Recreation, tourism and nature in a changing world : proceedings of the fifth international conference on monitoring and management of visitor flows in recreational and protected areas : Wageningen, the Netherlands, May 30-June 3, 2010

Proceedings of the fifth international conference on monitoring and management of visitor flows in recreational and protected areas : Wageningen, the Netherlands, May 30-June 3, 201

National Educators' Workshop: Update 1989 Standard Experiments in Engineering Materials Science and Technology

Presented here is a collection of experiments presented and demonstrated at the National Educators' Workshop: Update 89, held October 17 to 19, 1989 at the National Aeronautics and Space Administration, Hampton, Virginia. The experiments related to the nature and properties of engineering materials and provided information to assist in teaching about materials in the education community

Oceanus.

v. 28, no. 1 (1985

Establishing an economic value for the mangroves of the Mngazana Estuary in the Eastern Cape.

Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2004.This mini-dissertation contains the results of research to establish a[1 economic value for the mangroves of the Mngazana Estuary in the Eastern Cape. The research is presented in two parts. Component A comprises the literature review and also describes the scope and context for the study, its purpose and the proposed methodology. Component B presents the results of the research in the format of an article to be submitted for publication to the African Journal of Marine Science. Estuaries and mangroves are among the most threatened habitats in South Africa, with the third largest mangrove forest in South Africa at the Mngazana Estuary on the Wild Coast of the Eastern Cape gradually reducing in size. A lack of appreciation of their value has resulted in policies and decisions that promoted the conversion of estuary and mangrove ecosystems to alternative uses, and caused a large-scale loss of mangroves throughout the world. Apart from their key ecological role, the Mngazana Estuary mangroves provide important benefits to the 645 households in three villages that utilise the resources and the sustainable use and management of the mangroves is essential. Economic valuation ascribes values to traded and untraded environmental resources and is a tool that supports policy formulation and decision-making on sustainable management of resources like mangroves. The theory of total economic value provides the conceptual framework for estimating the economic value, but constraints limited this study to estimating the socio-economically significant benefits the mangroves bestow on the communities around the Mngazana Estuary. Using information collected in a household survey and focus group discussions, market-price methods were used to estimate the value of mangroves harvested for building materials and the subsistence consumption of fish by the communities. Values were estimated for mangrove-dependent canoe trails and honey production operations, while a recreational use value was estimated on the basis of travel costs and expenses incurred by visitors to the holiday cottages adjacent to the estuary. The results were incorporated in 20-year valuation models with the net annual benefits then discounted to present value terms. Sensitivity analysis was performed to estimate lower-bound, upper-bound and most-likely values for the benefits. The minimum economic value of the mangroves was estimated to be between R1.1 and R13.6 million, with a most-likely value at a real 5% discount rate of R7.4 million. This study has shown that policies for managing environmental resources must be ecologically, socially and economically sound. This requires an integrated approach to address the socio-economic needs of local communities while safe-guarding environmental resources

Celebrating Economies of Change: Brave Visions for Inclusive Futures

This issue has been inspired by a path-breaking conference held by the Canadian Society for Ecologi-cal Economics (CANSEE), which took place this past May 2019 in Waterloo, Ontario. Entitled Engaging Economies of Change, the conference aimed to ex-pand existing research networks in the economy-environment nexus by building connections beyond the academy in order to meaningfully engage with the practicalities of building and implementing change. This issue captures the rich content shared during the event, as well as descriptions of the pro-cesses and efforts made to create a welcoming and respectful space where academics and community activists could build alliances and discuss common challenges. The conference organizers – all graduate students and activists themselves -- called this ‘building a brave space’.This research was supported by the Social Sciences and Humanities Research Council of Canad

Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes

With the increasing occurrence and growing public health concern that cyanobacteria blooms pose, it is crucial that we continue to explore ways to improve our ability to accurately, efficiently, and safely monitor water quality in impacted lakes. Also known as blue-green algae, cyanobacteria are naturally occurring in many waters globally. Cyanobacteria harmful algal blooms (CHABs) release various toxins which can cause skin irritations and dog fatalities while the effects of long-term exposure to the neurotoxins for humans has stirred additional studies. Although possibly harmful if CHABs are present, monitoring this biological component is nonetheless an integral factor when studying freshwater ecosystems. The use of an unmanned aerial system (UAS), equipped with a high resolution multispectral dual imaging sensor, provides a novel and full waterbody approach to quantify CHABs. Using a DJI M300 RTK unmanned aerial copter equipped with a MicaSense dual camera system collecting data in 10 wavelengths, six NH lakes were monitored from May-September 2022. Five of these six lakes experienced cyanobacteria blooms during this field season. Using the UAS collected spectral data coupled with collected in-situ water quality data, we used the random forest algorithm to classify the remotely sensed data and predict water quality classification categories. The analysis yielded very high overall accuracies for cyanobacteria cell concentration (93%), chlorophyll-a concentration (87%), and phycocyanin concentration (92%). Reflectance data from the 475 nm wavelength, the normalized green blue difference index – version 4 (NGBDI_4), and the normalized green-red difference index – version 4 (NGRDI_4) indices in addition to a few others were the most important features for these classifications. Additionally, simple logarithmic regressions illuminated relationships between single bands and indices with water quality data. Particularly, cell concentration with NGBDI_4 (R2 = 0.31), chlorophyll-a concentration with 475 nm (R2 = 0.24), and phycocyanin concentration with NGBDI_4 (R2 = 0.27). Therefore, our proposed monitoring approach successfully classified cyanobacteria cell, chlorophyll-a, and phycocyanin concentrations in the sampled NH lakes using UAS multispectral data while identifying the multispectral properties most important for cyanobacteria identification

VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING

These days, detection of Visual Attention Regions (VAR), such as moving objects has become an integral part of many Computer Vision applications, viz. pattern recognition, object detection and classification, video surveillance, autonomous driving, human-machine interaction (HMI), and so forth. The moving object identification using bounding boxes has matured to the level of localizing the objects along their rigid borders and the process is called foreground localization (FGL). Over the decades, many image segmentation methodologies have been well studied, devised, and extended to suit the video FGL. Despite that, still, the problem of video foreground (FG) segmentation remains an intriguing task yet appealing due to its ill-posed nature and myriad of applications. Maintaining spatial and temporal coherence, particularly at object boundaries, persists challenging, and computationally burdensome. It even gets harder when the background possesses dynamic nature, like swaying tree branches or shimmering water body, and illumination variations, shadows cast by the moving objects, or when the video sequences have jittery frames caused by vibrating or unstable camera mounts on a surveillance post or moving robot. At the same time, in the analysis of traffic flow or human activity, the performance of an intelligent system substantially depends on its robustness of localizing the VAR, i.e., the FG. To this end, the natural question arises as what is the best way to deal with these challenges? Thus, the goal of this thesis is to investigate plausible real-time performant implementations from traditional approaches to modern-day deep learning (DL) models for FGL that can be applicable to many video content-aware applications (VCAA). It focuses mainly on improving existing methodologies through harnessing multimodal spatial and temporal cues for a delineated FGL. The first part of the dissertation is dedicated for enhancing conventional sample-based and Gaussian mixture model (GMM)-based video FGL using probability mass function (PMF), temporal median filtering, and fusing CIEDE2000 color similarity, color distortion, and illumination measures, and picking an appropriate adaptive threshold to extract the FG pixels. The subjective and objective evaluations are done to show the improvements over a number of similar conventional methods. The second part of the thesis focuses on exploiting and improving deep convolutional neural networks (DCNN) for the problem as mentioned earlier. Consequently, three models akin to encoder-decoder (EnDec) network are implemented with various innovative strategies to improve the quality of the FG segmentation. The strategies are not limited to double encoding - slow decoding feature learning, multi-view receptive field feature fusion, and incorporating spatiotemporal cues through long-shortterm memory (LSTM) units both in the subsampling and upsampling subnetworks. Experimental studies are carried out thoroughly on all conditions from baselines to challenging video sequences to prove the effectiveness of the proposed DCNNs. The analysis demonstrates that the architectural efficiency over other methods while quantitative and qualitative experiments show the competitive performance of the proposed models compared to the state-of-the-art

COMMODITY AUDIENCE, COMMODITY EVERYTHING: INTERROGATING T-COMMERCE IN THE UNITED STATES CABLE INDUSTRY

This thesis is a theoretical and historical investigation of interactive television commerce (t-commerce). T-commerce lets viewers buy the commodities appearing in advertisements and program content. Additionally, t-commerce utilizes advanced advertising formats that target consumers precisely with customized advertisements. This thesis is grounded in theories of the audience commodity. It is argued that t-commerce is consistent with the historical trajectory of advertiser-supported television in which profits are generated by producing audiences of consumers. The business of commercial television has always been structured to produce consumers as economic and social products. The linchpin of their value as commodities is their capacity to consume. T- commerce increases the value of audiences of consumers by situating viewers in a marketplace that exhorts impulse buying and monitors consumption-related behaviour