AgRISTARS: Agriculture and resources inventory surveys through aerospace remote sensing

The rationale, objectives, participants, and approach of the AgRISTARS program are described. Progress is reported in activities related to early warning and crop condition assessment; inventory technology development (formerly foreign commodity production forecasting); yield model development; supporting research; soil moisture; renewable resources inventory; domestic crops and land cover; and conservation and pollution. Emphasis is on technological highlights

Application of remote sensing to selected problems within the state of California

There are no author-identified significant results in this report

Application of remote sensing to selected problems within the state of California

Specific case studies undertaken to demonstrate the usefulness of remote sensing technology to resource managers in California are highlighted. Applications discussed include the mapping and quantization of wildland fire fuels in Mendocino and Shasta Counties as well as in the Central Valley; the development of a digital spectral/terrain data set for Colusa County; the Forsythe Planning Experiment to maximize the usefulness of inputs from LANDSAT and geographic information systems to county planning in Mendocino County; the development of a digital data bank for Big Basin State Park in Santa Cruz County; the detection of salinity related cotton canopy reflectance differences in the Central Valley; and the surveying of avocado acreage and that of other fruits and nut crops in Southern California. Special studies include the interpretability of high altitude, large format photography of forested areas for coordinated resource planning using U-2 photographs of the NASA Bucks Lake Forestry test site in the Plumas National Forest in the Sierra Nevada Mountains

Application of remote sensing to selected problems within the state of California

There are no author-identified significant results in this report

Scoping a public health impact assessment of aquaculture with particular reference to tilapia in the UK

Background. The paper explores shaping public health impact assessment tools for tilapia, a novel emergent aquaculture sector in the UK. This Research Council’s UK Rural Economy and Land Use project embraces technical, public health, and marketing perspectives scoping tools to assess possible impacts of the activity. Globally, aquaculture produced over 65 million tonnes of food in 2008 and will grow significantly requiring apposite global public health impact assessment tools.<p></p> Methods. Quantitative and qualitative methods incorporated data from a tridisciplinary literature. Holistic tools scoped tilapia farming impact assessments. Laboratory-based tilapia production generated data on impacts in UK and Thailand along with 11 UK focus groups involving 90 consumers, 30 interviews and site visits, 9 visits to UK tilapia growers and 2 in The Netherlands.<p></p> Results. The feasibility, challenges, strengths, and weaknesses of creating a tilapia Public Health Impact Assessment are analysed. Occupational and environmental health benefits and risks attached to tilapia production were identified.<p></p> Conclusions. Scoping public health impacts of tilapia production is possible at different levels and forms for producers, retailers, consumers, civil society and governmental bodies that may contribute to complex and interrelated public health assessments of aquaculture projects. Our assessment framework constitutes an innovatory perspective in the field

F as in Fat: How Obesity Policies Are Failing in America, 2005

Examines national and state obesity rates and government policies. Challenges the research community to focus on major research questions to inform policy decisions, and policymakers to pursue actions to combat the obesity crisis

Probabilistic and Deep Learning Algorithms for the Analysis of Imagery Data

Accurate object classification is a challenging problem for various low to high resolution imagery data. This applies to both natural as well as synthetic image datasets. However, each object recognition dataset poses its own distinct set of domain-specific problems. In order to address these issues, we need to devise intelligent learning algorithms which require a deep understanding and careful analysis of the feature space. In this thesis, we introduce three new learning frameworks for the analysis of both airborne images (NAIP dataset) and handwritten digit datasets without and with noise (MNIST and n-MNIST respectively). First, we propose a probabilistic framework for the analysis of the NAIP dataset which includes (1) an unsupervised segmentation module based on the Statistical Region Merging algorithm, (2) a feature extraction module that extracts a set of standard hand-crafted texture features from the images, (3) a supervised classification algorithm based on Feedforward Backpropagation Neural Networks, and (4) a structured prediction framework using Conditional Random Fields that integrates the results of the segmentation and classification modules into a single composite model to generate the final class labels. Next, we introduce two new datasets SAT-4 and SAT-6 sampled from the NAIP imagery and use them to evaluate a multitude of Deep Learning algorithms including Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Autoencoders (SAE) for generating class labels. Finally, we propose a learning framework by integrating hand-crafted texture features with a DBN. A DBN uses an unsupervised pre-training phase to perform initialization of the parameters of a Feedforward Backpropagation Neural Network to a global error basin which can then be improved using a round of supervised fine-tuning using Feedforward Backpropagation Neural Networks. These networks can subsequently be used for classification. In the following discussion, we show that the integration of hand-crafted features with DBN shows significant improvement in performance as compared to traditional DBN models which take raw image pixels as input. We also investigate why this integration proves to be particularly useful for aerial datasets using a statistical analysis based on Distribution Separability Criterion. Then we introduce a new dataset called noisy-MNIST (n-MNIST) by adding (1) additive white gaussian noise (AWGN), (2) motion blur and (3) Reduced contrast and AWGN to the MNIST dataset and present a learning algorithm by combining probabilistic quadtrees and Deep Belief Networks. This dynamic integration of the Deep Belief Network with the probabilistic quadtrees provide significant improvement over traditional DBN models on both the MNIST and the n-MNIST datasets. Finally, we extend our experiments on aerial imagery to the class of general texture images and present a theoretical analysis of Deep Neural Networks applied to texture classification. We derive the size of the feature space of textural features and also derive the Vapnik-Chervonenkis dimension of certain classes of Neural Networks. We also derive some useful results on intrinsic dimension and relative contrast of texture datasets and use these to highlight the differences between texture datasets and general object recognition datasets

Lower Mekong Portfolio: Interim Evaluation

This report summarizes a portfolio evaluation of the MacArthur Foundation's conservation investments in the Lower Mekong region since 2011. It is explicitly a portfolio-level evaluation, focusing on common themes rather than individual grants. The evaluation involved understanding the portfolio context through reviewing relevant documents and speaking with donor partners; gathering data from MacArthur grantees; calibrating initial evaluation findings through consultations with independent regional experts and donor partner grantees; improving future evaluation ability by cooperating with NatureServe to improve the Lower Mekong Dashboard; and presenting results in this evaluation report and to MacArthur directly

Activities of the Remote Sensing Information Sciences Research Group

Topics on the analysis and processing of remotely sensed data in the areas of vegetation analysis and modelling, georeferenced information systems, machine assisted information extraction from image data, and artificial intelligence are investigated. Discussions on support field data and specific applications of the proposed technologies are also included