scholarly journals Data collection tools for river geomorphology studies : LiDAR and traditional methods

2021 ◽  
Author(s):  
Christopher Haring
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Survey Methods ◽  
Geospatial Data ◽  
Lidar Data ◽  
Army Corps ◽  
Positional Accuracy ◽  
Us Army ◽  
Advantages And Disadvantages ◽  
American Society

The purpose of this review is to highlight LiDAR data usage for geomorphic studies and compare to other remote sensing technologies. This review further identifies survey efficiencies and issues that can be problematic in using LiDAR digital elevation models (DEMs) in completing surveys and geomorphic analysis. US Army Corps of Engineers (USACE) geospatial data collection guidance (EM 1110-1-1000) (USACE 2015) aligns with the American Society for Photogrammetry and Remote Sensing Positional Accuracy Standards for Digital Geospatial Data (ASPRS 2014). Geomorphic assessment technologies are rapidly evolving, and LiDAR data collection methods are at the forefront. The FluvialGeomorph (FG) toolbox, developed to support USACE watershed planning, is a recent example of the use of LiDAR high-resolution terrain data to provide a new, efficient approach for rapid watershed assessments (Haring et al. 2020; Haring and Biedenharn 2021). However, there are advantages and disadvantages in using LiDAR data compared to other remote sensing technologies and traditional topographic field survey methods.

scholarly journals Exploring the Potential of Unmanned Aerial Vehicle (UAV) Remote Sensing for Mapping Plucking Area of Tea Plantations

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1214
Author(s):  
Qingfan Zhang ◽  
Bo Wan ◽  
Zhenxiu Cao ◽  
Quanfa Zhang ◽  
Dezhi Wang
Keyword(s):  
Remote Sensing ◽  
Survey Methods ◽  
Remotely Sensed ◽  
Lidar Data ◽  
Remotely Sensed Data ◽  
Tea Plantation ◽  
Aerial Photogrammetry ◽  
Tea Plantations ◽  
Tea Garden ◽  
Optical Imagery

Mapping plucking areas of tea plantations is essential for tea plantation management and production estimation. However, on-ground survey methods are time-consuming and labor-intensive, and satellite-based remotely sensed data are not fine enough for plucking area mapping that is 0.5–1.5 m in width. Unmanned aerial vehicles (UAV) remote sensing can provide an alternative. This paper explores the potential of using UAV-derived remotely sensed data for identifying plucking areas of tea plantations. In particular, four classification models were built based on different UAV data (optical imagery, digital aerial photogrammetry, and lidar data). The results indicated that the integration of optical imagery and lidar data produced the highest overall accuracy using the random forest algorithm (94.39%), while the digital aerial photogrammetry data could be an alternative to lidar point clouds with only a ~3% accuracy loss. The plucking area of tea plantations in the Huashan Tea Garden was accurately measured for the first time with a total area of 6.41 ha, which accounts for 57.47% of the tea garden land. The most important features required for tea plantation mapping were the canopy height, variances of heights, blue band, and red band. Furthermore, a cost–benefit analysis was conducted. The novelty of this study is that it is the first specific exploration of UAV remote sensing in mapping plucking areas of tea plantations, demonstrating it to be an accurate and cost-effective method, and hence represents an advance in remote sensing of tea plantations.

Remote sensing of terrestrial non-photosynthetic vegetation using hyperspectral, multispectral, SAR, and LiDAR data

2015 ◽  
Vol 40 (2) ◽  
pp. 276-304 ◽  
Author(s):  
Zhaoqin Li ◽  
Xulin Guo
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Wildlife Habitat ◽  
Optical Data ◽  
Lidar Data ◽  
Natural Fire ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Sar Data ◽  
Ecological Importance

Quantifying non-photosynthetic vegetation (NPV) is important for ecosystem management and studies on climate change, ecology, and hydrology because it controls uptake of carbon, water, and nutrients together with frequency and intensity of natural fire, and serves as wildlife habitat. The ecological importance of NPV has driven considerable research on quantitatively estimating NPV in diverse ecosystems including croplands, forests, grasslands, savannah, and shrublands using remote sensing data. However, a comprehensive review is not available. This review highlights the theoretical bases and the critical elements of remote sensing for NPV estimation, and summarizes research on estimating fractional cover of NPV (NPV cover) and biomass using passive optical hyperspectral and multispectral remote sensing data, active synthetic aperture radar (SAR) and light detection and ranging (LiDAR), and integrated multi-sensorial data. We also discuss advantages and disadvantages of optical, LiDAR, and SAR data and pinpoint future direction on NPV estimation using remote sensing data. Currently, most NPV research has been mainly focused on NPV cover, not NPV biomass, using passive optical data, while a few studies have used LiDAR data to quantify NPV biomass in forests and SAR data on NPV estimation in croplands and grasslands. In the future, more efforts should be made to estimate NPV biomass and to investigate the best use of hyperspectral, LiDAR, SAR data, and their integration. The upcoming new optical sensor on Sentinel-2 satellites, Radarsat-2 constellation and NovaSAR, technological innovation in hyperspectral, LiDAR, and SAR, and improvements on methodology for information extraction and combining multi-sensorial data will provide more opportunities for NPV estimation.

Modernizing Rail Inspections for the U.S. Army Corps of Engineers

2012 ◽  
Author(s):  
Chad Gartrell ◽  
Judy Reagan ◽  
Caleb Carter
Keyword(s):  
Data Collection ◽  
Spatial Data ◽  
Quality Data ◽  
Army Corps ◽  
Army Corps Of Engineers ◽  
Mobile Data Collection ◽  
Us Army ◽  
The Us ◽  
Data Content ◽  
The U.S

The U.S. Army Engineer Research and Development Center (ERDC) executes inspection programs as part of the U.S. Army Transportation Infrastructure Program (ATIP). These inspection, monitoring and assessment programs include airfields, bridges, dams, railroads, waterfront facilities and ranges. To date, the process for these inspection programs has been manually intensive, time consuming and difficult to scale. The US Army Corps is bringing digital business and spatial data collection methods to its inspection program for the military’s railroad infrastructure. By combining GPS and GIS technologies into a mobile data collection solution, added efficiency and data quality has been brought to the field inspection workflow. This modernization effort also results in streamlined data processing and reporting. These improved processes will lead to higher quality data, better analysis of the new richer data content and better decisions made by the end users and stakeholders.

MOTIVASI KERJA PERAWAT DI RUANG RAWAT INAP RSUD MAJALAYA KABUPATEN BANDUNG

2020 ◽  
Author(s):  
Andria Pragholapati
Keyword(s):  
Data Collection ◽  
Work Environment ◽  
Sampling Method ◽  
Work Motivation ◽  
Survey Methods ◽  
Sampling Technique ◽  
Total Sample ◽  
Regional Hospital ◽  
High Work

Work motivation is an influential condition for arousing, directing, and maintaining behavior related to the work environment including nurse work motivation. The purpose of this study was to edit the Nurses' Work Motivation in the Inpatient Room of Majalaya Regional Hospital. This type of research uses analytic survey methods. The sampling method uses a total sampling technique with a total sample of 55 nurses in 6 inpatients. Data collection techniques using a work motivation questionnaire. The analysis used is univariate. The results of the study 28 people (50.9%) have high work motivation. The conclusion of the results of this study some nurses have work motivation of nurses in the inpatient room of Majalaya Regional Hospital. Based on the results of the study are expected to require motivation support to increase work motivation of nurses.

scholarly journals Inventory of Locations of Old Mining Works Using LiDAR Data: A Case Study in Slovakia

2021 ◽  
Vol 13 (12) ◽  
pp. 6981
Author(s):  
Marcela Bindzarova Gergelova ◽  
Slavomir Labant ◽  
Jozef Mizak ◽  
Pavel Sustek ◽  
Lubomir Leicher
Keyword(s):  
Satellite System ◽  
Precise Determination ◽  
Lidar Data ◽  
Positional Accuracy ◽  
Current Form ◽  
Mining History ◽  
Mining Works ◽  
Gnss Measurements ◽  
Global Navigation Satellite

The concept of further sustainable development in the area of administration of the register of old mining works and recent mining works in Slovakia requires precise determination of the locations of the objects that constitute it. The objects in this register have their uniqueness linked with the history of mining in Slovakia. The state of positional accuracy in the registration of objects in its current form is unsatisfactory. Different database sources containing the locations of the old mining works are insufficient and show significant locational deviations. For this reason, it is necessary to precisely locate old mining works using modern measuring technologies. The most effective approach to solving this problem is the use of LiDAR data, which at the same time allow determining the position and above-ground shape of old mining works. Two localities with significant mining history were selected for this case study. Positional deviations in the location of old mining works among the selected data were determined from the register of old mining works in Slovakia, global navigation satellite system (GNSS) measurements, multidirectional hill-shading using LiDAR, and accessible data from the open street map. To compare the positions of identical old mining works from the selected database sources, we established differences in the coordinates (ΔX, ΔY) and calculated the positional deviations of the same objects. The average positional deviation in the total count of nineteen objects comparing documents, LiDAR data, and the register was 33.6 m. Comparing the locations of twelve old mining works between the LiDAR data and the open street map, the average positional deviation was 16.3 m. Between the data sources from GNSS and the registry of old mining works, the average positional deviation of four selected objects was 39.17 m.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 692
Author(s):  
MD Abdul Mueed Choudhury ◽  
Ernesto Marcheggiani ◽  
Andrea Galli ◽  
Giuseppe Modica ◽  
Ben Somers
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Tree Species ◽  
Carbon Stock ◽  
Dominant Species ◽  
Remote Sensing Data ◽  
Data Sources ◽  
Lidar Data ◽  
Urban Tree ◽  
Sensing Data

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

scholarly journals A Review of Remote Sensing Image Dehazing

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3926
Author(s):  
Juping Liu ◽  
Shiju Wang ◽  
Xin Wang ◽  
Mingye Ju ◽  
Dengyin Zhang
Keyword(s):  
Remote Sensing ◽  
Data Driven ◽  
Future Research ◽  
Research Focus ◽  
Image Dehazing ◽  
Surface Information ◽  
Haze Removal ◽  
Advantages And Disadvantages ◽  
Extra Information ◽  
Removal Technique

Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated.

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