LiDAR – A new tool for forest measurements?

2006 ◽  
Vol 82 (2) ◽  
pp. 211-218 ◽  
Author(s):  
David L Evans ◽  
Scott D Roberts ◽  
Robert C Parker
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Measurement Tool ◽  
Mississippi State University ◽  
State University ◽  
Individual Tree ◽  
Sensing Technology ◽  
High Measurement ◽  
Application Potential ◽  
Forest Measurement

LiDAR (Light Detection and Ranging) is a remote sensing technology with strong application potential in forest resource management. It provides high measurement precision that can be used for tree and stand measurements. Although LiDAR has not been used widely as an operational measurement tool, there is a significant body of research and a number of projects at Mississippi State University (MSU) that illustrate the potential for this technology to be incorporated into operational forest assessments. This paper provides basic background on the capabilities of LiDAR in a forest measurement context that illustrates specific examples of LiDAR use including: 1) individual tree assessments, 2) a forest inventory protocol currently being operationally tested, 3) forest structure analysis, and 4) forest typing. Key words: LiDAR, remote sensing, tree identification, tree measurements, forest inventory, forest types

scholarly journals AIRBORNE TECHNOLOGIES FOR DISASTER MANAGEMENT

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 387-393
Author(s):  
J. Schulz
Keyword(s):  
Remote Sensing ◽  
Water Masses ◽  
Aerial Images ◽  
Airborne Remote Sensing ◽  
Optimal Method ◽  
Individual Tree ◽  
Flood Peak ◽  
Sensing Technology ◽  
Aircraft System ◽  
The Individual

<p><strong>Abstract.</strong> Currently, satellite-based systems and UAVs are very popular in the investigation of natural disasters. Both systems have their justification and advantages &amp;ndash; but one should not forget the airborne remote sensing technology. The presentation shows with three examples very clearly how airborne remote sensing is still making great progress and in many cases represents the optimal method of data acquisition.</p> <p>The airborne detection of forest damages (especially currently the bark beetle in spruce stands) can determine the pest attack using CIR aerial images in combination with ALS and hyperspectral systems &amp;ndash; down to the individual tree. Large forest areas of 100 sqkm and more can be recorded from planes on one day (100 sqkm with 10cm GSD on one day).</p> <p>Flood events &amp;ndash; such as on the Elbe in 2013 &amp;ndash; were recorded by many satellites. However, many evaluations require highresolution data (GSD 10cm), e.g. to clarify insurance claims. Here the aircraft system, which was able to fly below the cloud cover and was constantly flying at the height level of the flood peak, proved to be unbeatable.</p> <p>The phenomenon of urban flash floods is one of the consequences of climate change. Cities are not in a position to cope with the water masses of extreme rain events and so are confronted with major damages. In Germany, a number of cities are already preparing to manage short-term but extreme water masses. The complicated hydrographic and hydraulic calculations and simulations require above all one thing &amp;ndash; a precise data basis. This involves, for example, the height of kerbstones and the recording of every gully and every obstacle. Such city-wide data can only be collected effectively by photogrammetric analysis of aerial photography (GSD 5 to 10cm).</p>

scholarly journals Teaching of Remote Sensing Technology for Landscape Architecture in the Context of Spatial Information Technology

2021 ◽  
Vol 16 (15) ◽  
pp. 125
Author(s):  
Xueling Zhang ◽  
Dayu Zhang
Keyword(s):  
Remote Sensing ◽  
Information Technology ◽  
Information Integration ◽  
Landscape Architecture ◽  
Spatial Information ◽  
Sensing Technology ◽  
Application Potential ◽  
Professional Teaching ◽  
Spatial Information Technology ◽  
Digital Landscape

The research of digital landscape architecture springs up in recent years. The emerging digital technology provides a rational and objective method to mine and quantify the endogenous laws of landscape architecture. Remote sensing (RS) technology has become a new growth point in the current research and design of landscape spatial information. To develop the professional teaching of landscape architecture, it is important to fully integrate the RS technology into the teaching system of spatial information technology, carry out systematic spatial information quantification and research-based teaching of landscape architecture, and collaboratively promote the teaching of landscape architecture design. This paper firstly analyzes the integration and application potential of RS technology into landscape architecture. Considering the demand and trend of information-based teaching of landscape architecture, the authors integrated the relevant technologies into an RS teaching platform for landscape architecture, and summarized an application model of RS technology in the teaching of landscape architecture theories and practices. Moreover, a landscape spatial information chain, which is question-oriented, task-driven, and exploration-based, was constructed to promote the synergistic development between the students’ research and practice ability under spatial information integration.

Remote sensing curriculum for advanced technology education at the community college level - Mississippi State University and USDA cooperating

2002 ◽  
Author(s):  
Steven J. Thomson ◽  
Donald L. Sudbrink ◽  
Gretchen F. Sassenrath ◽  
Molly B. Walker ◽  
Patrick J. English ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Community College ◽  
Technology Education ◽  
Advanced Technology ◽  
Mississippi State University ◽  
State University ◽  
College Level

scholarly journals Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data

2019 ◽  
Vol 11 (7) ◽  
pp. 797 ◽  
Author(s):  
Atte Saukkola ◽  
Timo Melkas ◽  
Kirsi Riekki ◽  
Sanna Sirparanta ◽  
Jussi Peuhkurinen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Prediction Models ◽  
Basal Area ◽  
Sample Plot ◽  
Weighted Mean ◽  
Individual Tree ◽  
Plot Size ◽  
Head Positions ◽  
Mean Square Errors

The aim of the study was to develop a new method to use tree stem information recorded by harvesters along operative logging in remote sensing-based prediction of forest inventory attributes in mature stands. The reference sample plots were formed from harvester data, using two different tree positions: harvester positions (XYH) in global satellite navigation system and computationally improved harvester head positions (XYHH). Study materials consisted of 158 mature Norway-spruce-dominated stands located in Southern Finland that were clear-cut during 2015–16. Tree attributes were derived from the stem dimensions recorded by the harvester. The forest inventory attributes were compiled for both stands and sample plots generated for stands for four different sample plot sizes (254, 509, 761, and 1018 m2). Prediction models between the harvester-based forest inventory attributes and remote sensing features of sample plots were developed. The stand-level predictions were obtained, and basal-area weighted mean diameter (Dg) and basal-area weighted mean height (Hg) were nearly constant for all model alternatives with relative root-mean-square errors (RMSE) roughly 10–11% and 6–8%, respectively, and minor biases. For basal area (G) and volume (V), using either of the position methods, resulted in roughly similar predictions at best, with approximately 25% relative RMSE and 15% bias. With XYHH positions, the predictions of G and V were nearly independent of the sample plot size within 254–761 m2. Therefore, the harvester-based data can be used as ground truth for remote sensing forest inventory methods. In predicting the forest inventory attributes, it is advisable to utilize harvester head positions (XYHH) and a smallest plot size of 254 m2. Instead, if only harvester positions (XYH) are available, expanding the sample plot size to 761 m2 reaches a similar accuracy to that obtained using XYHH positions, as the larger sample plot moderates the uncertainties when determining the individual tree position.

scholarly journals Sensor Agnostic Semantic Segmentation of Structurally Diverse and Complex Forest Point Clouds Using Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1413
Author(s):  
Sean Krisanski ◽  
Mohammad Sadegh Taskhiri ◽  
Susana Gonzalez Aracil ◽  
David Herries ◽  
Paul Turner
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Woody Debris ◽  
Digital Terrain Model ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Measurement Tool ◽  
Terrain Model ◽  
Sensing Technology ◽  
Forest Measurement

Forest inventories play an important role in enabling informed decisions to be made for the management and conservation of forest resources; however, the process of collecting inventory information is laborious. Despite advancements in mapping technologies allowing forests to be digitized in finer granularity than ever before, it is still common for forest measurements to be collected using simple tools such as calipers, measuring tapes, and hypsometers. Dense understory vegetation and complex forest structures can present substantial challenges to point cloud processing tools, often leading to erroneous measurements, and making them of less utility in complex forests. To address this challenge, this research demonstrates an effective deep learning approach for semantically segmenting high-resolution forest point clouds from multiple different sensing systems in diverse forest conditions. Seven diverse point cloud datasets were manually segmented to train and evaluate this model, resulting in per-class segmentation accuracies of Terrain: 95.92%, Vegetation: 96.02%, Coarse Woody Debris: 54.98%, and Stem: 96.09%. By exploiting the segmented point cloud, we also present a method of extracting a Digital Terrain Model (DTM) from such segmented point clouds. This approach was applied to a set of six point clouds that were made publicly available as part of a benchmarking study to evaluate the DTM performance. The mean DTM error was 0.04 m relative to the reference with 99.9% completeness. These approaches serve as useful steps toward a fully automated and reliable measurement extraction tool, agnostic to the sensing technology used or the complexity of the forest, provided that the point cloud has sufficient coverage and accuracy. Ongoing work will see these models incorporated into a fully automated forest measurement tool for the extraction of structural metrics for applications in forestry, conservation, and research.

Hyperspectral Remote Sensing Technology (HRST) program

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

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

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.

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

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.

scholarly journals Non-Destructive Lumber and Engineered Pine Products Research in the Gulf South U.S. 2005–2020

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
R. Dan Seale ◽  
Rubin Shmulsky ◽  
Frederico Jose Nistal Franca
Keyword(s):  
Agricultural Research ◽  
Forest Products ◽  
Wood Products ◽  
Time Interval ◽  
Mississippi State University ◽  
State University ◽  
White Oak ◽  
Department Of Agriculture ◽  
Mass Timber ◽  
Structural Lumber

This review primarily describes nondestructive evaluation (NDE) work at Mississippi State University during the 2005–2020 time interval. Overall, NDE is becoming increasingly important as a means of maximizing and optimizing the value (economic, engineering, utilitarian, etc.) of every tree that comes from the forest. For the most part, it focuses on southern pine structural lumber, but other species such as red pine, spruce, Douglas fir, red oak, and white oak and other products such as engineered composites, mass timber, non-structural lumber, and others are included where appropriate. Much of the work has been completed in conjunction with the U.S. Department of Agriculture, Forest Service, Forest Products Laboratory as well as the Agricultural Research Service with the overall intent of improving lumber and wood products standards and valuation. To increase the future impacts and adoption of this NDE-related work, wherever possible graduate students have contributed to the research. As such, a stream of trained professionals is a secondary output of these works though it is not specifically detailed herein.

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