Using the electromagnetic induction survey method to examine the depth to clay soil layer (Bt horizon) in playa wetlands

Purpose Sediment accumulation has been and continues to be a significant threat to the integrity of the playa wetland ecosystem. The purpose of this study was to determine the vertical depth to the clay soil layer (Bt horizon) and thus to calculate the thickness of sediments accumulated in playa wetlands. Materials and methods This study used the electromagnetic induction (EMI) survey method, specifically EM38-MK2 equipment, to measure the vertical depth to the clay soil layer at the publicly managed wetlands in the Rainwater Basin, Nebraska, USA. Results and discussion The results indicated that the depth to the clay soil layer ranges from 21 to 78 cm (n = 279) with a mean sediment thickness of 39 cm. The annual sediment deposition rate since human settlement in the 1860s was calculated to be 0.26 cm year−1. The results provided science-based data to support future wetland restoration planning and the development of decision support tools that prioritize conservation delivery efforts. Conclusions Our research confirmed that the EMI technique is effective and efficient at determining the depth to the Bt horizon for playa wetlands. Additionally, these results supported previous studies and continue to indicate that a large amount of sediment has accrued in these playa wetlands within the Rainwater Basin area since settlement.Wetland restoration ecologists can use this information to prioritize future wetland restoration work that intends to remove culturally accumulated sediments above the clay soil layer. These findings provided a contemporary summary of wetland soil profile information that is typically used to develop restoration plans. This research also filled the critical knowledge gap about the thickness of the upper soils and the depth to Bt in publicly managed wetlands

Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management

Using the electromagnetic induction survey method to examine the depth to clay soil layer (Bt horizon) in playa wetlands

Purpose Sediment accumulation has been and continues to be a significant threat to the integrity of the playa wetland ecosystem. The purpose of this study was to determine the vertical depth to the clay soil layer (Bt horizon) and thus to calculate the thickness of sediments accumulated in playa wetlands. Materials and methods This study used the electromagnetic induction (EMI) survey method, specifically EM38-MK2 equipment, to measure the vertical depth to the clay soil layer at the publicly managed wetlands in the Rainwater Basin, Nebraska, USA. Results and discussion The results indicated that the depth to the clay soil layer ranges from 21 to 78 cm (n = 279) with a mean sediment thickness of 39 cm. The annual sediment deposition rate since human settlement in the 1860s was calculated to be 0.26 cm year−1. The results provided science-based data to support future wetland restoration planning and the development of decision support tools that prioritize conservation delivery efforts. Conclusions Our research confirmed that the EMI technique is effective and efficient at determining the depth to the Bt horizon for playa wetlands. Additionally, these results supported previous studies and continue to indicate that a large amount of sediment has accrued in these playa wetlands within the Rainwater Basin area since settlement.Wetland restoration ecologists can use this information to prioritize future wetland restoration work that intends to remove culturally accumulated sediments above the clay soil layer. These findings provided a contemporary summary of wetland soil profile information that is typically used to develop restoration plans. This research also filled the critical knowledge gap about the thickness of the upper soils and the depth to Bt in publicly managed wetlands

Using Sentinel-2 Imagery and Machine Learning Algorithms to Assess the Inundation Status of Nebraska Conservation Easements during 2018–2021

Conservation easements (CEs) play an important role in the provision of ecological services. This paper aims to use the open-access Sentinel-2 satellites to advance existing conservation management capacity to a new level of near-real-time monitoring and assessment for the conservation easements in Nebraska. This research uses machine learning and Google Earth Engine to classify inundation status using Sentinel-2 imagery during 2018–2021 for all CE sites in Nebraska, USA. The proposed machine learning approach helps monitor the CE sites at the landscape scale in an efficient and low-cost manner. The results confirmed effective inundation performance in these floodplain or wetland-related CE sites. The CE sites under the Emergency Watershed Protection-Floodplain Easement (EWPP-FPE) had the highest inundated area rate of 18.72%, indicating active hydrological inundation in the floodplain areas. The CE sites under the Wetlands Reserve Program (WRP) reached a mean annual surface water cover rate area of 8.07%, indicating the core wetland areas were inundated periodically or regularly. Other types of CEs serving upland conservation purposes had a lower level of inundation while these uplands conservation provided critical needs in soil erosion control. The mean annual surface water cover rate is 0.96% for the CE sites under the Grassland Reserve Program (GRP). The conservation of the CEs on uplands is an important component to reduce soil erosion and improve downstream wetland hydrological inundation performance. The findings support that the sites with higher inundation frequencies can be considered for future wetland-related conservation practices. The four typical wetland-based CE sites suggested that conservation performance can be improved by implementing hydrological restoration and soil erosion reduction at the watershed scale. The findings provided robust evidence to discover the surface water inundation information on conservation assessment to achieve the long-term goals of conservation easements

An examination of midwestern US cities’ preparedness for climate change and extreme hazards

The increasing occurrence of extreme weather and climate events raised concerns in regard to hazard mitigation and climate adaptation. Local municipal planning mechanisms play a fundamental role in increasing a community’s capacity toward long-term resiliency. This study employs the content analysis method to evaluate the 95 selected cities located in the US Federal Emergency Management Agency Region VII and examine how these local plans, including local comprehensive plans (CPs), hazard mitigation plans (HMPs), and local emergency operations plans (EOPs), prepare communities for climate change and possible extreme events. Results indicate that local plans delineated multiple resources and diverse strategies to reduce community climatic risks, where HMPs have medium-level preparation, and CPs and EOPs have limited level preparation. Local HMPs lead in mitigating for impacts from potential extreme events, but both local CPs and EOPs are proactively adapted for climatic risks. Common strengths and weaknesses exist between different planning mechanisms. Large variations exist among plans due to varying jurisdictions among cities. However, the plans score similarly overall—higher on strategies and factual base but are short of clear and detailed goals, objectives, and agendas. Finally, despite the diverse vertical and horizontal outreach, there is inadequate integration among local planning mechanisms to share climate hazard information

Effect of Semantic Web technologies on Distance Education

AbstractWe analyze the characteristics of the semantic web and the potential effect on the model of distance education starting with the key technologies of the Semantic Web. The conclusion shows that the development of Semantic Web will leverage the ability of the resources fusion, knowledge discovery and the knowledge retrieval, and finally, the Semantic Web will transform the study mode of the learners, from pulling knowledge from the web to pushing knowledge out of the web

An examination of midwestern US cities’ preparedness for climate change and extreme hazards

The increasing occurrence of extreme weather and climate events raised concerns in regard to hazard mitigation and climate adaptation. Local municipal planning mechanisms play a fundamental role in increasing a community’s capacity toward long-term resiliency. This study employs the content analysis method to evaluate the 95 selected cities located in the US Federal Emergency Management Agency Region VII and examine how these local plans, including local comprehensive plans (CPs), hazard mitigation plans (HMPs), and local emergency operations plans (EOPs), prepare communities for climate change and possible extreme events. Results indicate that local plans delineated multiple resources and diverse strategies to reduce community climatic risks, where HMPs have medium-level preparation, and CPs and EOPs have limited level preparation. Local HMPs lead in mitigating for impacts from potential extreme events, but both local CPs and EOPs are proactively adapted for climatic risks. Common strengths and weaknesses exist between different planning mechanisms. Large variations exist among plans due to varying jurisdictions among cities. However, the plans score similarly overall—higher on strategies and factual base but are short of clear and detailed goals, objectives, and agendas. Finally, despite the diverse vertical and horizontal outreach, there is inadequate integration among local planning mechanisms to share climate hazard information

The toxic leaching behavior of MSWI fly ash made green and non-sintered lightweight aggregates

With the rapid development of urbanization and the economy, the amount of municipal solid waste incineration fly ash (MSWI fly ash) dramatically increases and stacks up. Declining the leaching concentration of hazardous heavy metals in municipal solid waste incineration fly ash and enlarging the application range in wastewater and biosorption of non-sintered LWAs have always got more attention from more and more researchers. Green and non-sintered LWAs were prepared by using the waste solids (MSWI fly ash and coal fly ash) as raw materials through autoclave technology. In the meantime, the effect of severe leaching environment (pH = 1, 3, 5 and 7) on the stabilization of heavy metals in the LWAs with MSWI fly ash and extra heavy metals were systematically investigated by means of Inductively Coupled Plasma Optical Emission Spectrometer, X-ray diffraction, Fourier Transform Infrared Spectrometer, X-ray photoelectron spectra and Scanning electron microscope. The results revealed that the heavy metals are well immobilized in the LWA matrix through physical encapsulation and adsorption by hydration products (C-S-H). The leaching rate (LR) and cumulative leaching rate (CLR) of heavy metals, phase compositions and chemical structures in LWAs with heavy metals at pH of 1 are significantly changed, but the cumulative leaching rate of Pb2+ is lower than that of Zn2+ and Cu2+. The structure of hydration products in the matrix will be broken and reformed to gypsum under an acid environment (pH = 1). The leaching rate and cumulative leaching rate of heavy metals in LWAs with heavy metals under various leaching environments are much lower when the pH is above 1, which could meet the leaching requirement. This research could provide theoretical support for the application of non-sintered and municipal solid waste incineration fly ash based LWAs in concrete

Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management