Lidar measurement of snow depth: a review

AbstractLaser altimetry (lidar) is a remote-sensing technology that holds tremendous promise for mapping snow depth in snow hydrology and avalanche applications. Recently lidar has seen a dramatic widening of applications in the natural sciences, resulting in technological improvements and an increase in the availability of both airborne and ground-based sensors. Modern sensors allow mapping of vegetation heights and snow or ground surface elevations below forest canopies. Typical vertical accuracies for airborne datasets are decimeter-scale with order 1 m point spacings. Ground-based systems typically provide millimeter-scale range accuracy and sub-meter point spacing over 1 m to several kilometers. Many system parameters, such as scan angle, pulse rate and shot geometry relative to terrain gradients, require specification to achieve specific point coverage densities in forested and/or complex terrain. Additionally, snow has a significant volumetric scattering component, requiring different considerations for error estimation than for other Earth surface materials. We use published estimates of light penetration depth by wavelength to estimate radiative transfer error contributions. This paper presents a review of lidar mapping procedures and error sources, potential errors unique to snow surface remote sensing in the near-infrared and visible wavelengths, and recommendations for projects using lidar for snow-depth mapping.

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Application of Remote Sensing Technology in Sediment Estimating Entering the Dam Reservoirs due to Floods

Shock and Vibration ◽

10.1155/2021/4469744 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Mohammad Hadian ◽

Abolfazl Mosaedi

Keyword(s):

Remote Sensing ◽

Particulate Matter ◽

Near Infrared ◽

Spectral Response ◽

Landsat 8 ◽

Exponential Relationship ◽

Particulate Materials ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Dam Reservoirs

The present study aimed to use remote sensing technology to estimate the concentration of particulate materials in the water entering the reservoirs of dams and consequently investigate the possibility of estimating the amount of sediment carried to the reservoir by flood during the life of the dam and its annual estimate. Using an advanced spectrometer device (ASD), the reflectance values of water containing different amounts of particulate sediments were measured in the range of 400–2500 nm; then, these reflectance values were represented for the Landsat 8 satellite OLI bands using their spectral response functions. In the study of interband correlation with the number of particulate materials, band 2 (blue) and band 5 (near-infrared) were identified to prepare a specific and appropriate model. The specificity of the reflectance difference in the two abovementioned bands was presented as an exponential relationship between the concentration of particulate materials and spectral reflectance. In this model, the RMSE parameter for the maximum possible sediment concentration was equal to 1.57 and the parameter R2 was equal to 0.91. In the second step, at the same time as the satellite passed, the area was visited and the sediments of the Ardak dam reservoir were sampled by recording their location. To complete this research, two measures were performed simultaneously, calculating the concentration of particulate materials sampled in the laboratory environment and their location on the image. Then, the number of particulate materials is estimated by taking into account the coordinates recorded from the images on which the relevant corrections have been made. According to the extracted exponential model, the results of estimating the concentration of particulate matter obtained from the model and Landsat satellite images with the concentration of particulate matter obtained from sampling showed its complete compatibility with field surveys to validate this research.

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Archaeological Remote Sensing Using Multi-Temporal, Drone-Acquired Thermal and Near Infrared (NIR) Imagery: A Case Study at the Enfield Shaker Village, New Hampshire

Remote Sensing ◽

10.3390/rs12040690 ◽

2020 ◽

Vol 12 (4) ◽

pp. 690 ◽

Cited By ~ 7

Author(s):

Austin Chad Hill ◽

Elise Jakoby Laugier ◽

Jesse Casana

Keyword(s):

Remote Sensing ◽

New Hampshire ◽

Cultural Landscape ◽

Near Infrared ◽

Ground Surface ◽

Sensor Technology ◽

Aerial Imaging ◽

Wide Range ◽

Multi Temporal ◽

Remote Sensing Methods

While archaeologists have long understood that thermal and multi-spectral imagery can potentially reveal a wide range of ancient cultural landscape features, only recently have advances in drone and sensor technology enabled us to collect these data at sufficiently high spatial and temporal resolution for archaeological field settings. This paper presents results of a study at the Enfield Shaker Village, New Hampshire (USA), in which we collect a time-series of multi-spectral visible light, near-infrared (NIR), and thermal imagery in order to better understand the optimal contexts and environmental conditions for various sensors. We present new methods to remove noise from imagery and to combine multiple raster datasets in order to improve archaeological feature visibility. Analysis compares results of aerial imaging with ground-penetrating radar and magnetic gradiometry surveys, illustrating the complementary nature of these distinct remote sensing methods. Results demonstrate the value of high-resolution thermal and NIR imagery, as well as of multi-temporal image analysis, for the detection of archaeological features on and below the ground surface, offering an improved set of methods for the integration of these emerging technologies into archaeological field investigations.

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Multi-Sensor Approach to Improve Bathymetric Lidar Mapping of Semi-Arid Groundwater-Dependent Streams: Devils River, Texas

Remote Sensing ◽

10.3390/rs12152491 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2491 ◽

Cited By ~ 1

Author(s):

Kutalmis Saylam ◽

Aaron R. Averett ◽

Lucie Costard ◽

Brad D. Wolaver ◽

Sarah Robertson

Keyword(s):

Remote Sensing ◽

Near Infrared ◽

Aquatic Vegetation ◽

Survey Methods ◽

Airborne Lidar ◽

Active Sensors ◽

Geomorphic Mapping ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Complex Settings

Remote sensing technology enables detecting, acquiring, and recording certain information about objects and locations from distances relative to their geographic locations. Airborne Lidar bathymetry (ALB) is an active, non-imaging, remote sensing technology for measuring the depths of shallow and relatively transparent water bodies using light beams from an airborne platform. In this study, we acquired Lidar datasets using near-infrared and visible (green) wavelength with the Leica Airborne Hydrography AB Chiroptera-I system over the Devils River basin of southwestern Texas. Devils River is a highly groundwater-dependent stream that flows 150 km from source springs to Lake Amistad on the lower Rio Grande. To improve spatially distributed stream bathymetry in aquatic habitats of species of state and federal conservation interest, we conducted supplementary water-depth observations using other remote sensing technologies integrated with the airborne Lidar datasets. Ground penetrating radar (GPR) mapped the river bottom where vegetation impeded other active sensors in attaining depth measurements. We confirmed the accuracy of bathymetric Lidar datasets with a differential global positioning system (GPS) and compared the findings to sonar and GPR measurements. The study revealed that seamless bathymetric and geomorphic mapping of karst environments in complex settings (e.g., aquatic vegetation, entrained air bubbles, riparian zone obstructions) require the integration of a variety of terrestrial and remotely operated survey methods. We apply this approach to Devils River of Texas. However, the methods are applicable to similar streams globally.

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CLASSIFICATION OF LANDS OF REMOTE SENSITIVE DATA BY NDVI METHOD IN SMART AGRICULTURE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlvi-4-w5-2021-73-2021 ◽

2021 ◽

Vol XLVI-4/W5-2021 ◽

pp. 73-77

Author(s):

İ. Avcı ◽

E. Farzaliyev ◽

E. Kabullar

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Near Infrared ◽

Threshold Function ◽

Vegetation Density ◽

Sensitive Data ◽

Index Method ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Plant Groups

Abstract. A large share of the earth's surface is observed with remote sensing technology. Thanks to the data obtained from this process, information about the observed lands is obtained. In this study, NDVI (normalized difference), which is developed by applying mathematical operations on the reflection values of plants at different wavelengths from remote sensing technology and different application areas of this technology, electromagnetic rays, and spectral reflection values, and which is used as a method that provides a value expressing vegetation density. Vegetation index) method, NDVI value, and plant groups analyzed according to this value, sample MATLAB applications related to the NDVI method are mentioned. -Green-Blue) image of visible red and infrared regions, histogram graph showing the relationships between the intensities of values in NIR (near-infrared) and Red (visible Red) bands, NDVI image, and threshold function at the end. The NDVI image was obtained by using the direction (to detect areas that may have vegetation) is shown.

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Simulations of Leaf BSDF Effects on Lidar Waveforms

Remote Sensing ◽

10.3390/rs12182909 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2909

Author(s):

Benjamin D. Roth ◽

Adam A. Goodenough ◽

Scott D. Brown ◽

Jan A. van Aardt ◽

M. Grady Saunders ◽

...

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Forest Canopy ◽

Near Infrared ◽

Difficult Problem ◽

Leaf Angle ◽

Lidar Data ◽

Angle Distribution ◽

Visible Wavelengths ◽

Leaf Angle Distribution

Establishing linkages between light detection and ranging (lidar) data, produced from interrogating forest canopies, to the highly complex forest structures, composition, and traits that such forests contain, remains an extremely difficult problem. Radiative transfer models have been developed to help solve this problem and test new sensor platforms in a virtual environment. Many forest canopy studies include the major assumption of isotropic (Lambertian) reflecting and transmitting leaves or non-transmitting leaves. Here, we study when these assumptions may be valid and evaluate their associated impacts/effects on the lidar waveform, as well as its dependence on wavelength, lidar footprint, view angle, and leaf angle distribution (LAD), by using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) remote sensing radiative transfer simulation model. The largest effects of Lambertian assumptions on the waveform are observed at visible wavelengths, small footprints, and oblique interrogation angles relative to the mean leaf angle. For example, a 77% increase in return signal was observed with a configuration of a 550 nm wavelength, 10 cm footprint, and 45° interrogation angle to planophile leaves. These effects are attributed to (i) the bidirectional scattering distribution function (BSDF) becoming almost purely specular in the visible, (ii) small footprints having fewer leaf angles to integrate over, and (iii) oblique angles causing diminished backscatter due to forward scattering. Non-transmitting leaf assumptions have the greatest error for large footprints at near-infrared (NIR) wavelengths. Regardless of leaf angle distribution, all simulations with non-transmitting leaves with a 5 m footprint and 1064 nm wavelength saw around a 15% reduction in return signal. We attribute the signal reduction to the increased multiscatter contribution for larger fields of view, and increased transmission at NIR wavelengths. Armed with the knowledge from this study, researchers will be able to select appropriate sensor configurations to account for or limit BSDF effects in forest lidar data.

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Hyperspectral Remote Sensing Technology (HRST) program

10.2514/6.1997-3939 ◽

1997 ◽

Author(s):

Tom Wilson ◽

Rebecca Baugh ◽

Ron Contillo ◽

Tom Wilson ◽

Rebecca Baugh ◽

...

Keyword(s):

Remote Sensing ◽

Hyperspectral Remote Sensing ◽

Remote Sensing Technology ◽

Sensing Technology

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Application of remote sensing technology to the relation of submarine pipelines and coastal morphology

Water Science & Technology ◽

10.2166/wst.1995.0077 ◽

1995 ◽

Vol 32 (2) ◽

pp. 77-83

Author(s):

Y. Yüksel ◽

D. Maktav ◽

S. Kapdasli

Keyword(s):

Remote Sensing ◽

Sediment Transport ◽

Surf Zone ◽

Hydrodynamic Forces ◽

Construction Site ◽

Coastal Morphology ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Sea Bed

Submarine pipelines must be designed to resist wave and current induced hydrodynamic forces especially in and near the surf zone. They are buried as protection against forces in the surf zone, however this procedure is not always feasible particularly on a movable sea bed. For this reason the characteristics of the sediment transport on the construction site of beaches should be investigated. In this investigation, the application of the remote sensing method is introduced in order to determine and observe the coastal morphology, so that submarine pipelines may be protected against undesirable seabed movement.

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FEATURES OF CONSTRUCTION OF ON-BOARD HELICOPTER RADAR ICE EXPLORATION ON BASIS OF CIRCULAR GROUND SURFACE WITH SYNTHESIS OF APERTURE ROTATING ANTENNA

Issues of radio electronics ◽

10.21778/2218-5453-2019-2-31-37 ◽

2019 ◽

pp. 31-37

Author(s):

I. G. Antсev ◽

A. P. Aleshkin ◽

V. V. Vladimirov ◽

E. O. Kudrina ◽

O. L. Polonchik ◽

...

Keyword(s):

Remote Sensing ◽

Ground Surface ◽

Rotor Blades ◽

Navigation Systems ◽

Propeller Blade ◽

Antenna Aperture ◽

Sensing System ◽

Radar Systems ◽

Simulation Results ◽

Circular Antenna Array

The results of modeling the processes of receiving and processing the signals of remote sensing of the Earth’s surface using helicopter radar and synthesizing the antenna aperture due to its placement on the rotating rotor blades are presented. The mathematical correctness of the application of the developed algorithms for processing probing signals, as well as the uniqueness of the measurements, was confirmed. At the same time, the dimensions of the synthesized aperture due to the rotation of the radiator placed at the end of the propeller blade are equivalent to a circular antenna array with a diameter of tens of meters. The functionality of the remote sensing system based on this radar meets the requirements for ice observation and navigation systems for seagoing ships off the coast. The simulation results confirm the promise of further research in this direction and can be used in the development of radar systems with synthesized antenna aperture mounted on rotating rotor blades.

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Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

Applied Sciences ◽

10.3390/app11156923 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6923

Author(s):

Rui Zhang ◽

Zhanzhong Tang ◽

Dong Luo ◽

Hongxia Luo ◽

Shucheng You ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Identification Accuracy ◽

Optical Remote Sensing ◽

Sar Image ◽

Single Time ◽

Growing Period ◽

Sensing Technology ◽

Sar Imagery ◽

Cloudy Region

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

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Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

Arabian Journal of Geosciences ◽

10.1007/s12517-021-06699-y ◽

2021 ◽

Vol 14 (6) ◽

Author(s):

Jinming Yang ◽

Chengzhi Li

Keyword(s):

Remote Sensing ◽

Kalman Filter ◽

Water Resource ◽

Ensemble Kalman Filter ◽

Snow Depth ◽

Resource Assessment ◽

Measured Data ◽

Hydrological Process ◽

Depth Data ◽

Water Resource Assessment

AbstractSnow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.

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