Single Tree Detection from Airborne Laser Scanning Data: A Stochastic Approach

Characterizing and monitoring forests are of great scientific and managerial interests, such as understanding the global carbon circle, biodiversity conservation and management of natural resources. As an alternative or compliment to traditional remote sensing techniques, airborne laser scanning (ALS) has been placed in a very advantageous position in forest studies, for its unique ability to directly measure the distribution of vegetation materials in the vertical direction, as well as the terrain beneath the forest canopy. Serving as basis for tree-wise forest biophysical parameter and species information retrieval, single tree detection is a very motivating research topic in forest inventory. The objective of the study is to develop a method from the perspective of computer vision to detect single trees automatically from ALS data. For this purpose, this study explored different aspects of the problem. It starts from an improved pipeline for canopy height model (CHM) generation, which alleviates the distortion of tree crown shapes presented on CHMs resulted from conventional procedures due to the shadow effects of ALS data and produces pit-free CHM. The single tree detection method consists of a hybrid framework which integrates low-level image processing techniques, i.e. local maxima filtering (LM) and marker-controlled watershed segmentation (MCWS), into a high-level probabilistic model. In the proposed approach, tree crowns in the forest plot are modelled as a configuration of circular objects. The configuration containing the best possible set of detected tree objects is estimated by a global optimization solver in a probabilistic framework. The model features an accelerated optimization process compared with classical stochastic models, e.g. marked point processes. The parameter estimation is another issue: the study investigated both a reference-based supervised and an Expectation-Maximization (EM) based unsupervised method to estimate the parameters in the model. The model was tested in a temperate mature coniferous forest in Ontario, Canada, as well as simulated coniferous forest plots with various degrees of crown overlap. The experimental results showed the effectiveness of our proposed method, which was capable of reducing the commission errors produced by local maxima filtering based methods, thus increasing the overall detection accuracy by approximately 10% on all of the datasets

Evaluation of Tree Detection and Segmentation Algorithms in Peat Swamp Forest Based on LiDAR Point Clouds Data

Application of LiDAR for tree detection and tree canopy segmentation has been widely used in conifer plantation forest in temperate countries with high accuracy, however its application on tropical natural forest especially peat swamp forest hardly found. The objective of this study was evaluated algorithms of individual tree detection and canopy segmentation used LiDAR data in peat swamp forest. The algorithms included (a) Local Maxima (LM) with various variable window size combined with growing region, (b) LM with various variable window size combined with Voronoi Tessellation, (c) LM with various fixed window size combined with growing region, (d) LM with various fixed window size combined with Voronoi Tessellation, and (e) Tree Relative Distance algorithm. The results show that algorithm with the best accuracy was the Tree Relative Distance algorithm with the highest overall F-score of 0.63. The tree relative distance algorithm also provides the highest accuracy in determining three tree parameters which are position, height and diameter of tree canopy with a RMSE value 1.08 m, 6.45 m and 1.19 m, respectively

International Benchmarking of the Individual Tree Detection Methods for Modeling 3-D Canopy Structure for Silviculture and Forest Ecology Using Airborne Laser Scanning

Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data

Estimation of canopy structure and individual trees from laser scanning data

During the last fifteen years, laser scanning has emerged as a data source for forest inventory. Airborne laser scanning (ALS) provides 3D data, which may be used in an automated analysis chain to estimate vegetation properties for large areas. Terrestrial laser scanning (TLS) data are highly accurate 3D ground-based measurements, which may be used for detailed 3D modeling of vegetation elements. The objective of this thesis is to further develop methods to estimate forest information from laser scanning data. The aims are to estimate lists of individual trees from ALS data with accuracy comparable to area-based methods, to collect detailed field reference data using TLS, and to estimate canopy structure from ALS data. The studies were carried out in boreal and hemi-boreal forests in Sweden. Tree crowns were delineated in three dimensions with a model-based clustering approach. The model-based clustering identified more trees than delineation of a surface model, especially for small trees below the dominant tree layer. However, it also resulted in more erroneously delineated tree crowns. Individual trees were estimated with statistical methods from ALS data based on field-measured trees to obtain unbiased results at area level. The accuracy of the estimates was similar for delineation of a surface model (stem density root mean square error or RMSE 32.0%, bias 1.9%; stem volume RMSE 29.7%, bias 3.8%) as for model-based clustering (stem density RMSE 33.3%, bias 1.1%; stem volume RMSE 22.0%, bias 2.5%). Tree positions and stem diameters were estimated from TLS data with an automated method. Stem attributes were then estimated from ALS data trained with trees found from TLS data. The accuracy (diameter at breast height or DBH RMSE 15.4%; stem volume RMSE 34.0%) was almost the same as when trees from a manual field inventory were used as training data (DBH RMSE 15.1%; stem volume RMSE 34.5%). Canopy structure was estimated from discrete return and waveform ALS data. New models were developed based on the Beer-Lambert law to relate canopy volume to the fraction of laser light reaching the ground. Waveform ALS data (canopy volume RMSE 27.6%) described canopy structure better than discrete return ALS data (canopy volume RMSE 36.5%). The methods may be used to estimate canopy structure for large areas

Site index estimation using airborne laser scanner data in Eucalyptus dunnii maide stands in Uruguay

Intensive silviculture demands new inventory tools for better forest management and planning. Airborne laser scanning (ALS) was shown to be one of the best alternatives for high-precision inventories applied to productive plantations. The aim of this study was to generate multiple stand-scale maps of the site index (SI) using ALS data in the intensive silviculture of Eucalyptus dunnii Maide plantations in Uruguay. Forty-three plots (314.16 m3) were established in intensive E. dunnii plantations in the departments of Río Negro and Paysandú (Uruguay). ALS data were obtained for an area of 1995 ha. Linear and Random Forest models were fitted to estimate the height and site index, and OrpheoToolBox (OTB) software was used for stand segmentation. Linear models for dominant height (DH) estimation had a better fit (R2 = 0.84, RMSE = 0.94 m, MAPE = 0.04, Bias = 0.002) than the Random Forest (R2 = 0.85, RMSE = 1.27 m, MAPE = 7.20, Bias=−0.173) model when including only the 99th percentile metric. The coefficient between RMSE values of the cross-validation and RMSE of the model had a higher value for the linear model (0.93) than the Random Forest (0.75). The SI was estimated by applying the RF model, which included the ALS metrics corresponding to the 99th height percentile and the 80th height bicentile (R2 = 0.65; RMSE = 1.62 m). OTB segmentation made it possible to define a minimum segment size of 2.03 ha (spatial radius = 30, range radius = 1 and minimum region size = 64). This study provides a new tool for better forest management and promotes the need for further progress in the application of ALS data in the intensive silviculture of Eucalyptus spp. plantations in Uruguay

Site index estimation using airborne laser data in Eucalyptus dunnii Maide stands in Uruguay

Premio extraordinario de Trabajo Fin de Máster curso 2020/2021. Máster en Geomática, Teledetección y Modelos Espaciales Aplicados a la Gestión ForestalThe estimation of forest variables to support a forest inventory can be approached through the use of different technologies. Although field sampling is the most widely implemented technique, the development of remote sensing techniques increases the possibilities of action in this field. One of these technologies is the airborne LiDAR scanner (ALS). In this study, linear models and non-parametric models with Random Forest imputation were generated to estimate the total height (HT) and site index (SI) of Eucalyptus dunnii Maide, based on LiDAR metrics. High spatial resolution continuous rasters for HT and SI were created with these models. The use of a semi-automatic object-oriented segmentation algorithm for stand delimitation based on the SI raster was then carried out. To evaluate the performance of these models, the One Leave One cross-validation technique was implemented, determining for each model the ratio between the RMSE of the model and the RMSE of the cross-validation (RMSEcv). Linear models for HT estimation presented a better fit (R2=0.84, RMSE=0.84 m, MAPE=0.039, Bias=0.002) than the Random Forest (R2=0.85, RMSE=1.26 m, MAPE=7.19, Bias=-0.173) model including only independent variable the 99th percentile. The RMSE/RMSEcv ratio presented a higher value for the linear model (0.93) than Random Forest (0.75). For the estimation of the site index (SI), the Random Forest model was applied, which included the LiDAR metrics corresponding to the 99th percentile and the 80th bicentile. This model had an R2 value of 0.65 and an RMSE value of 1.62 m. Then, on the SI raster generated by the Random Forest model, automatic segmentation was applied, generating segments with high internal homogeneity and low homogeneity between segments. The methodology developed in this work provides accurate estimates and mapping of HT and SI at stand scale based on LiDAR data. In addition, an automatic segmentation method was applied, generating stands based on the SI. This segmentation is very useful for the sector as it is a tool that will improve forest management in terms of harvesting and future plantations

Two-phase forest inventory using very-high-resolution laser scanning

In this study, we compared a two-phase laser-scanning-based forest inventory of stands versus a traditional field inventory using sample plots. The two approaches were used to estimate stem volume (VOL), Lorey's mean height (HL), Lorey's stem diameter (DL), and VOL per tree species in a study area in Sweden. The estimates were compared at the stand level with the harvested reference values obtained using a forest harvester. In the first phase, a helicopter acquired airborne laser scanning (ALS) data with >500 points/m2 along 50-m wide strips across the stands. These strips intersected systematic plots in phase two, where terrestrial laser scanning (TLS) was used to model DL for individual trees. In total, phase two included 99 plots across 10 boreal forest stands in Sweden (lat 62.9 degrees N, long 16.9 degrees E). The single trees were segmented in both the ALS and TLS data and linked to each other. The very-high-resolution ALS data enabled us to directly measure tree heights and also classify tree species using a convolutional neural network. Stem volume was predicted from the predicted DBH and the estimated height, using national models, and aggregated at the stand level. The study demonstrates a workflow to derive forest variables and stand-level statistics that has potential to replace many manual field inventories thanks to its time efficiency and improved accuracy. To evaluate the inventories, we estimated bias, RMSE, and precision, expressed as standard error. The laser-scanning-based inventory provided estimates with an accuracy considerably higher than the field inventory. The RMSE was 17 m3/ha (7.24%), 0.9 m (5.63%), and 16 mm (5.99%) for VOL, HL, and DL respectively. The tree species classification was generally successful and improved the three species-specific VOL estimates by 9% to 74%, compared to field estimates. In conclusion, the demonstrated laser-scanning-based inventory shows potential to replace some future forest inventories, thanks to the increased accuracy demonstrated empirically in the Swedish forest study area

Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)

Developing a robust algorithm for automatic individual tree crown (ITC) detection from airborne laser scanning datasets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform less well than simple area-based approaches when estimating plot-level biomass. Here we describe a Multi-Class Graph Cut (MCGC) approach to tree crown delineation. This uses local three-dimensional geometry and density information, alongside knowledge of crown allometries, to segment individual tree crowns from airborne LiDAR point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognise small trees. From these three-dimensional crowns, we are able to measure individual tree biomass. Comparing these estimates to those from permanent inventory plots, our algorithm is able to produce robust estimates of hectare-scale carbon density, demonstrating the power of ITC approaches in monitoring forests. The flexibility of our method to add additional dimensions of information, such as spectral reflectance, make this approach an obvious avenue for future development and extension to other sources of three-dimensional data, such as structure from motion datasets.Jonathan Williams holds a NERC studentship [NE/N008952/1] which is a CASE partnership with support from Royal Society for the Protection of Birds (RSPB). David Coomes was supported by an International Academic Fellowship from the Leverhulme Trust. Carola-Bibiane Schoenlieb was supported by the RISE projects CHiPS and NoMADS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute. We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Quadro P6000 GPU used for this research

Effects of data uncertainty on dynamic treatment units

The purpose of this thesis was to study the effects of uncertainty in the pixel level estimates concerning the potential benefits of dynamic, pixel-based treatment units compared to traditional stand-based treatment allocation. Because of automatic stand delineation having benefits resulting in the possibility to be more commonly used in forest industry. In thesis assumption is made that the original ALS-dataset exactly represents the “Ground truth”. The study area is a part of the Östad foundation (Östad “stiftelse”) located in Southern Sweden. The property is owned by Östad foundation and is mainly used for industrial and education purpose. The analysis area is comprised of 1 848 ha. The forest is managed in the traditional Scandinavian clearcutting regimes. The results shows that uncertainty effects potential incomes in all cases, especially on the stands where traditional stand delineation was used. Target harvest volume (share of harvested pixels) also has an impact on the potential losses. When the target volume goes from 60 000 m3 to 80 000 m3 the financial trend is changing and the potential losses per m3 is decreasing. Due to this phenomenon the biggest potential loss in SEK per m3 is reached at the target volume of 60 000 m3. In terms of dynamic treatment units (DTU) the difference per m3 between the “Ground truth” and created rasters are relatively low, in some cases less than -1 SEK/m3. The results concerning the average difference between DTU and “Original borders” variants with simulated dNPV in all cases favor the DTU’s. In all tested cases DTU had an economical advantage over the planning within existing boundaries. Even with target volume of 20 000 m3 results improve by -3.2 SEK/m3 in average between DTU and “Original borders”. The largest difference between simulated dNPV data of DTU and “Original borders” is - 5.9 SEK/m3, this result is achieved by 60 000 m3 as the target harvest volume. The conclusion is drawn that DTU planning is more efficient than forest management planning based on “Original stand” borders even when the effect of volume data errors is considered. The data errors have larger effect on the planning results within the framework of original borders that within the DTU framework. Highest difference was achieved in case of minimum segment size 0.5 ha and target harvesting volume 60 000 m3 where, the difference was -6.9 SEK/m3