Multi-temporal land-cover classification and change analysis with conditional probability networks: The case of Lesvos Island (Greece)

This study uses a series of Landsat images to map the main land-cover types on the Mediterranean island of Lesvos, Greece. We compare a single-year maximum likelihood classification (MLC) with a multi-temporal maximum likelihood classification (MTMLC) approach, with time-series class labels modelled using a first-order hidden Markov model comprising continuous and discrete variables. A rigorous validation scheme shows statistically significant higher accuracy figures for the multi-temporal approach. Land-cover change accuracies were also greatly improved by the proposed methodology: from 46% to 70%. The results show that when only two dates are used, the mapping of land use/cover is unreliable and a large number of the changes identified are due to the individual-year commission and omission errors

Annual Forest Monitoring as part of Indonesia's National Carbon Accounting System

Land use and forest change, in particular deforestation, have contributed the largest proportion of Indonesia’s estimated greenhouse gas emissions. Indonesia’s remaining forests store globally significant carbon stocks, as well as biodiversity values. In 2010, the Government of Indonesia entered into a REDD+ partnership. A spatially detailed monitoring and reporting system for forest change which is national and operating in Indonesia is required for participation in such programs, as well as for national policy reasons including Monitoring, Reporting, and Verification (MRV), carbon accounting, and land-use and policy information. Indonesia’s National Carbon Accounting System (INCAS) has been designed to meet national and international policy requirements. The INCAS remote sensing program is producing spatially-detailed annual wall-to-wall monitoring of forest cover changes from time-series Landsat imagery for the whole of Indonesia from 2000 to the present day. Work on the program commenced in 2009, under the Indonesia-Australia Forest Carbon Partnership. A principal objective was to build an operational system in Indonesia through transfer of knowledge and experience, from Australia’s National Carbon Accounting System, and adaptation of this experience to Indonesia’s requirements and conditions. A semi-automated system of image pre-processing (ortho-rectification, calibration, cloud masking and mosaicing) and forest extent and change mapping (supervised classification of a ‘base’ year, semi-automated single-year classifications and classification within a multi-temporal probabilistic framework) was developed for Landsat 5 TM and Landsat 7 ETM+. Particular attention is paid to the accuracy of each step in the processing. With the advent of Landsat 8 data and parallel development of processing capability, capacity and international collaborations within the LAPAN Data Centre this processing is being increasingly automated. Research is continuing into improved processing methodology and integration of information from other data sources. This paper presents technical elements of the INCAS remote sensing program and some results of the 2000 – 2012 mapping

A multi-agency project of the Western Australian Salinity Action Plan supported by the Natural Heritage Trust

Land Monitor is a multi-agency project of the Western Australian Salinity Action Plan supported by the Natural Heritage Trust. It will provide land managers and administrators with baseline salinity and vegetation data for monitoring changes over time, and land height data from which contours accurate to two metre intervals can be produced. The Project will also provide estimates of areas at risk from secondary or future salinisation. Land Monitor will cover the 18 million hectares of agricultural area of south-west, Western Australia. Sequences of calibrated Landsat Thematic Mapper satellite images integrated with landform information derived from height data, ground truthing and other existing mapped data sets are used as the basis for monitoring changes in salinity and woody vegetation. Heights are derived on a 10m grid from stereo aerial photography flown at 1:40,000 scale, using soft-copy automatic terrain extraction (image correlation) techniques. Proposed Land Monitor products include salinity maps, predicted salinity maps, enhanced imagery, vegetation status maps and spectral / temporal statistics. These products will be available in a range of formats and scales, from paddock, farm to catchment and shire scales to suit customer needs

The Land Monitor Project

The Land Monitor Project is providing information over the southwest agricultural region of WA. It is assembling and processing sequences of Landsat TM data, a new highresolution digital elevation model (DEM) and other spatial data to provide monitoring information on the area of salt-affected land, and on changes in the area and status of perennial vegetation over the period 1988-2000. Land Monitor is a multi-agency project of the Western Australian Salinity Action Plan supported by the Natural Heritage Trust. The Project will also providing estimates of areas at risk from secondary or future salinisation, based on the historical salinity maps and a set of landform variables derived from the high resolution DEM. Sequences of calibrated Landsat Thematic Mapper satellite images integrated with landform information derived from height data, ground truthing and other existing mapped data are used as the basis for monitoring changes in salinity and woody vegetation. Procedures for accurate registration and calibration were developed by CSIRO Mathematical and Information Sciences (CMIS), as were the data integration procedures for salinity mapping and prediction. For the DEM, heights are derived on a 10m grid from stereo aerial photography flown at 1:40,000 scale, using soft-copy automatic terrain extraction (image correlation) techniques. Land Monitor products include: high resolution DEMs; calibrated sequences of Landsat imgery; present and historical salinity maps; predicted salinity maps; maps of change in vegetation status and spectral/temporal statistics. These products are available in a range of formats and scales, from paddock to catchment and shire scales to suit customer needs

Physical activity monitoring to assess disability progression in multiple sclerosis

Background: Clinical outcome measurement in multiple sclerosis (MS) usually requires a physical visit. Remote activity monitoring (RAM) using wearable technology provides a rational alternative, especially desirable when distance is involved or in a pandemic setting. Objective: To validate RAM in progressive MS using (1) traditional psychometric methods (2) brain atrophy. Methods: 56 people with progressive MS participated in a longitudinal study over 2.5 years. An arm-worn RAM device measured activity over six days, every six months, and incorporated triaxial accelerometry and transcutaneous physiological variable measurement. Five RAM variables were assessed: physical activity duration, step count, active energy expenditure, metabolic equivalents and a composite RAM score incorporating all four variables. Other assessments every six months included EDSS, MSFC, MSIS-29, Chalder Fatigue Scale and Beck’s Depression Inventory. Annualized brain atrophy was measured using SIENA. Results: RAM was tolerated well by people with MS; the device was worn 99.4% of the time. RAM had good convergent and divergent validity and was responsive, especially with respect to step count. Measurement of physical activity over one day was as responsive as six days. The composite RAM score positively correlated with brain volume loss. Conclusion: Remote activity monitoring is a valid and acceptable outcome measure in MS

Framework, principles and recommendations for utilising participatory methodologies in the co-creation and evaluation of public health interventions

Background: Due to the chronic disease burden on society, there is a need for preventive public health interventions to stimulate society towards a healthier lifestyle. To deal with the complex variability between individual lifestyles and settings, collaborating with end-users to develop interventions tailored to their unique circumstances has been suggested as a potential way to improve effectiveness and adherence. Co-creation of public health interventions using participatory methodologies has shown promise but lacks a framework to make this process systematic. The aim of this paper was to identify and set key principles and recommendations for systematically applying participatory methodologies to co-create and evaluate public health interventions. Methods: These principles and recommendations were derived using an iterative reflection process, combining key learning from published literature in addition to critical reflection on three case studies conducted by research groups in three European institutions, all of whom have expertise in co-creating public health interventions using different participatory methodologies. Results: Key principles and recommendations for using participatory methodologies in public health intervention co-creation are presented for the stages of: Planning (framing the aim of the study and identifying the appropriate sampling strategy); Conducting (defining the procedure, in addition to manifesting ownership); Evaluating (the process and the effectiveness) and Reporting (providing guidelines to report the findings). Three scaling models are proposed to demonstrate how to scale locally developed interventions to a population level. Conclusions: These recommendations aim to facilitate public health intervention co-creation and evaluation utilising participatory methodologies by ensuring the process is systematic and reproducible

Simultaneous genetic analysis of means and covariance structure: Pearson-Lawley selection rules

The object of this paper is to indicate that the Pearson-Lawley selection rules form a plausible general theory for the simultaneous genetic analysis of means and covariance structure. Models are presented based on phenotypic selection and latent selection. Previously presented quantitative genetic models to decompose means and covariance structure simultaneously are reconsidered as instances of latent selection. The selection rules are very useful in the context of behavior genetic modeling because they lead to testable models and a conceptual framework for explaining variation between and within groups by the same genetic and environmental factors. © 1994 Plenum Publishing Corporation

Assessing Causality in the Relationship Between Adolescents’ Risky Sexual Online Behavior and Their Perceptions of this Behavior

The main aim of this study was to investigate the causal nature of the relationship between adolescents’ risky sexual behavior on the internet and their perceptions of this behavior. Engagement in the following online behaviors was assessed: searching online for someone to talk about sex, searching online for someone to have sex, sending intimate photos or videos to someone online, and sending one’s telephone number and address to someone exclusively known online. The relationship between these behaviors and adolescents’ perceptions of peer involvement, personal invulnerability, and risks and benefits was investigated. A two-wave longitudinal study among a representative sample of 1,445 Dutch adolescents aged 12–17 was conducted (49% females). Autoregressive cross-lagged structural equation models revealed that perceived peer involvement, perceived vulnerability, and perceived risks were all significant predictors of risky sexual online behavior 6 months later. No reverse causal paths were found. When the relationships between perceptions and risky sexual online behavior were modeled simultaneously, only perceived peer involvement was a determinant of risky sexual online behavior. Findings highlight the importance of addressing peer involvement in future interventions to reduce adolescents’ risky sexual online behavior