Estimation of Carbon Storage in Urban Trees Using Multispectral Airborne Laser Scanning Data

With the continued growth of global population, urbanization becomes an inevitable trend. As substantial urban expansion undergoes, ecosystem and global land cover have been altered consequently. Urban development becomes the biggest contributor to global carbon emissions while the process of urbanization results in urban heat islands, climate change, and losses of carbon sinks. Urban vegetation has drawn direct attention of city planners and policy makers by considering the importance of vegetation in urban climate modification and energy conservation in different ways. For instance, tree shading and wind shielding effects can attenuate the direct solar heat and air infiltration into individual houses. In city wide, vegetation contributes the largest proportion of carbon storage which reduces climate warming and urban heat island effects by sequestering CO2 and storing carbon in biomass. The carbon content stored in individual trees can be estimated by dendrometric parameters such as the diameter at breast height (DBH) using allometry-based models. With the development of airborne laser scanning (ALS) technology, ALS data and very high resolution multispectral imagery have proven to be promising tools for deriving dendrometric parameters in forest. With the emerging multispectral ALS technology, it became possible to obtain both the range and spectral information from a single source meanwhile the intensity of multispectral ALS showed its power in vegetation mapping. This study aims to develop a workflow that can quantify the carbon storage in urban trees using multispectral ALS data. The workflow consists of four steps: multispectral ALS data processing, vegetation isolation, dendrometric parameters estimation, and carbon storage modeling. First, the raw multispectral ALS data is intensity-rectified and filtered to generate a normalized Digital Surface Model (nDSM) and multispectral ALS intensity information at wavelengths: 532 nm (Green), 1064 nm (Near-infrared, NIR), and 1550 nm (Shortwave Infrared, SWIR), respectively. Vegetation covers are isolated by the support vector machine (SVM) classifier using multispectral ALS intensity information and nDSM in which total six classes including two vegetation classes (grass and tree) are classified. Individual tree crown is delineated by local maxima filtering and marker-controlled watershed segmentation. Tree height and crown width are derived from the crown segments and compared with field measurements. An ALS-DBH (diameter at breast height) multiple linear regression model is developed to predict field-measured DBH using ALS-derived tree height and crown width and assessed by cross validation. Then the carbon storage in individual trees is calculated by allometric equations using ALS-estimated DBH and height. A total of 40 trees are sampled in the field that four attributes: height, crown width, DBH, and biomass are recorded for each single tree. The results show that the land cover classification with multispectral ALS intensity images and nDSM achieves above 90% overall accuracy. The result of local maxima filtering is improved by using both multispectral ALS intensity and nDSM as input data. The ALS-derived tree height has a root mean square error (RMSE) of 1.21 m (relative RMSE = 6.8%) and the ALS-derived crown width has a RMSE of 1.47 m (relative RMSE = 16.4%). The prediction performance of the ALS-DBH model achieves R2 over 0.80 with a RMSE of 4.6 cm. The predicted carbon storage using ALS-modeled DBH corresponded to a RMSE of 142 kg (28.6%) and a bias of 14.4 kg. Results suggest that ALS-based dendrometric parameter estimation and allometric models can yield consistent performance and accurate estimation. Citywide carbon storage estimation is derived in this study by extrapolating the values within the study area to the entire city based on the specific proportion of each land cover type in the entire city. The proposed workflow also reveals the potential of multispectral ALS data in estimating carbon storage at individual-tree level and mapping vegetation in the urban environment

Single-Sensor Solution to Tree Species Classification Using Multispectral Airborne Laser Scanning

This paper investigated the potential of multispectral airborne laser scanning (ALS) data for individual tree detection and tree species classification. The aim was to develop a single-sensor solution for forest mapping that is capable of providing species-specific information, required for forest management and planning purposes. Experiments were conducted using 1903 ground measured trees from 22 sample plots and multispectral ALS data, acquired with an Optech Titan scanner over a boreal forest, mainly consisting of Scots pine (Pinus Sylvestris), Norway spruce (Picea Abies), and birch (Betula sp.), in southern Finland. ALS-features used as predictors for tree species were extracted from segmented tree objects and used in random forest classification. Different combinations of features, including point cloud features, and intensity features of single and multiple channels, were tested. Among the field-measured trees, 61.3% were correctly detected. The best overall accuracy (OA) of tree species classification achieved for correctly-detected trees was 85.9% (Kappa = 0.75), using a point cloud and single-channel intensity features combination, which was not significantly different from the ones that were obtained either using all features (OA = 85.6%, Kappa = 0.75), or single-channel intensity features alone (OA = 85.4%, Kappa = 0.75). Point cloud features alone achieved the lowest accuracy, with an OA of 76.0%. Field-measured trees were also divided into four categories. An examination of the classification accuracy for four categories of trees showed that isolated and dominant trees can be detected with a detection rate of 91.9%, and classified with a high overall accuracy of 90.5%. The corresponding detection rate and accuracy were 81.5% and 89.8% for a group of trees, 26.4% and 79.1% for trees next to a larger tree, and 7.2% and 53.9% for trees situated under a larger tree, respectively. The results suggest that Channel 2 (1064 nm) contains more information for separating pine, spruce, and birch, followed by channel 1 (1550 nm) and channel 3 (532 nm) with an overall accuracy of 81.9%, 78.3%, and 69.1%, respectively. Our results indicate that the use of multispectral ALS data has great potential to lead to a single-sensor solution for forest mapping.Peer reviewe

Individual tree detection and modelling aboveground biomass and forest parameters using discrete return airborne LiDAR data

Individual tree detection and modelling forest parameters using Airborne Laser Scanner data (Light Detection and Ranging (LiDAR) is becoming increasingly important for the monitoring and sustainable management of forests. Remote sensing has been a useful tool for individual tree analysis in the past decade, although inadequate spatial resolution from satellites means that only airborne systems have sufficient spatial resolution to conduct individual tree analysis. Moreover, recent advances in airborne LiDAR now provide high horizontal resolution as well as information in the vertical dimension. However, it is challenging to fully exploit and utilize small-footprint LiDAR data for detailed tree analysis. Procedures for forest biomass quantification and forest attributes measurement using LiDAR data have improved at a rapid pace as more robust and sophisticated modelling used to improve the studies. This thesis contains an evaluation of three approaches of utilizing LiDAR data for individual tree forest measurement. The first explores the relationship between LiDAR metrics and field reference to assess the correlation between LiDAR and field data at the individual-tree level. The intention was not to detect trees automatically, but to develop a LiDAR-AGB model based on trees that were mapped in the field so as to evaluate the relationships between LiDAR-type metrics under controlled conditions for the study sites, and field-derived AGB. A non-linear AGB model based on field data and LiDAR data was developed and LiDAR height percentile h80 and crown width measurement (CW) was found to best fit the data as evidenced by and Adj-R2 value of 0.63, the root mean squared error of the model of 14.8% and analysis of the residuals. This paper provides the foundation for a predictive LiDAR-AGB model at tree level over two study sites, Pasoh Forest Reserve and FRIM Forest Reserve. The second part of the thesis then takes this AGB-LiDAR relationship and combines it with individual tree crown delineation. This chapter shows the contribution of performing an automatic individual tree crown delineation over the wider forest areas. The individual tree crown delineation is composed of a five-step framework, which is unique in its automated determination of dominant crown sizes in a forest area and its adaption of the LiDAR-AGB model developed for the purpose of validation the method. This framework correctly delineated 84% and 88% of the tree crowns in the two forest study areas which is mostly dominated with lowland dipterocarp trees. Thirdly, parametric and non-parametric modelling approaches are proposed for modelling forest structural attributes. Selected modelling methods are compared for predicting 4 forest attributes, volume (V), basal area (BA), height (Ht) and aboveground biomass (AGB) at the species level. The AGB modelling in this paper is extracted using the LiDAR derived variables from the automated individual tree crown delineation, in contrast to the earlier AGB modelling where it is derived based on the trees that were mapped in the field. The selected non-parametric method included, k-nearest neighbour (k-NN) imputation methods: Most Similar Neighbour (MSN) and Gradient Nearest Neighbour (GNN), Random Forest (RF) and parametric approach: Ordinary Least Square (OLS) regression. To compare and evaluate these approaches a scaled root mean squared error (RMSE) between observed and predicted forest attribute sampled from both forest site was computed. The best method varied according to response variable and performance measure. OLS regression was to found to be the best performance method overall evidenced by RMSE after cross validation for BA (1.40 m2), V (1.03 m3), Ht (2.22 m) and AGB (96 Kg/tree) respectively, showed its applicability to wider conditions, while RF produced best overall results among the non-parametric methods tested. This thesis concludes with a discussion of the potential of LiDAR data as an independent source of important forest inventory data source when combined with appropriate designed sample plots in the field, and with appropriate modelling tools

Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation

INDIVIDUAL TREE SEGMENTATION FROM BLS DATA BASED ON GRAPH AUTOENCODER

In the last two decades, Light detection and ranging (LiDAR) has been widely employed in forestry applications. Individual tree segmentation is essential to forest management because it is a prerequisite to tree reconstruction and biomass estimation. This paper introduces a general framework to extract individual trees from the LiDAR point cloud based on a graph link prediction problem. First, an undirected graph is generated from the point cloud based on K-nearest neighbors (KNN). Then, this graph is used to train a convolutional autoencoder that extracts the node embeddings to reconstruct the graph. Finally, the individual trees are defined by the separate sets of connected nodes of the reconstructed graph. A key advantage of the proposed method is that no further knowledge about tree or forest structure is required. Seven sample plots from a plantation forest with poplar and dawn redwood species have been employed in the experiments. Though the precision of the experimental results is up to 95 % for poplar species and 92 % for dawn redwood trees, the method still requires more investigations on natural forest types with mixed tree species

Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms

Remote sensing technology is an efficient tool for various practical applications of environmental resources management. Advances in this technology include the diverse range of high quality data sources and image analysis techniques. Object-based image analysis (OBIA) and machine learning algorithms are recent advances, which this thesis evaluates. OBIA and machine learning algorithms are first tested using a combination of multiple datasets for identifying individual tree species. These datasets include Quickbird, LiDAR, and GIS derived terrain data. Improvements in tree species classification were obtained and the best data combination was terrain context (based on slope, elevation, and wetness), tree height, canopy shape, and branch density (based on LiDAR return intensity). The availability of a range of classifiers and different data pre-processing techniques adds to the complexity of image analysis. The combinations of these techniques result in a large number of potential outcomes and these need to be evaluated. Therefore, the second part of this research investigated and compared tree species classification performance for different methods (Naïve Bayes - NB , Logistic Regression - LR, Random Forest - RF, and Support Vector Machine - SVM), combined with various dimensionality reduction (DR) methods (Correlation-based feature selection filter, Information Gain, Wrapper methods, and Principal Component Analysis). When DR was used prior to classification, only the NB classifier had a significant improvement in accuracy. SVM and RF had the best classification accuracy, and this was achieved without DR. The final part of this thesis demonstrates a new method using OBIA for mapping the biomass change of mangrove forests in Vietnam between 2000 and 2011 from SPOT images. First, three different mangrove associations were identified using two levels of image segmentation followed by a SVM classifier and a range of spectral, texture and GIS information for classification. The RF regression model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Ash Tree Identification Based on the Integration of Hyperspectral Imagery and High-density Lidar Data

Monitoring and management of ash trees has become particularly important in recent years due to the heightened risk of attack from the invasive pest, the emerald ash borer (EAB). However, distinguishing ash from other deciduous trees can be challenging. Both hyperspectral imagery and Light detection and ranging (LiDAR) data are two valuable data sources that are often used for tree species classification. Hyperspectral imagery measures detailed spectral reflectance related to the biochemical properties of vegetation, while LiDAR data measures the three-dimensional structure of tree crowns related to morphological characteristics. Thus, the accuracy of vegetation classification may be improved by combining both techniques. Therefore, the objective of this research is to integrate hyperspectral imagery and LiDAR data for improving ash tree identification. Specifically, the research aims include: 1) using LiDAR data for individual tree crowns segmentation; 2) using hyperspectral imagery for extraction of relative pure crown spectra; 3) fusing hyperspectral and LiDAR data for ash tree identification. It is expected that the classification accuracy of ash trees will be significantly improved with the integration of hyperspectral and LiDAR techniques. Analysis results suggest that, first, 3D crown structures of individual trees can be reconstructed using a set of generalized geometric models which optimally matched LiDAR-derived raster image, and crown widths can be further estimated using tree height and shape-related parameters as independent variables and ground measurement of crown widths as dependent variables. Second, with constrained linear spectral mixture analysis method, the fractions of all materials within a pixel can be extracted, and relative pure crown-scale spectra can be further calculated using illuminated-leaf fraction as weighting factors for tree species classification. Third, both crown shape index (SI) and coefficient of variation (CV) can be extracted from LiDAR data as invariant variables in tree’s life cycle, and improve ash tree identification by integrating with pixel-weighted crown spectra. Therefore, three major contributions of this research have been made in the field of tree species classification:1) the automatic estimation of individual tree crown width from LiDAR data by combining a generalized geometric model and a regression model, 2) the computation of relative pure crown-scale spectral reflectance using a pixel-weighting algorithm for tree species classification, 3) the fusion of shape-related structural features and pixel-weighted crown-scale spectral features for improving of ash tree identification