale.

The Little Sycamore Site and the Early Milling Stone Cultures of Southern California

1954 ◽  
Vol 20 (2) ◽  
pp. 112-123 ◽  
Author(s):  
William James Wallace
Keyword(s):  
San Diego ◽  
Southern California ◽  
Coastal Region ◽  
San Diego County ◽  
University Of Southern California ◽  
Santa Barbara ◽  
Field Methods ◽  
Analogous Complex ◽  
La Jolla ◽  
The University

The Presence in the southern California coastal region of prehistoric cultures showing considerable use of milling stones has been recognized for some years. Attention was called to this fact by the publication in 1929 of David Banks Rogers’ Prehistoric Man of the Santa Barbara Coast. Rogers distinguished a sequence of three aboriginal cultures in the Santa Barbara area, the earliest of which (Oak Grove) was characterized by the employment of this form of grinding implement almost to the exclusion of other artifacts. In the same year Malcolm J. Rogers noted a somewhat analogous complex (now La Jolla) in western San Diego County (M. J. Rogers 1929: 456-7). Occurrences of similar assemblages have been reported upon since (Treganza and Malamud 1950; Walker 1952).An investigation conducted at the Little Sycamore site (Ven 1) in Ventura County by a class in archaeological field methods from the University of Southern California uncovered evidence of yet another milling stone complex.

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