Economic Growth and Development of Countries in the Asia Pacific Region: Some Implications for Australian Food Exporters

From the early 1970s until the Asian currency crisis in 1997, economic growth rates in the countries of the Asia Pacific region were above OECD averages. This was partly because priority in these countries has been achieving growth in manufacturing industries. The shift in emphasis from agriculture to manufacturing has resulted in these countries as a group becoming more reliant on agricultural exporters such as Australia and the United States for their food requirements. This paper provides an overview of the economic growth and development of the countries in the Asia Pacific region since the mid 1960s, in terms of changing trade and investment patterns; liberalisation and deregulation; and the role of the agricultural sector. Economic growth and development of the region has been accompanied by increasing disposable incomes and a range of changes to these economies, as well as changes in diet. However, there are differences in food consumption patterns amongst these countries. These differences reflect a variety of factors, including variations in economic performance, religious beliefs, cultural factors, government policies and agricultural resources

Machine Learning for ARUP: Time to Redefine the Ground Truth

Research accepts that ML-based AI tools’ accuracy is a defining characteristic for AI implementation. Yet, the understanding of accuracy in relation to the “ground truth” remains under-researched, especially the understanding of universally recognized practices for the “ground truth” in specific knowledge domains. This short paper investigates how knowledge workers’ expertise can be used effectively to redefine the “ground truth” and produce training datasets conducive to more accurate ML predictions. It approaches the question empirically with a case study of ARUP, a global engineering and consultancy firm that uses various AI tools for its advisory services. The paper highlights how executives often overlook data preparation and the role of knowledge workers during this phase, thus questioning the meaning of “ground truth”. It provides valuable insights on how a total and constructive collaboration of stakeholders is essential for organizing existing data, contributing to existing literature on ML implementation and data in general

Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High-Resolution Satellite Images

Abstract This article presents a new method for burned area mapping using high-resolution satellite images in the Mediterranean ecosystem. In such a complex environment, high-resolution satellite images represent an appropriate data source for identifying fire-affected areas, and single postfire data are often the only available source of information. The method proposed here integrates several spectral indices into a fuzzy synthetic indicator of likelihood of burn. The indices are interpreted through fuzzy membership functions that have been derived with a partially data-driven approach exploiting training data and expert knowledge. The final map of fire-affected areas is produced by applying a region growing algorithm on the basis of seed pixels selected on a conservative threshold of the synthetic fuzzy score. The algorithm has been developed and tested on a set of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes acquired over Southern Italy. Validation showed that the accuracy of the burned area maps is comparable or even better [overall accuracy (OA) > 90%, K > 0.76] than that obtained with approaches based on single index thresholds adapted to each image. The method described here provides an automatic approach for mapping fire-affected areas with very few false alarms (low commission error), whereas omission errors are mainly related to undetected small burned areas and are located in heterogeneous sparse vegetation cover

Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation

AbstractLand Surface Temperature (LST) is a key variable in the interactions and energy fluxes between the Earth surface and the atmosphere. Satellite data provide consistent, continuous and spatially distributed information on the Earth's surface conditions among which LST. Ten years of NASA-MODIS day-time and night-time 1 km LST data over Southern Italy have been analyzed to quantify the influence of factors such as topography and the land cover on LST spatio-temporal variations. Results show that topography significantly influence LST variability as a function of the land cover and to a different extent for day-time and night-time data. Moreover, the relation between LST and the influential factors varies with the season during the year. This study contributes to a further understanding of the complex relationship between the spatio-temporal variability of the surface thermal conditions and its driving factors highlighting how these relationships might change within the year

Optical remote sensing of lakes: an overview on Lake Maggiore

Optical satellite remote sensing represents an opportunity to integrate traditional methods for assessing water quality of lakes: strengths of remote sensing methods are the good spatial and temporal coverage, the possibility to monitor many lakes simultaneously and the reduced costs. In this work we present an overview of optical remote sensing techniques applied to lake water monitoring. Then, examples of applications focused on lake Maggiore, the second largest lake in Italy are discussed by presenting the temporal trend of chlorophyll-a (chl-a), suspended particulate matter (SPM), coloured dissolved organic matter (CDOM) and the z90 signal depth (the latter indicating the water depth from which 90% of the reflected light comes from) as estimated from the images acquired by the Medium Resolution Imaging Spectrometer (MERIS) in the pelagic area of the lake from 2003 to 2011. Concerning the chl-a trend, the results are in agreement with the concentration values measured during field surveys, confirming the good status of lake Maggiore, although occasional events of water deterioration were observed (e.g., an average increase of chl-a concentration, with a decrease of transparency, as a consequence of an anomalous phytoplankton occurred in summer 2011). A series of MERIS-derived maps (summer period 2011) of the z90 signal are also analysed in order to show the spatial variability of lake waters, which on average were clearer in the central pelagic zones. We expect that the recently launched (e.g., Landsat-8) and the future satellite missions (e.g., Sentinel-3) carrying sensors with improved spectral and spatial resolution are going to lead to a larger use of remote sensing for the assessment and monitoring of water quality parameters, by also allowing further applications (e.g., classification of phytoplankton functional types) to be developed

Organizations and stakeholders : three papers on data leveraging for AI implementation

Leveraging data for AI implementation requires complex managerial approaches and sophisticated technology arrangements that ideally bridge the gaps between the needs of different stakeholders and an organization’s strategic objectives. Although organizations believe that they are in the “perfect” position to do so, in practice this is not good enough. The tandem between the managerial approaches and the technology catalyzes changes in the way an organization operates and distributes its services, keeping digital innovation at the center of the operation. However, the understanding of how different groups of stakeholders actually promote, support and participate in such innovation opens up a new path for further investigation. This thesis contributes to a better understanding of how organizations leverage data and implement AI within specific industry domains, promoting and maintaining digital innovation. It sheds light on how organizations leverage data and support AI implementation from a theoretical perspective on business analytics and AI, as well as an empirical perspective with two case studies of Lufthansa Industry Solutions and ARUP, and pulls together both the systematic literature review method and qualitative analysis to problematize the current scholars’ conversation on these topics. This thesis adds important insights to the current academic conversation on leveraging data for AI implementation in today’s business landscape. The insights do not emphasize failed attempts, but rather highlight how specific organizations face unique challenges compared to well-accepted and continuously discussed practices while working with data and AI. In doing so, the thesis also provides multiple future research directions, finally highlighting important theoretical and practical implications for organizations and practitioners

Towards an automated approach to map flooded areas from Sentinel-2 MSI data and soft integration of water spectral features

Abstract In this work we propose an approach for mapping flooded areas from Sentinel-2 MSI (Multispectral Instrument) data based on soft fuzzy integration of evidence scores derived from both band combinations (i.e. Spectral Indices - SIs) and components of the Hue, Saturation and Value (HSV) colour transformation. Evidence scores are integrated with Ordered Weighted Averaging (OWA) operators, which model user's decision attitude varying smoothly between optimistic and pessimistic approach. Output is a map of global evidence degree showing the plausibility of being flooded for each pixel of the input Sentinel-2 (S2) image. Algorithm set up and validation were carried out with data over three sites in Italy where water surfaces are extracted from stable water bodies (lakes and rivers), natural hazard flooding, and irrigated paddy rice fields. Validation showed more than satisfactory accuracy for the OR-like OWA operators (F-score > 0.90) with performance slightly decreased (F-scor

Assessing in-season crop classification performance using satellite data: a test case in Northern Italy

AbstractThis study investigated the feasibility of delivering a crop type map early during the growing season. Landsat 8 OLI multi-temporal data acquired in 2013 season were used to classify seven crop types in Northern Italy. The accuracy achieved with four supervised algorithms, fed with multi-temporal spectral indices (EVI, NDFI, RGRI), was assessed as a function of the crop map delivery time during the season. Overall accuracy (Kappa) exceeds 85% (0.83) starting from mid-July, five months before the end of the season, when maximum accuracy is reached (OA=92%, Kappa=0.91). Among crop types, rice is the most accurately classified, followed by forages, maize and arboriculture, while soybean or double crops can be confused with other classes

Comparison of global inventories of CO emissions from biomass burning derived from remotely sensed data

We compare five global inventories of monthly CO emissions named VGT, ATSR, MODIS, GFED3 and MOPITT based on remotely sensed active fires and/or burned area products for the year 2003. The objective is to highlight similarities and differences by focusing on the geographical and temporal distribution and on the emissions for three broad land cover classes (forest, savanna/grassland and agriculture). Globally, CO emissions for the year 2003 range between 365 Tg CO (GFED3) and 1422 Tg CO (VGT). Despite the large uncertainty in the total amounts, some common spatial patterns typical of biomass burning can be identified in the boreal forests of Siberia, in agricultural areas of Eastern Europe and Russia and in savanna ecosystems of South America, Africa and Australia. Regionally, the largest difference in terms of total amounts (CV > 100%) and seasonality is observed at the northernmost latitudes, especially in North America and Siberia where VGT appears to overestimate the area affected by fires. On the contrary, Africa shows the best agreement both in terms of total annual amounts (CV = 31%) and of seasonality despite some overestimation of emissions from forest and agriculture observed in the MODIS inventory. In Africa VGT provides the most reliable seasonality. Looking at the broad land cover types, the range of contribution to the global emissions of CO is 64–74%, 23–32% and 3–4% for forest, savanna/grassland and agriculture, respectively. These results suggest that there is still large uncertainty in global estimates of emissions and it increases if the comparison is carried by out taking into account the temporal (month) and spatial (0.5° × 0.5° cell) dimensions. Besides the area affected by fires, also vegetation characteristics and conditions at the time of burning should also be accurately parameterized since they can greatly influence the global estimates of CO emissions