Sub-Compartment Variation in Tree Height, Stem Diameter and Stocking in a Pinus radiata D. Don Plantation Examined Using Airborne LiDAR Data

Saremi, Hanieh; Kumar, Lalit; Stone, Christine; Melville, Gavin J.; Turner, Russell

doi:10.3390/rs6087592

Cited by 24 publications

(16 citation statements)

References 29 publications

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“…Digital surface models (DSMs) and ultra-fine resolution orthophotos can be directly utilized to estimate tree heights and canopy size. Usually, these data are difficult to extract correctly because the height of a tree depends on site conditions (higher/lower stem density; [33]), decreased interpretation of surface area at dense forest sites [30], or even exposure to light and wind [34,35]. In addition, directly estimating parameters in lower layers, such as DBH (Diameter at Breast Height), from DSM or orthophotos is also difficult because of tree canopies, particularly in dense forest environments.…”

Section: Introductionmentioning

confidence: 99%

Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

et al. 2017

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Methods for accurately measuring biophysical parameters are a key component for quantitative evaluation regarding to various forest applications. Conventional in situ measurements of these parameters take time and expense, encountering difficultness at locations with heterogeneous microtopography. To obtain precise biophysical data in such situations, we deployed an unmanned aerial system (UAS) multirotor drone in a cypress forest in a mountainous area of Japan. The structure from motion (SfM) method was used to construct a three-dimensional (3D) model of the forest (tree) structures from aerial photos. Tree height was estimated from the 3D model and compared to in situ ground data. We also analyzed the relationships between a biophysical parameter, diameter at breast height (DBH), of individual trees with canopy width and area measured from orthorectified images. Despite the constraints of ground exposure in a highly dense forest area, tree height was estimated at an accuracy of root mean square error = 1.712 m for observed tree heights ranging from 16 to 24 m. DBH was highly correlated with canopy width (R 2 = 0.7786) and canopy area (R 2 = 0.7923), where DBH ranged from 11 to 58 cm. The results of estimating forest parameters indicate that drone-based remote-sensing methods can be utilized to accurately analyze the spatial extent of forest structures.

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Section: Introductionmentioning

confidence: 99%

Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

et al. 2017

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“…This leads to more accurate estimations of basal area, crown size, tree height and stem volume. A number of studies have established strong correlations between LiDAR parameters and above-ground biomass [45][46][47][48][49][50][51][52][53][54][55].…”

Section: Role Of Remote Sensing In Mapping Above-ground Biomassmentioning

confidence: 99%

Remote Sensing of Above-Ground Biomass

2017

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Accurate measurement and mapping of biomass is a critical component of carbon stock quantification, climate change impact assessment, suitability and location of bio-energy processing plants, assessing fuel for forest fires, and assessing merchandisable timber. While above-ground biomass includes both live and dead plant material, most of the recent research effort on biomass estimation has focussed on the 'live' component (live trees) due to the prominence of this component. Accurate estimates of biomass is a prerequisite for better understanding of the impacts of deforestation and environmental degradation on climate change.The Intergovernmental Panel on Climate Change (IPCC) [1] has listed five terrestrial ecosystem carbon pools involving biomass: above-ground biomass, below-ground biomass, litter, woody debris and soil organic matter. Of these five, above-ground biomass is the most visible, dominant, dynamic and important pool of the terrestrial ecosystem, constituting around 30% of the total terrestrial ecosystem carbon pool. Above-ground biomass estimation, and especially forest biomass, has received considerable attention over the last few decades because of increased awareness of climate warming and the role forest biomass plays in carbon sequestration and release of greenhouse gases due to deforestation.Above-ground biomass estimates are the central basis for carbon inventories and most international negotiations in carbon trading schemes. Carbon trading markets require long-term information on carbon stocks, particularly on the above-ground 'live' biomass component as this is the most dynamic, changing and manipulable component of all the biomass pools. This is the 'merchantable' component of biomass.Above-ground forest biomass accounts for between 70% to 90% of total forest biomass [2]. While soil organic matter holds two to three times more carbon than biomass on a global scale, much of the soil carbon is more protected and not easily oxidised [3]. On the other hand, above-ground biomass is in a continuous state of flux due to fire, logging, storms, landuse changes, etc., and thus contributes to atmospheric carbon fluxes to a much greater extent and so is of much greater interest. Due to this dynamism of above-ground biomass, it is necessary to monitor it continuously and not measure once and forget.While detailed estimations of biomass is necessary for accurate carbon accounting, reliable estimation methods are few. Accurate estimates of stored carbon (biomass as dry weight is 50% carbon [4] and understanding sources and sinks can improve the accuracy of carbon flux models and thus lead to better projections of climate change and impacts. Initiatives such as Reducing Emissions from Deforestation and Forest Degradation (REDD) and REDD+ also rely heavily on above-ground biomass estimates [5,6]. REDD+ includes financing schemes and incentives with the aim of mitigating climate change by reducing deforestation and forest degradation through sustainable forest management and conservation, and enhancement...

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“…Stand-level estimates of the 300 Index are commonly made by averaging plot values, and these stand estimates are used to guide forest management. The resulting estimate aggregations may not be adequate to detect local growing variations within stands resulting from interactions between climate, soil, topography and genetic factors (Véga and St-Onge 2009;Saremi et al 2014). Consequently, predictive modelling methods should be developed to produce spatial layers that provide a finer spatial resolution for estimates of the 300 Index.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor modelling of a forest productivity index for radiata pine plantations

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et al. 2016

N.Z. j. of For. Sci.

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Background: An understanding of how plantation productivity varies spatially is important for forest planning, management and projection of future plantation yields and returns. The 300 Index is a volume productivity index developed for Pinus radiata D.Don that has been widely used within New Zealand to assess site productivity. Although the 300 Index is routinely characterised at the stand level, little research has investigated if remotely sensed data sources can be used in combination with environmental layers to precisely predict this metric at fine spatial resolution. Methods: This study uses an extensive dataset obtained from P. radiata plantations in the central North Island, New Zealand. Using this dataset, the objective of this research was to compare the precision of parametric and non-parametric models of the 300 Index that included explanatory variables extracted from aerially acquired light detection and ranging (LiDAR), satellite imagery (RapidEye) at 5-m resolution or environmental layers and combinations of these three data sources. Models were constructed both with and without stand age as an explanatory variable as managers may not always have access to stand age. A total of 28 models (14 data sources × two model methods) were constructed using data from 433 plots. Precision and bias of these models was determined using an independent dataset of 60 plots. Results: Of the non-parametric methods tested (k-most similar neighbour (k-MSN), k-nearest neighbour (k-NN)), k-NN using an optimised value of k-most precisely predicted the 300 Index for 11 of the 14 constructed models. The use of k-NN was found to be more precise than parametric models when age was not available but of overall similar precision to parametric models when stand age was available as a predictor. For models including stand age, the inclusion of LiDAR resulted in the most precise model (mean R 2 = 0.789; root mean square error (RMSE) = 2.48 m 3 ha −1 year

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Sub-Compartment Variation in Tree Height, Stem Diameter and Stocking in a Pinus radiata D. Don Plantation Examined Using Airborne LiDAR Data

Cited by 24 publications

References 29 publications

Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

Remote Sensing of Above-Ground Biomass

Multi-sensor modelling of a forest productivity index for radiata pine plantations

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