scholarly journals Rapid, nondestructive estimation of forest understory biomass using a handheld laser rangefinder

2018 ◽  
Vol 48 (7) ◽  
pp. 803-808 ◽  
Author(s):  
Mark J. Ducey ◽  
Rasmus Astrup
Keyword(s):  
Airborne Lidar ◽  
Forest Understory ◽  
Laser Rangefinder ◽  
Destructive Sampling ◽  
Laser Sampling ◽  
Biomass Assessment ◽  
Woody Shrubs ◽  
Cost Efficient ◽  
Understory Biomass ◽  
Nondestructive Estimation

The forest understory is often associated with rapid rates of carbon and nutrient cycling, but cost-efficient quantification of its biomass remains challenging. We tested a new field technique for understory biomass assessment using an off-the-shelf handheld laser rangefinder. We conducted laser sampling in a pine forest with an understory dominated by invasive woody shrubs, especially Rhamnus frangula L. Laser sampling was conducted using a rangefinder, mounted on a monopod to provide a consistent reference height, and pointed vertically downward. Subsequently, the understory biomass was measured with destructive sampling. A series of metrics derived from the airborne LiDAR literature were evaluated alone and in combination for prediction of understory biomass using best-subsets regression. Resulting fits were good (r2 = 0.85 and 0.84 for the best single metric and best additive metric, respectively, and R2 = 0.93 for the best multivariate model). The results indicate that laser sampling could substantially reduce the need for costly destructive sampling within a double-sampling context.

scholarly journals Testing the Ability of Airborne LiDAR to Measure Forage Resources for Wild Ungulates in Conifer Forests

2019 ◽  
Vol 117 (5) ◽  
pp. 492-503 ◽  
Author(s):  
Iver T Hull ◽  
Lisa A Shipley
Keyword(s):  
Food Resource ◽  
Canopy Cover ◽  
Laser Pulses ◽  
Understory Vegetation ◽  
Airborne Lidar ◽  
Forest Understory ◽  
Conifer Forests ◽  
Vegetation Density ◽  
Wild Ungulates ◽  
Vegetation Biomass

Abstract Vegetation in the forest understory is a key food resource for wild ungulates like deer (Odocoileus spp.) because the amount of nutritious forage influences animal productivity and density. Therefore, measuring the abundance of understory vegetation available to wildlife populations is often a key objective for wildlife managers. Field-based methods for measuring understory vegetation across remote landscapes are time- and resource-intensive, so we compared estimates of understory vegetation density derived from airborne light detection and ranging (LiDAR) returns with vegetation biomass sampled directly on 65 field plots across 4 years and >250,000 hectares of xeric conifer forests in northeastern Washington. We found that LiDAR-derived estimates of understory vegetation density were only able to predict field-sampled vegetation biomass when the two sampling methods occurred within 3 years of each other, and overstory canopy cover was <50 percent. Our results demonstrate limitations in the ability of LiDAR, at the intensity and frequency currently applied for multiuse purposes, to measure the quantity of forage. However, further testing with synchronous field sampling and higher-density laser pulses holds promise.

Understory biomass measurement based on SfM data by a manual low-flying drone under the canopy

2020 ◽  
Author(s):  
Yupan Zhang ◽  
Yuichi Onda ◽  
Hiroaki Kato ◽  
Xinchao Sun ◽  
Takashi Gomi
Keyword(s):  
Point Cloud ◽  
Light Availability ◽  
Understory Vegetation ◽  
Airborne Lidar ◽  
Vegetation Coverage ◽  
Coefficient Of Determination ◽  
Small Scale ◽  
Cloud Data ◽  
Dense Point ◽  
Understory Biomass

&lt;p&gt;Understory vegetation has the important effect that cannot be ignored on Evapotranspiration. In previous studies, laser scanner was used to measure small-scale biomass and airborne LiDAR was used to assess light availability to understory vegetation, which in turn was converted to understory biomass production. However, it is difficult to measure watershed-scale understory biomass with high resolution. In this study, Structure from Motion (SfM) was used to reconstruct understory vegetation structure by a manual low-flying drone under the canopy with radial paths in a line thinning plantation and a spot thinning plantation made by Japanese cedar and cypress. By generating Orthomosaic image and dense point cloud data, we then extracted Excess Green Index (ExG) and Canopy Height Model (CHM), combining with understory biomass data from field harvesting to establish a quantitative relationship between the CHM and biomass, which was then used to map biomass and vegetation coverage in the study area. The results indicated that (1) a flight height of 7-10 meters is more conducive to understory vegetation reconstruction, with a photo quality greater than 0.8 and a point cloud density of more than 20 points/cm&lt;sup&gt;2&lt;/sup&gt;. (2) a regression cubic model based on the CHM has acceptable accuracy and biomass estimate capability (P&lt;0.01), with a coefficient of determination of 0.75. (3) compared with the spot thinning, the understory biomass under the line thinning scenario was higher(average biomass 3.03kg/m&lt;sup&gt;2&lt;/sup&gt;). (4) vegetation coverage based on the ExG index of visible light analysis was affected by ambient light(strong sunlight on a sunny day), and it cannot reflect the seasonal changes of understory vegetation biomass. These results disclosed the potential of the dense point cloud from drone SfM for estimating understory biomass. With this method, we will measure more than 5000m&lt;sup&gt;2 &lt;/sup&gt;of headwater catchment and output a understory biomass map.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Seasonal Dynamics of Floodplain Forest Understory–Impacts of Degradation, Light Availability and Temperature on Biomass and Species Composition

Forests ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 22 ◽  
Author(s):  
Natalia Czapiewska ◽  
Marcin K. Dyderski ◽  
Andrzej M. Jagodziński
Keyword(s):  
Species Composition ◽  
Seasonal Dynamics ◽  
Light Availability ◽  
Floodplain Forest ◽  
Carbon Accumulation ◽  
Forest Understory ◽  
Floodplain Forests ◽  
Biomass Crop ◽  
Biomass Dynamics ◽  
Understory Biomass

Forest understory significantly contributes to matter cycling in ecosystems, but little is known about its carbon pool. This is especially poorly understood in floodplain forests, one of the most threatened ecosystems worldwide. We studied seasonal dynamics of biomass and species composition of understory vegetation in degraded and non-degraded floodplain forests, to improve our understanding of carbon pools in forest ecosystems. We hypothesized that degraded and non-degraded floodplain forests will differ in patterns of seasonal variability of biomass and species composition. The study was conducted in Poznań (W Poland) in two study plots (each with 10 samples) across 22 dates (March–November 2016). In each date, we collected understory aboveground biomass. We evaluated impact of light availability and soil temperature on biomass and species composition. Our study revealed high dynamics of biomass production. We found maximum biomass crop of understory in degraded floodplain forest on 24 April (930.12 ± 48.70 kg ha−1), whereas in non-degraded floodplain forest the maximum occurred on 30 May (768.99 ± 40.65 kg ha−1). At the beginning of the growing season, understory biomass was dominated by spring ephemerals and later these species were replaced by others present for the whole season. Additionally, we confirmed the positive impacts of light availability and temperature on understory primary production. The pattern revealed drove species composition shifts and low differences in biomass crop between consecutive dates. Patterns of understory biomass dynamics differed between degraded and non-degraded plots. Despite study limitations, we provided rare data about understory biomass dynamics of floodplain forests, increasing knowledge about carbon accumulation and cycling in floodplain forests, and contributing to global carbon assessments.

scholarly journals Puistu esimese rinde võrastiku alguse kõrguse hindamine lennukilidari mõõdistusandmete järgi / A simple model to estimate forest canopy base height from airborne lidar data

2013 ◽  
Vol 58 (1) ◽  
pp. 46-56 ◽  
Author(s):  
Tauri Arumäe ◽  
Mait Lang
Keyword(s):  
Forest Canopy ◽  
Laser Scanner ◽  
Digital Terrain Model ◽  
Test Site ◽  
Airborne Lidar ◽  
Forest Understory ◽  
Ground Vegetation ◽  
Height Distribution ◽  
Forest Stands ◽  
Tree Species Composition

Abstract Airborne laser scanner (ALS) measurements from two test sites in Estonia were used to estimate forest canopy-base height (HVL). The ALS data was collected by Estonian Land Board using Leica ALS50-II scanner. The HVL was estimated by using mode value and standard deviation of the ALS pulse reflection position height distribution. The pulse reflections which had height less than 0.5 m over the estimated digital terrain model were excluded from the analysis. In situ measurements of canopy base height (HVA) were carried out in 20 mesotrophic Norway spruce and silver birch forest stands in Järvselja and in 45, mostly Scots pine dominant, mesotrophic forest stands in Aegviidu. Determination coefficients of linear regression between HVL and HVA for both test sites were over 0.8 and the residual standard errors of the models were less than two meters. The influence of forest understory vegetation to the estimation of HVL was tested by excluding the near-to-ground vegetation reflections which had height less than 1.5 m. The test results revealed no significant impact of forest understory to the HVL models. The cross validation showed that the HVL models were independent of test sites and tree species composition. The Järvselja data based HVL model had 1.3 m negative bias if applied to Aegviidu forests and the Aegviidu data based HVL model had 1.4 m positive bias if applied to Järvselja forests. In the Aegviidu test site, difference of HVL models of coniferous and deciduous stands was tested and the difference was found not to be significant

Modern university as an economic growth driver: Models & missions

2017 ◽  
pp. 58-76 ◽  
Author(s):  
A. Karpov
Keyword(s):  
Economic Growth ◽  
Education Research ◽  
Advanced Technology ◽  
Global Competitiveness ◽  
Technology Markets ◽  
Fast Growing ◽  
Business Ecosystem ◽  
Cost Efficient ◽  
National Economies ◽  
The University

The paper considers the modern university as an economic growth driver within the University 3.0 concept (education, research, and commercialization of knowledge). It demonstrates how the University 3.0 is becoming the basis for global competitiveness of national economies and international alliances, and how its business ecosystem generates new fast-growing industries, advanced technology markets and cost-efficient administrative territories.

Unravelling a microbial synergy to boost caproate production via carboxylates chain elongation with ethanol

2019 ◽  
Vol 26 (2) ◽  
pp. 63-71
Author(s):  
Ling Leng ◽  
Ying Wang ◽  
Peixian Yang ◽  
Takashi Narihiro ◽  
Masaru Konishi Nobu ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Volatile Fatty Acids ◽  
Microbial Consortia ◽  
Chain Elongation ◽  
Desulfovibrio Vulgaris ◽  
Rrna Gene ◽  
Selective Enrichment ◽  
Microbial Network ◽  
Cost Efficient ◽  
Rrna Gene Sequencing

Chain elongation of volatile fatty acids for medium chain fatty acids production (e.g. caproate) is an attractive approach to treat wastewater anaerobically and recover resource simultaneously. Undefined microbial consortia can be tailored to achieve chain elongation process with selective enrichment from anaerobic digestion sludge, which has advantages over pure culture approach for cost-efficient application. Whilst the metabolic pathway of the dominant caproate producer, Clostridium kluyveri, has been annotated, the role of other coexisting abundant microbiomes remained unclear. To this end, an ethanol-acetate fermentation inoculated with fresh digestion sludge at optimal conditions was conducted. Also, physiological study, thermodynamics and 16 S rRNA gene sequencing to elucidate the biological process by linking the system performance and dominant microbiomes were integrated. Results revealed a possible synergistic network in which C. kluyveri and three co-dominant species, Desulfovibrio vulgaris, Fusobacterium varium and Acetoanaerobium sticklandii coexisted. D. vulgaris and A. sticklandii (F. varium) were likely to boost the carboxylates chain elongation by stimulating ethanol oxidation and butyrate production through a syntrophic partnership with hydrogen (H2) serving as an electron messenger. This study unveils a synergistic microbial network to boost caproate production in mixed culture carboxylates chain elongation.

Out-of-autoclave Process and Automation: A Successful Path to Highly Integrated and Cost Efficient Composite Wing Moveables

2019 ◽  
Author(s):  
Thibault de Lumley ◽  
François Mathieu ◽  
Didier Cornet ◽  
Dimitri Gueuning ◽  
Nicolas Van Hille
Keyword(s):  
Autoclave Process ◽  
Cost Efficient ◽  
Out Of Autoclave ◽  
Composite Wing

Urban energetics applications and Geomatic technologies in a Smart City perspective

2015 ◽  
Vol 6 (1) ◽  
pp. 19-29 ◽  
Author(s):  
G. Bitelli ◽  
P. Conte ◽  
T. Csoknyai ◽  
E. Mandanici
Keyword(s):  
Smart City ◽  
Laser Scanning ◽  
Near Infrared ◽  
Three Dimensional ◽  
Energy Sector ◽  
Primary Objective ◽  
Airborne Lidar ◽  
City Management ◽  
Urban Context ◽  
Research Areas

The management of an urban context in a Smart City perspective requires the development of innovative projects, with new applications in multidisciplinary research areas. They can be related to many aspects of city life and urban management: fuel consumption monitoring, energy efficiency issues, environment, social organization, traffic, urban transformations, etc. Geomatics, the modern discipline of gathering, storing, processing, and delivering digital spatially referenced information, can play a fundamental role in many of these areas, providing new efficient and productive methods for a precise mapping of different phenomena by traditional cartographic representation or by new methods of data visualization and manipulation (e.g. three-dimensional modelling, data fusion, etc.). The technologies involved are based on airborne or satellite remote sensing (in visible, near infrared, thermal bands), laser scanning, digital photogrammetry, satellite positioning and, first of all, appropriate sensor integration (online or offline). The aim of this work is to present and analyse some new opportunities offered by Geomatics technologies for a Smart City management, with a specific interest towards the energy sector related to buildings. Reducing consumption and CO2 emissions is a primary objective to be pursued for a sustainable development and, in this direction, an accurate knowledge of energy consumptions and waste for heating of single houses, blocks or districts is needed. A synoptic information regarding a city or a portion of a city can be acquired through sensors on board of airplanes or satellite platforms, operating in the thermal band. A problem to be investigated at the scale A problem to be investigated at the scale of the whole urban context is the Urban Heat Island (UHI), a phenomenon known and studied in the last decades. UHI is related not only to sensible heat released by anthropic activities, but also to land use variations and evapotranspiration reduction. The availability of thermal satellite sensors is fundamental to carry out multi-temporal studies in order to evaluate the dynamic behaviour of the UHI for a city. Working with a greater detail, districts or single buildings can be analysed by specifically designed airborne surveys. The activity has been recently carried out in the EnergyCity project, developed in the framework of the Central Europe programme established by UE. As demonstrated by the project, such data can be successfully integrated in a GIS storing all relevant data about buildings and energy supply, in order to create a powerful geospatial database for a Decision Support System assisting to reduce energy losses and CO2 emissions. Today, aerial thermal mapping could be furthermore integrated by terrestrial 3D surveys realized with Mobile Mapping Systems through multisensor platforms comprising thermal camera/s, laser scanning, GPS, inertial systems, etc. In this way the product can be a true 3D thermal model with good geometric properties, enlarging the possibilities in respect to conventional qualitative 2D images with simple colour palettes. Finally, some applications in the energy sector could benefit from the availability of a true 3D City Model, where the buildings are carefully described through three-dimensional elements. The processing of airborne LiDAR datasets for automated and semi-automated extraction of 3D buildings can provide such new generation of 3D city models.

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