Connie Ko

GIS modeling for predicting river runoff volume in ungauged drainages in the Greater Toronto Area, Canada

This paper introduces two parsimonious models for predicting runoff for ungauged basins from the observed river flow data in gauged basins and precipitation data from rain gauging stations in the same area. The models assume the runoff is related to the precipitation subject to variance of drainage basins. The runoff can be modeled as a function of precipitation and parameters determined by basin descriptive properties. The parameter values of the models can be calibrated statistically on the basis of observed historical runoff data and precipitation data. Further the parameters can be regressed to associate with the areas of different landcover types occupying the drainage basins. This regression model can be used for estimating parameter values for ungauged basins in the same study area which can be further used together with precipitation data to predict the runoff in the ungauged basins. An example of adopted Soil Conservation Service (SCS) method with application to the Oak Ridges Moraine area was introduced to demonstrate the implementation of the models introduced. The information used for the modeling and prediction includes: surficial geology, DEM, Landsat TM images, historical river flow data, and precipitation and temperature data from weather stations.

Tree genera classification with geometric features from high-density airborne LiDAR

Categorical recognition of a trees genus is known to be valuable information for the effective management of forest inventories. In this paper, we present a method for learning a discriminative model using Random Forests to classify individual trees into three genera: pine, poplar, and maple. We believe that both internal and external geometric characteristics of the tree crown are related to tree form and therefore useful in classifying trees to the genus level. Our approach involves the extraction of both internal and external geometric features from a LiDAR point cloud as we believe that geometric features provide important information about the organization of the points inside the tree crown along with overall tree shape and form. We developed 24 geometric features and then reduced the number of features to increase efficiency. These geometric characteristics, computed for 160 sampled trees from eight field sites, were classified using Random Forests and achieved an 88.3% average accuracy rate by using 25% (40 trees) of the data for training.

GIS spatial modeling of river flow and precipitation in the Oak Ridges Moraine area, Ontario

The spatial pattern of storm runoff volume is of interest in many environmental concerns and storm runoff is an important component in the hydrologic cycle because of its relationship to issues such as flooding and water quality. The isolation of the groundwater component allows the net effect of meteorology to stream flow rate. Hence, the time lag for the stream in respond to the precipitation event depends mainly on the physical and hydrological characteristics of the drainage basins such as land use, topography and geometry of the drainage basins. This paper uses different types of statistical and GIS techniques in an attempt to relate the meteorological data to the hydrological data, showing the spatial pattern of different respond time for different watersheds in Oak Ridges Moraine Area in the southern part of Ontario, Canada. The results were then related to the different physical properties of the watersheds delineated by GIS software.

Hybrid Ensemble Classification of Tree Genera Using Airborne LiDAR Data

Abstract: This paper presents a hybrid ensemble method that is comprised of a sequential and a parallel architecture for the classification of tree genus using LiDAR (Light Detection and Ranging) data. The two classifiers use different sets of features: (1) features derived from geometric information, and (2) features derived from vertical profiles using Random Forests as the base classifier. This classification result is also compared with that obtained by replacing the base classifier by LDA (Linear Discriminant Analysis), kNN (k Nearest Neighbor) and SVM (Support Vector Machine). The uniqueness of this research is in the development, implementation and application of three main ideas: (1) the hybrid ensemble method, which aims to improve classification accuracy, (2) a pseudo-margin criterion for assessing the quality of predictions and (3) an automatic feature reduction method using results drawn from Random Forests. An additional point-density analysis is performed to study the influence of decreased point density on classification accuracy results. By using Random Forests as the base classifier, the average classification accuracies for the geometric classifier and vertical profile classifier are 88.0% and 88.8%, respectively,

Mapping tree genera using discrete LiDAR and geometric tree metrics

Los mapas de generos de arboles son utiles para el inventario forestal, planificacion urbana y el mantenimiento de la infraestructura de lineas de transmision. Se presenta un estudio de caso de uso de datos LiDAR de alta densidad para el mapeo de generos de arboles a lo largo del derecho de paso (ROW) de un corredor de linea de transmision. El objetivo de la investigacion fue identificar arboles individuales que mostraban o poseian una amenaza potencial a la infraestructura de la linea de transmision. Mediante el uso de mapas tridimensionales de LiDAR se derivaron metricas de arboles que estan relacionadas con la geometria de estos (formas del arbol). Por ejemplo, la direccion del crecimiento dominante de los arboles es util para identificar arboles que crecen inclinados hacia las lineas de transmision. Tambien se derivaron otras metricas geometricas que son utiles para determinar los generos de los arboles, tales como altura, forma de la copa, tamano y estructura de ramas. El area de estudio se ubico al norte de Thessalon, Ontario, Canada, a lo largo de los principales corredores de ROW y en los bosques aledanos. Los atributos geometricos usados para la clasificacion de los generos fueron categorizados en cinco amplias clases: 1) lineas, 2) agrupamiento, 3) volumenes, 4) amortiguamiento en 3D de puntos, y 5) forma general del arbol que provee parametros como una entrada para el clasificador forestal aleatorio.

Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density LiDAR Data

Recent research into improving the effectiveness of forest inventory management using airborne LiDAR data has focused on developing advanced theories in data analytics. Furthermore, supervised learning as a predictive model for classifying tree genera (and species, where possible) has been gaining popularity in order to minimize this labor-intensive task. However, bottlenecks remain that hinder the immediate adoption of supervised learning methods. With supervised classification, training samples are required for learning the parameters that govern the performance of a classifier, yet the selection of training data is often subjective and the quality of such samples is critically important. For LiDAR scanning in forest environments, the quantification of data quality is somewhat abstract, normally referring to some metric related to the completeness of individual tree crowns; however, this is not an issue that has received much attention in the literature. Intuitively the choice of training samples having varying quality will affect classification accuracy. In this paper a Diversity Index (DI) is proposed that characterizes the diversity of data quality (Qi) among selected training samples required for constructing a classification model of tree genera. The training sample is diversified in terms of data quality as opposed to the number of samples per class. The diversified training sample allows the classifier to better learn the positive and negative instances and; therefore; has a higher classification accuracy in discriminating the “unknown” class samples from the “known” samples. Our algorithm is implemented within the Random Forests base classifiers with six derived geometric features from LiDAR data. The training sample contains three tree genera (pine; poplar; and maple) and the validation samples contains four labels (pine; poplar; maple; and “unknown”). Classification accuracy improved from 72.8%; when training samples were selected randomly (with stratified sample size); to 93.8%; when samples were selected with additional criteria; and from 88.4% to 93.8% when an ensemble method was used.

Airborne LiDAR Applications in Forest Landscapes

This chapter provides an introduction and overview of using light detection and ranging (LiDAR) in forest applications. The first section explains the principles and basic terminology for LiDAR and introduces the use of LiDAR on three different platforms (spaceborne, airborne, and terrestrial) for forest applications. The second section discusses applications in relation to the primary measurements from a LiDAR point cloud, primarily information derived from distance (from the aircraft to the target). We cover concepts related to different representations of surfaces (e.g., digital surface model, digital terrain model, digital elevation model, and canopy height model). Typically, single trees can be identified from the canopy height model and there are two different ways to assign LiDAR points to individual trees, the surface-based method and the point-based method. The third section discusses forest applications in relation to secondary measurement from a LiDAR point cloud, information derived from point cloud geometry rather than direct distance measurements. This section covers tree genera classification; the use of allometric equations for deriving DBH, biomass, and other forest attributes; and the classification of vegetation types. Three ways of getting genera information are discussed, including the vertical profile method, methods relying on geometry derived from individual tree point clouds, and methods that incorporate spectral information. The fourth section provides a case study for identifying potential tree hazards along a powerline corridor in Ontario, Canada. We conclude by discussing the future of this technology.