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Article ; Online: Geospatial Management and Analysis of Microstructural Data from San Andreas Fault Observatory at Depth (SAFOD) Core Samples

Elliott M. Holmes / Andrea E. Gaughan / Donald J. Biddle / Forrest R. Stevens / Jafar Hadizadeh

ISPRS International Journal of Geo-Information, Vol 10, Iss 332, p

2021 Volume 332

Abstract: Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying ... ...

Abstract	Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying microstructural data within a spatially referenced Geographic Information System (GIS) environment provides an opportunity to readily locate, visualize, correlate, and apply remote sensing techniques to the data. Using 26 core billet samples from the San Andreas Fault Observatory at Depth (SAFOD), this study developed GIS-based procedures for: 1. Spatially referenced visualization and storage of various microstructural data from core billets; 2. 3D modeling of billets and thin section positions within each billet, which serve as a digital record after irreversible fragmentation of the physical billets; and 3. Vector feature creation and unsupervised classification of a multi-generation calcite vein network from cathodluminescence (CL) imagery. Building on existing work which is predominantly limited to the 2D space of single thin sections, our results indicate that a GIS can facilitate spatial treatment of data even at centimeter to nanometer scales, but also revealed challenges involving intensive 3D representations and complex matrix transformations required to create geographically translated forms of the within-billet coordinate systems, which are suggested for consideration in future studies.
Keywords	Geographic Information Systems (GIS) ; remote sensing ; structural geology ; 3D visualization ; spatial analyses ; Geography (General) ; G1-922
Subject code	910
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Evaluating the Accuracy of Gridded Population Estimates in Slums

Dana R. Thomson / Andrea E. Gaughan / Forrest R. Stevens / Gregory Yetman / Peter Elias / Robert Chen

Urban Science, Vol 5, Iss 48, p

A Case Study in Nigeria and Kenya

2021 Volume 48

Abstract: Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in ... ...

Abstract	Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. In this study, we compared field-referenced boundaries and population counts from Slum Dwellers International in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) with nine gridded population datasets to assess their statistical accuracy in slums. We found that all gridded population estimates vastly underestimated population in slums (RMSE: 4958 to 14,422, Bias: −2853 to −7638), with the most accurate dataset (HRSL) estimating just 39 per cent of slum residents. Using a modelled map of all slums in Lagos to compare gridded population datasets in terms of SDG 11.1.1 (percent of population living in deprived areas), all gridded population datasets estimated this indicator at just 1–3 per cent compared to 56 per cent using UN-Habitat’s approach. We outline steps that might improve that accuracy of each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG 11.1.1, we are optimistic that some could be used in the future following updates to their modelling approaches.
Keywords	SDG11 ; urban ; deprivation ; informal settlement ; poverty ; mapping ; Geography. Anthropology. Recreation ; G ; Social Sciences ; H
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Using Very-High-Resolution Multispectral Classification to Estimate Savanna Fractional Vegetation Components

Andrea E. Gaughan / Nicholas E. Kolarik / Forrest R. Stevens / Narcisa G. Pricope / Lin Cassidy / Jonathan Salerno / Karen M. Bailey / Michael Drake / Kyle Woodward / Joel Hartter

Remote Sensing, Vol 14, Iss 551, p

2022 Volume 551

Abstract: Characterizing compositional and structural aspects of vegetation is critical to effectively assessing land function. When priorities are placed on ecological integrity, remotely sensed estimates of fractional vegetation components (FVCs) are useful for ... ...

Abstract	Characterizing compositional and structural aspects of vegetation is critical to effectively assessing land function. When priorities are placed on ecological integrity, remotely sensed estimates of fractional vegetation components (FVCs) are useful for measuring landscape-level habitat structure and function. In this study, we address whether FVC estimates, stratified by dominant vegetation type, vary with different classification approaches applied to very-high-resolution small unoccupied aerial system (UAS)-derived imagery. Using Parrot Sequoia imagery, flown on a DJI Mavic Pro micro-quadcopter, we compare pixel- and segment-based random forest classifiers alongside a vegetation height-threshold model for characterizing the FVC in a southern African dryland savanna. Results show differences in agreement between each classification method, with the most disagreement in shrub-dominated sites. When compared to vegetation classes chosen by visual identification, the pixel-based random forest classifier had the highest overall agreement and was the only classifier not to differ significantly from the hand-delineated FVC estimation. However, when separating out woody biomass components of tree and shrub, the vegetation height-threshold performed better than both random-forest approaches. These findings underscore the utility and challenges represented by very-high-resolution multispectral UAS-derived data (~10 cm ground resolution) and their uses to estimate FVC. Semi-automated approaches statistically differ from by-hand estimation in most cases; however, we present insights for approaches that are applicable across varying vegetation types and structural conditions. Importantly, characterization of savanna land function cannot rely only on a “greenness” measure but also requires a structural vegetation component. Underscoring these insights is that the spatial heterogeneity of vegetation structure on the landscape broadly informs land management, from land allocation, wildlife habitat use, natural resource ...
Keywords	savannas ; vegetation composition ; Africa ; random forest classifier ; vegetation structure ; unoccupied aerial systems ; Science ; Q
Subject code	710
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Measuring the contribution of built-settlement data to global population mapping

Jeremiah J. Nieves / Maksym Bondarenko / David Kerr / Nikolas Ves / Greg Yetman / Parmanand Sinha / Donna J. Clarke / Alessandro Sorichetta / Forrest R. Stevens / Andrea E. Gaughan / Andrew J. Tatem

Social Sciences and Humanities Open, Vol 3, Iss 1, Pp 100102- (2021)

2021

Abstract: Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built- ...

Abstract	Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built-environment and settlements as key predictive covariates. While these RS-derived data, which are global in extent, have become more advanced and more available, gaps in spatial and temporal coverage remain. These gaps have prompted the interpolation of the built-environment and settlements, but the utility of such interpolated data in further population modelling applications has garnered little research. Thus, our objective was to determine the utility of modelled built-settlement extents in a top-down population modelling application. Here we take modelled global built-settlement extents between 2000 and 2012, created using a spatio-temporal disaggregation of observed settlement growth. We then demonstrate the applied utility of such annually modelled settlement data within the application of annually modelling population, using random forest informed dasymetric disaggregations, across 172 countries and a 13-year period. We demonstrate that the modelled built-settlement data are consistently the 2nd most important covariate in predicting population density, behind annual lights at night, across the globe and across the study period. Further, we demonstrate that this modelled built-settlement data often provides more information than current annually available RS-derived data and last observed built-settlement extents.
Keywords	Urban ; Population ; Growth model ; Built ; Settlement ; Machine learning ; History of scholarship and learning. The humanities ; AZ20-999 ; Social sciences (General) ; H1-99
Subject code	910
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape

Kyle D. Woodward / Narcisa G. Pricope / Forrest R. Stevens / Andrea E. Gaughan / Nicholas E. Kolarik / Michael D. Drake / Jonathan Salerno / Lin Cassidy / Joel Hartter / Karen M. Bailey / Henry Maseka Luwaya

Remote Sensing, Vol 13, Iss 4, p

Integrating Remote Sensing and Participatory Mapping

2021 Volume 631

Abstract: Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging ... ...

Abstract	Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.
Keywords	remote sensing ; participatory mapping ; NTFP ; grazing ; random forest ; natural resources ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Climate change beliefs and forest management in eastern Oregon

Angela E. Boag / Joel Hartter / Lawrence C. Hamilton / Nils D. Christoffersen / Forrest R. Stevens / Michael W. Palace / Mark J. Ducey

Ecology and Society, Vol 23, Iss 4, p

implications for individual adaptive capacity

2018 Volume 1

Abstract: The management decisions of private landowners affect forest structure and composition, and may impact the resilience of forested regions. In this case study we assessed barriers to both intentional and incidental climate-adaptive forest management among ...

Abstract	The management decisions of private landowners affect forest structure and composition, and may impact the resilience of forested regions. In this case study we assessed barriers to both intentional and incidental climate-adaptive forest management among nonindustrial private forest owners in eastern Oregon, USA. In this context, incidental adaptations result from synergies between climate-adaptive forest management and actions motivated by goals such as wildfire mitigation, which landowners may prioritize regardless of concerns about climate change. Through semistructured interviews we used qualitative analyses to identify barriers to adaptation, including subjective (cognitive and experiential) and structural barriers (social, political, and economic) by comparing individual cases. Overall, we found that intentional climate change adaptation had low salience among participants, though a large majority of forest owners were active managers motivated by other goals, contributing to widespread incidental adaptation. We found that nonindustrial private forest owners who engaged in or considered intentional climate adaptation actions generally believed that anthropogenic climate change is occurring. Many respondents perceived local environmental change, notably reduced snowpack, but this was not associated with adaptive actions or intentions. The few participants who considered or implemented intentional climate adaptation actions generally had written forest management plans containing both forest inventories and specific management goals. Improving access to resources for forest management planning may enhance fire- and climate-smart forest management by facilitating scenario visioning and formalizing intentions. Although climate change beliefs were subjective barriers to intentional climate adaptation, many of the same structural barriers limited intentional and incidental adaptation. Place-based education, reliable funding mechanisms, and cooperative approaches among landowners may enhance adaptive capacity and ...
Keywords	adaptive capacity ; climate change ; climate change adaptation ; drought ; forest management ; private land ; resilience ; Biology (General) ; QH301-705.5 ; Ecology ; QH540-549.5
Subject code	333
Language	English
Publishing date	2018-12-01T00:00:00Z
Publisher	Resilience Alliance
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Gridded Population Maps Informed by Different Built Settlement Products

Fennis J. Reed / Andrea E. Gaughan / Forrest R. Stevens / Greg Yetman / Alessandro Sorichetta / Andrew J. Tatem

Data, Vol 3, Iss 3, p

2018 Volume 33

Abstract: The spatial distribution of humans on the earth is critical knowledge that informs many disciplines and is available in a spatially explicit manner through gridded population techniques. While many approaches exist to produce specialized gridded ... ...

Abstract	The spatial distribution of humans on the earth is critical knowledge that informs many disciplines and is available in a spatially explicit manner through gridded population techniques. While many approaches exist to produce specialized gridded population maps, little has been done to explore how remotely sensed, built-area datasets might be used to dasymetrically constrain these estimates. This study presents the effectiveness of three different high-resolution built area datasets for producing gridded population estimates through the dasymetric disaggregation of census counts in Haiti, Malawi, Madagascar, Nepal, Rwanda, and Thailand. Modeling techniques include a binary dasymetric redistribution, a random forest with a dasymetric component, and a hybrid of the previous two. The relative merits of these approaches and the data are discussed with regards to studying human populations and related spatially explicit phenomena. Results showed that the accuracy of random forest and hybrid models was comparable in five of six countries.
Keywords	gridded population distribution ; geography ; built areas ; remote sensing ; geographic information systems ; random forest ; regression ; binary dasymetric ; Bibliography. Library science. Information resources ; Z
Subject code	910 ; 333
Language	English
Publishing date	2018-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations

Jeremiah J. Nieves / Maksym Bondarenko / Alessandro Sorichetta / Jessica E. Steele / David Kerr / Alessandra Carioli / Forrest R. Stevens / Andrea E. Gaughan / Andrew J. Tatem

Remote Sensing, Vol 12, Iss 1545, p

2020 Volume 1545

Abstract: Advances in the availability of multi-temporal, remote sensing-derived global built-/human-settlements datasets can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time- ... ...

Abstract	Advances in the availability of multi-temporal, remote sensing-derived global built-/human-settlements datasets can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time-series of past extents and urban growth/expansion models have not had parallel advances at high-spatial resolution. Here our goal was to present a globally applicable predictive modelling framework, as informed by a short, preceding time-series of built-settlement extents, capable of producing annual, near-future built-settlement extents. To do so, we integrated a random forest, dasymetric redistribution, and autoregressive temporal models with open and globally available subnational data, estimates of built-settlement population, and environmental covariates. Using this approach, we trained the model on a 11 year time-series (2000–2010) of European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover “Urban Areas” class and predicted annual, 100m resolution, binary settlement extents five years beyond the last observations (2011–2015) within varying environmental, urban morphological, and data quality contexts. We found that our model framework performed consistently across all sampled countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and the withheld validation settlement extents. When comparing manually delineated building footprints of small settlements to the modelled extents, we saw that the modelling framework had a 12 percent increase in accuracy compared to withheld validation settlement extents. However, how this framework performs when using different input definitions of “urban” or settlement remains unknown. While this model framework is predictive and not explanatory in nature, it shows that globally available “off-the-shelf” datasets and relative changes in subnational population can be sufficient for accurate prediction of future settlement ...
Keywords	Urban ; growth model ; forecast ; built ; settlement ; machine learning ; Science ; Q
Subject code	333
Language	English
Publishing date	2020-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data.

Forrest R Stevens / Andrea E Gaughan / Catherine Linard / Andrew J Tatem

PLoS ONE, Vol 10, Iss 2, p e

2015 Volume 0107042

Abstract: High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census ... ...

Abstract	High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, "Random Forest" estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2015-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: GridSample

Dana R. Thomson / Forrest R. Stevens / Nick W. Ruktanonchai / Andrew J. Tatem / Marcia C. Castro

International Journal of Health Geographics, Vol 16, Iss 1, Pp 1-

an R package to generate household survey primary sampling units (PSUs) from gridded population data

2017 Volume 19

Abstract: Abstract Background Household survey data are collected by governments, international organizations, and companies to prioritize policies and allocate billions of dollars. Surveys are typically selected from recent census data; however, census data are ... ...

Abstract	Abstract Background Household survey data are collected by governments, international organizations, and companies to prioritize policies and allocate billions of dollars. Surveys are typically selected from recent census data; however, census data are often outdated or inaccurate. This paper describes how gridded population data might instead be used as a sample frame, and introduces the R GridSample algorithm for selecting primary sampling units (PSU) for complex household surveys with gridded population data. With a gridded population dataset and geographic boundary of the study area, GridSample allows a two-step process to sample “seed” cells with probability proportionate to estimated population size, then “grows” PSUs until a minimum population is achieved in each PSU. The algorithm permits stratification and oversampling of urban or rural areas. The approximately uniform size and shape of grid cells allows for spatial oversampling, not possible in typical surveys, possibly improving small area estimates with survey results. Results We replicated the 2010 Rwanda Demographic and Health Survey (DHS) in GridSample by sampling the WorldPop 2010 UN-adjusted 100 m × 100 m gridded population dataset, stratifying by Rwanda’s 30 districts, and oversampling in urban areas. The 2010 Rwanda DHS had 79 urban PSUs, 413 rural PSUs, with an average PSU population of 610 people. An equivalent sample in GridSample had 75 urban PSUs, 405 rural PSUs, and a median PSU population of 612 people. The number of PSUs differed because DHS added urban PSUs from specific districts while GridSample reallocated rural-to-urban PSUs across all districts. Conclusions Gridded population sampling is a promising alternative to typical census-based sampling when census data are moderately outdated or inaccurate. Four approaches to implementation have been tried: (1) using gridded PSU boundaries produced by GridSample, (2) manually segmenting gridded PSU using satellite imagery, (3) non-probability sampling (e.g. random-walk, “spin-the-pen”), ...
Keywords	Cluster survey ; Multi-stage ; Cluster sample ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	310
Language	English
Publishing date	2017-07-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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