Innovative technologies for terrestrial remote sensing

[In lieu of abstract, extract from first page] Characterizing and monitoring terrestrial, or land, surface features, such as forests, deserts, and cities, are fundamental and continuing goals of Earth Observation (EO). EO imagery and related technologies are essential for increasing our scientific understanding of environmental processes, such as carbon capture and albedo change, and to manage and safeguard environmental resources, such as tropical forests, particularly over large areas or the entire globe. This measurement or observation of some property of the land surface is central to a wide range of scientific investigations and industrial operations, involving individuals and organizations from many different backgrounds and disciplines. However, the process of observing the land provides a unifying theme for these investigations, and in practice there is much consistency in the instruments used for observation and the techniques used to map and model the environmental phenomena of interest. There is therefore great potential benefit in exchanging technological knowledge and experience among the many and diverse members of the terrestrial EO community

Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach

Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with varying spatial and spectral characteristics, and these are classified using three different approaches (maximum likelihood (ML), support vector machine (SVM) and object-based image analysis (OBIA)) to yield 36 output land cover maps. Classification accuracy is evaluated independently and McNemar tests are conducted between all paired outputs (630 pairs in total) to determine which classifications are significantly different. Overall accuracy varied between 35% for ML classification of 30 m spatial resolution, 4-band imagery and 91% for OBIA classification of 2 m spatial resolution, 8-band imagery. The results demonstrate that spatial resolution is clearly the most influential factor when mapping complex urban environments, and modern “very high resolution” or VHR sensors offer great advantage here. However, the advanced spectral capabilities provided by some recent sensors, coupled with contemporary classification approaches (especially SVMs and OBIA), can also lead to significant gains in mapping accuracy. Ongoing development in instrumentation and methodology offer huge potential here and imply that urban mapping opportunities will continue to grow

The natural resources of Bolinas Lagoon: their status and future

This publication is an integral part of the Department's high-priority inventory and assessment of coastal marshland and tideflat resources. It is intended as a guide for citizens, planners, administrators, and all others interested in the use and development of coastal lands and waters. Although the resources and problems of Bolinas Lagoon have probably been the subject of more biological and physical investigations than any small estuarine area of the California coast, many of the pertinent reports and information are not readily available to the public. Consequently, it is one purpose of this report to summarize the lagoon's history, ecological attractions, educational values and the problems facing its continued existence. At the same time, it should provide concerned citizens with a knowledge of the sources of additional and more specific information. Publication of this report is consistent with the obligation of the Department of Fish and Game to do everything in its power to protect and maintain the State's fish and wildlife resources. Therefore, its purpose transcends local issues on pollution and development, and the Department is, in fact, submitting a report to the people on the status and future of part of its inheritance and the dowry of coming generations. The report is the third of a scheduled series. It follows similar releases on Upper Newport Bay (Orange County) and Goleta Slough (Santa Barbara county) in March and June of 1970. Documentation of the resources of other critical areas is in progress. There will be future reports of this nature on Elkhorn Slough, Morro Bay, Tomales Bay, Humboldt Bay, and highly threatened marshlands in southern California. (137 pp.

Time-Series Satellite Imagery Demonstrates the Progressive Failure of a City Master Plan to Control Urbanization in Abuja, Nigeria

Urbanization is a global phenomenon, but its negative effects are most pronounced in developing countries. While much urbanization in the global South is unplanned, there have been occasional attempts at strategic, large-scale urban planning. One example is Abuja, Nigeria, a new city with origins in a 1970s Master Plan. Here, we use multi-temporal remote sensing to investigate four decades of urbanization in Abuja, showing the extent to which urban development has matched original intentions. Seven Landsat images from 1975 to 2014 were selected to correspond with Master Plan milestones and turning points in Nigeria’s socio-political development. Land cover classification and change detection results show built-up land increasing rapidly, from 1,167 ha in 1975 to 18,623 ha in 2014, mostly converted from grassland, often via a pioneer bare soil class. Comparing image analysis against the Master Plan shows that, in the early years, Abuja’s development matched broad planning intentions fairly closely. Later, though, unplanned development proliferated, and the city’s resemblance to the Master Plan diminished progressively. Level of adherence to the Master Plan varied widely according to the system of government. Notably, after long-term military rule was replaced by a democratic government around the turn of the millennium, unplanned development increased sharply

Scrubbing up: multi-scale investigation of woody encroachment in a southern African savannah

Changes in the extent of woody vegetation represent a major conservation question in many savannah systems around the globe. To address the problem of the current lack of broad-scale cost-effective tools for land cover monitoring in complex savannah environments, we use a multi-scale approach to quantifying vegetation change in Kruger National Park (KNP), South Africa. We test whether medium spatial resolution satellite data (Landsat, existing back to the 1970s), which have pixel sizes larger than typical vegetation patches, can nevertheless capture the thematic detail required to detect woody encroachment in savannahs. We quantify vegetation change over a 13-year period in KNP, examine the changes that have occurred, assess the drivers of these changes, and compare appropriate remote sensing data sources for monitoring change. We generate land cover maps for three areas of southern KNP using very high resolution (VHR) and medium resolution satellite sensor imagery from February 2001 to 2014. Considerable land cover change has occurred, with large increases in shrubs replacing both trees and grassland. Examination of exclosure areas and potential environmental driver data suggests two mechanisms: elephant herbivory removing trees and at least one separate mechanism responsible for conversion of grassland to shrubs, theorised to be increasing atmospheric CO2. Thus, the combination of these mechanisms causes the novel two-directional shrub encroachment that we observe (tree loss and grassland conversion). Multi-scale comparison of classifications indicates that although spatial detail is lost when using medium resolution rather than VHR imagery for land cover classification (e.g., Landsat imagery cannot readily distinguish between tree and shrub classes, while VHR imagery can), the thematic detail contained within both VHR and medium resolution classifications is remarkably congruent. This suggests that medium resolution imagery contains sufficient thematic information for most broad-scale land cover monitoring requirements in heterogeneous savannahs, while having the benefits of being cost-free and providing a longer historical archive of data than VHR sources. We conclude that monitoring of broad-scale land cover change using remote sensing has considerable potential as a cost-effective tool for both better informing land management practitioners, and for monitoring the future landscape-scale impacts of management policies in savannahs

Methane emissions from tree stems in neotropical peatlands

1.Neotropical peatlands emit large amounts of methane (CH4) from the soil surface, but fluxes from tree stems in these ecosystems are unknown. In this study we investigated CH4 emissions from five tree species in two forest types common to neotropical lowland peatlands in Panama.2.Methane from tree stems accounted for up to 30% of net ecosystem CH4 emissions. Peak CH4 fluxes were greater during the wet season when the water table was high and temperatures were lower. Emissions were greatest from the hardwood tree Campnosperma panamensis, but most species acted as emitters, with emissions declining exponentially with height along the stem for all species. 3.Overall, species identity, stem diameter, water level, soil temperature and soil CH4 fluxes explained 54% of the variance in stem CH4 emissions from individual trees. On the landscape level, the high high emission from Campnosperma panamensis forest these emitted comparable amounts of CH4 from tree stems as mixed forests at 340 kg CH4 day‐1 during flooded periods despite their substantially lower areal cover. 4.We conclude that emission from tree stems is an important emission pathway for CH4 flux from Neotropical peatlands, and that these emissions vary strongly with season and forest type

Probabilistic Mapping and Spatial Pattern Analysis of Grazing Lawns in Southern African Savannahs Using WorldView-3 Imagery and Machine Learning Techniques

Savannah grazing lawns are a key food resource for large herbivores such as blue wildebeest (Connochaetes taurinus), hippopotamus (Hippopotamus amphibius) and white rhino (Ceratotherium simum), and impact herbivore densities, movement and recruitment rates. They also exert a strong influence on fire behaviour including frequency, intensity and spread. Thus, variation in grazing lawn cover can have a profound impact on broader savannah ecosystem dynamics. However, knowledge of their present cover and distribution is limited. Importantly, we lack a robust, broad-scale approach for detecting and monitoring grazing lawns, which is critical to enhancing understanding of the ecology of these vital grassland systems. We selected two sites in the Lower Sabie and Satara regions of Kruger National Park, South Africa with mesic and semiarid conditions, respectively. Using spectral and texture features derived from WorldView-3 imagery, we (i) parameterised and assessed the quality of Random Forest (RF), Support Vector Machines (SVM), Classification and Regression Trees (CART) and Multilayer Perceptron (MLP) models for general discrimination of plant functional types (PFTs) within a sub-area of the Lower Sabie landscape, and (ii) compared model performance for probabilistic mapping of grazing lawns in the broader Lower Sabie and Satara landscapes. Further, we used spatial metrics to analyse spatial patterns in grazing lawn distribution in both landscapes along a gradient of distance from waterbodies. All machine learning models achieved high F-scores (F1) and overall accuracy (OA) scores in general savannah PFTs classification, with RF (F1 = 95.73±0.004%, OA = 94.16±0.004%), SVM (F1 = 95.64±0.002%, OA = 94.02±0.002%) and MLP (F1 = 95.71±0.003%, OA = 94.27±0.003%) forming a cluster of the better performing models and marginally outperforming CART (F1 = 92.74±0.006%, OA = 90.93±0.003%). Grazing lawn detection accuracy followed a similar trend within the Lower Sabie landscape, with RF, SVM, MLP and CART achieving F-scores of 0.89, 0.93, 0.94 and 0.81, respectively. Transferring models to the Satara landscape however resulted in relatively lower but high grazing lawn detection accuracies across models (RF = 0.87, SVM = 0.88, MLP = 0.85 and CART = 0.75). Results from spatial pattern analysis revealed a relatively higher proportion of grazing lawn cover under semiarid savannah conditions (Satara) compared to the mesic savannah landscape (Lower Sabie). Additionally, the results show strong negative correlation between grazing lawn spatial structure (fractional cover, patch size and connectivity) and distance from waterbodies, with larger and contiguous grazing lawn patches occurring in close proximity to waterbodies in both landscapes. The proposed machine learning approach provides a novel and robust workflow for accurate and consistent landscape-scale monitoring of grazing lawns, while our findings and research outputs provide timely information critical for understanding habitat heterogeneity in southern African savannah

Disengagement from care in a decentralised primary health care antiretroviral treatment programme: cohort study in rural South Africa.

OBJECTIVE: To determine rates of, and factors associated with, disengagement from care in a decentralised antiretroviral programme. METHODS: Adults (≥16 years) who initiated antiretroviral therapy (ART) in the Hlabisa HIV Treatment and Care Programme August 2004-March 2011 were included. Disengagement from care was defined as no clinic visit for 180 days, after adjustment for mortality. Cumulative incidence functions for disengagement from care, stratified by year of ART initiation, were obtained; competing-risks regression was used to explore factors associated with disengagement from care. RESULTS: A total of 4,674 individuals (median age 34 years, 29% male) contributed 13 610 person-years of follow-up. After adjustment for mortality, incidence of disengagement from care was 3.4 per 100 person-years (95% confidence interval (CI) 3.1-3.8). Estimated retention at 5 years was 61%. The risk of disengagement from care increased with each calendar year of ART initiation (P for trend 200 cells/μl respectively, compared with CD4 count <50 cells/μl). Of those disengaged from care with known outcomes, the majority (206/303, 68.0%) remained resident within the local community. CONCLUSIONS: Increasing disengagement from care threatens to limit the population impact of expanded antiretroviral coverage. The influence of both individual and programmatic factors suggests that alternative service delivery strategies will be required to achieve high rates of long-term retention