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Article ; Online: A Conceptual Approach towards Improving Monitoring of Living Conditions for Populations Affected by Desertification, Land Degradation, and Drought

David López-Carr / Narcisa G. Pricope / Kevin M. Mwenda / Gabriel Antunes Daldegan / Alex Zvoleff

Sustainability, Vol 15, Iss 9400, p

2023 Volume 9400

Abstract: Addressing the global challenges of desertification, land degradation, and drought (DLDD), and their impacts on achieving sustainable development goals for coupled human-environmental systems is a key component of the 2030 Agenda for Sustainable ... ...

Abstract	Addressing the global challenges of desertification, land degradation, and drought (DLDD), and their impacts on achieving sustainable development goals for coupled human-environmental systems is a key component of the 2030 Agenda for Sustainable Development. In particular, Sustainable Development Goal (SDG) 15.3 aims to, “ by 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world” . Addressing this challenge is essential for improving the livelihoods of those most affected by DLDD and for safeguarding against the most extreme effects of climate change. This paper introduces a conceptual framework for improved monitoring of DLDD in the context of United Nations Convention to Combat Desertification (UNCCD) Strategic Objective 2 (SO2) and its expected impacts: food security and adequate access to water for people in affected areas are improved; the livelihoods of people in affected areas are improved and diversified; local people, especially women and youth, are empowered and participate in decision-making processes in combating DLDD; and migration forced by desertification and land degradation is substantially reduced. While it is critical to develop methods and tools for assessing DLDD, work is needed first to provide a conceptual roadmap of the human dimensions of vulnerability in relation to DLDD, especially when attempting to create a globally standardized monitoring approach.
Keywords	sustainable development goals ; desertification ; land degradation ; and drought (DLDD) ; UNCCD Strategic Objectives ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
Subject code	910
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Evaluating SWAT Model Performance for Runoff, Percolation, and Sediment Loss Estimation in Low-Gradient Watersheds of the Atlantic Coastal Plain

Kerry L. Mapes / Narcisa G. Pricope

Hydrology, Vol 7, Iss 21, p

2020 Volume 21

Abstract: With predicted alterations in climate and land use, managing water resources is of the utmost importance, especially in areas such as the United States (U.S.) Coastal Plain where extensive connections exist between surface and groundwater systems. These ... ...

Abstract	With predicted alterations in climate and land use, managing water resources is of the utmost importance, especially in areas such as the United States (U.S.) Coastal Plain where extensive connections exist between surface and groundwater systems. These changes create the need for models that effectively assess shifting hydrologic regimes and, in that context, we examine the performance of the Soil and Water Assessment Tool (SWAT) in a low-gradient, shallow-aquifer-dominated watershed of the U.S. Coastal Plain using a gridded reanalysis dataset. We evaluate accuracy, uncertainty, and parameter sensitivity by comparing observed and predicted streamflow at two gaging stations and assess model predictions for yearly average runoff (SURQ), percolation (PERC), and sediment loss (SYLD). Streamflow performance was acceptable during calibration (NSE = 0.67 and 0.60) and very good during validation (NSE = 0.84 and 0.91). Model predictions for SURQ, PERC, and SYLD coincided with expected ranges for this region. Parameters related to shallow aquifer properties or groundwater were highly sensitive, which indicates the need for continued study of spatial and temporal variability within the sub-surface components of these hydrologic systems. Our findings highlight the applicability of this reanalysis dataset for modeling hydrologic processes in poorly gaged watersheds and adds to the body of research that seeks to develop effective assessment tools for shallow-aquifer-dominated systems. Our methodology can effectively assist watershed managers in establishing baseline rates of hydrologic processes as is crucial with future predicted shifts in hydrologic regimes due to land-use alteration and climate change.
Keywords	SWAT ; hydrologic modeling ; groundwater ; Atlantic Coastal Plain ; watershed management ; Science ; Q
Subject code	550
Language	English
Publishing date	2020-04-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Geospatial datasets in support of high-resolution spatial assessment of population vulnerability to climate change in Nepal

Janardan Mainali / Narcisa G. Pricope

Data in Brief, Vol 12, Iss C, Pp 459-

2017 Volume 462

Abstract: We present a geographic information system (GIS) dataset with a nominal spatial resolution of one-kilometer composed of grid polygons originally derived and utilized in a high-resolution climate vulnerability model for Nepal. The different data sets ... ...

Abstract	We present a geographic information system (GIS) dataset with a nominal spatial resolution of one-kilometer composed of grid polygons originally derived and utilized in a high-resolution climate vulnerability model for Nepal. The different data sets described and shared in this article are processed and tailored to the specific objectives of our research paper entitled “High-resolution Spatial Assessment of Population Vulnerability to Climate Change in Nepal” (Mainali and Pricope, In press) [1]. We share these data recognizing that there is a significant gap in regards to data availability, the spatial patterns of different biophysical and socioeconomic variables, and the overall population vulnerability to climatic variability and disasters in Nepal. Individual variables, as well as the entire set presented in this dataset, can be used to better understand the spatial pattern of different physical, biological, climatic, and vulnerability characteristics in Nepal. The datasets presented in this article are sourced from different national and global databases and have been statistically treated to meet the needs of the article. The data are in GIS-ready ESRI shapefile file format of one-kilometer grid polygon with various fields (columns) for each dataset.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Science (General) ; Q1-390
Subject code	333
Language	English
Publishing date	2017-06-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: High-resolution spatial assessment of population vulnerability to climate change in Nepal

Mainali, Janardan / Narcisa G. Pricope

Applied geography. 2017 May, v. 82

2017

Abstract: Current, spatially explicit, and high-resolution assessments of population vulnerability to climate change and variability in developing countries can be difficult to create due to lack of data or financial and technical capacity constraints. We propose ... ...

Abstract	Current, spatially explicit, and high-resolution assessments of population vulnerability to climate change and variability in developing countries can be difficult to create due to lack of data or financial and technical capacity constraints. We propose a comparative, multiple-approach framework to assess the spatial variation of population vulnerability to climatic changes using several high-resolution variables related to climate, topography, and socioeconomic conditions with an objective to detect the spatial variability of climate vulnerability in Nepal. Nepal is one of the most vulnerable countries to the effects of climate change due to frequent climatic hazards and poor socio-economic capacity. We used a climate vulnerability index (CVI) approach to derive climate vulnerability maps at the one-kilometer resolution and test an additive and a principal components-based composite method of data aggregation. In this work, we attempt to answer three questions. 1) How do different methods of assessment inform the spatial variation of the climate vulnerability in Nepal? 2) How do different variables interact to shape climate vulnerability in Nepal? 3) What proportions of the population in Nepal are vulnerable to climatic disasters and why? Our analysis uncovered significant spatial variations in population vulnerability to climate change across Nepal, with the highest vulnerability being experienced by the High Mountain region followed by the regions in the lower elevations. We find that although the lack of adaptive capacity is the biggest cause of population vulnerability to climate change in Nepal, a resilient community is shaped by both biophysical and socioeconomic characteristics. By performing an iterative sensitivity analysis of our thirteen variables both at the aggregate level (nationally) as well as at the more disaggregated (physiographic region) level, we contribute to identifying important, multi-scalar driving factors for vulnerability that can be employed as leverage points for lowering vulnerability at different scales. After performing analyses at multiple regions, we conclude that region-specific variable selection is needed for more detailed assessments and in order to prioritize adaptation strategies at scales that go beyond the hierarchy of administrative divisions.
Keywords	altitude ; climate ; climate change ; developing countries ; disasters ; geography ; socioeconomics ; Nepal
Language	English
Dates of publication	2017-05
Size	p. 66-82.
Publishing place	Elsevier Ltd
Document type	Article
ISSN	0143-6228
DOI	10.1016/j.apgeog.2017.03.008
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery

Samuel Pizarro / Narcisa G. Pricope / Deyanira Figueroa / Carlos Carbajal / Miriam Quispe / Jesús Vera / Lidiana Alejandro / Lino Achallma / Izamar Gonzalez / Wilian Salazar / Hildo Loayza / Juancarlos Cruz / Carlos I. Arbizu

Remote Sensing, Vol 15, Iss 3203, p

2023 Volume 3203

Abstract: The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil ... ...

Abstract	The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R 2 , to select the best predicted maps (R 2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.
Keywords	soil mapping ; UAV ; Google Earth Engine ; machine learning ; cloud computing ; Science ; Q
Subject code	910
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Thermal Imagery-Derived Surface Inundation Modeling to Assess Flood Risk in a Flood-Pulsed Savannah Watershed in Botswana and Namibia

Jeri J. Burke / Narcisa G. Pricope / James Blum

Remote Sensing, Vol 8, Iss 8, p

2016 Volume 676

Abstract: The Chobe River Basin (CRB), a sub-basin of the Upper Zambezi Basin shared by Namibia and Botswana, is a complex hydrologic system that lies at the center of the world’s largest transfrontier conservation area. Despite its regional importance for ... ...

Abstract	The Chobe River Basin (CRB), a sub-basin of the Upper Zambezi Basin shared by Namibia and Botswana, is a complex hydrologic system that lies at the center of the world’s largest transfrontier conservation area. Despite its regional importance for livelihoods and biodiversity, its hydrology, controlled by the timing and relative contributions of water from two regional rivers, remains poorly understood. An increase in the magnitude of flooding in this region since 2009 has resulted in significant displacements of rural communities. We use an innovative approach that employs time-series of thermal imagery and station discharge data to model seasonal flooding patterns, identify the driving forces that control the magnitude of flooding and the high population density areas that are most at risk of high magnitude floods throughout the watershed. Spatio-temporal changes in surface inundation determined using NASA Moderate-resolution Imaging Spectroradiometer (MODIS) thermal imagery (2000–2015) revealed that flooding extent in the CRB is extremely variable, ranging from 401 km2 to 5779 km2 over the last 15 years. A multiple regression model of lagged discharge of surface contributor basins and flooding extent in the CRB indicated that the best predictor of flooding in this region is the discharge of the Zambezi River 64 days prior to flooding. The seasonal floods have increased drastically in magnitude since 2008 causing large populations to be displaced. Over 46,000 people (53% of Zambezi Region population) are living in high magnitude flood risk areas, making the need for resettlement planning and mitigation strategies increasingly important.
Keywords	inundation modeling ; MODIS thermal imagery ; flood-pulsed savanna ; Africa ; Science ; Q
Subject code	910 ; 550
Language	English
Publishing date	2016-08-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Residential flood vulnerability along the developed North Carolina, USA coast

Narcisa G. Pricope / Joanne N. Halls / Lauren M. Rosul / Christopher Hidalgo

Data in Brief, Vol 24, Iss , Pp - (2019)

High resolution social and physical data for decision support

2019

Abstract: This article presents an ArcGIS geodatabase of socio-demographic and physical characteristics derived from recent high resolution data sources to construct measures of population vulnerability to inundation in the 28 counties of coastal North Carolina, U. ...

Abstract	This article presents an ArcGIS geodatabase of socio-demographic and physical characteristics derived from recent high resolution data sources to construct measures of population vulnerability to inundation in the 28 counties of coastal North Carolina, U.S.A. as presented in Pricope et al., 2019. The region is simultaneously densely populated, low-lying and exposed to recurrent inundation related to storms and incremental sea level rise. The data presented here can be used as a decision support tool in coastal planning, emergency management preparedness, designing adaptation strategies and developing strategies for coastal resilience. The socio-demographic data (population and housing) was derived from 228 tables at the block-group level of geography from the 2010 U.S. Census Bureau. These data were statistically analyzed, using Principal Component Analysis, to identify key factors and then used to construct a Social Vulnerability Index (SOVI) at the block-group level of geography which highlighted regions where socio-demographic characteristics such as family structure, race, housing (primarily owner vs. renter-occupied), special needs populations (e.g. elderly and group living), and household/family size play an overwhelmingly important role in determining community vulnerability from a social perspective. An index of physical exposure was developed using the National Flood Hazards Maps (available from North Carolina's Flood Risk Information System and FEMA) along with a novel building inventory dataset available from the North Carolina Department of Public Safety that contains the Finished-Floor Elevation of every structure in the state. We took advantage of the unprecedented high spatial resolution nature of the building inventory dataset to calculate an index of physical vulnerability to inundation of every block group in the 28 coastal counties relative to Base Flood elevations and identified hotspots where this intersection predisposes people to an increased risk of flooding. Here, we present the final ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Science (General) ; Q1-390
Subject code	710
Language	English
Publishing date	2019-06-01T00:00:00Z
Publisher	Elsevier
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: Biodiversity Areas under Threat

Juliann E Aukema / Narcisa G Pricope / Gregory J Husak / David Lopez-Carr

PLoS ONE, Vol 12, Iss 1, p e

Overlap of Climate Change and Population Pressures on the World's Biodiversity Priorities.

2017 Volume 0170615

Abstract: Humans and the ecosystem services they depend on are threatened by climate change. Places with high or growing human population as well as increasing climate variability, have a reduced ability to provide ecosystem services just as the need for these ... ...

Abstract	Humans and the ecosystem services they depend on are threatened by climate change. Places with high or growing human population as well as increasing climate variability, have a reduced ability to provide ecosystem services just as the need for these services is most critical. A spiral of vulnerability and ecosystem degradation often ensues in such places. We apply different global conservation schemes as proxies to examine the spatial relation between wet season precipitation, population change over three decades, and natural resource conservation. We pose two research questions: 1) Where are biodiversity and ecosystem services vulnerable to the combined effects of climate change and population growth? 2) Where are human populations vulnerable to degraded ecosystem services? Results suggest that globally only about 20% of the area between 50 degrees latitude North and South has experienced significant change-largely wetting-in wet season precipitation. Approximately 40% of rangelands and 30% of rainfed agriculture lands have experienced significant precipitation changes, with important implications for food security. Over recent decades a number of critical conservation areas experienced high population growth concurrent with significant wetting or drying (e.g. the Horn of Africa, Himalaya, Western Ghats, and Sri Lanka), posing challenges not only for human adaptation but also to the protection and sustenance of biodiversity and ecosystem services. Identifying areas of climate and population risk and their overlap with conservation priorities can help to target activities and resources that promote biodiversity and ecosystem services while improving human well-being.
Keywords	Medicine ; R ; Science ; Q
Subject code	333
Language	English
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: 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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