Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon

Background： Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning （ALS） produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites. Methods： The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests. Results： Power-law models were slightly less accurate than tree-centric models （RMSE 35% vs 32%） but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth. Conclusion： Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind.

Mapping tree canopies in urban environments using airborne laser scanning (ALS):a Vancouver case study

Background: The distribution of forest vegetation within urban environments is critically important as it influences urban environmental conditions and the energy exchange through the absorption of solar radiation and modulation of evapotranspiration. It also plays an important role filtering urban water systems and reducing storm water runoff.Methods: We investigate the capacity of ALS data to individually detect, map and characterize large(taller than15 m) trees within the City of Vancouver. Large trees are critical for the function and character of Vancouver’s urban forest. We used an object-based approach for individual tree detection and segmentation to determine tree locations(position of the stem), to delineate the shape of the crowns and to categorize the latter either as coniferous or deciduous.Results: Results indicate a detection rate of 76.6% for trees > 15 m with a positioning error of 2.11 m(stem location). Extracted tree heights possessed a RMSE of 2.60 m and a bias of-1.87 m, whereas crown diameter was derived with a RMSE of 3.85 m and a bias of-2.06 m. Missed trees are principally a result of undetected treetops occurring in dense, overlapping canopies with more accurate detection and delineation of trees in open areas.Conclusion: By identifying key structural trees across Vancouver’s urban forests, we can better understand their role in providing ecosystem goods and services for city residents.

Using mixed integer programming and airborne laser scanning to generate forest management units

Airborne laser scanning(ALS)has been widely applied to estimate tree and forest attributes,but it can also drive the segmentation of forest areas.Clustering algorithms are the dominant technique in segmentation but spatial optimization using exact methods remains untested.This study presents a novel approach to segmentation based on mixed integer programming to create forest management units(FMUs).This investigation focuses on using raster information derived from ALS surveys.Two mainstream clustering algorithms were compared to the new MIP formula that simultaneously accounts for area and adjacency restrictions,FMUs size and homogeneity in terms of vegetation height.The optimal problem solution was found when using less than 150 cells,showing the problem formulation is solvable.The results for MIP were better than for the clustering algorithms;FMUs were more compact based on the intravariation of canopy height and the variability in size was lower.The MIP model allows the user to strictly control the size of FMUs,which is not possible in heuristic optimization and in the clustering algorithms tested.The definition of forest management units based on remote sensing data is an important operation and our study pioneers the use of MIP ALS-based optimal segmentation.

Influence of sampling intensity on performance of two-phase forest inventory using airborne laser scanning

Background: Forest inventories have always been a primary information source concerning the forest ecosystem state. Various applied survey approaches arise from the numerous important factors during sampling scheme planning. Paramount aspects include the survey goal and scale, target population inherent variation and patterns,and available resources. The last factor commonly inhibits the goal, and compromises have to be made. Airborne laser scanning(ALS) has been intensively tested as a cost-effective option for forest inventories. Despite existing foundations, research has provided disparate results. Environmental conditions are one of the factors greatly influencing inventory performance. Therefore, a need for site-related sampling optimization is well founded.Moreover, as stands are the basic operational unit of managed forest holdings, few related studies have presented stand-level results. As such, herein, we tested the sampling intensity influence on the performance of the ALSenhanced stand-level inventory.Results: Distributions of possible errors were plotted by comparing ALS model estimates, with reference values derived from field surveys of 3300 sample plots and more than 300 control stands located in 5 forest districts. No improvement in results was observed due to the scanning density. The variance in obtained errors stabilized in the interval of 200–300 sample plots, maintaining the bias within +/-5% and the precision above 80%. The sample plot area affected scores mostly when transitioning from 100 to 200 m2. Only a slight gain was observed when bigger plots were used.Conclusions: ALS-enhanced inventories effectively address the demand for comprehensive and detailed information on the structure of single stands over vast areas. Knowledge of the relation between the sampling intensity and accuracy of ALS estimates allows the determination of certain sampling intensity thresholds. This should be useful when matching the required sample size and accuracy with available resources. Site optimization may be necessary, as certain errors may occur due to the sampling scheme, estimator type or forest site, making these factors worth further consideration.

Using object-based analysis to derive surface complexity information for improved filtering of airborne laser scanning data

Airborne laser scanning （ALS） is a technique used to obtain Digital Surface Models （DSM） and Digital Terrain Models （DTM） efficiently, and filtering is the key procedure used to derive DTM from point clouds. Generating seed points is an initial step for most filtering algorithms, whereas existing algorithms usually define a regular window size to generate seed points. This may lead to an inadequate density of seed points, and further introduce error type I, especially in steep terrain and forested areas. In this study, we propose the use of object- based analysis to derive surface complexity information from ALS datasets, which can then be used to improve seed point generation. We assume that an area is complex if it is composed of many small objects, with no buildings within the area. Using these assumptions, we propose and implement a new segmentation algorithm based on a grid index, which we call the Edge and Slope Restricted Region Growing （ESRGG） algorithm. Surface complexity information is obtained by statistical analysis of the number of objects derived by segmentation in each area. Then, for complex areas, a smaller window size is defined to generate seed points. Experimental results show that the proposed algorithm could greatly improve the filtering results in complex areas, especially in steep terrain and forested areas.

Building reconstruction from airborne laser scanning data

The research on building model reconstruction has been a long-term hot topic in both the photogrammetry and computer vision areas.The airborne laser scanning technique provides new opportunities for building model reconstruction.Despite many investigations on building reconstruction using point clouds,there are still many unresolved problems that need further research,especially fully automatic methods and intelligent user-friendly operations.This article surveys the methods,tools and problems of building model reconstruction using point clouds data.The article also points out some important but unnoticed problems in building reconstruction according to our previous experience.We hope our comments article can be helpful for researchers in understanding their position and for new researcher in acquiring general information.

Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data:a case study in the Sierra Nevada Mountains,California

Improved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth modeling.Airborne Laser Scanning(ALS)can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy terrains.This study proposed an ALSbased framework to quantify tree growth and competition.Bi-temporal ALS data were used to quantify tree growth in height(ΔH),crown area(ΔA),crown volume(ΔV),and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada forests.We analyzed the correlations between tree growth attributes and controlling factors(i.e.tree sizes,competition,forest structure,and topographic parameters)at multiple levels.At the individual tree level,ΔH had no consistent correlations with controlling factors,ΔA andΔV were positively related to original tree sizes(R>0.3)and negatively related to competition indices(R<−0.3).At the forest-stand level,ΔH andΔA were highly correlated to topographic wetness index(|R|>0.7),ΔV was positively related to original tree sizes(|R|>0.8).Multivariate regression models were simulated at individual tree level forΔH,ΔA,andΔV with the R2 ranged from 0.1 to 0.43.The ALS-based tree height estimation and growth analysis results were consistent with field measurements.

Automated tree detection and crown delineation using airborne laser scanner data in heterogeneous East-Central Europe forest with different species mix

Identifying tree locations is a basic step in the derivation of other tree parameters using remote sensing techniques, particularly when using airborne laser scanning. There are several techniques for identifying tree positions. In this paper, we present a raster-based method for determining tree position and delineating crown coverage. We collected data from nine research plots that supported different mixes of species. We applied a raster-based method to raster layers with six different spatial resolutions and used terrestrial measurement data as reference data. Tree identification at a spatial resolution of 1.5?m was demonstrated to be the most accurate, with an average identification ratio (IR) of 95% and average detection ratio of 68% being observed. At a higher spatial resolution of 0.5?m, IR was overestimated by more than 600%. At a lower spatial resolution of 3?m, IR was underestimated at less than 44% of terrestrial measurements. The inventory process was timed to enable evaluation of the time efficiency of automatic methods.

Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters

Sustainable forest management heavily relies on the accurate estimation of tree parameters.Among others,the diameter at breast height(DBH) is important for extracting the volume and mass of an individual tree.For systematically estimating the volume of entire plots,airborne laser scanning(ALS) data are used.The estimation model is frequently calibrated using manual DBH measurements or static terrestrial laser scans(STLS) of sample plots.Although reliable,this method is time-consuming,which greatly hampers its use.Here,a handheld mobile terrestrial laser scanning(HMTLS) was demonstrated to be a useful alternative technique to precisely and efficiently calculate DBH.Different data acquisition techniques were applied at a sample plot,then the resulting parameters were comparatively analysed.The calculated DBH values were comparable to the manual measurements for HMTLS,STLS,and ALS data sets.Given the comparability of the extracted parameters,with a reduced point density of HTMLS compared to STLS data,and the reasonable increase of performance,with a reduction of acquisition time with a factor of5 compared to conventional STLS techniques and a factor of3 compared to manual measurements,HMTLS is considered a useful alternative technique.

A Novel Airborne 3D Laser Point Cloud Hole Repair Algorithm Considering Topographic Features

Hole repair processing is an important part of point cloud data processing in airborne 3-dimensional(3D)laser scanning technology.Due to the fragmentation and irregularity of the surface morphology,when applying the 3D laser scanning technology to mountain mapping,the conventional mathematical cloud-based point cloud hole repair method is not ideal in practical applications.In order to solve this problem,we propose to repair the valley and ridge line first,and then repair the point cloud hole.The main technical steps of the method include the following points:First,the valley and ridge feature lines are extracted by the GIS slope analysis method;Then,the valley and ridge line missing from the hole are repaired by the mathematical interpolation method,and the repaired results are edited and inserted to the original point cloud;Finally,the traditional repair method is used to repair the point cloud hole whose valley line and ridge line have been repaired.Three experiments were designed and implemented in the east bank of the Xiaobaini River to test the performance of the proposed method.The results showed that compared with the direct point cloud hole repair method in Geomagic Studio software,the average repair accuracy of the proposed method,in the 16 m buffer zone of valley line and ridge line,is increased from 56.31 cm to 31.49 cm.The repair performance is significantly improved.

Analysis of forest structural complexity using airborne LiDAR data and aerial photography in a mixed conifer–broadleaf forest in northern Japan

Determining forest structural complexity,i.e.,a measure of the number of different attributes of a forest and the relative abundance of each attribute,is important for forest management and conservation.In this study,we examined the structural complexity of mixed conifer–broadleaf forests by integrating multiple forest structural attributes derived from airborne Li DAR data and aerial photography.We sampled 76 plots from an unmanaged mixed conifer–broadleaf forest reserve in northern Japan.Plot-level metrics were computed for all plots using both field and remote sensing data to assess their ability to capture the vertical and horizontal variations of forest structure.A multivariate set of forest structural attributes that included three Li DAR metrics(95 th percentile canopy height,canopy density and surface area ratio) and one image metric(proportion of broadleaf cover),was used to classify forest structure into structural complexity classes.Our results revealed significant correlation between field and remote sensing metrics,indicating that these two sets of measurements captured similar patterns of structure in mixed conifer–broadleaf forests.Further,cluster analysis identified six forest structural complexity classes includingtwo low-complexity classes and four high-complexity classes that were distributed in different elevation ranges.In this study,we could reliably analyze the structural complexity of mixed conifer–broadleaf forests using a simple and easy to calculate set of forest structural attributes derived from airborne Li DAR data and high-resolution aerial photography.This study provides a good example of the use of airborne Li DAR data sets for wider purposes in forest ecology as well as in forest management.

Natural forest ALS-TLS point cloud data registration without control points

Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information below the forest canopy due to the occlusion of trees in natural forests.In contrast,TLS is unable to gather fi ne structure information about the upper canopy.To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform,this study proposes data registration without control points.The ALS and TLS original data were cropped according to sample plot size,and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin.The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data.The initial registered point cloud data was fi nely and optimally registered via the iterative closest point(ICP)algorithm.The results show that the proposed method achieved highprecision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch.and Picea asperata Mast.which included diff erent species and environments.An average registration accuracy of 0.06 m and 0.09 m were obtained for P.yunnanensis and P.asperata,respectively.

Application of 3D Digital Modeling Technology in the Construction of Digital Cities

The concepts of “digital twins”, “3D real scene”, “metacosm” and others were the technical paths for building digital cities with the development of emerging surveying and mapping science and technology, which was to build a digital and virtualized city that matched the real physical world, to achieve a one-to-one correspondence between all elements of the physical world and the digital virtual world. And one of its basic geographic information data was a highly similar, virtual simulation of the 3D real scene. After exploring the traditional manual 3DsMax modeling, UAV low-altitude digital oblique photogrammetry modeling, airborne laser scanning modeling and other single modeling technologies, this paper discussed the 3D digital modeling technology used by the UAV airborne laser scanning point cloud and low-altitude digital oblique photogrammetry for complementary integration, constructing the 3D scene of the digital city. This paper expounded the technical route and production process of 3D digital modeling, in order to provide technical references for related projects.

Combining spatial and economic criteria in tree-level harvest planning

Background:Modern remote sensing methods enable the prediction of tree-level forest resource data.However,the benefits of using tree-level data in forest or harvest planning is not clear given a relative paucity of research.In particular,there is a need for tree-level methods that simultaneously account for the spatial distribution of trees and other objectives.In this study,we developed a spatial tree selection method that considers tree-level(relative value increment),neighborhood related(proximity of cut trees)and global objectives(total harvest).Methods:We partitioned the whole surface area of the stand to trees,with the assumption that a large tree occupies a larger area than a small tree.This was implemented using a power diagram.We also utilized spatially explicit tree-level growth models that accounted for competition by neighboring trees.Optimization was conducted with a variant of cellular automata.The proposed method was tested in stone pine(Pinus pinea L.)stands in Spain where we implemented basic individual tree detection with airborne laser scanning data.Results:We showed how to mimic four different spatial distributions of cut trees using alternative weightings of objective variables.The Non-spatial selection did not aim at a particular spatial layout,the Single-tree selection dispersed the trees to be cut,and the Tree group and Clearcut selections clustered harvested trees at different magnitudes.Conclusions:The proposed method can be used to control the spatial layout of trees while extracting trees that are the most economically mature.

Do historical maps show the maximal anthropopressure in the Carpathians?

Many landscapes bear the marks of historical land use.These marks can be the basis for a reconstruction of a historical land use structure as some of them are typical of different types of human activity.The aim of this paper is to determine whether Austrian cadastral maps from the 19th century present the image of the most transformed environment in the Western Carpathians as a result of agricultural activity.Land use structure and terrain forms were detected based on Austrian cadastral maps from 1848,airborne laser scanning and field studies.In two of the test areas,the percentage of arable fields was higher among the plots with stone mounds than the percentage among the plots without them.In the third test area,the relationship was reversed.Also,lynchets,terraces and stone walls sometimes occur in plots that were not arable fields in 1848.Thus,the Austrian cadastral maps from 1848 could not reflect the maximal range of arable fields in the Carpathians in the 19th century.However,it is impossible to determine the historical structure of land use precisely.Nevertheless,an inventory of terrain forms can be used to assess land use when historical maps have not preserved or when available maps do not present land use in detail.

Charcoal kiln sites, associated landscape attributes and historic forest conditions:DTM-based investigations in Hesse(Germany)

Background： An examination of the distribution of ancient charcoal kiln sites in the forest landscape seems to be worthwhile, since general trends in the selection of suitable kiln site locations in the past might become obvious. In this way forest landscape elements with a more intense usage by charcoal burning can be identified. By doing this, we can expect to gain information on the former condition and tree species composition of woodland. Investigations on the spatial distribution of charcoal kiln sites in relation to landscape attributes are sparse, however, probably due to the high on-site mapping effort. The outstanding suitability of LiDAR-derived digital terrain models （DTMs） for the detection of charcoal kiln sites has been recently proved. Hence, DTM-based surveys of charcoal kiln sites represent a promising attempt to fill this research gap. Methods： Based on DTM-based surveys, we analyzed the spatial distribution of charcoal kiln sites in two forest landscapes in the German federal state of Hesse： Reinhardswald and Kellerwald-Edersee National Park. In doing so, we considered the landscape attibutes ＂tree species composition＂, ＂water supply status＂, ＂nutrient supply status＂, ＂soil complex classes＂, ＂altitude＂, ＂exposition＂, and ＂inclination＂. Results： We found that charcoal kiln sites were established preferably on hillside locations that provided optimal growing and regeneration conditions for European beech （Fagus sylvatico） due to their acidic brown soils and sufficient water supply. These results are in line with instructions for the selection of appropriate kiln site locations, found in literature from the 18th to the 19th century. Conclusions： We conclude that there were well-stocked, beech-dominated deciduous forest stands in northern Hesse before 1800, particularly at poorly accessible hillside locations. These large stocks of beech wood were utilized by the governments of the different Hessian territories through the establishment of ironworks and hammer mills. Our argumentation is well in line with findings which underline that not all Hessian forests were overexploited in the 18th century. Frequently repeated complaints about ＂wood shortage＂ seemed to be more a political instrument than reality, not only in Hesse, but all over Europe. Consequently, a differentiated assessment of woodland conditions in proto-industrial times is strictly advised, even if contemporary sources draw a dark picture of the historic situation.

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral(MS)sensors,we suggest the inclusion of normalized difference vegetation index(NDVI)metrics along with the more traditional LIDAR height metrics.Here the data fusion method consists of back-projecting LIDAR returns onto original MS images,avoiding co-registration errors.The prediction method is based on nonparametric imputation(the most similar neighbor).Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods.Results show improvements when using combinations of LIDAR and MS compared to using either of them alone.The MS sensor has little explanatory capacity for forest variables dependent on tree height,already well determined from LIDAR alone.However,there is potential for variables dependent on tree diameters and their density.The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity,which are best described from synergies between LIDAR heights and NDVI dispersion.Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes.Their inclusion in the predictor dataset may,however,in a few cases be detrimental to accuracy,and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.

Updating digital elevation models via change detection and fusion of human and remote sensor data in urban environments

OpenStreetMap(OSM)currently represents the most popular project of Volunteered Geographic Information(VGI):geodata are collected by common people and made available for public use.Airborne Laser Scanning(ALS)enables the acquisition of high-resolution digital elevation models that are used for many applications.This study combines the advantages of both ALS and OSM,offering a promising new approach that enhances data quality and allows change detection:the mainly up-to-date 2D data of OSM can be combined with the high-resolution–but rarely updated–elevation information provided by ALS.This case study investigates building objects of OSM and ALS data of the city of Bregenz,Austria.Data quality of OSM is discerned by the comparison of building footprints using different true positive definitions(e.g.overlapping area).High quality of OSM data is revealed,yet also limitations of each method with respect to heterogeneous regions and building outlines are identified.For the first time,an up-to-date Digital Surface Model(DSM)combining 2D OSM and ALS data is achieved.A multitude of applications such as flood simulations and solar potential assessments can directly benefit from this data combination,since their value and reliability strongly depend on an up-to-date DSM.

Environmental landscape determinants of maximum forest canopy height of boreal forests

Aims Canopy height is a key driver of forest biodiversity and carbon cycling.Accurate estimates of canopy height are needed for assess-ing mechanisms relating to ecological patterns and processes of tree height limitations.At global scales forest canopy height patterns are largely controlled by climate,while local variation at fine scales is due to differences in disturbance history and local patterns in envir-onmental conditions.The relative effect of local environmental driv-ers on canopy height is poorly understood partly due to gaps in data on canopy height and methods for examining limiting factors.Here,we used airborne laser scanning(ALS)data on vegetation structure of boreal forests to examine the effects of environmental factors on potential maximum forest canopy height.Methods Relationships between maximum canopy height from ALS meas-ures and environmental variables were examined to assess factors limiting tree height.Specifically,we used quantile regression at the 0.90 quantile to relate maximum canopy height with environmental characteristics of climate(i.e.mean annual temperature[MAT]and mean annual precipitation),terrain(i.e.slope)and depth-to-water(DTW)across a 33000 km2 multiple use boreal forest landscape in northeast Alberta,Canada.Important Findings Maximum canopy height was positively associated with MAT,ter-rain slope and terrain-derived DTW,collectively explaining 33.2%of the variation in heights.The strongest explanatory variable was DTW explaining 26%of canopy height variation with peatland forests having naturally shorter maximum canopy heights,but also more sites currently at their maximum potential height.In con-trast,the most productive forests(i.e.mesic to xeric upland forests)had the fewest sites at their potential maximum height,illustrating the effects of long-term forest management,wildfires and general anthropogenic footprints on reducing the extent and abundance of older,taller forest habitat in Alberta’s boreal forest.