Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data

Donoghue, Daniel N.M.; Watt, P. J.; Cox, Nicholas J.; Wilson, J.

doi:10.1016/j.rse.2007.02.032

Cited by 196 publications

(119 citation statements)

References 46 publications

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“…Airborne Lidar (Light Detection and Ranging) is an active remote sensing system well suited to measure specific forest information, including tree height [8], basal area [9], leaf area index [10], canopy cover [11,12], vertical vegetation strata [13], successional stages [14], and canopy architecture [15]. In the case of tropical forests, Asner et al [16,17] assessed the capability of mean canopy height (MCH) derived from airborne Lidar to estimate the above-ground carbon in Madagascar and Colombia, demonstrating that Lidar-derived MCH could be used to accurately estimate above ground carbon.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

et al. 2014

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Abstract:In this study, we test and demonstrate the utility of disturbance and recovery information derived from annual Landsat time series to predict current forest vertical structure (as compared to the more common approaches, that consider a sample of airborne Lidar and single-date Landsat derived variables). Mean Canopy Height (MCH) was estimated separately using single date, time series, and the combination of single date and time series variables in multiple regression and random forest (RF) models. The combination of single date and time series variables, which integrate disturbance history over the entire time series, overall provided better MCH prediction than using either of the two sets of variables separately. In general, the RF models resulted in improved performance in all estimates over those using multiple regression. The lowest validation error was obtained using Landsat time series variables in a RF model (R 2 = 0.75 and RMSE = 2.81 m). Combining single date and time series data was more effective when the RF model was used (opposed to multiple regression). The RMSE for RF mean canopy height prediction was reduced by 13.5% when combining the two sets of variables as compared to the 3.6% RMSE decline presented by multiple regression. This study demonstrates the value of airborne Lidar and long term Landsat observations to generate estimates of forest canopy height using the random forest algorithm.

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Section: Introductionmentioning

confidence: 99%

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

et al. 2014

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“…Range corrected LIDAR intensity data has been used lately for species classification [8,9]. In Donoghue et al [8] range corrected intensity measures were computed for different height quantiles. These quantiles were used to quantify the volume of spruce in even aged, mixed spruce and pine stands.…”

Section: Lidar and Aerial Image Based Methodsmentioning

confidence: 99%

Individual Tree Species Classification by Illuminated—Shaded Area Separation

2009

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Abstract:A new method, called Illumination Dependent Colour Channels (IDCC), is presented to improve individual tree species classification. The method is based on tree crown division into illuminated and shaded parts on a digital aerial image. Colour values of both sides of the tree crown are then used in species classification. Tree crown division is achieved by comparing the projected location of an aerial image pixel with its neighbours on a Canopy Height Model (CHM), which is calculated from a synchronized LIDAR point cloud. The sun position together with the mapping aircraft position are also utilised in illumination status detection. The new method was tested on a dataset of 295 trees and the classification results were compared with ones measured with two other feature extraction methods. The results of the developed method gave a clear improvement in overall tree species classification accuracy.

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“…The capacity to resolve forest structure parameters provides a great opportunity for developing vegetation-mapping strategies (Kramer et al 2014). Donoghue et al (2007) and Heinzel and Koch (2011) explored the possibility of identifying tree species mixtures from parameters derived from LiDAR data. Ørka et al (2009) and Kim et al (2009) used LiDAR intensity data to differentiate broadleaf and needleleaf trees.…”

Section: Introductionmentioning

confidence: 99%

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

Fry

Collins

et al. 2015

Canadian Journal of Remote Sensing

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Abstract. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure characteristics, which are critical for differentiating vegetation communities in compositionally homogeneous forests. Light detection and ranging (LiDAR) can accurately measure the forest vertical and horizontal structures and provide a great opportunity for solving this problem. This study introduces a strategy using both multispectral aerial imagery and LiDAR data to map vegetation composition and structure over large spatial scales. Our approach included the use of a Bayesian information criterion algorithm to determine the optimized number of vegetation groups within mixed conifer forests in two study areas in the Sierra Nevada, California, and an unsupervised classification technique and post hoc analysis to map these vegetation groups across both study areas. The results show that the proposed strategy can recognize four and seven vegetation groups at the two study areas, respectively. Each vegetation group has its unique vegetation structure characteristics or vegetation species composition. The overall accuracy and kappa coefficient of the vegetation mapping results are over 78% and 0.64 for both study sites.Résumé. La cartographie précise de la végétation est essentielle entre autres pour la gestion des ressources naturelles, l'analysé ecologique, et la modélisation hydrologique. Les approches d'imagerie multispectrale et hyperspectrale par télédétection se sont avérées de précieuses contributions au processus de la cartographie de la végétation, mais elles ne peuvent fournir qu'un nombre limité de caractéristiques sur la structure de la végétation, qui sont essentielles pour différencier les communautés végétales dans les forêts de composition homogènes. La télédétection par laser «light detection and ranging» (LiDAR) peut mesurer avec précision les structures verticales et horizontales de la forêt, et fournit une formidable opportunité de résoudre ce problème. Cetté etude présente une stratégie qui utiliseà la fois l'imagerie multispectrale aérienne et des données LiDAR pour cartographier la composition et la structure de la végétationà grandeséchelles spatiales. Notre approche comprenait l'utilisation d'un algorithme du critère d'information Bayésien pour déterminer le nombre optimal de groupes de végétation dans les forêts mixtes de conifères sur deux zones d'étude dans les Sierra Nevada, en Californie, ainsi qu'une technique de classification non supervisée et une analyse post hoc pour cartographier ces groupes de végétation dans les deux zones d'étude. Les résultats montrent que la stratégie proposée peut reconnaitre quatre et sept groupes de végétation dans les deux zones d'étude respectivement. Chaque groupe de végétation a des caractéristiques uniques de structure de...

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Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data

Cited by 196 publications

References 46 publications

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

Individual Tree Species Classification by Illuminated—Shaded Area Separation

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

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