20th International Congress of Biometeorology

isbThe ICB is organized and sponsored by the International Society of Biometeorology (ISB). Since 1956, the ISB has provided an international forum for the promotion of interdisciplinary collaboration between meteorologists, health professionals, biologists, climatologists, ecologists and other scientists. For over 60 years, the ISB has served as a professional group of colleagues and researchers. A Congress is held every three years, which gathers biometeorologists from across the globe. This meeting is no exception with over 260 presentations or posters that collectively include authors from 42 countries. The theme for this Congress is “Adaptation to Climate Risks,”. True to the diversity of biometeorological studies, there is a large variety of topics across the individual sessions, including human biometeorology, animal biometeorology, phenology, agriculture and forestry, the built environment, tourism, thermal comfort, aerobiology, extreme events and disasters, and infectious diseases. The societal challenges presented through these studies are addressed through other fields, including risk communication, climate change, adaptation, and challenges particular to the developing world.


This year the conference was held in Cleveland, Ohio, on the 28th September to 1st October.  Victor F. Rodriguez-Galiano and Sarchil Qader presented their research at the conference. Both abstracts for the presentations are below. Victors titled: European vegetation response to climate drivers in the last decade: using 1 km MERIS data for modelling changes in land surface phenology, and Sarchils titled: Crop area estimation in Iraq based on satellite derived phenological metrics and the influence of war and drought.


European vegetation response to climate drivers in the last decade: using 1 km MERIS data for modelling changes in land surface phenology 
Victor F. Rodriguez-Galiano, Dr. Jadu Dash and Prof. Pete M. Atkinson
Phenological events, such as onset on greenness and senescence, occur at a specific time depending upon the local climatic conditions. Given this dependency between phenology and climate, the former has emerged as an important focus for scientific research because phenological events are regarded as an indicator of global warming. On the other hand, phenology also affects climate, playing an important role in many feedbacks of the climate system by influencing albedo, and fluxes of water, energy and CO2. Thus, a better understanding of the drivers of phenology is of paramount importance, especially for the senescence phenophases, to which the controlling factors are not well documented.
Temperature is one of the key parameters to regulate vegetation growing states in high latitude regions such as Europe, changes in air temperature will lead to changes in vegetation growth. Numerous studies have been conducted to evaluate the sensitivity of spring phenology to warming using plant phenological records. Additionally, others studies have used time series of satellite
sensor derived vegetation indexes to up-scale phenology (Land Surface Phenology; LSP) and study the influence of climate at global or continental scales. These studies performed linear regression
between phenology trends or anomalies and temperature values. However, the relation between phenology and climatological drivers is complex, and it is not necessarily linear. Therefore, there is a need for the application of new generation computational tools to assist in extracting as much information as possible from the rapidly growing volumes of digital data. This is the case of the
present research, related to a considerably large phenological and climatological dataset retrieved for the whole Pan-European Continent in the last decade.
Regression Trees (RT), a machine learning technique, appears as an alternative to traditional regression (global single predictive models), allowing for multiple regressions using recursive
partitioning. When the database has many variables which interact in complicated, nonlinear ways, assembling a single global model can be very difficult and hopelessly confusing. An alternative
approach to nonlinear regression is to sub-divide, or partition, the space into smaller regions, where the interactions are more manageable. The application of machine learning techniques has different advantages: i) ability to learn complex patterns, considering nonlinear relationships between explanatory and dependent variables; ii) generalisation ability, hence applicable to incomplete or noisy databases; iii) integration of different types of data in the analysis due to the absence of assumptions about the data used (e.g. normality); and iv) interpretability of results, since RT allows obtaining patterns for a better explanation of a given phenomenon, showing the most important variables and their threshold values.
This contribution reports the application of RT to model the differences in phenology for the natural vegetation of Europe in the last decade using temperature and precipitation data. Multi-temporal Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data at 1 km spatial resolution were used to derive key phenological metrics (onset on greenness and end of senescence) for a 10-year time series data from 2002 to 2012. Differences in phenology were computed as the difference from the decadal median. Surface air temperature data and precipitation were acquired from the European Climatic Assessment Dataset and interpolated at the satellite data
spatial resolution from an original of 0.25°. We used the daily mean temperature and precipitation and computed monthly and trimestral averages, as well as growing degree days and chilling
requirements for every year. All these variables were used as input to the Regression Tree model. This approach is, to the knowledge of the authors, attempted here for the first time. The goal is to
gain access to novel information regarding relationships and potential interactions between differences in phenology (synergy between different climatological drivers and threshold values in
temperature, growing degree days, etc), not directly or easily provided by more traditional statistical methods. Apart from focusing on the present case, this research aims to encourage other
researchers dealing with complex and interacting systems or processes to further contribute with new insights to this novel line of research.


Crop Area Estimation in Iraq Based on Satellite Derived Phenological Metrics and the Influence of
War and Drought
Sarchil Qader, Dr Jadu Dash and Prof. Pete M. Atkinson
War and political conflicts can affect the land use practices, particularly agriculture in a country and in turn could affect the availability of food grain and food security of a country. Over last decade, Iraq had been involved in ‘Post-Gulf’ war mainly to oppose the previous regime. Due to the political instability and fear for life during the war many farmers were unable to grown any crops, which
affected the overall production of the country. In addition to the war, due to its geographical location, the region is affected by irregularities in precipitation resulting in frequent occurrence of
drought. Both these factors made the region vulnerable to sustained food production.
However,  at present there are no reliable estimation of both crop areas and crop yield across the country. Therefore, the current research will attempt to use the phonological information to classify
the country’s land cover type in order to provide an accurate estimation of crop area and their changes through time. Thirteen successive years of 8 days Normalized Difference Vegetation Index
(NDVI) with the spatial resolution of 250 m derived from the Moderate Resolution Imagery Spectrometer (MODIS) were analysed. Fourier technique will use to smooth the phonological signal. Eleven phenology metrics were extracted from MODIS NDVI time series with elevation from Shuttle Radar Topography Mission (SRTM) for Iraq to classify the crop areas. A decision tree based
classifier was used to discriminate crop types (irrigated and rainfed) to natural vegetation. Initial results suggested significant changes in crop area in Iraq from 2001 to 2013 mostly attributed to Post-Gulf war and occurrence of drought. A Detail quantitative estimate of the impact of these factors on total crop area and resulting crop yield will be presented.


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