scholarly journals Utilization of Image, LiDAR and Gamma-Ray Information to Improve Environmental Sustainability of Cut-to-Length Wood Harvesting Operations in Peatlands: A Management Systems Perspective

2021 ◽  
Vol 10 (5) ◽  
pp. 273
Author(s):  
Teijo Palander ◽  
Kalle Kärhä
Keyword(s):  
Environmental Sustainability ◽  
Gamma Ray ◽  
A Priori ◽  
Field Measurements ◽  
Airborne Lidar ◽  
Quality Data ◽  
Management Systems ◽  
Terrestrial Lidar ◽  
Wood Harvesting ◽  
Wood Procurement

Forest industry corporations use quality management systems in their wood procurement operations. Spatial quality data are used to improve the quality of wood harvesting and to achieve environmental sustainability. Some studies have proposed new management systems based on LiDAR. The main aim of this study was to investigate how efficiently planning systems can select areas for wood harvesting a priori with respect to avoiding harvesting damage caused by forest machinery. A literature review revealed the possibility of using GISs, and case studies showed the criteria required to predict the required quality levels. Terrestrial LiDAR can be utilized in authorities’ quality control systems, but it is inefficient for preplanning without terrestrial gamma-ray data collection. Airborne LiDAR and gamma-ray information about forest soils can only be used for planning larger regions at the forest level because the information includes too much uncertainty to allow it to be used for planning in small-sized areas before wood harvesting operations involving wood procurement. In addition, airborne LiDAR is not accurate enough, even at the forest level, for the planning of wood procurement systems because wood harvesting remains challenging without field measurements. Therefore, there is a need for the use of manual ground-penetrating radar for determining the peat layer thickness and the depth to the groundwater table.

scholarly journals Reconstruction of the cosmic equation of state for high redshift

2019 ◽  
Vol 79 (12) ◽  
Author(s):  
A. M. Velasquez-Toribio ◽  
M. M. Machado ◽  
Julio C. Fabris
Keyword(s):  
Equation Of State ◽  
Gamma Ray ◽  
High Redshift ◽  
A Priori ◽  
Parametric Method ◽  
Simulated Data ◽  
Quality Data ◽  
Model Independent ◽  
Priori Information ◽  
Non Parametric

AbstractWe investigate the possibilities of reconstructing the cosmic equation of state (EoS) for high redshift. In order to obtain general results, we use two model-independent approaches. The first reconstructs the EoS using comoving distance and the second makes use of the Hubble parameter data. To implement the first method, we use a recent set of Gamma-Ray Bursts (GRBs) measures. To implement the second method, we generate simulated data using the Sandage–Loeb (SL) effect; for the fiducial model, we use the $$\Lambda CDM$$ΛCDM model. In both cases, the statistical analysis is conducted through the Gaussian processes (non-parametric). In general, we demonstrate that this methodology for reconstructing the EoS using a non-parametric method plus a model-independent approach works appropriately due to the feasibility of calculation and the ease of introducing a priori information ($$H_ {0}$$H0 and $$\Omega _{m0}$$Ωm0). In the near future, following this methodology with a higher number of high quality data will help obtain strong restrictions for the EoS.

Bayesian deconvolution of gamma‐ray logs

Geophysics ◽  
1987 ◽  
Vol 52 (11) ◽  
pp. 1535-1546 ◽  
Author(s):  
Ping Sheng ◽  
Benjamin White ◽  
Balan Nair ◽  
Sandra Kerford
Keyword(s):  
High Frequency ◽  
Gamma Ray ◽  
Simulated Data ◽  
Field Measurements ◽  
Bayesian Optimization ◽  
Frequency Noise ◽  
High Frequency Noise ◽  
Data Application ◽  
Gamma Ray Detector ◽  
Gamma Ray Log

The spatial resolution of gamma‐ray logs is defined by the length 𝓁 of the gamma‐ray detector. To resolve thin beds whose thickness is less than 𝓁, it is generally desirable to deconvolve the data to reduce the averaging effect of the detector. However, inherent in the deconvolution operation is an amplification of high‐frequency noise, which can be a detriment to the intended goal of increased resolution. We propose a Bayesian statistical approach to gamma‐ray log deconvolution which is based on optimization of a probability function which takes into account the statistics of gamma‐ray log measurements as well as the empirical information derived from the data. Application of this method to simulated data and to field measurements shows that it is effective in suppressing high‐frequency noise encountered in the deconvolution of gamma‐ray logs. In particular, a comparison with the least‐squares deconvolution approach indicates that the incorporation of physical and statistical information in the Bayesian optimization process results in optimal filtering of the deconvolved results.

Estimating local CH4 emissions in the Upper Silesian Coal Basin using inverse modelling

2021 ◽  
Author(s):  
Sebastian Wolff ◽  
Friedemann Reum ◽  
Christoph Kiemle ◽  
Gerhard Ehret ◽  
Mathieu Quatrevalet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
A Priori ◽  
Point Sources ◽  
Airborne Lidar ◽  
Inverse Modelling ◽  
Emission Rates ◽  
Coal Basin ◽  
Upper Silesian Coal Basin ◽  
The Individual ◽  
Modelling Approach

<p>Methane (CH<sub>4</sub>) is the second most important anthropogenic greenhouse gas (GHG) with respect to radiative forcing. Since pre-industrial times, the globally averaged CH<sub>4</sub> concentration in the atmosphere has risen by a factor of 2.5. A large fraction of global anthropogenic CH<sub>4</sub> emissions originates from localized point sources, e.g. coal mine ventilation shafts. International treaties foresee GHG emission reductions, entailing independent monitoring and verification support capacities. Considering the spatially widespread distribution of point sources, remote sensing approaches are favourable, in order to enable rapid survey of larger areas. In this respect, active remote sensing by airborne lidar is promising, such as provided by the integrated-path differential-absorption lidar CHARM-F operated by DLR. Installed onboard the German research aircraft HALO, CHARM-F serves as a demonstrator for future satellite missions, e.g. MERLIN. CHARM-F simultaneously measures weighted vertical column mixing ratios of CO<sub>2</sub> and CH<sub>4</sub> below the aircraft. In spring 2018, during the CoMet field campaign, measurements were taken in the Upper Silesian Coal Basin (USCB) in Poland. The USCB is considered to be a European hotspot of CH<sub>4</sub> emissions, covering an area of approximately 50 km × 50 km. Due to the high number of coal mines and density of ventilation shafts in the USCB, individual CH<sub>4</sub> exhaust plumes can overlap. This makes simple approaches to determine the emission rates of single shafts, i.e. the cross-sectional flux method, difficult. Therefore, we use an inverse modelling approach to obtain an estimate of the individual emission rates. Specifically, we employ the Weather Research and Forecast Model (WRF) coupled to the CarbonTracker Data Assimilation Shell (CTDAS), an Ensemble Kalman Filter. CTDAS-WRF propagates an ensemble realization of the a priori CH<sub>4</sub> emissions forward in space and time, samples the simulated CH<sub>4</sub> concentrations along the measurement’s flight path, and scales the a priori emission rates to optimally fit the measured values, while remaining tied to the prior. Hereby, we obtain a regularized a posteriori best emission estimate for the individual ventilation shafts. Here, we report on the results of this inverse modelling approach, including individual and aggregated emission estimates, their uncertainties, and to which extent the data are able to constrain individual emitters independently.</p>

scholarly journals Dune migration and volume change from airborne LiDAR, terrestrial LiDAR and Structure from Motion-Multi View Stereo

2020 ◽  
Vol 143 ◽  
pp. 104569 ◽  
Author(s):  
Carlos H. Grohmann ◽  
Guilherme P.B. Garcia ◽  
Alynne Almeida Affonso ◽  
Rafael Walter Albuquerque
Keyword(s):  
Volume Change ◽  
Structure From Motion ◽  
Airborne Lidar ◽  
Terrestrial Lidar ◽  
Dune Migration

The responsibility of the pulp and paper sector with regard to sustainable development: how much is enough?

2007 ◽  
Vol 55 (6) ◽  
pp. 65-71 ◽  
Author(s):  
A. Kiperstok ◽  
C.M. Silva
Keyword(s):  
Environmental Management ◽  
Sustainable Development ◽  
Continuous Improvement ◽  
Environmental Sustainability ◽  
Pulp And Paper ◽  
Management Systems ◽  
Environmental Management Systems ◽  
The World

Pulp and paper companies all over the world certify their environmental management systems assuming public commitments for the continuous improvement of their relationship with the environment. Once certified, they consider themselves having done their part. But is this enough? This work has been carried out with the clear intention of provoking the professionals who can give the much needed answers for the construction of environmental sustainability in the pulp and paper sector.

scholarly journals Characterizing a New England Saltmarsh with NASA G-LiHT Airborne Lidar

2019 ◽  
Vol 11 (5) ◽  
pp. 509 ◽  
Author(s):  
Ian Paynter ◽  
Crystal Schaaf ◽  
Jennifer Bowen ◽  
Linda Deegan ◽  
Francesco Peri ◽  
...  
Keyword(s):  
New England ◽  
Spartina Alterniflora ◽  
Regional Scale ◽  
Satellite Observations ◽  
Airborne Lidar ◽  
Aerial Imagery ◽  
Lidar Data ◽  
Terrestrial Lidar ◽  
Airborne Lidar Data ◽  
Lidar Observations

Airborne lidar can observe saltmarshes on a regional scale, targeting phenological and tidal states to provide the information to more effectively utilize frequent multispectral satellite observations to monitor change. Airborne lidar observations from NASA Goddard Lidar Hyperspectral and Thermal (G-LiHT) of a well-studied region of saltmarsh (Plum Island, Massachusetts, United States) were acquired in multiple years (2014, 2015 and 2016). These airborne lidar data provide characterizations of important saltmarsh components, as well as specifications for effective surveys. The invasive Phragmites australis was observed to increase in extent from 8374 m2 in 2014, to 8882 m2 in 2015 (+6.1%), and again to 13,819 m2 in 2016 (+55.6%). Validation with terrestrial lidar supported this increase, but suggested the total extent was still underestimated. Estimates of Spartina alterniflora extent from airborne lidar were within 7% of those from terrestrial lidar, but overestimation of height of Spartina alterniflora was found to occur at the edges of creeks (+83.9%). Capturing algae was found to require observations within ±15° of nadir, and capturing creek structure required observations within ±10° of nadir. In addition, 90.33% of creeks and ditches were successfully captured in the airborne lidar data (8206.3 m out of 9084.3 m found in aerial imagery).

scholarly journals Derivation of Effective Aerodynamic Surface Roughness in Urban Areas from Airborne Lidar Terrain Data

2008 ◽  
Vol 47 (10) ◽  
pp. 2614-2626 ◽  
Author(s):  
Donald E. Holland ◽  
Judith A. Berglund ◽  
Joseph P. Spruce ◽  
Rodney D. McKellip
Keyword(s):  
Surface Roughness ◽  
Urban Areas ◽  
A Priori ◽  
Airborne Lidar ◽  
Broward County ◽  
Effective Surface ◽  
Geometric Models ◽  
Automated Technique ◽  
Variable Wind ◽  
The Impact

Abstract An automated technique was developed that uses only airborne lidar terrain data to derive the necessary parameters for calculation of effective aerodynamic surface roughness in urban areas. The technique provides parameters for geometric models that have been used over the past 40+ years by automatically deriving the relevant geometry, orientation, and spacing of buildings and trees. In its prototypical form, this technique subsequently calculates an effective surface roughness for 1-km2 parcels of land for each of five geometric models. The user can define several constraints to guide processing based on a priori knowledge of the urban area or lidar data characteristics. Any given wind direction (or range of directions) can be selected to simulate conditions of variable wind flow and the impact on effective surface roughness. The operation, capabilities, and limitations of the technique were demonstrated using lidar terrain data from Broward County, Florida.

LESS: Large-scale remote sensing data and image simulation framework over Vegetated Areas

2020 ◽  
Author(s):  
Jianbo Qi ◽  
Donghui Xie
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Large Scale ◽  
Computational Cost ◽  
Field Measurements ◽  
Airborne Lidar ◽  
Graphic User Interface ◽  
Inversion Algorithm ◽  
Modeling Framework ◽  
Tracing Method

<p>Three-dimensional (3D) radiative transfer (RT) modeling and simulation of the transport of radiation through earth surfaces is a challenging and difficult task. The difficulties lie in the complexity of the landscapes and also the intensive computational cost of 3D RT simulations. Current models usually work with abstract landscape elements to reduce complexity or only consider relatively small realistic scenes. In this study, a new 3D RT modeling framework (called LESS) is proposed. It employs a forward photon tracing method to simulate bidirectional reflectance factor (BRF) or flux-related data (e.g., downwelling radiation) and a backward path tracing method to generate sensor images (e.g., fisheye images) or large-scale (e.g. 1 km<sup>2</sup>) spectral images from visible to thermal infrared band. In this framework, a graphic user interface (GUI) and a set of tools are also provided to help to construct the landscape and set parameters, e.g., extracting tree crowns from airborne LiDAR data, which makes it more accessible to common users. The accuracy of LESS is evaluated with other models and field measurements in terms of directional BRF and pixel-wise comparisons. It shows that the accuracy of LESS is consistent with the reference models from RAMI model inter-comparison website (http://rami-benchmark.jrc.ec.europa.eu/HTML/Home.php) as well as field measurements. LESS has also been extended to simulate atmosphere, LiDAR and in-situ sensors. It provides as a useful tool for studying the radiative transfer process over complex forest canopies from leaf to canopy scales. The simulated datasets can be used as benchmarks for validating other physical remote sensing inversion algorithm and developing parameterized models for retrieving bio-geophysical variables of canopy. LESS can be accessed from http://lessrt.org.</p>

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