Comparing patterns in forest stand structure following variable harvests using airborne laser scanning data

Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands required for estimating and frequently updating forest data. Here we demonstrate the capability of alternative solutions based on the use of low-cost color infrared (CIR) cameras to estimate tree-level parameters, providing a cost-effective solution for forest inventories. ALS data were acquired with a Leica ALS60 laser scanner and digital aerial imagery (DAI) was acquired with a consumer-grade camera modified for color infrared detection and synchronized with a GPS unit. In this paper we evaluate the generation of a DAI-based canopy height model (CHM) from imagery obtained with low-cost CIR cameras using structure from motion (SfM) and spatial interpolation methods in the context of a complex canopy, as in forestry. Metrics were calculated from the DAI-based CHM and the DAI-based Normalized Difference Vegetation Index (NDVI) for the estimation of tree height and LAI, respectively. Results were compared with the models estimated from ALS point cloud metrics. Field measurements of tree height and effective leaf area index (LAIe) were acquired from a total of 200 and 26 trees, respectively. Comparable accuracies were obtained in the tree height and LAI estimations using ALS and DAI data independently. Tree height estimated from DAI-based metrics (Percentile 90 (P90) and minimum height (MinH)) yielded a coefficient of determination (R2) = 0.71 and a root mean square error (RMSE) = 0.71 m while models derived from ALS-based metrics (P90) yielded an R2 = 0.80 and an RMSE = 0.55 m. The estimation of LAI from DAI-based NDVI using Percentile 99 (P99) yielded an R2 = 0.62 and an RMSE = 0.17 m2/m−2. A comparative analysis of LAI estimation using ALS-based metrics (laser penetration index (LPI), interquartile distance (IQ), and Percentile 30 (P30)) yielded an R2 = 0.75 and an RMSE = 0.14 m2/m−2. The results provide insight on the appropriateness of using cost-effective 3D photo-reconstruction methods for targeting single trees with irregular and heterogeneous tree crowns in complex open-canopy forests. It quantitatively demonstrates that low-cost CIR cameras can be used to estimate both single-tree height and LAI in forest inventories.

Riparian areas are an integral component of the forest hydrologic system. However, systematic management and conservation of riparian vegetation remains a challenge when the extent and nature of riparian vegetation is not easily characterized. Thus, describing riparian vegetation structural attributes and mapping riparian extent is important for stream protection and restoration. This study discriminated riparian and upland vegetation zones in two watersheds on Vancouver Island, Canada, and characterized vegetation attributes ascribed to streamside forest vegetation using fine-scale airborne laser scanning (ALS) data with field-based measurements for controls. Comparison of ALS metrics for riparian and upland vegetation plots indicated metrics of canopy cover, height, and vertical canopy complexity were significantly different. Machine learning was used to model the probability of riparian and upland vegetation zone extent in each watershed and to determine which ALS metrics were the most important for discriminating between these two zones. Separate models were built for each watershed and results indicated that each watershed's model had different ALS metrics that were important for model development, reflecting the different management histories associated with each watershed. Models achieved overall classification accuracies of 65–77% using a 10-fold cross validation. Probability maps were then developed to characterize riparian extent, indicating that the majority of riparian areas in these watersheds were within 20 m of a stream. Additionally, riparian areas were found up to 200 m away from the stream, which is beyond the current provincial regulation widths set for riparian buffers in forested areas in British Columbia, Canada. However, the majority (roughly 75%) of vegetation within existing management buffers was riparian, although this differed by stream classification. We conclude that ALS data provides a potentially useful source of information for determining the characteristics and extent of riparian vegetation.

Airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have both been demonstrated as reliable sources of information on forest stand inventory attributes. The increasing availability of both datasets provides a means for improving stand dynamics information over time; however, the cost of multi-temporal ALS can be prohibitive in some circumstances. As a result, a combination of ALS at an initial time step and subsequent updates using DAP has been proposed as a cost-effective alternative for maintaining forest inventories. In this study we used low density ALS and DAP point clouds acquired in 2007 and 2015, respectively, to quantify changes in forest structure, in a highly disturbed boreal mixedwood forest in Alberta, Canada. We examined the capacity of the two technologies to model changes in top height (H), volume (V), and basal area (BA) using both direct and indirect approaches for estimation. Results indicate that the proportion of explained variance (adjusted R2) for the models derived from the ALS (Time 1; T1) and DAP (Time 2; T2) data were highest for models predicting H at T1, and lowest for BA at T1 and T2 (R2 was 0.66–0.70). The indirect estimates of change in H, BA, and V were calculated by subtracting the T1 and T2 predictions. For the direct approach, separate regression models were developed that used the differences in point cloud metrics between T1 and T2 as predictors. Results indicated that the accuracy of the estimates generated using the indirect approach were markedly lower than the estimates generated using the direct approach, with especially poor results for ∆BA and ∆V. Best results were achieved for ∆H using the direct approach with an R2 coefficient of 0.65 and an root mean square error (RMSE)% of 190.06%. We found that the error associated with change estimates of H, BA and V increased with the increase or decrease in mortality. We conclude that forest managers should act carefully when applying the multi-temporal and multi-sensor analysis of forest growth if forest growth is slow and the level of mortality is high.

ABSTRACTWe examine the nearest neighbor (NN) imputation of species-specific logwood volumes using airborne laser scanning (ALS) data and aerial images. We compare different remote sensing (RS) data combinations as predictor variables in an area-based prediction of logwood volumes using separate training and validation data. We include multispectral leaf-on ALS data, bi-temporal leaf-off ALS data and aerial images in the analyses. Two response configurations are used in the NN imputations: (1) simultaneous imputation in which species-specific logwood volumes are response variables, and (2) separate imputation by tree species in which the attributes of one tree species at a time are response variables. Although an unrealistic alternative in practical implementation, the combination of leaf-on and leaf-off ALS metrics as predictors proved to be the most successful RS data combination, according to the RMSE values associated with the predicted species-specific and dominant logwood volumes. The results showed that older leaf-off ALS data perform well in combination with leaf-on ALS data. In general, predictive performance was better with simultaneous imputation than with separate imputation by tree species. Our finding promotes an awareness of how best to utilize various RS data in future forest inventories.

Accurate extraction of tree parameters is vital for the calculation of high-quality forest volume or biomass. The Light Detection and Ranging (Lidar) technology with its ability to acquire three-dimensional forest stand structures is an important data source for extracting tree parameters. However, complex forest stand structures due to dense canopy and abundant understory vegetation can seriously affect the extraction performance of tree parameters. This study selected Chinese fir plantations to examine how different forest stand structure conditions affected tree parameter extraction based on the integration of handheld laser scanning (HLS) and airborne laser scanning (ALS) data. This study also explored the ability of developing models using extracted tree height and crown width, or directly using the calculated variables from point clouds to estimate the diameter at breast height (DBH) and stem volume. The methods mainly consisted of two parts: (1) Selecting appropriate height to identify seed points for tree segmentation, and subsequently extracting tree parameters; (2) Taking tree height, crown width, and tree-level point cloud metrics as independent variables to establish regression and random forest models for estimating tree DBHs and stem volumes. Results showed that (1) tree DBHs were accurately extracted with relative root mean square error (RMSEr) of 7.1%−10.5% and stem volume with an RMSEr of 12.3%−16.8% under different understory conditions; (2) As stand structure became complex, direct extraction of tree DBHs from HLS data became a challenging task. However, developing a DBH estimation model using tree height and crown width proved feasible; (3) In the complex-understory condition, the volume estimation model utilizing point cloud variables achieved good performance with RMSEr of 28.0%. This research provides new insights for extracting tree parameters using HLS and ALS data, offering a potential replacement for field measurements of tree DBH, height, and stem volume for Chinese fir plantations.

A critical component of the forest ecosystem, the understorey supports the vast majority of wildlife habitat and total ecosystem floristic diversity. Remote sensing data have been developed to provide information at different scales for surveys of forest ecosystems, but obtaining information about the understorey remains a challenge. As rapid and efficient tools for forest structure attribute estimation, Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) have attracted much attention. We examine the relationship between ALS and TLS data and detect changes in the forest understorey caused by forest-tending events in the study area. We conducted trials in five plots within a young Khasi pine (Pinus kesiya Royle ex Gord.) plantation in Yunnan province, China, before and after forest tending. We collected bi-temporal ALS data in this area and TLS data from 10 scanning stations. Canopy height profiles were retrieved from ALS and TLS data, and understorey material volume retrieved from filled TLS voxels volume agreed well with the understorey point clouds percentile distribution (PD) obtained from ALS data. The PD value for the understorey from ALS multiplied by the percentage of ALS return points in the overstorey had a stronger correlation (R2 = 0.90) with the TLS-derived understorey material volume than did the ALS PD value for the understorey only (R2 = 0.71). Taking the effect of the overstorey into consideration will improve evaluations of forest understorey parameters from ALS data. This study confirmed the potential of TLS as a validation tool to assess the accuracy of forest understorey material volume estimation at large scales using ALS data.

Determination of forest fuels characteristics in mortality-affected Pinus forests using integrated hyperspectral and ALS data

This dissertation addresses the problem of automated vector extraction from Airborne Laser Scanner (ALS) data in urban areas. The recent popularity of Geographic Information Systems (GIS) has stimulated research on automated object extraction in order to simplify the data acquisition and update process. By automatically generating GIS inputs and updates from a single data source, the cost of data acquisition and processing will be kept to a minimum. Compared with other remote sensing techniques, extraction of objects from ALS data is in its infancy. ALS sensor technology has evolved rapidly and now allows the acquisition of very dense point clouds in a short period of time. ALS data is unique in that it explicitly contains 3D information and is acquired from an active sensor. As such, there are several benefits that can be immediately realised by using an ALS only data approach; data acquisition will not be limited to daylight hours as with other sensors and accurate height information is contained in the data. This means that registration of different data sources is not required and as only one data source is used, acquisition costs are minimised. Apart from these facts, ALS data has some other unique properties that have not been utilised to their full potential. An ALS sensor can record both height and intensity information from multiple returns in a single swath. To date, all the available information has rarely been used. For example, the intensity of a laser return has regularly been dismissed as it was considered under-sampled and noisy. This data still contains usable information and until this information is used, the optimum object recognition results will not be achieved; thus the use of as much of this information as possible is a major focus of this thesis. As ALS is an explicit 3D data source, the early stages of development were primarily focused on topographic mapping of terrain in forested areas in order to generate Digital Terrain Models (DTMs). As sensor technology has improved, so has the achievable resolution of point clouds from ALS data, and methods to extract objects from stand-alone ALS data have emerged. Attempts have been made to create city models and maps from ALS data that included buildings, roads, trees and powerlines. Each of these spatial object types has unique attributes, which means that automatic map creation is not an easy task. For example, buildings in general are easily detected in ALS data but the building outline is not easily delineated. Another difficulty with building detection is the separation of buildings and bridges as they have many of the same properties as observed by an ALS system. Bridges can usually be found in a road network but road extraction techniques typically produce poor detection rates and often require existing data and / or user interaction in semi-automatic techniques. These simple examples highlight the complexity of automatically generating vectorised maps from ALS data or in fact any data source. In this thesis, new methods are presented for the automatic creation of vectorised maps from ALS data. A two-step processing paradigm is adopted for this purpose, namely the classification of the ALS data and the vectorisation of the classification results. A classification strategy is introduced that creates a hierarchy for object detection with respect to the ALS data itself. This approach develops an ontology between the spatial object classes and the ALS data. The hierarchical framework highlights the fact that one object might not be discernable within the data without considering another. New classification algorithms are then presented within this framework. Each algorithm attempts to exploit the attributes of the data that are consistent within the spatial object class being considered as described by the classification framework. New algorithms for the classification of roads, trees and powerlines are all introduced whilst an extension to existing building classification methods is presented. Once classification is complete, a vectorisation process specific to the task at hand can be employed to yield vectorised results. These developed vectorisation processes are new and include an algorithm that has been generalised to allow the vectorisation of thick lines in images by detecting the centreline, direction and width. The primary goal of this thesis is to present a framework of new algorithms that will allow automatic spatial object detection and vectorisation whilst providing results of an acceptable quality. The algorithms presented rely solely on ALS data and require minimal operator knowledge. Each algorithm has been designed to exploit the way in which the object exhibits itself in the data. The new algorithms are integrated into a software package called JTD (Join The Dots) that will facilitate the effective automatic processing of ALS data. The results of the new algorithms have been evaluated over four 2 x 2 km areas that have been sampled with medium resolution ALS data. The results for each area are displayed and analysed to show the applicability of the whole process in an exemplary way.

Tree biomass estimate is essential for carbon accounting, bioenergy feasibility studies, and forest sustainable management. This fact, added to the availability of airborne laser scanning (ALS) information, provided by the Spanish National Plan for Aerial Orthophotography (PNOA), and the existence of little research focusing on the use of ALS technology in Mediterranean Aleppo pine (Pinus halepensis Mill.) forest, determined the main objective of this research. Thus, this study aims to test the suitability of the low point density (0.5 points/m2), discrete, multiple-return, PNOA-ALS data, to estimate and map the total biomass (TB) and its carbon content in Pinus halepensis Mill. forest stands, located in Aragon (north-eastern Spain). TB was calculated in 45 field plots, using allometric equations, and related through a multivariate linear regression analysis with a collection of independent variables extracted from the ALS data. The predictive model was validated using a leave-one-out cross-validation (LOOCV) technique. Then, a regular grid with cell size 25 x 25 m corresponding to the sample plot size was generated by means of GIS, in order to compute TB at stand level and convert biomass to carbon by using the 0.5 conversion factor. The maximum height, kurtosis and the percentage of returns above 1 meter, were the ALS metrics included in the fitted model, which presented a R2 value of 0.89. The implementation of the model in a GIS showed an average of 68633 kg/ha of TB and 34247.95 kg/ha of carbon fixed. The results indicate that despite the low point density of the ALS data, the final model is accurate enough to be used in forestry applications.

Reliable information concerning stand volume is fundamental to making strategic decisions in sustainable forest management. A variety of remotely sensed data and different inventory methods have been used for the estimation of forest biometric parameters. Particularly, airborne laser scanning (ALS) point clouds are widely used for the estimation of stand volume and forest biomass using an area-based approach (ABA) framework. This method relies on the reference measurements of field plots with the necessary prerequisite of a precise co-registration between ground reference plots and the corresponding ALS samples. In this research, the allometric area-based approach (AABA) is proposed in the context of stand volume estimation of Scots pine (Pinus sylvestris L.) stands. The proposed method does not require detailed information about the coordinates of the field plots. We applied Polish National Forest Inventory data from 9400 circular field plots (400 m2) to develop a plot level stand volume allometric model using two independent variables: top height (TH) and relative spacing index (RSI). The model was developed using the multiple linear regression method with a log–log transformation of variables. The hypothesis was that, the field measurements of TH and RSI could be replaced with corresponding ALS-derived metrics. It was assumed that TH could be represented by the maximum height of the ALS point cloud, while RSI can be calculated based on the number of tree crowns delineated within the ALS-derived canopy height model. Performance of the developed AABA model was compared with the semi-empirical ABASE (with two predictors: TH and RSI) and empirical ABAE (several point cloud metrics as predictors). The models were validated at the plot level using 315 forest management inventory plots (400 m2) and at the stand level using the complete field measurements from 42 Scots pine dominated forest stands in the Milicz forest district (Poland). The AABA model showed a comparable accuracy to the traditional ABA models with relatively high accuracy at the plot (relative root mean square error (RMSE) = 22.8 per cent; R2 = 0.63) and stand levels (RMSE = 17.8 per cent, R2 = 0.65). The proposed novel approach reduces time- and cost-consuming field work required for the classic ABA method, without a significant reduction in the accuracy of stand volume estimations. The AABA is potentially applicable in the context of forest management inventory without the necessity for field measurements at local scale. The transportability of the approach to other species and more complex stands needs to be explored in future studies.

Improving species-specific plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced field data

Airborne laser scanning (ALS) has demonstrated utility for forestry applications and has renewed interest in other forms of remotely sensed data, especially those that capture three-dimensional (3-D) forest characteristics. One such data source results from the advanced processing of high spatial resolution digital stereo imagery (DSI) to generate 3-D point clouds. From the derived point cloud, a digital surface model and forest vertical information with similarities to ALS can be generated. A key consideration is that when developing forestry related products such as a canopy height model (CHM), a high spatial resolution digital terrain model (DTM), typically from ALS, is required to normalize DSI elevations to heights above ground. In this paper we report on our investigations into the use of DSI-derived vertical information for capturing variations in forest structure and compare these results to those acquired using ALS. An ALS-derived DTM was used to provide the spatially detailed ground surface elevations to normalize DSI-derived heights. Similar metrics were calculated from the vertical information provided by both DSI and ALS. Comparisons revealed that ALS metrics provided a more detailed characterization of the canopy surface including canopy openings. Both DSI and ALS metrics had similar levels of correlation with forest structural attributes (e.g., height, volume, and biomass). DSI-based models predicted height, diameter, basal area, stem volume, and biomass with root mean square (RMS) accuracies of 11.2%, 21.7%, 23.6%, 24.5%, and 23.7%, respectively. The respective accuracies for the ALS-based predictions were 7.8%, 19.1%, 17.8%, 17.9%, and 17.5%. Change detection between ALS-derived CHM (time 1) and DSI-derived CHM (time 2) provided change estimates that demonstrated good agreement (r = 0.71) with two-date, ALS only, change outputs. For the single-layered, even-aged stands under investigation in this study, the DSI-derived vertical information is an appropriate and cost-effective data source for estimating and updating forest information. The accuracy of DSI information is based on a capability to measure the height of the upper canopy envelope with performance analogous to ALS. Forest attributes that are well captured and subsequently modeled from height metrics are best suited to estimation from DSI metrics, whereas ALS is more suitable for capturing stand density. Further investigation is required to better understand the performance of DSI-derived height products in more complex forest environments. Furthermore, the difference in variance captured between ALS and DSI-derived CHM also needs to be better understood in the context of change detection and inventory update considerations.

This study aimed to map the stem biomass of an even-aged eucalyptus plantation in southeastern Brazil based on canopy height profile (CHPs) statistics using wall-to-wall discrete return airborne laser scanning (ALS), and compare the results with alternative maps generated by ordinary kriging interpolation from field-derived measurements. The assessment of stem biomass with ALS data was carried out using regression analysis methods. Initially, CHPs were determined to express the distribution of laser point heights in the ALS cloud for each sample plot. The probability density function (pdf) used was the Weibull distribution, with two parameters that in a secondary task, were used as explanatory variables to model stem biomass. ALS metrics such as height percentiles, dispersion of heights, and proportion of points were also investigated. A simple linear regression model of stem biomass as a function of the Weibull scale parameter showed high correlation (adj.R2 = 0.89). The alternative model considering the 30th percentile and the Weibull shape parameter slightly improved the quality of the estimation (adj.R2 = 0.93). Stem biomass maps based on the Weibull scale parameter doubled the accuracy of the ordinary kriging approach (relative root mean square error = 6 % and 13 %, respectively).

Bridging spatio-temporal gaps in ALS data using Landsat time series and forest disturbance-recovery metrics via multi-task neural networks

The invasive phytopathogen Phytophthora ramorum has caused extensive infection of larch forest across areas of the UK, particularly in Southwest England, South Wales and Southwest Scotland. At present, landscape level assessment of the disease in these areas is conducted manually by tree health surveyors during helicopter surveys. Airborne laser scanning (ALS), also known as LiDAR, has previously been applied to the segmentation of larch tree crowns infected by P. ramorum infection and the detection of insect pests in coniferous tree species. This study evaluates metrics from high-density discrete ALS point clouds (24points/m2) and canopy height models (CHMs) to identify individual trees infected with P. ramorum and to discriminate between four disease severity categories (NI: not infected, 1: light, 2: moderate, 3: heavy). The metrics derived from ALS point clouds include canopy cover, skewness, and bicentiles (B60, B70, B80 and B90) calculated using both a static (1m) and a variable (50% of tree height) cut-off height. Significant differences are found between all disease severity categories, except in the case of healthy individuals (NI) and those in the early stages of infection (category 1). In addition, fragmentation metrics are shown to identify the increased patchiness and intra-crown height irregularities of CHMs associated with individual trees subject to heavy infection (category 3) of P. ramorum. Classifications using a k-nearest neighbour (k-NN) classifier and ALS point cloud metrics to classify disease presence/absence and severity yielded overall accuracies of 72% and 65% respectively. The results indicate that ALS can be used to identify individual tree crowns subject to moderate and heavy P. ramorum infection in larch forests. This information demonstrates the potential applications of ALS for the development of a targeted phytosanitary approach for the management of P. ramorum.