Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees

Data aggregation is a necessity when working with big data. Data reduction steps without loss of information are a scientific and computational challenge but are critical to enable effective data processing and information delineation in data-rich studies. We investigated the effect of four spatial aggregation schemes on Landsat imagery on prediction accuracy of green photosynthetic vegetation (PV) based on fractional cover (FCover). To reduce data volume we created an evenly spaced grid, overlaid that on the PV band and delineated the arithmetic mean of PV fractions contained within each grid cell. The aggregated fractions and the corresponding geographic grid cell coordinates were then used for boosted regression tree prediction models. Model goodness of fit was evaluated by the Root Mean Squared Error (RMSE). Two spatial resolutions (3000 m and 6000 m) offer good prediction accuracy whereas others show either too much unexplained variability model prediction results or the aggregation resolution smoothed out local PV in heterogeneous land. We further demonstrate the suitability of our aggregation scheme, offering an increased processing time without losing significant topographic information. These findings support the feasibility of using geographic coordinates in the prediction of PV and yield satisfying accuracy in our study area.</jats:p

Towards a Capacity-based optimal allocation of storm pipes’ replacements: Considering the effects of Climate Change and Urbanization

Storm pipes are a major component of any municipal infrastructure system, however, poor attention is commonly paid to their management and replacement of pipes is typically guided by condition criteria. Pipes replacement also faces the issue that their capacity is not considered, and the proposed pipes replacements are of the same diameter as the previous ones. However, lagged replacements of storm pipes can compromise urban system ability to drain runoff-water from rainfall and this could lead into flooding. This research extents optimal condition-based allocation of pipes by considering demand-capacity ratios, in order to prevent flooding. The effects of urbanization and climate change are also incorporated. A general method for detecting the impacts of urbanization currently exist, however, a simplified approach due to the lack of data is suggested. A case study of the city of Kindersley in Saskatchewan is used to illustrate the application of the method. Hydrological models based on current and future land uses were used to estimate changes in demand-capacity ratios for each pipe in the system. Performance curves for capacity and condition were developed, and validated by change detection of observed historical land use cover for the past 25 years. An extra 18% rainfall intensity was used to model the impacts of climate change. It was found that CAN$40,000 were enough to sustain current levels of condition and capacity-demand ratios, however, condition was at unacceptable level. Budget was raised and weights were adjusted until a combination with 45$100,000 was found to be the departure at which compliance begins to reach desirable point minimum levels of demand-capacity ratios and condition, possibly higher values of budget would be necessary as the municipality adjust such minimum requirements to the desired ones

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Enhancing Landsat time series through multi-sensor fusion and integration of meteorological data

Over 50 years ago, the United States Interior Secretary, Stewart Udall, directed space agencies to gather "facts about the natural resources of the earth." Today global climate change and human modification make earth observations from all variety of sensors essential to understand and adapt to environmental change. The Landsat program has been an invaluable source for understanding the history of the land surface, with consistent observations from the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors since 1982. This dissertation develops and explores methods for enhancing the TM/ETM+ record by fusing other data sources, specifically, Landsat 8 for future continuity, radar data for tropical forest monitoring, and meteorological data for semi-arid vegetation dynamics. Landsat 8 data may be incorporated into existing time series of Landsat 4-7 data for applications like change detection, but vegetation trend analysis requires calibration, especially when using the near-infrared band. The improvements in radiometric quality and cloud masking provided by Landsat 8 data reduce noise compared to previous sensors. Tropical forests are notoriously difficult to monitor with Landsat alone because of clouds. This dissertation developed and compared two approaches for fusing Synthetic Aperture Radar (SAR) data from the Advanced Land Observation Satellite (ALOS-1) with Landsat in Peru, and found that radar data increased accuracy of deforestation. Simulations indicate that the benefit of using radar data increased with higher cloud cover. Time series analysis of vegetation indices from Landsat in semi-arid environments is complicated by the response of vegetation to high variability in timing and amount of precipitation. We found that quantifying dynamics in precipitation and drought index data improved land cover change detection performance compared to more traditional harmonic modeling for grasslands and shrublands in California. This dissertation enhances the value of Landsat data by combining it with other data sources, including other optical sensors, SAR data, and meteorological data. The methods developed here show the potential for data fusion and are especially important in light of recent and upcoming missions, like Sentinel-1, Sentinel-2, and NASA-ISRO Synthetic Aperture Radar (NISAR)