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

Prédiction de la distribution des diamètres des arbres à l’aide de métriques tirées de la donnée lidar aéroporté pour les forêts boréales du Québec et de l’ouest de Terre-Neuve

La forêt occupe une proportion importante du territoire Canadien et son exploitation nécessite une connaissance approfondie de la structure des peuplements forestiers. La distribution des diamètres des arbres (DDA) permet d’estimer plusieurs attributs forestiers, notamment le volume de bois ou le taux de croissance. Cette étude vise à prédire la DDA à une résolution fine (20 m²) à partir de données de LiDAR aéroporté pour les forêts boréales de conifères au Québec et à l’ouest de Terre Neuve. La donnée LiDAR aéroportée permet de produire un modèle de hauteur de canopée. Conséquemment, le premier objectif vise à améliorer les estimés de DDA avec la contribution de métriques texturales dérivées du modèle de hauteur de canopée combinées aux métriques LiDAR standards. Le deuxième objectif consiste à déterminer la meilleure approche pour modéliser les DDA : soit en différentiant a priori la modalité des DDA selon leur modalité (uni/bimodales) ou non. La modélisation de la DDA passe par la prédiction des paramètres de la fonction Weibull ajustée aux DDA unimodales et non-différentiées. Pour les DDA bimodales, un finite mixture model, composé de deux fonctions Weibull, permet d’extraire les paramètres des deux composantes Weibull ajustées à la DDA. Les paramètres, échelle et forme, des fonctions Weibull décrivant les DDA unimodales et non différenciées ont été prédits avec des R² acceptables (0.40-0.55) comparativement aux paramètres moyenne, proportion et écart type des DDA bimodales (R² moyen<0.30). L’utilisation de métriques de texture a permis d’améliorer la précision globale de la différentiation des modalités de 4%, ce qui a fait augmenter en moyenne de 0.10 le R² pour les paramètres des DDA unimodales et non-différentiées, et 0.17 pour les DDA différentiés bimodales. De plus, les DDA unimodales ont prédit en moyenne 79% des diamètres mesurés et 75% pour les bimodales. Les DDA non-différentiées ont prédit 76% des diamètres mesurés. Cependant, les DDA bimodales présentaient des R² faibles, causé par l’absence de deux modes clairement distincts ainsi eu par la difficulté de prédire les faibles diamètres

Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data

Multispectral airborne laser scanning (MS-ALS) provides information about 3D structure as well as the intensity of the reflected light and is a promising technique for acquiring forest information. Data from MS-ALS have been used for tree species classification and tree health evaluation. This paper investigates its potential for individual tree detection (ITD) when using intensity as an additional metric. To this end, rasters of height, point density, vegetation ratio, and intensity at three wavelengths were used for template matching to detect individual trees. Optimal combinations of metrics were identified for ITD in plots with different levels of canopy complexity. The F-scores for detection by template matching ranged from 0.94 to 0.73, depending on the choice of template derivation and raster generalization methods. Using intensity and point density as metrics instead of height increased the F-scores by up to 14% for the plots with the most understorey trees

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Développement d’une méthode de télédétection pour l’identification d’espèces exotiques envahissantes dans l’agglomération de Québec

Les espèces exotiques envahissantes végétales (EEEv) sont actuellement considérées comme étant à l’origine de plusieurs types d’impacts négatifs dont la perte de la biodiversité et l’altération du fonctionnement des écosystèmes. Dans l’agglomération de Québec, la présence de plusieurs EEEv et les informations partielles sur leur distribution territoriale limitent la mise en place de stratégies efficaces de contrôle et d’éradication. Ces données sur la distribution territoriale peuvent être acquises à partir des inventaires in situ. Cependant, ces derniers nécessitent beaucoup de temps surtout dans les milieux envahis par plusieurs EEEv en même temps tels que les milieux urbains. Ces inventaires ne sont également pas adaptés financièrement et techniquement, lorsqu’il s’agit de grandes étendues ou lorsque les conditions topographiques ne sont pas favorables. La télédétection pourrait être utilisée pour contrer ces limites afin de cartographier les EEEv, suivre leur prolifération et intervenir rapidement. Le but de cette étude consistait donc à élaborer une méthode de cartographie multi-espèces par télédétection de cinq EEEv terrestres présentes dans l’agglomération de Québec, à savoir la renouée du Japon (Fallopia japonica), le phragmite (Phragmites australis), la berce du Caucase (Heracleum mantegazzianum), le nerprun bourdaine (Frangula alnus) et le nerprun cathartique (Rhamnus cathartica). L’approche méthodologique consistait à réaliser une cartographie mono-date et multi-date à l’aide d’images satellitaires WorldView-3 acquises en été, SPOT-7 et GeoEye-1 acquises en automne. Une classification orientée-objet combinée à des méthodes d’apprentissage automatique non paramétriques, à savoir Support Vector Machine (SVM), Random Forest (RF) et Extreme Gradient Boosting (XGBoost) a été utilisée afin de produire des probabilités de présence de ces EEEv. La cartographie des nerpruns a été réalisée à part car leur faible présence sur la zone d’étude et leur distribution sous-couvert à faible densité a nécessité un ajout de l’image GeoEye-1 et un paramétrage des méthodes différent de celui utilisé pour les trois premières EEEv. La combinaison des images WorldView-3 et SPOT-7 a permis d’atteindre d’excellentes performances pour les trois premières EEEv, avec un coefficient Kappa de 0,85 et une précision globale de 91 % en utilisant RF. Les performances individuelles des classes basées sur l’indicateur F1-score ont montré que la renouée du Japon est mieux détectée (F1-score maximal = 0,95), que la berce du Caucase (F1-score maximal = 0,91) et le phragmite (F1-score maximal = 0,87). La classification multi-date des nerpruns est, par contre, moins performante par rapport à celle des autres espèces avec un coefficient Kappa égal à 0,72, une précision globale de 83 % et F1-score maximal égal 0,62. Cette étude montre la possibilité de cartographie et suivi des principales EEEv selon une approche multi-date. Les limites de cette étude, à savoir la faible quantité de données de référence d’EEEv, les coûts élevés d’acquisition et la faible disponibilité des images satellitaires à très haute résolution spatiale ainsi que la distribution des nerpruns en sous-couvert (dans notre zone d’étude) pourraient être réduites en utilisant des images plus accessibles en combinaison avec les techniques de super-résolution. Les données LiDAR à haute densité pourraient également être intégrées à l’imagerie optique afin d’améliorer les performances de cartographie des nerpruns

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion

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