ALBERT

Description: We investigated the use of C-band RADARSAT Constellation Mission (RCM) synthetic aperture radar (SAR) for retrieval of ocean surface wind speeds by using four new channels (right circular transmit, vertical receive (RV); right circular transmit, horizontal receive (RH); right circular transmit, left circular transmit (RL); and right circular transmit, right circular receive (RR)) in compact polarimetry (CP) mode. Using 256 buoy measurements collocated with RADARSAT-2 fine beam quad-polarized scenes, RCM CP data was simulated using a “CP simulator”. Provided that the relative wind direction is known, our results demonstrate that wind speed can be retrieved from RV, RH and RL polarization channels using existing C-band model (CMOD) geophysical model function (GMF) and polarization ratio (PR) models. Simulated RR-polarized radar returns have a strong linear relationship with speed and are less sensitive to relative wind direction and incidence angle. Therefore, a model is proposed for the RR-polarized synthetic aperture radar (SAR) data. Our results show that the proposed model can provide an efficient methodology for wind speed retrieval.

Description: The numerical simulation of estuarine dynamics requires accurate prediction for the transport of tracers, such as temperature and salinity. During the simulation of these processes, all the numerical models introduce two kinds of tracer mixing: (1) by parameterizing the tracer eddy diffusivity through turbulence models leading to a source of physical mixing and (2) discretization of the tracer advection term that leads to numerical mixing. Physical and numerical mixing both vary with the choice of horizontal advection schemes, grid resolution, and time step. By simulating four idealized cases, this study compares the physical and numerical mixing for three different tracer advection schemes. Idealized domains only involving physical and numerical mixing are used to verify the implementation of mixing terms by equating them to total tracer variance. Among the three horizontal advection schemes, the scheme that causes the least numerical mixing while maintaining a sharp front also results in larger physical mixing. Instantaneous spatial comparison of mixing components shows that physical mixing is dominant in regions of large vertical gradients, while numerical mixing dominates at sharp fronts that contain large horizontal tracer gradients. In the case of estuaries, numerical mixing might locally dominate over physical mixing; however, the amount of volume integrated numerical mixing through the domain compared to integrated physical mixing remains relatively small for this particular modeling system.

Description: China and India are not only the two most populous nations on Earth, they are also two of the most rapidly growing economies. Historically, economic and social development have been subsidized by cheap and abundant fossil-fuels. Climate change from fossil-fuel emissions has resulted in the need to reduce fossil-fuel emissions in order to avoid catastrophic warming. If climate goals are achieved, China and India will have been the first major economies to develop via renewable energy sources. In this article, we examine the factors of projected population growth, available fossil-fuel reserves, and renewable energy installations required to develop scenarios in which both China and India may increase per capita energy consumption while remaining on trach to meet ambitious climate goals. Here, we show that China and India will have to expand their renewable energy infrastructure at unprecedented rates in order to support both population growth and development goals. In the larger scope of the literature, we recommend community-based approaches to microgrid and cookstove development in both China and India.

Description: RADARSAT Constellation Mission (RCM) compact polarimetry (CP) data were simulated using 504 RADARSAT-2 quad-pol SAR images. These images were used to samples CP data in three RCM modes to build a data set with co-located ocean wind vector observations from in situ buoys on the West and East coasts of Canada. Wind speeds up to 18 m/s were included. CP and linear polarization parameters were related to the C-band model (CMOD) geophysical model functions CMOD-IFR2 and CMOD5n. These were evaluated for their wind retrieval potential in each RCM mode. The CP parameter Conformity was investigated to establish a data-quality threshold (〉0.2), to ensure high-quality data for model validation. An accuracy analysis shows that the first Stokes vector (SV0) and the right-transmit vertical-receive backscatter (RV) parameters were as good as the VV backscatter with CMOD inversion. SV0 produced wind speed retrieval accuracies between 2.13 m/s and 2.22 m/s, depending on the RCM mode. The RCM Medium Resolution 50 m mode produced the best results. The Low Resolution 100 m and Low Noise modes provided similar results. The efficacy of SV0 and RV imparts confidence in the continuity of robust wind speed retrieval with RCM CP data. Three image-based case studies illustrate the potential for the application of CP parameters and RCM modes in operational wind retrieval systems. The results of this study provide guidance to direct research objectives once RCM is launched. The results also provide guidance for operational RCM data implementation in Canada’s National SAR winds system, which provides near-real-time wind speed estimates to operational marine forecasters and meteorologists within Environment and Climate Change Canada.

Description: Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture Imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km2. We obtained an overall accuracy of 96.7% and a Kappa statistic of 0.886 using a combination of NAIP orthophotography and ancillary data. Despite the high overall classification accuracy, some classes were difficult to differentiate, as highlight by the low user’s and producer’s accuracies for the barren, impervious, and mixed developed classes. In contrast, forest, low vegetation, and water were generally mapped with accuracy. The inclusion of ancillary data and first- and second-order textural measures generally improved classification accuracy whereas band indices and object geometric measures were less valuable. Including super-object attributes improved the classification slightly; however, this increased the computational time and complexity. From the findings of this research and previous studies, recommendations are provided for mapping large spatial extents.

Description: The Congo River is the deepest in the world and second-longest in Africa. Harnessing its full hydropower potential has been an ongoing development dream of the Democratic Republic of Congo (DRC) and its more powerful regional allies. If completed, the Grand Inga complex near Kinshasa, the capital of the DRC, will be the largest dam project in the world. Its eight separate dams (Inga 1–8) are envisioned to be “lighting up and powering Africa”. Opponents claim, however, that the rewards will be outsourced to corporate mining interests rather than meeting the needs of the local population, and that the project is flawed economically, socially and environmentally. The planned construction of the Inga dams and associated infrastructure has been stuck in limbo since it was mooted in the 1960s; a fantasy rather than a reality. This article attempts to analyse the rivalry underlying the Grand Inga scheme beyond the “pro” and “contra” reports. Embracing Lacanian psychoanalysis and triangulating multiple sources, we seek to unmask Grand Inga as a potent fantasy. Whilst exhibiting its purpose to serve as a screen to protect both proponents of and opponents to the dam from encountering their own self-deception, we conclude the scheme to be at its most powerful whilst the dream remains unfulfilled.

Description: We propose two new ocean wind retrieval models for right circular-vertical (RV) and right circular-horizontal (RH) polarizations respectively from the compact-polarimetry (CP) mode of the RADARSAT Constellation Mission (RCM), which is scheduled to be launched in 2019. For compact RV-polarization (right circular transmit and vertical receive), we build the wind retrieval model (denoted CoVe-Pol model) by employing the geophysical model function (GMF) framework and a sensitivity analysis. For compact RH polarization (right circular transmit and horizontal receive), we build the wind retrieval model (denoted the CoHo-Pol model) by using a quadratic function to describe the relationship between wind speed and RH-polarized normalized radar cross-sections (NRCSs) along with radar incidence angles. The parameters of the two retrieval models are derived from a database including wind vectors measured by in situ National Data Buoy Center (NDBC) buoys and simulated RV- and RH-polarized NRCSs and incidence angles. The RV- and RH-polarized NRCSs are generated by a RCM simulator using C-band RADARSAT-2 quad-polarized synthetic aperture radar (SAR) images. Our results show that the two new RCM CP models, CoVe-Pol and CoHo-POL, can provide efficient methodologies for wind retrieval.

Description: High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to map land covers over large geographic areas using supervised machine learning algorithms. Although many studies have compared machine learning classification methods, sample selection methods for acquiring training and validation data for machine learning, and cross-validation techniques for tuning classifier parameters are rarely investigated, particularly on large, high spatial resolution datasets. This work, therefore, examines four sample selection methods—simple random, proportional stratified random, disproportional stratified random, and deliberative sampling—as well as three cross-validation tuning approaches—k-fold, leave-one-out, and Monte Carlo methods. In addition, the effect on the accuracy of localizing sample selections to a small geographic subset of the entire area, an approach that is sometimes used to reduce costs associated with training data collection, is investigated. These methods are investigated in the context of support vector machines (SVM) classification and geographic object-based image analysis (GEOBIA), using high spatial resolution National Agricultural Imagery Program (NAIP) orthoimagery and LIDAR-derived rasters, covering a 2,609 km2 regional-scale area in northeastern West Virginia, USA. Stratified-statistical-based sampling methods were found to generate the highest classification accuracy. Using a small number of training samples collected from only a subset of the study area provided a similar level of overall accuracy to a sample of equivalent size collected in a dispersed manner across the entire regional-scale dataset. There were minimal differences in accuracy for the different cross-validation tuning methods. The processing time for Monte Carlo and leave-one-out cross-validation were high, especially with large training sets. For this reason, k-fold cross-validation appears to be a good choice. Classifications trained with samples collected deliberately (i.e., not randomly) were less accurate than classifiers trained from statistical-based samples. This may be due to the high positive spatial autocorrelation in the deliberative training set. Thus, if possible, samples for training should be selected randomly; deliberative samples should be avoided.

Description: Countries across the world aspire towards climate resilient sustainable development. The interacting processes of climate change, land change, and unprecedented social and technological change pose significant obstacles to these aspirations. The pace, intensity, and scale of these sizeable risks and vulnerabilities affect the central issues in sustainable development: how and where people live and work, access to essential resources and ecosystem services needed to sustain people in given locations, and the social and economic means to improve human wellbeing in the face of disruptions. This paper addresses the question: What are the characteristics of transformational adaptation and development in the context of profound changes in land and climate? To explore this question, this paper contains four case studies: managing storm water runoff related to the conversion of rural land to urban land in Indonesia; using a basket of interventions to manage social impacts of flooding in Nepal; combining a national glacier protection law with water rights management in Argentina; and community-based relocation in response to permafrost thaw and coastal erosion in Alaska. These case studies contribute to understanding characteristics of adaptation which is commensurate to sizeable risks and vulnerabilities to society in changing climate and land systems. Transformational adaptation is often perceived as a major large-scale intervention. In practice, the case studies in this article reveal that transformational adaptation is more likely to involve a bundle of adaptation interventions that are aimed at flexibly adjusting to change rather than reinforcing the status quo in ways of doing things. As a global mosaic, transformational change at a grand scale will occur through an inestimable number of smaller steps to adjust the central elements of human systems proportionate to the changes in climate and land systems. Understanding the characteristics of transformational adaptation will be essential to design and implement adaptation that keeps society in step with reconfiguring climate and land systems as they depart from current states.

Description: The Sonoma County Water Agency (SWCA) uses six radial collector wells along the Russian River west of Santa Rosa, to provide water for several municipalities and water districts in north-western California. Three collector wells (1, 2, and 6) are located in the Wohler area, and three collector wells (3, 4, and 5) are located in the Mirabel area. The objective of this paper is to highlight the performance of the three collector wells located in the Mirabel area since their construction. The 2015 investigation showed a lower performance of Collectors 3 and 4 compared to their original performances after construction in 1975, while the performance of Collector 5 was relatively stable since 1982. The potential change in capacity could be due to the increase in encrustation observed during the visual inspection of laterals in all three collector wells. Overall, the three collectors are still within the optimal design parameters (screen entrance velocity 〈 0.305 m min−1 and axial flow velocity of lateral screens 〈 1.524 m s−1).