Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

Generative models of the human connectome

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.Comment: 38 pages, 5 figures + 19 supplemental figures, 1 tabl

The role of tropical forests in supporting biodiversity and hydrological integrity: a synoptic overview

Conservation of high-biodiversity tropical forests is sometimes justified on the basis of assumed hydrological benefits - in particular, the reduction of flooding hazards for downstream floodplain populations. However, the"far-field"link between deforestation and distant flooding has been difficult to demonstrate empirically. This simulation study assesses the relationship between forest cover and hydrology for all river basins intersecting the world's tropical forest biomes. The study develops a consistent set of pan-tropical land cover maps gridded at one-half degree latitude and longitude. It integrates these data with existing global biogeophysical data. The study applies the Water Balance Model - a coarse-scale process-based hydrological model - to assess the impact of land cover changes on runoff. It quantifies the impacts of forest conversion on biodiversity and hydrology for two scenarios - historical forest conversion and the potential future conversion of the most threatened remaining tropical forests. A worst-case scenario of complete conversion of the most threatened of the remaining forested areas would mean the loss of another three million km2 of tropical forests. Increased annual yield from the conversion of threatened tropical forests would be less than 5 percent of contemporary yield in aggregate. However, about 100 million people - 80 million of them in floodplains - would experience increases of more than 25 percent in annual water flows. This might be associated with commensurate increases in peak flows, though further analysis would be necessary to gauge the impact on flooding. The study highlights basins in Southeast Asia, southern China, and Latin America that warrant further study.Wetlands,Forestry,Climate Change,Drylands&Desertification,Earth Sciences&GIS

Effects of publication bias on conservation planning

Conservation planning needs reliable information on spatial patterns of biodiversity. However, existing data sets are skewed: some habitats, taxa, and locations are under-represented. Here, we map geographic publication density at the sub-national scale of individual 'provinces'. We query the Web of Science catalogues SCI and SSCI for biodiversity-related publications including country and province names (for the period 1993-2016). We combine these data with other provincial-scale factors hypothesised to affect research (i.e. economic development, human presence, infrastructure and remoteness). We show that sites that appear to be understudied, compared with the biodiversity expected from their bioclimatic conditions, are likely to have been inaccessible to researchers for a diversity of reasons amongst which current or recent armed conflicts are notable. Finally, we create a priority list of provinces where geographic publication bias is of most concern, and discuss how our provincial-scale model can assist in adjusting for publication biases in conservation planning.Comment: 10 pages; 3 figures; 1 table;R code on https://github.com/raffael-hickisch; data at https://zenodo.org/record/998889; interactive at http://bit.ly/publication_density_ma

Mapping Mangrove Extent and Change: A Globally Applicable Approach

This study demonstrates a globally applicable method for monitoring mangrove forest extent at high spatial resolution. A 2010 mangrove baseline was classified for 16 study areas using a combination of ALOS PALSAR and Landsat composite imagery within a random forests classifier. A novel map-to-image change method was used to detect annual and decadal changes in extent using ALOS PALSAR/JERS-1 imagery. The map-to-image method presented makes fewer assumptions of the data than existing methods, is less sensitive to variation between scenes due to environmental factors (e.g., tide or soil moisture) and is able to automatically identify a change threshold. Change maps were derived from the 2010 baseline to 1996 using JERS-1 SAR and to 2007, 2008 and 2009 using ALOS PALSAR. This study demonstrated results for 16 known hotspots of mangrove change distributed globally, with a total mangrove area of 2,529,760 ha. The method was demonstrated to have accuracies consistently in excess of 90% (overall accuracy: 92.293.3%, kappa: 0.86) for mapping baseline extent. The accuracies of the change maps were more variable and were dependent upon the time period between images and number of change features. Total change from 1996 to 2010 was 204,850 ha (127,990 ha gain, 76,860 ha loss), with the highest gains observed in French Guiana (15,570 ha) and the highest losses observed in East Kalimantan, Indonesia (23,003 ha). Changes in mangrove extent were the consequence of both natural and anthropogenic drivers, yielding net increases or decreases in extent dependent upon the study site. These updated maps are of importance to the mangrove research community, particularly as the continual updating of the baseline with currently available and anticipated spaceborne sensors. It is recommended that mangrove baselines are updated on at least a 5-year interval to suit the requirements of policy makers

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

TaLAM: Mapping Land Cover in Lowlands and Uplands with Satellite Imagery

End-of-Project ReportThe Towards Land Cover Accounting and Monitoring (TaLAM) project is part of Ireland’s response to creating a national land cover mapping programme. Its aims are to demonstrate how the new digital map of Ireland, Prime2, from Ordnance Survey Ireland (OSI), can be combined with satellite imagery to produce land cover maps