Background: The Atlantic Forest is one, if not the most, diverse ecosystem in the planet. The Atlantic Forest contains an estimated 250 species of mammals (55 endemic), 340 amphibians (90 endemic), 1,023 birds (188 endemic), and approximately 20,000 trees, half of them endemic. Unfortunately, several of these species are currently threatened to become extinct. Amongst those, a tree species from the Araucariaceae family called Araucaria angustifolia can be found. Known as Brazilian Pine or just araucaria, this tree occupies the higher forest stratum, characterizing the landscape of the Atlantic Forest highlands, thus being considered a symbol of the Brazilian southern region. Araucaria trees are profitable regarding non-wood utilisation. The species produces a highly nutritious seed (pinhão in Portuguese), appreciated by humans and animals. There is a culturally established market around the seed, which, unfortunately, is not developed enough to allow sufficient economical return. Araucaria trees are also attractive due to its high quality and aesthetically pleasing wood, which led to its intense exploration in the 1960s and 1970s. Nowadays, the species is classified as critically endangered and is protected by law against illegal logging. However, such restrictive laws have resulted in further threat to the species. Knowing the legal difficulties to remove a grown araucaria tree from their property, landowners remove new natural saplings. Practices have been proposed to promote other sustainable uses for the species. Still, lawmakers in consort with researchers and specialists can only legislate upon detailed data regarding the species. In order to collect such paramount information throughout the species occurrence areas (approx. 200,000 km2 ), specialists face an extremely very fragmented environment, which poses operational and financial difficulties to acquire the data.
Aims: The aim of this thesis was to introduce a methodology to automatically detect and measure Araucaria angustifolia in complex native forest formations in southern Brazil. The proposed methodology leverages on light detection and ranging (LiDAR) as well as high resolution aerial imagery. Normally, species mapping and measurement is conducted by combining LiDAR and spectral information (e.g. aerial, satellite or drone imagery). In this study, an analysis was performed to determine if there is, in fact, the necessity to add spectral information to map araucaria trees. Lastly, an alternative method was proposed, where no LiDAR data is required. A novel methodology was developed to detect araucarias from unmanned aerial vehicles (UAV), as an alternative to LiDAR-based methodologies.
Research questions:
1. Is spectral information imperative to map A. angustifolia or LiDAR data alone is enough?
2. Can A. angustifolia trees be detected and measured in dense forest formation using Remote Sensing data?
3. If araucaria trees are detectable, can tree parameters (e.g. total height, diameter at breast height (DBH) and crown area) be acquired with reasonable accuracy?
4. Can A. angustifolia trees be detected based on their morphology, i.e. branches distribution?
Study site and data: The data analysed in this thesis come from a municipality called Lages, located in the state of Santa Catarina in the Brazilian southern region. The two study sites contain fragments of the Atlantic Forest, where the target species A. angustifolia can be found with 38 and 34 ha for study site A and B, respectively. Both study sites are covered with LiDAR data with an average point density of 14 points/m2 and aerial imagery with 0.1 m spatial resolution. The datasets were collected in the same flight performed in June 2019. In addition, field data from March 2016 was available from 10 plots, each with 0.2 ha (total sampled area of 2 ha), located in site A, where all araucaria trees within the plots were measured and georeferenced. Lastly, UAV imagery with ground sample distance of 5 cm was also available for study site A, also collected in March 2016. Methodological approach: The methodology implemented in this thesis consisted mainly of two parts: (1) araucaria tree mapping and forest parameter estimation using LiDAR and aerial imagery; (2) detection of araucarias based on branch recognition from UAV imagery.
1. In order to map araucaria trees employing LiDAR and aerial imagery, a Random Forest classification was conducted. An analysis was performed to determine the efficiency of the classification when using only LiDAR data and when adding spectral information to it. Moreover, the random forest classifier was trained in site A and tested in site B. With the result of the mapping, a clipping mask was generated and used to clip the LiDAR point cloud. The clipped point cloud was assessed in terms of individual tree detection (ITD) as a means to determine the number of stems per hectare, total tree height and crown area,
as well as estimate DBH.
2. A new methodology was developed as an alternative for LiDAR-based approaches. The approach consists of recognizing araucaria branches and use their orientation to determine A. angustifolia tree locations. The approach was implemented using a computer vision method called Probabilistic Hough Transform associated with other image processing techniques such as morphological filtering and image segmentation. By employing such techniques, the branches were detected as lines, which then could be used to calculate branch orientation, culminating on tree location.
Results and discussion: LiDAR data is commonly used for commercial conifer tree species mapping and have been used for inventory purposes operationally in many countries such as Finland, Sweden, Canada, the United States and others. However, fewer have explored the applications of LiDAR data in complex environments such as the Atlantic Forest. The reason is mainly due to the multi layer structure in native forests and the high occurrence of tree occlusion, which affects the stems count, an important forest parameter for inventory purposes. In this study, such reasons were also noticed, even considering araucarias’ crown
size and the fact that the adult individuals of the species usually are located in the upper layer of the forest canopy. Nonetheless, it was possible to determine that the majority of A. angustifolia trees were successfully mapped employing LiDAR data. Moreover, there was enough statistical evidence to state that no difference was found when mapping A. angustifolia employing only LiDAR data and combined spectral information and LiDAR data. When comparing both maps derived from the Random Forest classification, it was possible to observe similar performance from both datasets. Overall accuracies of 90.8% and 89.8% were observed for sites A and B, respectively. Even though adult trees of araucaria are usually visible in the upper forest canopy, a more basic operation such as a height threshold would not be able to separate araucarias from the rest of the species. This happens since there are many other species that occupy the same height level, which could result in commission errors. Hence, a supervised classification such as RF was efficient in removing the remainder of the tree species. When addressing the stems count, after running a ITD approach using local maxima detection an overall accuracy of 73.34% was reached. That resulted in a density of 43 stems/ha, which is below the 61 stems/ha calculated from the field data. This difference in mainly due to tree occlusion, which is often observed in multi layer structure of complex
natural environments such as araucaria forests. If a comparison is performed against the actual visible trees, the accuracy would be increased to 87.9%. There is still 12.1% error considering the upper visible forest canopy, which is caused by smaller trees close to dominant ones, resulting in further omissions. One of the challenges of working with LiDAR and araucarias is the species morphology. Araucarias possess a unique crown shape, commonly described in the literature as being similar to a wine glass or an inverted chandelier. However, a combination of uneven terrain and oddly shaped crowns results in a distorted normalized point cloud, which in turn, affects the total tree height generated from it. As a solution, the ITD was performed using the digital surface model (DSM) to detect the local maxima. Once the coordinate of the highest point of a tree was determined, these coordinates were used to retrieve the tree height from the CHM. As a result, the total height and crown diameter measurements reached errors of 1.44 m and 1.72 m, respectively. However, point cloud normalization was not the only probable source of variation. The field measurements were performed in March 2016, while the LiDAR data was acquired in June 2019. This represents a difference of 39 months between measurements. Evidently, adult trees are not expected to grow too much, specially when a slow-growth native species such as araucaria is concerned. Yet, this
discrepancy needs to be considered when assessing the results. Hence, it was not possible to determine if the height and crown measurements were affected by the field measurements procedure (field measurement errors), the time difference between LiDAR and field data acquisitions or the methodology proposed in this study. Lastly, considering that the DBH was estimated from the total height and crown diameter, these inconsistencies are carried over to the estimates, yielding a DBH error of 9.89 cm. Araucaria trees are easy to distinguish from other tree species when observed from nadir. Due to the unique format of the species crown, a novel approach based on the branch distribution from an orthogonal view was proposed to automatically detect araucaria trees.After implementing and testing this approach, an overall accuracy of 93% was achieved.
During the analysis, a difficulty index was introduced, in which trees easily distinguishable were assigned difficulty level 1 (easy), partially occluded ones received index 2 (medium)
and severely occluded but still partially visible ones were assigned index 3 (hard). The highest accuracy was achieved with difficulty index 1 with overall accuracy of 98%, followed by 92% and 89% for difficulty index 2 and 3, respectively. If the difficulty index is ignored and the tree detection is assessed as a whole, an overall accuracy of 93% was reached. The methodology demonstrated to be robust, considering it relies solely on branches to determine the tree location. Nonetheless, limitations were observed with this approach. Considering that branches are the main element of this methodology, if they are not visible,
trees are simply not detected. Adult araucaria trees present visible branches, which are commonly very thick (reaching up to 30 cm in diameter based on field observations). However, a high density of secondary and tertiary branches may interfere in the visibility of primary branches. Lastly, adjacent trees with branches similarly oriented also result in omissions, since they seem merged in the image, resulting in only one branch being detected.
Conclusions: In this thesis, evidences demonstrated that LiDAR data can be used for A. angustifolia mapping and forest parameter estimation. Moreover, when considering the test sites addressed in this study, the addition of spectral information didn’t significantly improve the mapping, leading to the conclusion that LiDAR data alone is enough for A. angustifolia mapping. Lastly, when working with native species, conventional methods might not be the best practice and approaching issues with different perspectives can yield new solutions. The new proposed method is a clear example of that. By using the species unique morphology as basis, the approach has showed promising results, which could be further improved in future research. This thesis constitutes the first study to provide an in-depth analysis on the use of LiDAR data to automatically map Araucaria angustifolia in natural dense forest formations. Moreover, considering the current situation of the species, this work contributes to a better understanding of the challenges when working with araucaria trees as well as working with complex forest structures. In addition, further work can be developed based on this study, which could provide even more accurate large-scale information to lawmakers, researchers and specialists when developing new strategies to sustainably manage the species.