scholarly journals Remote Sensing of Forest Structural Changes Due to the Recent Boom of Unconventional Shale Gas Extraction Activities in Appalachian Ohio

2021 ◽  
Vol 13 (8) ◽  
pp. 1453
Author(s):  
Yang Liu
Keyword(s):  
Forest Fragmentation ◽  
Shale Gas ◽  
Structural Changes ◽  
Point Clouds ◽  
Aerial Images ◽  
Gas Extraction ◽  
River Watershed ◽  
Forest Connectivity ◽  
Image Objects ◽  
Appalachian Ohio

Dense unconventional shale gas extraction activities have occurred in Appalachian Ohio since 2010 and they have caused various landcover changes and forest fragmentation issues. This research investigated the most recent boom of unconventional shale gas extraction activities and their impacts on the landcover changes and forest structural changes in the Muskingum River Watershed in Appalachian Ohio. Triple-temporal high-resolution natural-color aerial images from 2006 to 2017 and a group of ancillary geographic information system (GIS) data were first used to digitize the landcover changes due to the recent boom of these unconventional shale gas extraction activities. Geographic object-based image analysis (GEOBIA) was then employed to form forest patches as image objects and to accurately quantify the forest connectivity. Lastly, the initial and updated forest image objects were used to quantify the loss of core forest as the two-dimensional (2D) forest structural changes, and initial and updated canopy height models (CHMs) derived from airborne light detection and ranging (LiDAR) point clouds were used to quantify the loss of forest volume as three-dimensional (3D) forest structural changes. The results indicate a consistent format but uneven spatiotemporal development of these unconventional shale gas extraction activities. Dense unconventional shale gas extraction activities formed two apparent hotspots. Two-thirds of the well pad facilities and half of the pipeline right-of-way (ROW) corridors were constructed during the raising phase of the boom. At the end of the boom, significant forest fragmentation already occurred in both hotspots of these active unconventional shale gas extraction activities, and the areal loss of core forest reached up to 14.60% in the densest concentrated regions of these activities. These results call for attention to the ecological studies targeted on the forest fragmentation in the Muskingum River Watershed and the broader Appalachian Ohio regions.

Fracking boomtowns? Proximity, intensity, and perceptions of shale gas extraction in Hughesville and Jersey Shore, Pennsylvania

2021 ◽  
Vol 81 ◽  
pp. 102250
Author(s):  
Christopher W. Podeschi ◽  
Jeffrey C. Brunskill ◽  
Gene L. Theodori
Keyword(s):  
Shale Gas ◽  
Gas Extraction

scholarly journals A CASE STUDY ON THROUGH-WATER DENSE IMAGE MATCHING

2018 ◽  
Vol XLII-2 ◽  
pp. 659-666 ◽  
Author(s):  
G. Mandlburger
Keyword(s):  
Image Matching ◽  
Water Surface ◽  
Point Clouds ◽  
Water Clarity ◽  
Aerial Images ◽  
Mean Deviation ◽  
Range Image ◽  
Surface Mapping ◽  
Dense Image

In the last years, the tremendous progress in image processing and camera technology has reactivated the interest in photogrammetrybased surface mapping. With the advent of Dense Image Matching (DIM), the derivation of height values on a per-pixel basis became feasible, allowing the derivation of Digital Elevation Models (DEM) with a spatial resolution in the range of the ground sampling distance of the aerial images, which is often below 10 cm today. While mapping topography and vegetation constitutes the primary field of application for image based surface reconstruction, multi-spectral images also allow to see through the water surface to the bottom underneath provided sufficient water clarity. In this contribution, the feasibility of through-water dense image matching for mapping shallow water bathymetry using off-the-shelf software is evaluated. In a case study, the SURE software is applied to three different coastal and inland water bodies. After refraction correction, the DIM point clouds and the DEMs derived thereof are compared to concurrently acquired laser bathymetry data. The results confirm the general suitability of through-water dense image matching, but sufficient bottom texture and favorable environmental conditions (clear water, calm water surface) are a preconditions for achieving accurate results. Water depths of up to 5 m could be mapped with a mean deviation between laser and trough-water DIM in the dm-range. Image based water depth estimates, however, become unreliable in case of turbid or wavy water and poor bottom texture.

scholarly journals MULTISPECTRAL AIRBORNE LASER SCANNING FOR AUTOMATED MAP UPDATING

2016 ◽  
Vol XLI-B3 ◽  
pp. 323-330 ◽  
Author(s):  
Leena Matikainen ◽  
Juha Hyyppä ◽  
Paula Litkey
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Sensor Technology ◽  
Classification Error ◽  
Surface Model ◽  
Airborne Laser ◽  
Map Updating

During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

scholarly journals EXPLORING ALS AND DIM DATA FOR SEMANTIC SEGMENTATION USING CNNS

2018 ◽  
Vol XLII-1 ◽  
pp. 347-354 ◽  
Author(s):  
F. Politz ◽  
M. Sester
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Good Alternative ◽  
Aerial Images ◽  
Learning Approaches ◽  
Advantages And Disadvantages ◽  
Sensing Applications ◽  
High Level

<p><strong>Abstract.</strong> Over the past years, the algorithms for dense image matching (DIM) to obtain point clouds from aerial images improved significantly. Consequently, DIM point clouds are now a good alternative to the established Airborne Laser Scanning (ALS) point clouds for remote sensing applications. In order to derive high-level applications such as digital terrain models or city models, each point within a point cloud must be assigned a class label. Usually, ALS and DIM are labelled with different classifiers due to their varying characteristics. In this work, we explore both point cloud types in a fully convolutional encoder-decoder network, which learns to classify ALS as well as DIM point clouds. As input, we project the point clouds onto a 2D image raster plane and calculate the minimal, average and maximal height values for each raster cell. The network then differentiates between the classes ground, non-ground, building and no data. We test our network in six training setups using only one point cloud type, both point clouds as well as several transfer-learning approaches. We quantitatively and qualitatively compare all results and discuss the advantages and disadvantages of all setups. The best network achieves an overall accuracy of 96<span class="thinspace"></span>% in an ALS and 83<span class="thinspace"></span>% in a DIM test set.</p>

scholarly journals Effect of Land Cover Change on Atlantic Forest Fragmentation in Rio Largo, Al, Brazil

2020 ◽  
pp. 102-114
Author(s):  
Lucas Galdino da Silva ◽  
Arthur Costa Falcão Tavares ◽  
Carlos Frederico Lins E. Silva Brandão ◽  
João Pedro dos Santos Verçosa ◽  
Raquel Elvira Cola ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Forest Fragmentation ◽  
Atlantic Forest ◽  
Pearson Correlation ◽  
Aerial Images ◽  
Forest Fragments ◽  
Case Scenario ◽  
Worst Case ◽  
Digital Format

This study's objective was to analyze the effect of land cover change, between 1965 and 2018, using statistical metrics and geoprocessing tools. And consequently, to provide information of area (ha) and spatial fragmentation of the Atlantic Forest in the municipality of Rio Largo/AL, Brazil. The samples were collected and transferred by CECA, CADEH, and INCRA, between November 2019 and April 2020. The basic materials used in this work were multi-temporal aerial images in digital format, derived from the 1965 aerophotogrametric survey on the scale 1:25000, belonging to the collection of the Engineering and Agrarian Sciences Campus - UFAL, and images of Landsat satellites (5 and 8) processed and made available by the Mapbiomas Project. The statistic landscape metrics were calculated using Landscape ecology Statistics (LECOs), a QGIS plugin. The analysis of forest fragmentation areas over the 53 years showed a reduction between 32.17% (1965) and 12.04% (2018) concerning the total extension of the municipality. In 1965, the average area obtained from 49 fragments was 201.13 ha. The values show a higher distance of forest fragments between 1965 and 1989, and disappearance by 2018.The Pearson correlation coefficient for 1965 and 2018 presented the value of r = -0.525, indicating a moderate and negative correlation between the mean values of areas (ha) of forest fragments and the number of forest fragments. The worst-case scenario for the maintenance of native forests occurred in 1989, where the reduction of continuous forest areas had 10.87 ha for forest area average, being spaced in 327 fragments. In the period 1986 and 1996, there was a decrease in fragmentation, reaching 200 fragments. In 1996 and 1997, there was an imbalance in forest maintenance, again increasing the number of fragments to 250 areas, and being explained by the loosening of surveillance in previous years, followed by deforestation.

scholarly journals AN AUTOMATIC FILTER ALGORITHM FOR DENSE IMAGE MATCHING POINT CLOUDS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 703-709 ◽  
Author(s):  
Y. Q. Dong ◽  
L. Zhang ◽  
X. M. Cui ◽  
H. B. Ai
Keyword(s):  
Image Matching ◽  
Iterative Process ◽  
Point Clouds ◽  
High Density ◽  
Aerial Images ◽  
Dense Image ◽  
Seed Points ◽  
Filter Algorithms

Although many filter algorithms have been presented over past decades, these algorithms are usually designed for the Lidar point clouds and can’t separate the ground points from the DIM (dense image matching, DIM) point clouds derived from the oblique aerial images owing to the high density and variation of the DIM point clouds completely. To solve this problem, a new automatic filter algorithm is developed on the basis of adaptive TIN models. At first, the differences between Lidar and DIM point clouds which influence the filtering results are analysed in this paper. To avoid the influences of the plants which can’t be penetrated by the DIM point clouds in the searching seed pointes process, the algorithm makes use of the facades of buildings to get ground points located on the roads as seed points and construct the initial TIN. Then a new densification strategy is applied to deal with the problem that the densification thresholds do not change as described in other methods in each iterative process. Finally, we use the DIM point clouds located in Potsdam produced by Photo-Scan to evaluate the method proposed in this paper. The experiment results show that the method proposed in this paper can not only separate the ground points from the DIM point clouds completely but also obtain the better filter results compared with TerraSolid. 1.

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

scholarly journals Prospects of "Slate Revolution" and Problem of Power Industry of China

2013 ◽  
pp. 118-124
Author(s):  
M. Scryabina
Keyword(s):  
New York ◽  
Shale Gas ◽  
Energy Security ◽  
Chinese Economy ◽  
Gdp Growth ◽  
Gas Extraction ◽  
Extraction Technology ◽  
Us States ◽  
The Core ◽  
The Usa

The remarkable results of “shale gas revolution” in the USA have provoked a huge interest in Chinese energy circles. Beijing seriously considers the prospects of developing its abandoned domestic shale gas resources, which might result in a second “shale gas revolution”, this time in Asian region. Developing shale gas would help Beijing to bridge the gap between energy consumption and supply, and would also create a viable alternative to coal. However, the technology of shale gas extraction (hydraulic fracturing) is highly controversial, and raises a lot of concerns among environmentalists. “Fracking” has already been banned in a number of European states and there, and is a subject to moratorium in US states of New York, Connecticut and New Jersey. The core question is whether China can successfully adapt the extraction technology to its geologic conditions, and most importantly whether “fracking” of shale gas will help to alleviate the environmental degradation caused by rapid GDP growth, and help to increase energy security of Chinese economy.

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