scholarly journals Accuracy of selected methods of measurement of tree heights

2021 ◽  
Vol 12 (1) ◽  
pp. 6-16
Author(s):  
A. M. Bilous ◽  
P. P. Diachuk ◽  
R. M. Zadorozhniuk ◽  
M. S. Matsala ◽  
M. M. Burianchuk
Keyword(s):  
Forest Ecosystems ◽  
Remote Sensing Data ◽  
Original Data ◽  
Aerial Images ◽  
Random Measurement Error ◽  
Laser Altimeter ◽  
Measuring Devices ◽  
Direct Measurements ◽  
Height Model

In this paper, the possibilities of using stereophotogrammetry methods for measurements using unmanned aerial vehicles (UAVs) for the conditions of a mature pine stand with uneven density are examined. Here, we carried out a comparison of measurements using altimeters and remote sensing data collected with a UAV optical camera. In particular, the height of growing trees was estimated by three different field-based devices and applying the three methods of data collection and processing with UAVs. Specifically, one method implied the direct measurements using on-board UAV equipment. The following two methods are based on the data provided by the stereophotogrammetrical approach, while the aerial images for that were collected using a UAV optical camera. In particular, there was a modeling of the points cloud from one-sided vertical shooting of sample trees and determination of height of trees from digital canopy height model (CHM) from data of aerial photography of horizontal spans over a stand. Our investigation confirmed the highest accuracy of laser measuring tools among the ground measuring devices used in research. Respective value of the average random measurement error was less than 3 % (0.88 m). Among the results obtained from the analysis of the original data collected by UAVs, the best method was to utilize the CHM, namely, the average random error was less than 2% (0.64 m). This exceeds the accuracy of laser altimeter measurements 33 %. Thus, this method of measuring height in pine stands meets the standards of accuracy in determining the height for production assessment, according to the “Inventory guidelines for the forest fund of Ukraine”, and can be used for survey, inventory, forest management and other works related to forestry and monitoring the changes in forest ecosystems.

scholarly journals Kombination von Landesforstinventar- und Fernerkundungsdaten für Kleingebietsschätzungen

2011 ◽  
Vol 162 (9) ◽  
pp. 290-299 ◽  
Author(s):  
Katharina Steinmann ◽  
Christian Ginzler ◽  
Adrian Lanz
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
National Forest Inventory ◽  
Aerial Images ◽  
Coefficient Of Determination ◽  
National Forest ◽  
Target Variable ◽  
Ancillary Data ◽  
Timber Volume

Combining data from the Swiss National Forest Inventory and from remote sensing for small-area estimations in forestry A design-unbiased small estimator was tested in this study. This estimator combines terrestrial data from the Swiss National Forest Inventory (LFI) with ancillary data from stereo aerial images and airborne laser scanner (ALS) data. The estimator was tested for the two target variables: the percentage of forest and the timber volume. The efficiency of the estimator depends on the model precision of the target variable obtained with remote sensing data and other ancillary spatial data, which can potentially explain the spatial variation of the target variable. Canopy heights derived from stereo aerial images (ADS40) and ALS data were used as ancillary data. Regression models for the timber volume and the forest/non-forest decision of the LFI samples were calibrated within the cantons Appenzell Inner Rhodes and Appenzell Outer Rhodes and provided a coefficient of determination of roughly 60%. Adding the forest/non-forest decision from the aerial photo interpretation of the LFI as an explanatory variable slightly improved the models and allowed to gain a coefficient of determination of 65% for the timber volume and 85% for the forest/non-forest decision. Within the forest area, the canopy height models explained nearly 40% (ALS data) and 20% (ADS40 data) of the observed timber volume variability. This case study shows that using remote sensing data can increase the precision (in terms of the standard error) of the timber volume estimation in canton Appenzell Inner Rhodes by roughly 30%. The same is valid for the estimation of the percentage of forest. A reduction in the standard error of about 10% for the latter estimation was obtained by using the aerial images and nearly 25% using the ALS data.

scholarly journals Alignment determination of the Hayabusa2 laser altimeter (LIDAR)

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Hirotomo Noda ◽  
Hiroki Senshu ◽  
Koji Matsumoto ◽  
Noriyuki Namiki ◽  
Takahide Mizuno ◽  
...  
Keyword(s):  
Altimeter Data ◽  
Gravity Assist ◽  
Laser Altimeter ◽  
Trajectory Error ◽  
Flight Data ◽  
Camera Image ◽  
Operation Period ◽  
The Cross ◽  
Along Track

AbstractIn this study, we determined the alignment of the laser altimeter aboard Hayabusa2 with respect to the spacecraft using in-flight data. Since the laser altimeter data were used to estimate the trajectory of the Hayabusa2 spacecraft, the pointing direction of the altimeter needed to be accurately determined. The boresight direction of the receiving telescope was estimated by comparing elevations of the laser altimeter data and camera images, and was confirmed by identifying prominent terrains of other datasets. The estimated boresight direction obtained by the laser link experiment in the winter of 2015, during the Earth’s gravity assist operation period, differed from the direction estimated in this study, which fell on another part of the candidate direction; this was not selected in a previous study. Assuming that the uncertainty of alignment determination of the laser altimeter boresight was 4.6 pixels in the camera image, the trajectory error of the spacecraft in the cross- and/or along-track directions was determined to be 0.4, 2.1, or 8.6 m for altitudes of 1, 5, or 20 km, respectively.

Determination of multi pesticide residues in leaf and needle samples using a modified QuEChERS approach and gas chromatography-tandem mass spectrometry

2021 ◽  
Author(s):  
Arnelle Löbbert ◽  
Sonja Schanzer ◽  
Henrik Krehenwinkel ◽  
Franz Bracher ◽  
Christoph Müller
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Tandem Mass Spectrometry ◽  
Forest Ecosystems ◽  
Tandem Mass ◽  
Modified Quechers ◽  
Chromatography Tandem Mass Spectrometry ◽  
The Impact ◽  
Impact Of Pesticides

A novel, validated QuEChERS-based GC-MS/MS method was developed, which will allow the assessment of the impact of pesticides on forest ecosystems.

THE MEASUREMENT AND ANALYSIS OF TRIPLE CORRELATIONS

1964 ◽  
Vol 42 (6) ◽  
pp. 1101-1115 ◽  
Author(s):  
Philip B. Smith
Keyword(s):  
Gamma Rays ◽  
Heavy Particle ◽  
Original Data ◽  
Population Parameters ◽  
True Solution ◽  
Error Matrix ◽  
Mixing Ratios ◽  
Triple Correlation ◽  
Measurement And Analysis

The measurement and analysis of the intensity–direction correlation of gamma rays emitted in cascade following heavy-particle capture are treated. A procedure is discussed which is based upon the expansion of the triple-correlation intensity in terms of the set of angular functions orthogonal over the space of the emission (or absorption) directions. This is in contrast to the usual method which expresses the correlation in terms of Legendre polynomials. In the analysis procedure proposed, the population parameters are found directly from the original data, with the gamma-radiation mixing ratios assigned. The least-squares equations representing the best fit to the data contain the population parameters linearly and are solved by a standard computer program which also gives the value of χ2. The true solution is then found by varying the mixing ratios until a minimum in χ2 is reached. In addition to the determination of the population parameters of the decaying state and the mixing ratios of the gamma rays in the cascade, the calculation of the error matrix of these quantities, and the calculation of the formation parameters in simple capture, are described.

Corrosion Metal Loss Interaction From In-Line Inspection and How it Can Affect the Calculated Failure Pressure

2012 ◽  
Author(s):  
Lucinda Smart ◽  
Richard McNealy ◽  
Harvey Haines
Keyword(s):  
Field Measurements ◽  
Metal Loss ◽  
Data Correlation ◽  
Failure Pressure ◽  
Industry Standards ◽  
Direct Measurements ◽  
Non Destructive ◽  
Flux Leakage

In-Line Inspection (ILI) is used to prioritize metal loss conditions based on predicted failure pressure in accordance with methods prescribed in industry standards such as ASME B31G-2009. Corrosion may occur in multiple areas of metal loss that interact and may result in a lower failure pressure than if flaws were analyzed separately. The B31G standard recommends a flaw interaction criterion for ILI metal loss predictions within a longitudinal and circumferential spacing of 3 times wall thickness, but cautions that methods employed for clustering of ILI anomalies should be validated with results from direct measurements in the ditch. Recent advances in non-destructive examination (NDE) and data correlation software have enabled reliable comparisons of ILI burst pressure predictions with the results from in-ditch examination. Data correlation using pattern matching algorithms allows the consideration of detection and reporting thresholds for both ILI and field measurements, and determination of error in the calculated failure pressure prediction attributable to the flaw interaction criterion. This paper presents a case study of magnetic flux leakage ILI failure pressure predictions compared with field results obtained during excavations. The effect of interaction criterion on calculated failure pressure and the probability of an ILI measurement underestimating failure pressure have been studied. We concluded a reason failure pressure specifications do not exist for ILI measurements is because of the variety of possible interaction criteria and data thresholds that can be employed, and demonstrate herein a method for their validation.

scholarly journals Retrieving Forest Canopy Elements Clumping Index Using ICESat GLAS Lidar Data

2021 ◽  
Vol 13 (5) ◽  
pp. 948
Author(s):  
Lei Cui ◽  
Ziti Jiao ◽  
Kaiguang Zhao ◽  
Mei Sun ◽  
Yadong Dong ◽  
...  
Keyword(s):  
Large Scale ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Alternative Methods ◽  
High Signal ◽  
Laser Altimeter ◽  
Waveform Data ◽  
Forest Park ◽  
Clumping Index ◽  
The Impact

Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical reflectance data. However, these reflectance-based estimates have well-known limitations, such as the mere use of a linear relationship between the normalized difference hotspot and darkspot (NDHD) and CI, uncertainties in bidirectional reflectance distribution function (BRDF) models used to calculate the NDHD, and coarse spatial resolutions (e.g., hundreds of meters to several kilometers). To remedy these limitations and develop alternative methods for large-scale CI mapping, here we explored the use of spaceborne lidar—the Geoscience Laser Altimeter System (GLAS)—and proposed a semi-physical algorithm to estimate CI at the footprint level. Our algorithm was formulated to leverage the full vertical canopy profile information of the GLAS full-waveform data; it converted raw waveforms to forest canopy gap distributions and gap fractions of random canopies, which was used to estimate CI based on the radiative transfer theory and a revised Beer–Lambert model. We tested our algorithm over two areas in China—the Saihanba National Forest Park and Heilongjiang Province—and assessed its relative accuracies against field-measured CI and MODIS CI products. We found that reliable estimation of CI was possible only for GLAS waveforms with high signal-to-noise ratios (e.g., >65) and at gentle slopes (e.g., <12°). Our GLAS-based CI estimates for high-quality waveforms compared well to field-based CI (i.e., R2 = 0.72, RMSE = 0.07, and bias = 0.02), but they showed less correlation to MODIS CI (e.g., R2 = 0.26, RMSE = 0.12, and bias = 0.04). The difference highlights the impact of the scale effect in conducting comparisons of products with huge differences resolution. Overall, our analyses represent the first attempt to use spaceborne lidar to retrieve high-resolution forest CI and our algorithm holds promise for mapping CI globally.

scholarly journals Remote Sensing for Property Valuation: A Data Source Comparison in Support of Fair Land Taxation in Rwanda

2021 ◽  
Vol 13 (18) ◽  
pp. 3563
Author(s):  
Mila Koeva ◽  
Oscar Gasuku ◽  
Monica Lengoiboni ◽  
Kwabena Asiama ◽  
Rohan Mark Bennett ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Aerial Images ◽  
Primary Data ◽  
Structured Interviews ◽  
Remotely Sensed Data ◽  
Quality Of Data ◽  
New Approach ◽  
Property Valuation ◽  
Land Administration

Remotely sensed data is increasingly applied across many domains, including fit-for-purpose land administration (FFPLA), where the focus is on fast, affordable, and accurate property information collection. Property valuation, as one of the main functions of land administration systems, is influenced by locational, physical, legal, and economic factors. Despite the importance of property valuation to economic development, there are often no standardized rules or strict data requirements for property valuation for taxation in developing contexts, such as Rwanda. This study aims at assessing different remote sensing data in support of developing a new approach for property valuation for taxation in Rwanda; one that aligns with the FFPLA philosophy. Three different remote sensing technologies, (i) aerial images acquired with a digital camera, (ii) WorldView2 satellite images, and (iii) unmanned aerial vehicle (UAV) images obtained with a DJI Phantom 2 Vision Plus quadcopter, are compared and analyzed in terms of their fitness to fulfil the requirements for valuation for taxation purposes. Quantitative and qualitative methods are applied for the comparative analysis. Prior to the field visit, the fundamental concepts of property valuation for taxation and remote sensing were reviewed. In the field, reference data using high precision GNSS (Leica) was collected and used for quantitative assessment. Primary data was further collected via semi-structured interviews and focus group discussions. The results show that UAVs have the highest potential for collecting data to support property valuation for taxation. The main reasons are the prime need for accurate-enough and up-to-date information. The comparison of the different remote sensing techniques and the provided new approach can support land valuers and professionals in the field in bottom-up activities following the FFPLA principles and maintaining the temporal quality of data needed for fair taxation.

scholarly journals Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images

2020 ◽  
Author(s):  
Matheus B. Pereira ◽  
Jefersson Alex Dos Santos
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
The Other ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Remote Sensing Images ◽  
Low Resolution

High-resolution aerial images are usually not accessible or affordable. On the other hand, low-resolution remote sensing data is easily found in public open repositories. The problem is that the low-resolution representation can compromise pattern recognition algorithms, especially semantic segmentation. In this M.Sc. dissertation1 , we design two frameworks in order to evaluate the effectiveness of super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on different remote sensing datasets. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery, outperforming unsupervised interpolation and achieving semantic segmentation results comparable to highresolution data.

scholarly journals Finding the Number of Clusters in Data and Better Initial Centers for K-means Algorithm

2020 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Ahmed Fahim ◽  
Keyword(s):  
Data Clustering ◽  
Linear Time ◽  
Original Data ◽  
Local Minima ◽  
Expected Number ◽  
Open Problems ◽  
Number Of Clusters ◽  
Benchmark Datasets ◽  
Selection Of

The k-means is the most well-known algorithm for data clustering in data mining. Its simplicity and speed of convergence to local minima are the most important advantages of it, in addition to its linear time complexity. The most important open problems in this algorithm are the selection of initial centers and the determination of the exact number of clusters in advance. This paper proposes a solution for these two problems together; by adding a preprocess step to get the expected number of clusters in data and better initial centers. There are many researches to solve each of these problems separately, but there is no research to solve both problems together. The preprocess step requires o(n log n); where n is size of the dataset. This preprocess step aims to get initial portioning of data without determining the number of clusters in advance, then computes the means of initial clusters. After that we apply k-means on original data using the resulting information from the preprocess step to get the final clusters. We use many benchmark datasets to test the proposed method. The experimental results show the efficiency of the proposed method.

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