Detecção de árvores em povoamentos de Pinus spp. a partir de nuvens de pontos derivados de imagens ópticas de RPAS a partir da análise de máximo global

Orientadora: Professora Drª. Christel LingnauCoorientador: Professor Dr. Daniel Rodrigues dos SantosDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Programa de Pós-Graduação em Engenharia Florestal. Defesa : Curitiba, 09/03/2018Inclui referênciasÁrea de concentração: Manejo florestalResumo: Esta pesquisa teve como objetivo a criação de um algoritmo para detecção automática de árvores a partir de nuvens de pontos 3D gerados de imagens ópticas de RPAS. O algoritmo criado, denominado DAA (Detecção Automática de Árvores), cujo funcionamento baseia-se na metodologia de filtro de máximos globais, foi aplicado em dois tipos de povoamentos florestais de Pinus spp.: plantio jovem, de 2 anos de idade e plantio adulto, de 11 anos recém desbastado. Para analisar o potencial de funcionamento do DAA, este foi inicialmente aplicado na nuvem de pontos 3D pré-processada do plantio jovem, de 2 anos de idade. O resultado obtido mostrou-se satisfatório, com 91% das árvores detectadas automaticamente pelo algoritmo. Quando aplicado no plantio adulto, o DAA foi comparado a outro algoritmo de detecção automática de árvores, o ITD (Individual Tree Detection), cujo princípio de funcionamento tem como base o método de filtro de máximos locais, o mais utilizado para este objetivo. Ao comparar os dois algoritmos com os métodos de contagem em campo das árvores e contagem por fotointerepretação de ortofoto, o DAA apresentou melhores resultados, com 93% de árvores detectadas corretamente, quando comparado a contagem pela ortofoto e 89% em relação ao censo florestal. Em contrapartida, o ITD apresentou valores de 71% de acertos em relação a ortofoto e 74% em relação ao censo florestal. Portanto, o algoritmo ITD subestimou o número total de árvores do talhão adulto, e o DAA obteve melhor desempenho para esta finalidade. Palavras-chaves: Automatização. Filtro de máximos globais. Censo florestal. RPAS. Sensoriamento Remoto 3D.Abstract: The goal of this research was the creation of an algorithm for automatic detection of trees from point cloud generated by RPAS's optical images. The algorithm, called DAA (Detecção Automática de Árvores), whose operation is based on the methodology of global maxima filter, was applied in two types of Pinus taeda's forests plantings: young planting, 2 years of age and adult planting, of 11 years old. To analyze the potential for DAA, it was first applied in a pre-processed 3D spot cloud of young, 2-year-old planting. The result obtained was satisfactory, with 91% of the seedlings detected automatically by the algorithm. When applied to adult planting, the DAA was compared to another algorithm of automatic tree detection, the ITD (Individual Tree Detection), whose principle of operation is based on the method of local maxima filter, the most used for this type of activity. When comparing the two algorithms with the counting methods in the field and counting by photointerpretation of orthophoto, the DAA presented better results, with 93% of correctly detected trees when compared to orthophoto counts and 89% in relation to the forest census. On the other hand, the ITD presented values of 71% of hits in relation to orthophoto and 74% in relation to the forest census. Therefore, the ITD algorithm underestimated the total number of trees in the adult field, and DAA obtained better performance for this purpose. Key-words: Automation. 3D Remote Sensing. Forest census. Global maxima filter. RPAS

AUTOMATIC DETECTION OF PLANTED TREES AND THEIR HEIGHTS USING PHOTOGRAMMETRIC RPA POINT CLOUDS

This work aims to analyze the potential of the Photogrammetric Point Cloud (PPC) obtained from Remote Piloted Aircraft (RPA) optical images for detecting and obtaining tree heights in a loblolly pine plantation using a global maximum filter. The enhanced algorithm used in this study is then named STD (Single Tree Detection). Field surveys were conducted to count all the trees in the field (Forest Census) and measure the trees’ height with a vertex hypsometer. The results were faced to PCC outcomes. The detection rate (r) was equal to the precision rate (p), indicating that the algorithm reaches a high tree detection performance. In summary, the STD algorithm segmented 2,192 trees, representing 89% of trees recorded in the forest census. The retrieved tree height reached, on average, a height of 17.05 m, whereas slightly higher by the traditional forest inventory (17.42 m). The root-mean-square error (RMSE) and Bias were 47 cm (2.8%) and -37 cm (-2.2%), respectively. The Dunnett test showed that the tree height did not significantly differ between the results obtained by traditional forest inventory from those generated by the STD. It confirms the potential use of PPC for forest inventory procedures

A LIGHTWEIGHT UAV-BASED LASER SCANNING SYSTEM FOR FOREST APPLICATION

Lightweight Unmanned Aerial Vehicles (UAVs) have become a cost effective alternative for studies which use aerial Remote Sensing with high temporal frequency requirements for small areas. Laser scanner devices are widely used for rapid tridimensional data acquisition, mainly as a complementary data source to photogrammetric surveying. Recent studies using laser scanner systems onboard UAVs for forestry inventory and mapping applications have presented encouraging results. This work describes the development and accuracy assessment of a low cost mapping platform composed by an Ibeo Lux scanner, a GNSS (Global Navigation Satellite System) antenna, an Inertial Navigation System Novatel Span-IGM-S1, integrating a GNSS receiver and an IMU (Inertial Measurement Unit), a Raspberry PI portable computer and an octopter UAV. The system was assessed in aerial mode using an UAV octopter developed by SensorMap Company. The resulting point density in a plot with trees concentration was also evaluated. The point density of this device is lower than conventional Airborne Laser Systems but the results showed that altimetric accuracy with this system is around 30 cm, which is acceptable for forest applications. The main advantages of this system are their low weight and low cost, which make it attractive for several applications

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

Forest Understory Trees Can Be Segmented Accurately Within Sufficiently Dense Airborne Laser Scanning Point Clouds

Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and multiple understory) and also pinpoint the required density for reasonable tree segmentation (where accuracy plateaus). We show that at a density of ~170 pt/m² understory trees can likely be segmented as accurately as overstory trees. Given the advancements of LiDAR sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources. The methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis

Forest Understory Trees Can Be Segmented Accurately Within Sufficiently Dense Airborne Laser Scanning Point Clouds

Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and multiple understory) and also pinpoint the required density for reasonable tree segmentation (where accuracy plateaus). We show that at a density of ~170 pt/m² understory trees can likely be segmented as accurately as overstory trees. Given the advancements of LiDAR sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources. The methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis

Opportunities for the use of drone technology in forest ecosystems

Au cours de la dernière décennie, la technologie drone a suscité un grand intérêt et a été largement utilisée pour des applications civiles. Ainsi, les drones ont rapidement prouvé leurs efficiences dans les ressources naturelles, l’environnement, l’agriculture et la foresterie. Étant une plate-forme de télédétection, les drones ont le potentiel d'augmenter l'efficacité d'acquisition des données forestières en ayant des résolutions spatiale et temporelle beaucoup plus importantes que celles des autres techniques de télédétection. Dans cet article, nous présentons une synthèse des travaux de recherche portant sur l’utilisation de la technologie drone dans diverses applications forestières, dont la modélisation de la canopée forestière, l’évaluation des paramètres de l’inventaire forestier, le suivi de la santé des forêts et la discrimination des essences forestières. L’analyse de ces travaux a montré que l’utilisation de la technologie drone a concerné plusieurs aspects diversifiés, tandis que d’autres thématiques de recherche sont encore peu étudiées, notamment l’évaluation de la régénération naturelle, le suivi des projets de réhabilitation des écosystèmes naturels, l’étude des impacts des changements climatiques et des impacts anthropogènes sur un écosystème forestier. Le drone offre des opportunités d’utilisation de la technologie drone dans la gestion du domaine forestier Marocain, afin de remédier aux limites des techniques de télédétection utilisées actuellement en termes de résolution spatiale, de flexibilité du choix du temps d’acquisition des données et en terme du coût. Mots clés: Drone, Écosystème forestier, Inventaire forestier, Modélisation de la canopée forestière, Photogrammétrie, LidarOver the past decade, UAV technology has attracted a great deal of interest and has been widely used for civilian applications. UAVs have rapidly proven their efficiency in natural resources, the environment, agriculture and forestry. As a remote sensing platform, UAVs have the potential to increase the efficiency of forest data acquisition, having much higher spatial and temporal resolutions than other remote sensing techniques. In this paper, we present a synthesis of research on the use of UAV technology in various forestry applications, such as forest canopy modelling, forest inventory parameter assessment, forest health monitoring and forest species discrimination. The analysis of this research has shown that the use of UAV technology has concerned several diversified aspects, while other research themes are less studied, notably the assessment of natural regeneration, the monitoring of natural ecosystem rehabilitation projects, the study of climate and anthropogenic change impacts forest ecosystems. UAV technology is an opportunities for management of the Moroccan forest ecosystem, in order to overcome the limitations of the remote sensing techniques currently used, in terms of spatial resolution, flexibility in the choice of data acquisition time and in terms of cost. Keywords: UAV, Forest ecosystem, Forest inventory, Forest canopy modelling, Photogrammetry, Lida

Modèle d'ajustement pour réduire le biais sur le modèle numérique de terrain et le modèle de hauteur de canopée à partir de données LiDAR acquises selon divers paramètres et conditions forestières

La sous-estimation des hauteurs LiDAR est très largement connue, mais n’a jamais été étudiée pour plusieurs capteurs et diverses conditions forestières. Cette sous-estimation varie en fonction de la probabilité que le faisceau atteigne le sol et le sommet de la végétation. Les principales causes de cette sous-estimation sont la densité des faisceaux, le patron de balayage (capteur), l'angle des faisceaux, les paramètres spécifiques du survol (altitude de vol, fréquence des faisceaux) et les caractéristiques du territoire (pente, densité du peuplement et composition d’essences). Cette étude, réalisée à une résolution de 1 x 1 m, a d’abord évalué la possibilité de faire un modèle d’ajustement pour corriger le biais du modèle numérique de terrain (MNT) et ensuite un modèle d’ajustement global pour corriger le biais sur le modèle de hauteur de canopée (MHC). Pour cette étude, le MNT et le MHC ont été calculés en soustrayant deux jeux de données LiDAR: l’un avec des pixels comportant un minimum de 20 retours (valeur de référence) et l’autre avec des pixels à faible densité (valeur à corriger). Les premières analyses ont permis de conclure que le MNT ne nécessitait pas d’ajustement spécifique contrairement au MHC. Parmi toutes les variables étudiées, trois ont été retenues pour calibrer le modèle d’ajustement final du MHC : la hauteur du point le plus haut dans le pixel, la densité de premiers retours par mètre carré et l’écart type des hauteurs maximales du voisinage à 9 cellules. La modélisation s'est déroulée en trois étapes. Les deux premières ont permis de trouver les paramètres significatifs et la forme de l'équation (modèle linéaire mixte (1) et modèle non linéaire (2)).La troisième étape a permis d’obtenir une équation empirique à l’aide d’un modèle non linéaire mixte (3) applicable à un MHC d’une résolution de 1x 1m. La correction de la sous-estimation du MHC peut être utilisée comme étape préliminaire à plusieurs utilisations du MHC comme le calcul de volumes et la création de modèles de croissance ou d’analyses multi-temporelles