Developing a Hyperspectral Non-Technical Survey for Minefields via UAV and Helicopter

The main topic discussed in this paper is the research, development, and implementation of hyperspectral remote sensing technology in humanitarian mine action (HMA), mainly for non-technical survey (NTS) from aerial platforms. NTS should be conducted to determine whether landmines or unexploded ordnance (UXO) exist in considered areas and whether clearance is needed and if so, in what scope. The availability of the hyperspectral sensors (2011–2012) that are suitable for use with multi-engine unmanned aerial vehicles (UAV) enabled the development of systems for HMA. While large and costly airborne hyperspectral systems have been available for over two decades, they had little to no impact on HMA. Optical spectroscopy brought out interest in the relationship between vegetation and explosives, and this relationship is considered herein. Therefore, we briefly analyzed the state of landmine detection via hyperspectral technology, particularly the efforts to detect the mines due to the influence of explosives on vegetation reflectance spectra

Restoration of Oyster (Crassostrea virginica) Habitat for Multiple Estuarine Species Benefits

Increase in nitrogen concentration and declining eelgrass beds in Great Bay Estuary have been observed in the last decades. These two parameters are clear indicators of the impending problems for NH’s estuaries. The NH Department of Environmental Services (DES) in collaboration with the New Hampshire Estuaries Project (NHEP) adopted the assumption that eelgrass survival can be used as the water quality target for nutrient criteria development for NH’s estuaries. One of the hypotheses put forward regarding eelgrass decline is that a possible eutrophication response to nutrient increases in the Great Bay Estuary has been the proliferation of nuisance macroalgae, which has reduced eelgrass area in Great Bay Estuary. To test this hypothesis, mapping of eelgrass and nuisance macroalgae beds using hyperspectral imagery was suggested. A hyperspectral imagery was conducted by SpecTIR in August 2007 using an AISA Eagle sensor. The collected dataset was used to map eelgrass and nuisance macroalgae throughout the Great Bay Estuary. This report outlines the configured procedure for mapping the macroalgae and eelgrass beds using hyperspectral imagery. No ground truth measurements of eelgrass or macroalgae were collected as part of this project, although eelgrass ground truth data was collected as part of a separate project. Guidance from eelgrass and macroalgae experts was used for identifying training sets and evaluating the classification results. The results produced a comprehensive eelgrass and macroalgae map of the estuary. Three recommendations are suggested following the experience gained in this study: conducting ground truth measurements at the time of the HS survey, acquiring the current DEM model of Great Bay Estuary, and examining additional HS datasets with expert eelgrass and macroalgae guidance. These three issues can improve the classification results and allow more advanced applications, such as identification of macroalgae types

Macroalgae and eelgrass mapping in Great Bay Estuary using AISA hyperspectral imagery

Increase in nitrogen concentration and declining eelgrass beds in Great Bay Estuary have been observed in the last decades. These two parameters are clear indicators of the impending problems for NH’s estuaries. The NH Department of Environmental Services (DES) in collaboration with the New Hampshire Estuaries Project (NHEP) adopted the assumption that eelgrass survival can be used as the water quality target for nutrient criteria development for NH’s estuaries. One of the hypotheses put forward regarding eelgrass decline is that a possible eutrophication response to nutrient increases in the Great Bay Estuary has been the proliferation of nuisance macroalgae, which has reduced eelgrass area in Great Bay Estuary. To test this hypothesis, mapping of eelgrass and nuisance macroalgae beds using hyperspectral imagery was suggested. A hyperspectral imagery was conducted by SpecTIR in August 2007 using an AISA Eagle sensor. The collected dataset was used to map eelgrass and nuisance macroalgae throughout the Great Bay Estuary. This report outlines the configured procedure for mapping the macroalgae and eelgrass beds using hyperspectral imagery. No ground truth measurements of eelgrass or macroalgae were collected as part of this project, although eelgrass ground truth data was collected as part of a separate project. Guidance from eelgrass and macroalgae experts was used for identifying training sets and evaluating the classification results. The results produced a comprehensive eelgrass and macroalgae map of the estuary. Three recommendations are suggested following the experience gained in this study: conducting ground truth measurements at the time of the HS survey, acquiring the current DEM model of Great Bay Estuary, and examining additional HS datasets with expert eelgrass and macroalgae guidance. These three issues can improve the classification results and allow more advanced applications, such as identification of macroalgae types

How the Small Object Detection via Machine Learning and UAS-Based Remote-Sensing Imagery Can Support the Achievement of SDG2: A Case Study of Vole Burrows

Small mammals, and particularly rodents, are common inhabitants of farmlands, where they play key roles in the ecosystem, but when overabundant, they can be major pests, able to reduce crop production and farmers' incomes, with tangible effects on the achievement of Sustainable Development Goals no 2 (SDG2, Zero Hunger) of the United Nations. Farmers do not currently have a standardized, accurate method of detecting the presence, abundance, and locations of rodents in their fields, and hence do not have environmentally efficient methods of rodent control able to promote sustainable agriculture oriented to reduce the environmental impacts of cultivation. New developments in unmanned aerial system (UAS) platforms and sensor technology facilitate cost-effective data collection through simultaneous multimodal data collection approaches at very high spatial resolutions in environmental and agricultural contexts. Object detection from remote-sensing images has been an active research topic over the last decade. With recent increases in computational resources and data availability, deep learning-based object detection methods are beginning to play an important role in advancing remote-sensing commercial and scientific applications. However, the performance of current detectors on various UAS-based datasets, including multimodal spatial and physical datasets, remains limited in terms of small object detection. In particular, the ability to quickly detect small objects from a large observed scene (at field scale) is still an open question. In this paper, we compare the efficiencies of applying one- and two-stage detector models to a single UAS-based image and a processed (via Pix4D mapper photogrammetric program) UAS-based orthophoto product to detect rodent burrows, for agriculture/environmental applications as to support farmer activities in the achievements of SDG2. Our results indicate that the use of multimodal data from low-cost UASs within a self-training YOLOv3 model can provide relatively accurate and robust detection for small objects (mAP of 0.86 and an F1-score of 93.39%), and can deliver valuable insights for field management with high spatial precision able to reduce the environmental costs of crop production in the direction of precision agriculture management

Detailed Investigation of Spectral Vegetation Indices for Fine Field-Scale Phenotyping

Spectral vegetation indices (VIs) are a well-known and widely used method for crop state estimation. These technologies have great importance for plant state monitoring, especially for agriculture. The main aim is to assess the performance level of the selected VIs calculated from space-borne multispectral imagery and point-based field spectroscopy in application to crop state estimation. The results obtained indicate that space-borne VIs react on phenology. This feature makes it an appropriate data source for monitoring crop development, crop water needs and yield prediction. Field spectrometer VIs were sensitive for estimating pigment concentration and photosynthesis rate. Yet, a hypersensitivity of field spectral measures might lead to a very high variability of the calculated values. The results obtained in the second part of the presented study were reported on crop state estimated by 17 VIs known as sensitive to plant drought. An alternative approach for identification early stress by VIs proposed in this study is Principal Component Analysis (PCA). The results show that PCA has identified the degree of similarity of the different states and together with reference stress states from the control plot clearly estimated stress in the actual irrigated field, which was hard to detect by VIs values only

Diatom Proteomics Reveals Unique Acclimation Strategies to Mitigate Fe Limitation

Phytoplankton growth rates are limited by the supply of iron (Fe) in approximately one third of the open ocean, with major implications for carbon dioxide sequestration and carbon (C) biogeochemistry. To date, understanding how alteration of Fe supply changes phytoplankton physiology has focused on traditional metrics such as growth rate, elemental composition, and biophysical measurements such as photosynthetic competence (Fv/Fm). Researchers have subsequently employed transcriptomics to probe relationships between changes in Fe supply and phytoplankton physiology. Recently, studies have investigated longer-term (i.e. following acclimation) responses of phytoplankton to various Fe conditions. In the present study, the coastal diatom, Thalassiosira pseudonana, was acclimated (10 generations) to either low or high Fe conditions, i.e. Fe-limiting and Fe-replete. Quantitative proteomics and a newly developed proteomic profiling technique that identifies low abundance proteins were employed to examine the full complement of expressed proteins and consequently the metabolic pathways utilized by the diatom under the two Fe conditions. A total of 1850 proteins were confidently identified, nearly tripling previous identifications made from differential expression in diatoms. Given sufficient time to acclimate to Fe limitation, T. pseudonana up-regulates proteins involved in pathways associated with intracellular protein recycling, thereby decreasing dependence on extracellular nitrogen (N), C and Fe. The relative increase in the abundance of photorespiration and pentose phosphate pathway proteins reveal novel metabolic shifts, which create substrates that could support other well-established physiological responses, such as heavily silicified frustules observed for Fe-limited diatoms. Here, we discovered that proteins and hence pathways observed to be down-regulated in short-term Fe starvation studies are constitutively expressed when T. pseudonana is acclimated (i.e., nitrate and nitrite transporters, Photosystem II and Photosystem I complexes). Acclimation of the diatom to the desired Fe conditions and the comprehensive proteomic approach provides a more robust interpretation of this dynamic proteome than previous studies.This work was supported by National Science Foundation grants OCE1233014 (BLN) and the Office of Polar Programs Postdoctoral Fellowship grant 0444148 (BLN). DRG was supported by National Institutes of Health 5P30ES007033-10. AH and MTM were supported by Natural Sciences and Engineering Research Council of Canada. RFS and PWB were supported by the New Zealand Royal Society Marsden Fund and the Ministry of Science. This work is supported in part by the University of Washington's Proteomics Computer Resource Centre (UWPR95794). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Effects of a Commercial Insurance Policy Restriction on Lumbar Fusion in North Carolina and the Implications for National Adoption

Analysis of the State Inpatient Database of North Carolina, 2005–2012, and the Nationwide Inpatient Sample, including all inpatient lumbar fusion admissions from non-federal hospitals

A new approach to identify on-ground lamp types from night-time ISS images

Artificial night-time light (NTL), emitted by various on-ground human activities, has become intensive in many regions worldwide. Its adverse effects on human and ecosystem health crucially depend on the light spectrum, making the remote discrimination between different lamp types a highly important task. However, such studies remain extremely limited, and none of them exploit freely available satellite imagery. In the present analysis, the possibility to remotely assess the relative contribution of different lamp types into outdoor lighting is tested. For this sake, we match two data sources: (i) the radiometrically calibrated RGB image provided by the ISS (coarse spectral resolution data), and (ii) a set of in situ measurements with detailed spectral signatures conducted by ourselves (fine spectral resolution data). First, we analyze the fine spectral resolution data: using spectral signatures of standard lamp types from the LICA UCM library as endmembers, we perform an unmixing analysis upon NTL in situ measurements; by this, we obtain the estimates for relative contributions of the standard lamp types in each examined in situ measurement. Afterward, we focus on the coarse spectral resolution data: by using various types of statistical models, we predict the estimated relative contributions of each lamp type via RGB characteristics of spatially corresponding pixels of the ISS image. The built models predict sufficiently well (with R-2 reaching similar to 0.87) the contributions of two standard lamp types: high-pressure sodium (HPS) and metal-halide (MH) lamps, the most widespread lamp types in the study area (Haifa, Israel). The restored map for HPS allocation demonstrates high concordance with the network of municipal roads, while that for MH shows notable coincidence with the industrial facilities and the airport