Quantifying Four Decades of Arid-region Agricultural Development in Arequipa, Peru Using Landsat

The Arequipa Nexus Institute for Food, Energy and the Environment (Nexus Institute) is located in Southwestern Peru, generally bounded by the city of Arequipa to the east, the Majes River to the west, the Pacific Ocean to the south, and the Andes mountains to the north. Though agriculture has been practiced in parts of this cool desert region (MAT~15°C, MA

Sustainable Environment: Nexus project

Arequipa region is locaed in Southwestern Peru. The Arequipa Nexus Institute for food, energy, water and the environment aims to address the key challenges to a sustainable furture for the people in the region. This roundtable discusses about the sustainable water management, geosaptial analysis and environment sharing, long range sensor network solution for soil health monitoring and data management and sharing in this Nexus project

Ideas and perspectives: strengthening the biogeosciences in environmental research networks

Many scientific approaches are improving our understanding and management of the rapidly changing environment. Long-term environmental research networks are one approach to advancing local, regional, and global environmental science and education. A remarkable number and wide variety of environmental research networks operate around the world today. These are diverse in funding, infrastructure, motivating questions, scientific strengths, and the sciences that birthed and maintained the networks. Some networks have individual sites that were selected because they had produced invaluable long-term data, while other networks have new sites selected to span ecological gradients. However, all long-term environmental networks share two challenges. Networks must keep pace with scientific advances and interact with both the scientific community and society at large. If networks fall short of successfully addressing these challenges, they risk becoming irrelevant. The objective of this paper is to assert that the biogeosciences offer environmental research networks a number of opportunities to expand scientific impact and public engagement. We explore some of these opportunities with four networks: the International Long Term Ecological Research programs (ILTERs), the Critical Zone Observatories (CZOs), the Earth and Ecological Observatory networks (EONs), and the FLUXNET program of eddy flux sites. While these networks were founded and grown by interdisciplinary scientists, the preponderance of expertise and funding have gravitated activities of ILTERs and EONs toward ecology and biology, CZOs toward the Earth sciences and geology, and FLUXNET toward ecophysiology and micrometeorology. Our point is not to homogenize networks, nor to diminish disciplinary science. Rather, we argue that by more fully incorporating the integration of biology and geology in long-term environmental research networks, scientists can better leverage network assets, keep pace with the ever-changing science of the environment, and engage with larger scientific and public audiences

Feasibility of diffusion tensor imaging (DTI) with fibre tractography of the normal female pelvic floor

To prospectively determine the feasibility of diffusion tensor imaging (DTI) with fibre tractography as a tool for the three-dimensional (3D) visualisation of normal pelvic floor anatomy. Five young female nulliparous subjects (mean age 28 ± 3 years) underwent DTI at 3.0T. Two-dimensional diffusion-weighted axial spin-echo echo-planar (SP-EPI) pulse sequence of the pelvic floor was performed, with additional T2-TSE multiplanar sequences for anatomical reference. Fibre tractography for visualisation of predefined pelvic floor and pelvic wall muscles was performed offline by two observers, applying a consensus method. Three eigenvalues (λ1, λ2, λ3), fractional anisotropy (FA) and mean diffusivity (MD) were calculated from the fibre trajectories. In all subjects fibre tractography resulted in a satisfactory anatomical representation of the pubovisceral muscle, perineal body, anal - and urethral sphincter complex and internal obturator muscle. Mean FA values ranged from 0.23 ± 0.02 to 0.30 ± 0.04, MD values from 1.30 ± 0.08 to 1.73 ± 0.12 × 10(-)³ mm²/s. Muscular structures in the superficial layer of the pelvic floor could not be satisfactorily identified. This study demonstrates the feasibility of visualising the complex three-dimensional pelvic floor architecture using 3T-DTI with fibre tractography. DTI of the deep female pelvic floor may provide new insights into pelvic floor disorder

Theoretically-Efficient and Practical Parallel DBSCAN

The DBSCAN method for spatial clustering has received significant attention due to its applicability in a variety of data analysis tasks. There are fast sequential algorithms for DBSCAN in Euclidean space that take $O(n\log n)$ work for two dimensions, sub-quadratic work for three or more dimensions, and can be computed approximately in linear work for any constant number of dimensions. However, existing parallel DBSCAN algorithms require quadratic work in the worst case, making them inefficient for large datasets. This paper bridges the gap between theory and practice of parallel DBSCAN by presenting new parallel algorithms for Euclidean exact DBSCAN and approximate DBSCAN that match the work bounds of their sequential counterparts, and are highly parallel (polylogarithmic depth). We present implementations of our algorithms along with optimizations that improve their practical performance. We perform a comprehensive experimental evaluation of our algorithms on a variety of datasets and parameter settings. Our experiments on a 36-core machine with hyper-threading show that we outperform existing parallel DBSCAN implementations by up to several orders of magnitude, and achieve speedups by up to 33x over the best sequential algorithms

Performance evaluation of a distributed clustering approach for spatial datasets

The analysis of big data requires powerful, scalable, and accurate data analytics techniques that the traditional data mining and machine learning do not have as a whole. Therefore, new data analytics frameworks are needed to deal with the big data challenges such as volumes, velocity, veracity, variety of the data. Distributed data mining constitutes a promising approach for big data sets, as they are usually produced in distributed locations, and processing them on their local sites will reduce significantly the response times, communications, etc. In this paper, we propose to study the performance of a distributed clustering, called Dynamic Distributed Clustering (DDC). DDC has the ability to remotely generate clusters and then aggregate them using an efficient aggregation algorithm. The technique is developed for spatial datasets. We evaluated the DDC using two types of communications (synchronous and asynchronous), and tested using various load distributions. The experimental results show that the approach has super-linear speed-up, scales up very well, and can take advantage of the recent programming models, such as MapReduce model, as its results are not affected by the types of communication

Development and deployment of a field-portable soil O2 and CO2 gas analyzer and sampler.

Here we present novel method development and instruction in the construction and use of Field Portable Gas Analyzers study of belowground aerobic respiration dynamics of deep soil systems. Our Field-Portable Gas Analysis (FPGA) platform has been developed at the Calhoun Critical Zone Observatory (CCZO) for the measurement and monitoring of soil O2 and CO2 in a variety of ecosystems around the world. The FPGA platform presented here is cost-effective, lightweight, compact, and reliable for monitoring dynamic soil gasses in-situ in the field. The FPGA platform integrates off-the-shelf components for non-dispersive infrared (NDIR) CO2 measurement and electro-chemical O2 measurement via flow-through soil gas analyses. More than 2000 soil gas measurements have been made to date using these devices over 4 years of observations. Measurement accuracy of FPGAs is consistently high as validated via conventional bench-top gas chromatography. Further, time series representations of paired CO2 and O2 measurement under hardwood forests at the CCZO demonstrate the ability to observe and track seasonal and climatic patterns belowground with this FPGA platform. Lastly, the ability to analyze the apparent respiratory quotient, the ratio of apparent CO2 accumulation divided by apparent O2 consumption relative to the aboveground atmosphere, indicates a high degree of nuanced analyses are made possible with tools like FPGAs. In sum, the accuracy and reliability of the FPGA platform for soil gas monitoring allows for low-cost temporally extensive and spatially expansive field studies of deep soil respiration

Clustering multidimensional sequences in spatial and temporal databases

Many environmental, scientific, technical or medical database applications require effective and efficient mining of time series, sequences or trajectories of measurements taken at different time points and positions forming large temporal or spatial databases. Particularly the analysis of concurrent andmultidimensional sequences poses newchallenges in finding clusters of arbitrary length and varying number of attributes. We present a novel algorithm capable of finding parallel clusters in different subspaces and demonstrate our results for temporal and spatial applications. Our analysis of structural quality parameters in rivers is successfully used by hydrologists to develop measures for river quality improvements