Modeling Soil Erosion in the Upper Green River, KY

Off-site soil erosion has tremendous impacts on the present state of most river systems throughout the United States, contributing sediments to channels mainly as nonpoint pollution resulting from land-use and agricultural practices and leading to sedimentation downstream and downwind, a decrease in the transport capacity of streams, increase in the risk of flooding, filling reservoirs, and eutrophication. A primary focus in examining the problems associated with soil erosion arid ultimately in proposing control measures should be on identifying the sources of the sediment. Therefore, a model that would be able to assess soil erosion needs to start by identifying the sediment sources and delivery paths to channels, link these sediment supply processes to in-channel sediment transport and storage and ultimately to basin sediment yield. This study focuses on the Upper Green River Basin in Kentucky and is concerned with analyzing hillslope erosion rates using The Unit Stream Power Erosion and Deposition soil erosion model (Mitas and Mitasova, 1996) and GIS, and thereby estimating patterns of sediment supply to rivers in order to predict which portions of the channel network are more likely to store large amounts of fine sediments. Results indicate that much of the eroded sediments are redistributed within the hillslope system, but also that a large proportion is delivered to the channel. These predictions have been tested by sampling the fine sediment content of the streambed at key locations along the channel network and comparing the observed patterns to those predicted by the soil erosion model. By linking topographic and soil characteristics with land cover data, it has been concluded that high intensity erosion tends to occur at contact between different vegetation covers, on barren lands and croplands, and 15-25% slopes poorly protected by vegetation. Erosion hot spots have been identified in the Pitman Creek HUC 05110001-90-130 and 05110001-90-050, both part of the Big Pitman Creek sub-basin, as well as in Mill and Falling Timber Creeks with lower intensity

Mapping Land Cover Types for Highland Andean Ecosystems in Peru Using Google Earth Engine

Highland Andean ecosystems sustain high levels of floral and faunal biodiversity in areas with diverse topography and provide varied ecosystem services, including the supply of water to cities and downstream agricultural valleys. Google (™) has developed a product specifically designed for mapping purposes (Earth Engine), which enables users to harness the computing power of a cloud-based solution in near-real time for land cover change mapping and monitoring. We explore the feasibility of using this platform for mapping land cover types in topographically complex terrain with highly mixed vegetation types (Nor Yauyos Cochas Landscape Reserve located in the central Andes of Peru) using classification machine learning (ML) algorithms in combination with different sets of remote sensing data. The algorithms were trained using 3601 sampling pixels of (a) normalized spectral bands between the visible and near infrared spectrum of the Landsat 8 OLI sensor for the 2018 period, (b) spectral indices of vegetation, soil, water, snow, burned areas and bare ground and (c) topographic-derived indices (elevation, slope and aspect). Six ML algorithms were tested, including CART, random forest, gradient tree boosting, minimum distance, naïve Bayes and support vector machine. The results reveal that ML algorithms produce accurate classifications when spectral bands are used in conjunction with topographic indices, resulting in better discrimination among classes with similar spectral signatures such as pajonal (tussock grass-dominated cover) and short grasses or rocky groups, and moraines, agricultural and forested areas. The model with the highest explanatory power was obtained from the combination of spectral bands and topographic indices using the random forest algorithm (Kappa = 0.81). Our study presents a first approach of its kind in topographically complex Cordilleran terrain and we show that GEE is particularly useful in large-scale land cover mapping and monitoring in mountainous ecosystems subject to rapid changes and conversions, with replicability and scalability to other areas with similar characteristics

Costs of elephant crop depredation exceed the benefits of trophy hunting in a community-based conservation area of Namibia

The Kazavango-Zambezi Transfrontier Conservation Area is home to the largest remaining elephant population in Africa but is also the site of high levels of human-elephant conflict through crop depredation. Offsetting the costs of coexisting with elephants in this area is critical to incentivizing elephant conservation within community-based conservation (CBC) areas, and trophy hunting has long been touted as a method for generating revenue for communities from wildlife. However, the idea that sustainable elephant hunting can offset the costs of crop depredation remains largely untested. We combined household survey data, financial records, and elephant population data to compare the potential benefits of sustainable hunting with the costs of crop depredation in a CBC area in northeastern Namibia. We determined that sustainable trophy hunting only returns ~30% of the value of crops lost to the community and cannot alone offset the current costs of coexistence with elephants. As core institutions supporting the practice of conservation, CBC efforts must promote community management capacity to combine multiple wildlife-based income streams and build partnerships at multiple scales of governance to address the challenges of elephant management

Using New Methods and Data to Assess and Address Population, Fertility, and Environment links in the Lake Victoria Basin

Lake Victoria is the largest lake in Africa, and the lake and its watershed are a critical resourcefor the livelihoods of millions of people across five countries: Tanzania, Kenya, Uganda,Burundi, and Rwanda. The FAO, however, reports that the population living around in the LakeVictoria Basin is among the poorest and most food insecure in all of East Africa due to decliningland productivity, soil degradation, desertification, loss of biodiversity, livestock and cropdiseases, declining fisheries, and poor development and trade policies. Policymakers concernedwith the sustainable management of the Lake Victoria Basin, perceive population growth to be akey driving force of rapidly changing and degrading ecosystems, along with inadequategovernment policies and planning, regulations, provision of services. Data on basic population,health, and environment indicators, however, are unavailable at the landscape level of the basinmaking it difficult to assess, monitor, and communicate effectively about these connections. Thevarying data sources across the five countries and the lack of alignment of administrative unitswith the Lake Victoria Basin watershed are the principle data challenges. In this paper wehighlight the efforts of a multidisciplinary research team to overcome these data challenges anddevelop demographic and health indicators for policy makers in the Lake Victoria BasinCommission as well as for program staff in an integrated Population, Health, EnvironmentProgram working with District leaders and communities on these issues in Uganda and Kenya

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

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

Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions

The role of remote sensing and human–environment interactions (HEI) research in social and environmental decision-making has steadily increased along with numerous technological and methodological advances in the global environmental change field. Given the growing inter- and trans-disciplinary nature of studies focused on understanding the human dimensions of global change (HDGC), the need for a synchronization of agendas is evident. We conduct a bibliometric assessment and review of the last two decades of peer-reviewed literature to ascertain what the trends and current directions of integrating remote sensing into HEI research have been and discuss emerging themes, challenges, and opportunities. Despite advances in applying remote sensing to understanding ever more complex HEI fields such as land use/land cover change and landscape degradation, agricultural dynamics, urban geography and ecology, natural hazards, water resources, epidemiology, or paleo HEIs, challenges remain in acquiring and leveraging accurately georeferenced social data and establishing transferable protocols for data integration. However, recent advances in micro-satellite, unmanned aerial systems (UASs), and sensor technology are opening new avenues of integration of remotely sensed data into HEI research at scales relevant for decision-making purposes that simultaneously catalyze developments in HDGC research. Emerging or underutilized methodologies and technologies such as thermal sensing, digital soil mapping, citizen science, UASs, cloud computing, mobile mapping, or the use of “humans as sensors” will continue to enhance the relevance of HEI research in achieving sustainable development goals and driving the science of HDGC further

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

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

Leveraging the NEON Airborne Observation Platform for socio-environmental systems research

During the 21st century, human–environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human–environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi-resolution information for analysis. Climate change, land-use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high-density, and high-resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) for Socio-Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross-site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio-environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data

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

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

Operationalizing an integrative socio-ecological framework in support of global monitoring of land degradation

Despite sustained global efforts to avoid, reduce, and reverse land degradation, estimates of land degradation nationally and regionally vary considerably. Land degradation reduces agricultural productivity, impacts the provision of vital ecosystem services, and disproportionately affects vulnerable populations. The 2030 Agenda for Sustainable Development, through Sustainable Development Goal 15.3, sets out to achieve land degradation neutrality (LDN) by improving the livelihoods of those most affected and building resilience in areas affected by or at risk from degradation. The United Nations Convention to Combat Desertification (UNCCD) leads the charge in creating a spatially explicit framework for monitoring and reporting on LDN goals that countries can integrate into their land planning policies. However, it remains difficult to operationalize the integration of biophysical indicators of land degradation with climatic and socio-economic indicators to assess the impact of land degradation on vulnerable populations. We present an integrative framework that demonstrates how freely available global geospatial data sets can be leveraged through an open-source platform (Trends.Earth) to simplify and operationalize monitoring and reporting on progress towards achieving LDN. Then, we summarize a suite of data sets and approaches that can be used to understand and quantify the socio-ecological interactions between drought, land degradation and population exposed to desertification, land degradation and drought. We discuss how improvements in Earth observation data sets and algorithms will allow UNCCD land-based progress sub-indicators (changes in primary productivity, land cover, soil organic carbon, drought, and population exposure) to be computed at enhanced spatial resolutions