Top PDF Estimation of shrub biomass by airborne LiDAR data in small forest stands

Estimation of shrub biomass by airborne LiDAR data in small forest stands

Estimation of shrub biomass by airborne LiDAR data in small forest stands

1. Introduction LiDAR system has been widely used in forestry to estimate dendrometric and dasometric variables such as height, biomass and tree volume (Zimble et al., 2003; Andersen et al., 2005; Hall et al., 2008; Li et al., 2008). Airborne laser scanning systems are found on the measurement of the time delay from pulse emission by an airborne sensor, to its return after reaching the earth’s surface. These data contain coordinates of points where the reflections take place, as if they occur on ground as in any object above it, such as vegetation and buildings. Applying algorithms (ground filters) that allow to select points belonging to the ground and reject those above this surface, a digital terrain model (DTM) is calculated. With non-ground points a digital surface model (DSM) can be computed. The difference between DSM and DTM generate the normalized DSM. This surface contains the heights of all overlying features, named canopy height model (CHM) when includes vegetation heights. Raw LiDAR data and the DTM can be overlapped to convert point elevations into heights above ground. From these data a variety of statistical parameters, which describe height distribution of these points, can be derived to be used in the regression models for estimating height, biomass, volume ( Næsset 2004; Van Aardt et al., 2006, Li et al., 2008).
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Towards scale-invariant aboveground biomass estimation in Savanna ecosystems using small-footprint waveform lidar

Towards scale-invariant aboveground biomass estimation in Savanna ecosystems using small-footprint waveform lidar

The algorithm is summarized as follows: First, pre-processing is done on the raw waveform data to extract the location of canopy interactions in 3D space. Height-variable smoothing is then performed on the canopy height model, which is then inverted and segmented with the watershed transform. Validation is performed to optimize tree (and shrub) delineation at the site level. The DBH of field-GPS’ed trees are then regressed against lidar-derived metrics of corresponding trees. Results of the regression model are subsequently applied to predict the DBH for each segmented tree in the lidar data. DBH is used as the metric of interest because of its estimation reliability 3 and ease of input into well developed biomass allometric equations. 19 Finally, a
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Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta

Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta

with a large range in heights, more representative of uneven aged forests. The high structural variation found within the lidar data also correlate well with the field-based stand structure analysis, which found that abundance of small trees were representative of strong recruitment in all height classes and that the Zambezi delta mangroves are regularly regenerating (Trettin et al., 2015). The factors regulating the composition and structure of mangroves are highly complex and depend on a range of environmental factors such as salinity, nutrient availability, soil type, disturbance regime among others (Smith, 1992; Ellison, 2002). Variations in these factors result in diverse patterns of forest structures, such as those found in the Zambezi Delta. The maximum canopy height in our area was 6 m taller than the SRTM-based estimate of maximum canopy height that was generated previously for all of Mozambique (Fatoyinbo et al., 2008). The height class distribution also shows a much larger proportion of tall trees (>15 m) than previous maps (Fatoyinbo et al, 2013) have, primarily due to the ALS survey design, which was developed to cover the tallest area of the deltaic mangroves. The difference in height ranges between the ALS map and previous, SRTM-based map (Fatoyinbo et al, 2013) can be attributed to resolution, differences in sensors (C-band Interferometry versus ALS) and 14 years between acquisitions.
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A Signal processing approach for preprocessing and 3d analysis of airborne small-footprint full waveform lidar data

A Signal processing approach for preprocessing and 3d analysis of airborne small-footprint full waveform lidar data

We successfully extracted structural metrics from LiDAR waveforms and related these to woody and foliar biomass measurements from a savanna region. The results show that waveform LiDAR technology has significant potential for estimating woody and foliar biomass at the tree-level, or fine scales, in this savanna environment. Although we did not evaluate the performance of more traditional discrete return LiDAR, it was evident that the waveform approach was especially useful for foliar biomass estimation. This parameter evidently corresponds to the tree crown volume, which can be effectively measured using waveform LiDAR. We concluded that waveform LiDAR has a unique advantage over discrete return LiDAR in this case, since the latter typically records the response from the canopy and subsequent lower-level returns at a per-determined minimum distance based on the sensor’s reset time (see Asner et al., 2009) [62]. Waveform LiDAR, on the other hand, contains intensity data at a higher temporal/vertical resolution; these data points can be effectively associated with crown thickness or volume. We therefore propose to use more detailed, accurate, and precise ground-truth field data, in the form of 3D models, to better relate LiDAR waveforms to vegetation structural characteristics. Additionally, the current research can be extended from individual tree to plot-, site-, and landscape level for land degradation assessment in future efforts.
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Airborne LiDAR for the detection of archaeological vegetation marks using biomass as a proxy

Airborne LiDAR for the detection of archaeological vegetation marks using biomass as a proxy

considered as a single narrow bandwidth imaging spectrometer. As such, Challis et al. [23,24] used the intensity values to detect both archaeological and geological crop marks and Briese et al. [25,26] demonstrated a significant improvement in detection using the intensity data centered at 550 nm from an airborne LiDAR instrument array employing multiple wavelengths. Briese et al. also employed the full waveform (FWF) recorded by an airborne LiDAR with some success. It is well established that, via advanced processing methods (e.g., signal decomposition or modelling [27]), FWF data offers increased pulse detection reliability, accuracy, and resolution and, therefore, improves the accuracy of topographic models that could be used for archaeological detection [28–30]. Furthermore, these data have been shown to provide additional information about the structure and physical backscattering properties of the objects they interact with [31]. In particular, significant information on the roughness, slope and reflectivity of surface materials may be retrieved from full waveform data (using both the range and intensity data). For example, differences in the properties of isolated echoes (e.g., width, amplitude or cross-section) and their number (e.g., echo ratio) have been found for vegetation and ploughed fields versus roads, or meadow areas and between grass and bare earth, or between different roof materials [32,33]. Common application of these data have been in forest studies, both at the level of the stand and the individual tree, for the estimation and modelling of forest properties [34] and tree species mapping [35]. It is commonly assumed that for studies other than that of forests, FWF LiDAR cannot be expected to enhance the information content already provided by the discrete return data [36]. However, Doneus et al. [28] have used FWF for the detection of archaeological features in a forest understory and produced improved detection confidence over discrete returns.
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MODELS OF FOREST INVENTORY FOR ISTANBUL FOREST USING AIRBORNE LiDAR AND SPACEBORNE IMAGERY

MODELS OF FOREST INVENTORY FOR ISTANBUL FOREST USING AIRBORNE LiDAR AND SPACEBORNE IMAGERY

The LiDAR point clouds give some information about the structure of stands and individual trees, and are useful when paired with ground data (Lillesand et al., 2014). LiDAR can be used for many forest applications such as; forest management (Wulder et al., 2008, Sasaki et al., 2016), forest fire management (Almeida et al., 2016, Hudak et al., 2016), forest biomass and carbon storage (Hopkinson et al, 2016, Singh et al., 2016), and forest inventory (Maack et al., 2016, Hu et al., 2016). Recent research shows that LiDAR can be used for different areas. For instance, Almeida et al., (2016) used a portable profiling LiDAR for fire susceptibility and contrasting fire damage in the Central Amazon. Hopkinson et al., (2016) monitored biomass and carbon storage change in the boreal forest using airborne laser scanning. Moreover, LiDAR and very high resolution images can be used for the horizontal structure characterization in the tropical forest canopy (Dupuy et al, 2013)
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Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

Abstract: Effective sustainable forest management for broad areas needs consistent country-wide forest inventory data. A stand-level inventory is appropriate as a minimum unit for local and regional forest management. South Korea currently produces a forest type map that contains only four categorical parameters. Stand height is a crucial forest attribute for understanding forest ecosystems that is currently missing and should be included in future forest type maps. Estimation of forest stand height is challenging in South Korea because stands exist in small and irregular patches on highly rugged terrain. In this study, we proposed stand height estimation models suitable for rugged terrain with highly mixed tree species. An arithmetic mean height was used as a target variable. Plot-level height estimation models were first developed using 20 descriptive statistics from airborne Light Detection and Ranging (LiDAR) data and three machine learning approaches—support vector regression (SVR), modified regression trees (RT) and random forest (RF). Two schemes (i.e., central plot-based (Scheme 1) and stand-based (Scheme 2)) for expanding from the plot level to the stand level were then investigated. The results showed varied performance metrics (i.e., coefficient of determination, root mean square error, and mean bias) by model for forest height estimation at the plot level. There was no statistically significant difference among the three mean plot height models (i.e., SVR, RT and RF) in terms of estimated heights and bias (p-values > 0.05). The stand-level validation based on all tree measurements for three selected stands produced varied results by scheme and machine learning used. It implies that additional reference data should be used for a more thorough stand-level validation to identify statistically robust approaches in the future. Nonetheless, the research findings from this study can be used as a guide for estimating stand heights for forests in rugged terrain and with complex composition of tree species.
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Use of UAV photogrammetric data for estimation of biophysical properties in forest stands under regeneration

Use of UAV photogrammetric data for estimation of biophysical properties in forest stands under regeneration

In order to increase the cost-effectiveness of the inventory of stands under regeneration, several authors have attempted to use remotely sensed data such as space borne imagery [ 10 – 12 ], aerial imagery [ 13 ] or airborne laser scanning (ALS) data [ 14 – 16 ] to model such biophysical properties. While these data sources may be valuable for mapping stands under regeneration properties on a large scale, the associated uncertainties are often too large for operational forest management and the derived data can be rapidly outdated for the planning of silvicultural treatments. In particular, these studies highlighted the poor predictive accuracy of tree density, which is a crucial variable for decision-making in stands under regeneration. This is due both to the profound impact of tree density on the need for pre-commercial thinning as well as the associated labour costs [ 17 ]. As a result, current operational practices rely on field checks to estimate stand level values for mean height and tree density. One of the main limitations of using ALS data is that the point density of ALS data commonly acquired for forest inventory (1–5 points m −2 ) is not adequate to detect small objects such as young trees. Pitt et al. [ 18 ] highlighted the importance of using very high-resolution remotely sensed data for stands under regeneration. More specifically, Pouliot et al. [ 13 ] suggested that a crown size to pixel or point ratio of 15:1 (i.e., 15 pixel or points needed within each tree crown) is required to detect single trees in stands under regeneration. In this regard, the flexible acquisition of very high-resolution (1–5 cm) 3D and spectral data by UAVs has significant potential for inventorying stands under regeneration. Further advantages of using UAVs over field checks are that they may allow for i) reduced time spent in the field and ii) production of high-quality maps of the variables of interest.
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Urban forest ecosystem analysis using fused airborne hyperspectral and lidar data

Urban forest ecosystem analysis using fused airborne hyperspectral and lidar data

Finally, we mapped urban tree carbon storage using lidar measurements of crown structure such as height and width, with allometric models. Results from lidar models separated by leaf type corresponded well to field-based estimates on the more urban plots (i.e., low fractional cover, low biomass) while results from the pooled model proved superior for plots in naturally-occurring, high biomass stands. While our remote sensing based maps do not have the sampling error of a field inventory, they do have uncertainties stemming from the application of the biomass equations used in i-Tree, remote sensing classification to the leaf type level, and extrapolation of the stepwise regression model beyond the values encountered in the field-measured training data. Still, the final, spatially explicit product offers finer grain insight into the capacity for urban areas to store carbon and how urbanization patterns mediate this process. A map like this could also be of immediate use for emergency managers: knowing the spatial distribution of tree biomass can improve planning for clean-up after storm events.
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Modeling Plot-Level Biomass and Volume Using Airborne and Terrestrial Lidar Measurements

Modeling Plot-Level Biomass and Volume Using Airborne and Terrestrial Lidar Measurements

Three sets of point cloud-based ALS metrics were calculated for the 56 subplots at 16 FIA plot locations dispersed throughout the study area. Four of the FIA locations were chosen because TLS data had been collected for at least one subplot at a given location. The 12 other FIA locations were selected at random, without replacement, from the remaining 87 FIA plot locations in the study area. An initial review of the FIA data for the selected subplots identified five with no tree records. In this case, the subplots either contained only dead trees, or trees too small to meet the FIA measurement criteria mentioned in 2.2.2.1. The subplots from the selected FIA locations cover a range of slopes and other forest conditions present in the Malheur National Forest. Table 1 and Table 2 provide summaries of the data collection year for each subplot and the frequency of each tree species in each crown class. Descriptive statistics for FIA measured DBH and tree height, as well as FIA estimated total aboveground biomass and volume are presented in Table 3 through Table 5.
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Airborne LiDAR for the detection of archaeological vegetation marks using biomass as a proxy

Airborne LiDAR for the detection of archaeological vegetation marks using biomass as a proxy

considered as a single narrow bandwidth imaging spectrometer. As such, Challis et al. [23,24] used the intensity values to detect both archaeological and geological crop marks and Briese et al. [25,26] demonstrated a significant improvement in detection using the intensity data centered at 550 nm from an airborne LiDAR instrument array employing multiple wavelengths. Briese et al. also employed the full waveform (FWF) recorded by an airborne LiDAR with some success. It is well established that, via advanced processing methods (e.g., signal decomposition or modelling [27]), FWF data offers increased pulse detection reliability, accuracy, and resolution and, therefore, improves the accuracy of topographic models that could be used for archaeological detection [28–30]. Furthermore, these data have been shown to provide additional information about the structure and physical backscattering properties of the objects they interact with [31]. In particular, significant information on the roughness, slope and reflectivity of surface materials may be retrieved from full waveform data (using both the range and intensity data). For example, differences in the properties of isolated echoes (e.g., width, amplitude or cross-section) and their number (e.g., echo ratio) have been found for vegetation and ploughed fields versus roads, or meadow areas and between grass and bare earth, or between different roof materials [32,33]. Common application of these data have been in forest studies, both at the level of the stand and the individual tree, for the estimation and modelling of forest properties [34] and tree species mapping [35]. It is commonly assumed that for studies other than that of forests, FWF LiDAR cannot be expected to enhance the information content already provided by the discrete return data [36]. However, Doneus et al. [28] have used FWF for the detection of archaeological features in a forest understory and produced improved detection confidence over discrete returns.
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Effect of forest stand density on the estimation of above ground biomass/carbon stock using airborne and terrestrial LIDAR derived tree parameters in tropical rain forest, Malaysia

Effect of forest stand density on the estimation of above ground biomass/carbon stock using airborne and terrestrial LIDAR derived tree parameters in tropical rain forest, Malaysia

Background: Forest stand density in tropical rainforests is crucial functional and structural variable of forest ecosys- tems in which above ground biomass can be derived. Currently, there is a growing demand for airborne and terres- trial LIDAR in measuring forest trees parameters for accurate assessment of forest biomass/carbon stock to meet the requirements of UN-REDD + program. Although several studies have been conducted on above ground biomass/ carbon stock in tropical rainforest using forest inventory parameters derived from airborne and terrestrial LIDAR, no research was conducted on how the estimation of above ground biomass/carbon stock using airborne and terrestrial LIDAR derived parameters is affected by forest stand density in a tropical rainforest. Therefore, this study aims to analyze and investigate the strength of the relationship between forest stand density and its above ground biomass estimated using airborne and terrestrial LIDAR derived trees parameters. Purposive sampling approach was adopted for the selec- tion of the unit of analysis. Results are based on data collected from 32 sample plots measured and scanned in the field. Airborne LIDAR was used to derive upper canopy trees height, while terrestrial LIDAR was used to derive the height of lower canopy trees and DBH of all lower and upper canopy trees. The DBH measured in the field was used to compute forest stand density and to validate the DBH manually extracted from TLS point cloud data. The DBH manually derived from TLS point cloud data was used to estimate AGB of the sampled plots for both upper and lower canopy trees. Results: Descriptive statistics, linear regression and correlation analysis were used to answer the research questions of this study. Out of 1033 trees measured and scanned in the field, 855 trees (82.7%) were extracted from TLS point cloud data and 178 trees (17.3%) were missed due to occlusion. The Pearson correlation coefficient (r) between a total number of trees measured and scanned in the field and the total number of trees extracted from TLS point cloud data was 0.95. R 2 of 0.89 was found to explain the relationship between number of missed trees per plot against a number of trees
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Estimating logged-over lowland rainforest aboveground biomass in Sabah, Malaysia using airborne LiDAR data

Estimating logged-over lowland rainforest aboveground biomass in Sabah, Malaysia using airborne LiDAR data

Most of the lowland dipterocarps in Borneo were re- peatedly logged and disturbed by anthropogenic activities. These logged-over forests have a highly heterogeneous for- est structure. A disturbed forest recovers by undergoing growth in horizontal (e.g., diameter at breast height, DBH) and vertical structure (e.g., stand height) accompanied with the overall increase in AGB. Horizontal and vertical struc- tures are inter-related with AGB, thus creating an opportu- nity for LiDAR to examine the forest in different structural conditions (Lefsky et al. 1999, 2002; Drake et al. 2002). High point density and multiple discrete heights from small footprint airborne LiDAR can retrieve such forest structures and predict AGB in fine spatial scale (Houghton 2005). Studies on the use of LiDAR to estimate logged-over low- land dipterocarp forests are relatively few. The objective of this study is to examine the use of airborne LiDAR data to estimate AGB in a logged-over lowland dipterocarp forest in Sabah, Malaysia.
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Interest of integrating spaceborne LiDAR data to improve the estimation of biomass in high biomass forested areas

Interest of integrating spaceborne LiDAR data to improve the estimation of biomass in high biomass forested areas

LiDAR data were acquired between 2003 and 2009 by the Geoscience Laser Altimeter System (GLAS) sensor on board the Ice, Cloud, and Land Elevation (ICESat) Satellite. The GLAS sensor operates in the near-infrared (1064 nm) wavelength and illuminates footprints with a nearly circular shape that are approximately 70 m in diameter. GLAS LiDAR data are free and available for all continents. Footprints are separated by approximately 172 m in the along-track direction. The horizontal geolocation accuracy of the GLAS footprints is 3.7 m (on average), and the vertical accuracy is between 0 and 3.2 cm over flat surfaces, on average [33,34]. Only the GLA01 and GLA14 data products available from ICESAT/GLAS were used in this study. The GLA01 product contains the full recorded waveform data. The GLA14 product, derived from the GLA01 product, contains several useful data for each footprint, such as the cloud flag index, saturation waveform index, land surface elevation from SRTM, centroid elevation derived from the waveform, and background noise. To eliminate unreliable GLAS data (i.e., data affected by atmospheric conditions), several filters were applied: (1) footprints with associated centroid elevations significantly different than the corresponding SRTM elevations were excluded (|GLAS − SRTM| > 100 m); (2) footprints corresponding to waveforms with a low signal to noise ratio (SNR) were also removed (SNR < 15) [33]; (3) saturated waveforms were eliminated (saturation index satNdx # 0); and (4) only the cloud- free footprints were conserved (cloud flag FRir_qaFlag = 15). In addition, GLAS footprints located inside forest stands (selected using the existing AGB map) were conserved. From the original database of 1,772,000 footprints, 48,247 footprints that respect all criteria mentioned above were kept (Figure 1). The density of GLAS footprints in forested areas is 0.52 points/km 2 .
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Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in aWestern Amazonian Forest Using Airborne LiDAR

Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in aWestern Amazonian Forest Using Airborne LiDAR

We selected a regular grid of 1-ha subplots based on an underlying assumption that field plots (ca. 1 ha scale) are an unbiased sample of the landscape (ca. 10 2 - to 10 4 -ha scale) [28,55], and previous findings of diminishing uncertainties between field-based and LiDAR-based estimates at this resolution [20]. The 1-ha grid scheme and the horizontal precision of the 50-ha plot corners (0.050 m) helped to reduce co-registration errors related to misalignment between field subplots and LiDAR data, as well as plot-edge effects. There is a tendency for errors to decrease in biomass estimates with increasing plot size, because large plots reduce the likelihood of plot-edge effects, which occur when the canopy of trees are found along the plot boundary [21]. Edge effects are likely more pronounced in less dense stands and where plot sizes are smaller [56]. The accuracy of plot-aggregate allometry used in this study appears to increase when averaging over more vegetation in larger plots since larger plots minimize the edge effects related to uncertainty in including or excluding a tree in the field survey. In the LiDAR calibration phase, use of small plots will always lead to inflated scatter and thus increased RMSE between LiDAR TCH (or any metric) and field-estimated ACD [17]. Although implementing larger plot sizes increase the costs and time needed for field sampling, large plots results in models with better performance, increase the accuracy of ACD predictions and reduce the variation in ALS-derived metrics [43].
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Forest biomass mapping from lidar and radar synergies

Forest biomass mapping from lidar and radar synergies

2. Study site and data 2.1. Site description and field data The test site for this project is the mixed hardwood and softwood forest of the Northern Experimental Forest (NEF), Howland, Maine (45°15 ′N, 68°45′W). This site, about 10 Km by 10 Km in size, is used for interdisciplinary forest research and experimental forestry practices. The natural stands in this northern hardwood –boreal transitional forest consist of hemlock –spruce–fir, aspen–birch, and hemlock– hardwood mixtures. Topographically, the region varies from flat to gently rolling, with a maximum elevation change of less than 135 m within the study area. Due to the region's glacial history, soil drainage classes within a small area may vary widely, from excessively drained to poorly drained. Consequently, an elaborate patchwork of forest communities has developed, supporting exceptional diversity in forest structure ( Ranson & Sun, 1994 ). While a signi ficant part of forests was preserved for research purposes, various forest management and harvesting practices have changed the forest structure. Fig. 1 is a false color ASTER image of July 22, 2002 (15 m pixel resolution) showing different types of forests in the study area.
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Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR

Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR

For each dataset the height of the returns was normal- ized using a digital elevation model (DEM) provided along with the datasets. After normalizing the datasets we derived 10 metrics that describe the vertical and hori- zontal distribution of vegetation and that are commonly used to estimate AGB from LiDAR data. These metrics included the mean height, percentiles of the height (25, 50, 75 and 90), the maximum height, the standard devia- tion, the coefficient of variation of the height distribu- tions and the fractional cover. Finally we estimated the area under the canopy waveform as described in Garcia et  al. [25]. These metrics were estimated by consider- ing only canopy returns (h ≥ 2 m). For BCI, however, a threshold of 27 m was applied to compute FC after ana- lyzing the relationship between crown area and biomass for that study area (Meyer, personal communication).
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Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico

Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico

Since LiDAR data can be acquired for much larger areas than field inventory data, LiDAR is an extremely important tool for repetitive reference data acquisitions over large areas, in particular in areas where the amount of NFI data are limited (e.g., restricted or inaccessible areas). Furthermore, in contrast to point measurements of field data, LiDAR captures spatial variability, which is beneficial at heterogeneous tropical forests. Neverthe- less, we showed here that a two-stage up-scaling method needs to be analyzed and validated with great care. Field inventory is an essential tool to measure and observe eco- logical processes at local scale as it can provide a higher level of data richness when compared to LiDAR. We believe though that LiDAR can be used as an extension to NFI, for example, for areas that are difficult or not pos- sible to access. Therefore, future research can investigate an integration of airborne LiDAR data into field inven- tory for forests carbon stock assessments (e.g., a trade- off between map accuracy (i.e., user requirements) and resulting costs (i.e., number of NFI and LiDAR data)).
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Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

Systematic sampling strategy outcomes are highly connected with site conditions, modeling technique, and the use of auxiliary data [60,61]. To test the uncertainty associated with site condition variability, we explored the impact of the systematic sampling start point for strip sampling as well as the random selection of sampling strips in terms of plot-based RMSE. Our study demonstrated that even with consistency in terms of modeling technique and auxiliary data inputs, RMSE values varied substantially (Figure 7) when we used different starting points to sample three strips at a constant distance interval. RMSE values also varied substantially when we varied the location sampled through random strip selection (Figure 8). These RMSE values varied from outperforming all other systematic sampling strategies in Table 5 to be the worst sampling strategy. With such high variability of RMSE, it is hard to discern the impact of sample percentages on the AGB estimation. From a practical standpoint, it would be almost impossible to discern which systematic strategy would return a good outcome since you cannot typically explore multiple systematic sampling combinations and would not be considering sampling if the full lidar coverage was available. In our study, there was no general trend in terms of the changes in accuracy with variation in systematic sampling intervals and sampling pattern. This variability may have been linked to differences in forest condition in different regions. Gregoire et al. [62] recommended considering the AGB gradient during the sampling stage. Although Chen and Hay [19] got similar performance from N-S and E-W direction lidar samplings, this might be attributed to the complexity of the forest ecosystem in their study site, which had no general trend in any direction. If there was a general trend shown in a site, as might be the case for plantation areas, considering sampling direction is highly recommended. Our study supported prior work that demonstrated that systematic sampling is easy to apply, but the instability of the outputs suggests it has lower transferability for AGB estimation at other sites.
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Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR

Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR

Popescu et al. [29] combined small-footprint LiDAR and multispectral airborne data to estimate the plot-level VOL and AGB in deciduous and pine (Pinus L.) forests, using ITD in which the average VOL ranged between 123 and 163 m 3 /ha. The maximum R 2 values for AGB were 0.32 for deciduous trees and 0.82 for pines. The respective RMSEs were 44 t/ha and 29 t/ha. Bortolot and Wynne [30] also used ITD for AGB estimation in young forests (ages between 11 and 16 yr) in which the correlation (r) varied from 0.59 to 0.82 and RMSEs from 13.6 to 140.4 t/ha. Van Aardt et al. [31] estimated the VOL and AGB with LiDAR point height metrics as predictors in a per-segment estimation in deciduous, coniferous and mixed forests. The adjusted R 2 and RMSE values for deciduous AGBs were 0.58 and 37.41 t/ha. Næsset [32] used regression methods to estimate AGB for 143 sample plots in young and mature coniferous forests. The sample plot data were divided into three strata (I: young forest, II: mature forest with poor site quality and III: mature forest with favourable site quality). The regression models explained 92% of the variability in the AGB for all the forest types. Jochem et al. [33] used a semiempirical model that was originally developed for VOL estimation to estimate the AGB in Norway spruce (Picea abies L.) dominated alpine forests. The model was extended with tree canopy transparency parameters (CTPs) extracted from LiDAR. The model was calibrated, using 196 selected sample plots. The R 2 values for the fitted AGB models were 0.70 with no CTP and varied from 0.64 to 0.71 with different CTPs. The standard deviations (stds) varied from 87.4 t/ha (35.8%) to 101.9 t/ha (41.7%). Latifi et al. [34] tested the ABA in southwestern Germany in VOL and AGB mapping. They found that the random forest method was superior to other nearest neighbour (NN) methods and achieved relative errors of 23.3%–31.4% in plot-level VOL and 22.4%–33.2% in AGB prediction, depending on the feature sets and feature selection used.
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