GNSS transpolar earth reflectometry exploriNg system (G-TERN): mission concept

The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploriNg system (G-TERN) was proposed in response to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a “dynamic mapper”of the ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (<;10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025-2030 or optimally 2025-2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance.Peer ReviewedPostprint (published version

GNSS Reflectometry and Remote Sensing: New Objectives and Results

The Global Navigation Satellite System (GNSS) has been a very powerful and important contributor to all scientific questions related to precise positioning on Earth's surface, particularly as a mature technique in geodesy and geosciences. With the development of GNSS as a satellite microwave (L-band) technique, more and wider applications and new potentials are explored and utilized. The versatile and available GNSS signals can image the Earth's surface environments as a new, highly precise, continuous, all-weather and near-real-time remote sensing tool. The refracted signals from GNSS Radio Occultation satellites together with ground GNSS observations can provide the high-resolution tropospheric water vapor, temperature and pressure, tropopause parameters and ionospheric total electron content (TEC) and electron density profile as well. The GNSS reflected signals from the ocean and land surface could determine the ocean height, wind speed and wind direction of ocean surface, soil moisture, ice and snow thickness. In this paper, GNSS remote sensing applications in the atmosphere, oceans, land and hydrology are presented as well as new objectives and results discussed.Comment: Advances in Space Research, 46(2), 111-117, 201

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs

Application of Thermal and Ultraviolet Sensors in Remote Sensing of Upland Ducks

Detection, mapping, and monitoring of wildlife populations can provide significant insight into the health and trajectory of the ecosystems they rely on. In fact, it was not until recently that the benefits of wetland ecosystems were fully understood. Unfortunately, by that point, the United States had removed more than 50% of its native wetlands. The Prairie Pothole Region in North America is the premier breeding location for ducks; responsible for producing more than 50% of the North American ducks annually. The current survey methods for obtaining duck population counts are accomplished primarily using manned flights with observers manually identifying and counting the ducks below with coordinated ground surveys at a subset of these areas to obtain breeding pair estimates. The current industry standard for in situ assessment of nest locations for reproductive effort estimates is known as the “chain drag method”, a manually intensive ground survey technique. However, recent improvements to small unmanned aerial systems (sUAS), coupled with the increased performance of lightweight sensors provide the potential for an alternative survey method. Our objective for this study was to assess the feasibility of utilizing sUAS based thermal longwave infrared (LWIR) imagery for detecting duck nests and ultraviolet (UV) imagery to classify breeding pairs in the Prairie Pothole Region. Our team deployed a DRS Tamarisk 640 LWIR sensor aboard a DJI Matrice 600 hexa-copter at Ducks Unlimited’s Coteau Ranch in Sheridan County, North Dakota, to obtain the thermal imagery. At the ranch, 24 nests were imaged at two altitudes (40m and 80m) during the early morning (04h00-06h00), morning (06h00-08h00), and midday (11h00-13h00). Three main parameters, namely altitude, time of day, and terrain, were varied between flights and the impact that each had on detection accuracy was examined. Each nest image was min-max normalized and contrast enhanced using a high-pass filter, prior to input into the detection algorithm. We determined that the variable with the highest impact on detection accuracies was altitude. We were able to achieve detection accuracies of 58% and 69% for the 80m and 40m flights, respectively. We also determined that flights in the early morning yielded the highest detection accuracies, which was attributed to the increased contrast between the landscape and the nests after the prairie cooled overnight. Additionally, the detection accuracies were lowest during morning flights when the hens might be off the nests on a recess break from incubation. Therefore, we determined that with increases in spatial resolution, the use of sUAS based thermal imagery is feasible for detecting nests across the prairie and that flights should occur early in the morning while the hens are on the nest, in order to maximize detection potential. To assess the feasibility of classifying breeding duck pairs using UV imagery, our team took a preliminary step in simulating UAS reflectance imagery by collecting 260 scans across nine species of upland ducks with a fixed measurement geometry using an OceanOptic’s spectroradiometer. We established baseline accuracies of 83%, 83%, and 76% for classifying age, sex, and species, respectively, by using a random forest (RF) classifier with simulated panchromatic (250-850nm) image sets. When using imagery at narrow UV bands with the same RF classifier, we were able to increase classification accuracies for age and species by 7%. Therefore, we demonstrated the potential for the use of sUAS based imagery as an alternate method for surveying nesting ducks, as well as potential improvements in age and species classification using UV imagery during breeding pair aerial surveys. Next steps should include efforts to extend these findings to airborne sensing systems, toward eventual operational implementation. Such an approach could alleviate environmental impacts associated with in situ surveys, while increasing the scale (scope and exhaustiveness) of surveys

Object-based machine learning correction of LiDAR using RTK-GNSS to model the potential effects of sea-level rise in Swanquarter National Wildlife Refuge, North Carolina

Coastal wetland systems are a vital habitat that provide many beneficial services; however, the complexity of these habitats makes it difficult for conservation managers to preserve these environments and predict future changes. Sea-level rise (SLR) is a growing and accelerating threat to coastal wetlands making its predictability essential for conservation planners. Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) have become an important component in monitoring coastal wildlife refuges and are implemented into models like Sea Level Affecting Marshes Model (SLAMM) to produce SLR vulnerability assessments. Although, with dense vegetation in these environments LiDAR penetration is reduced and DEMs in turn are less accurate. This study implemented an Object-Based Machine Learning (OBML) technique to improve DEM accuracy at Swanquarter National Wildlife Refuge (SNWR) and was implemented into SLAMM to provide land cover maps of the year 2050 for land cover change analysis. The corrected OBML DEM was compared with the original LiDAR DEM obtained from North Carolina Floodplain Mapping Program (NCFMP), which found the OBML DEM to provide a more reliable depiction of the potential impacts of future SLR on the coastal wetlands in North Carolina. Conservation managers may find the OBML approach in this study to be a useful option for SLR analysis

UNCERTAINTY IN LANDSLIDES VOLUME ESTIMATION USING DEMs GENERATED BY AIRBORNE LASER SCANNER AND PHOTOGRAMMETRY DATA

Abstract. The purpose of this paper is to identify an approach able to estimate the uncertainty related to the measure of terrain volume generated after a landslide. The survey of the area interested of landslide can be performed by Photogrammetry &amp;amp; Remote Sensing (PaRS) techniques. Indeed, depending on the method and technology used for the survey, a different level of accuracy is achievable. The estimate of the quantity of the terrain implicated in the landslide influences the type of geological and geotechnical approach, the civil engineering project on the area and of consequence, the costs to sustain for a community. According to the experiences and recommendations reported in the ASPRS guidelines, an example of the approach used to estimate volumetric accuracy concerning one of the most important landslide in Europe is shown in this paper. In this case study, the dataset is constituted by a Digital Elevation Model (DEM) obtained by photogrammetric (stereo-images) method (pre-landslide) and another by Airborne Laser Scanner (after-landslide). By the comparisons of Airborne Laser Scanner (ALS) and photogrammetry DEMs obtained from successive surveys, it has been possible to produce maps of differences and of consequence, to calculate the volume of the terrain (eroded or accumulated). In order to calculate the uncertainty of volume, a procedure that takes in account even the different accuracy achievable in the vegetation area is explained and discussed.</p

Application of Reflected Global Navigation Satellite System (GNSS-R) Signals in the Estimation of Sea Roughness Effects in Microwave Radiometry

In February-March 2009 NASA JPL conducted an airborne field campaign using the Passive Active L-band System (PALS) and the Ku-band Polarimetric Scatterometer (PolSCAT) collecting measurements of brightness temperature and near surface wind speeds. Flights were conducted over a region of expected high-speed winds in the Atlantic Ocean, for the purposes of algorithm development for salinity retrievals. Wind speeds encountered were in the range of 5 to 25 m/s during the two weeks deployment. The NASA-Langley GPS delay-mapping receiver (DMR) was also flown to collect GPS signals reflected from the ocean surface and generate post-correlation power vs. delay measurements. This data was used to estimate ocean surface roughness and a strong correlation with brightness temperature was found. Initial results suggest that reflected GPS signals, using small low-power instruments, will provide an additional source of data for correcting brightness temperature measurements for the purpose of sea surface salinity retrievals

Image Processing in Dense Forest Areas using Unmanned Aerial System (UAS)

Description: A detailed workflow using Structure from Motion (SfM) techniques for processing high-resolution Unmanned Aerial System (UAS) NIR and RGB imagery in a dense forest environment where obtaining control points is difficult due to limited access and safety issues. Abstract: Imagery collected via Unmanned Aerial System (UAS) platforms has become popular in recent years due to improvements in a Digital Single-Lens Reflex (DSLR) camera (centimeter and sub-centimeter), lower operation costs as compared to human piloted aircraft, and the ability to collect data over areas with limited ground access. Many different application (e.g., forestry, agriculture, geology, archaeology) are already using and utilizing the advantages of UAS data. Although, there are numerous UAS image processing workflows, for each application the approach can be different. In this study, we developed a processing workflow of UAS imagery collected in a dense forest (e.g., coniferous/deciduous forest and contiguous wetlands) area allowing users to process large datasets with acceptable mosaicking and georeferencing errors. Imagery was acquired with near-infrared (NIR) and red, green, blue (RGB) cameras with no ground control points. Image quality of two different UAS collection platforms were observed. Agisoft Metashape, a photogrammetric suite, which uses SfM (Structure from Motion) techniques, was used to process the imagery. The results showed that an UAS having a consumer grade Global Navigation Satellite System (GNSS) onboard had better image alignment than an UAS with lower quality GNSS

FINE SCALE MAPPING OF LAURENTIAN MIXED FOREST NATURAL HABITAT COMMUNITIES USING MULTISPECTRAL NAIP AND UAV DATASETS COMBINED WITH MACHINE LEARNING METHODS

Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes