UAV Photogrammetric Surveys for Tree Height Estimation

In the context of precision agriculture (PA), geomatic surveys exploiting UAV (unmanned aerial vehicle) platforms allow the dimensional characterization of trees. This paper focuses on the use of low-cost UAV photogrammetry to estimate tree height, as part of a project for the phytoremediation of contaminated soils. Two study areas with different characteristics in terms of mean tree height (5 m; 0.7 m) are chosen to test the procedure even in a challenging context. Three campaigns are performed in an olive grove (Area 1) at different flying altitudes (30 m, 40 m, and 50 m), and one UAV flight is available for Area 2 (42 m of altitude), where three species are present: oleander, lentisk, and poplar. The workflow involves the elaboration of the UAV point clouds through the SfM (structure from motion) approach, digital surface models (DSMs), vegetation filtering, and a GIS-based analysis to obtain canopy height models (CHMs) for height extraction based on a local maxima approach. UAV-derived heights are compared with in-field measurements, and promising results are obtained for Area 1, confirming the applicability of the procedure for tree height extraction, while the application in Area 2 (shorter tree seedlings) is more problematic

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Assessing the Ability of Image Based Point Clouds Captured from a UAV to Measure the Terrain in the Presence of Canopy Cover

Point clouds captured from Unmanned Aerial Systems are increasingly relied upon to provide information describing the structure of forests. The quality of the information derived from these point clouds is dependent on a range of variables, including the type and structure of the forest, weather conditions and flying parameters. A key requirement to achieve accurate estimates of height based metrics describing forest structure is a source of ground information. This study explores the availability and reliability of ground surface points available within point clouds captured in six forests of different structure (canopy cover and height), using three image capture and processing strategies, consisting of nadir, oblique and composite nadir/oblique image networks. The ground information was extracted through manual segmentation of the point clouds as well as through the use of two commonly used ground filters, LAStools lasground and the Cloth Simulation Filter. The outcomes of these strategies were assessed against ground control captured with a Total Station. Results indicate that a small increase in the number of ground points captured (between 0 and 5% of a 10 m radius plot) can be achieved through the use of a composite image network. In the case of manually identified ground points, this reduced the root mean square error (RMSE) error of the terrain model by between 1 and 11 cm, with greater reductions seen in plots with high canopy cover. The ground filters trialled were not able to exploit the extra information in the point clouds and inconsistent results in terrain RMSE were obtained across the various plots and imaging network configurations. The use of a composite network also provided greater penetration into the canopy, which is likely to improve the representation of mid-canopy elements

UNMANNED AERIAL SYSTEMS (UAS) AS A TOOL FOR INVESTIGATING EDGE INFLUENCES IN NEW HAMPSHIRE FORESTS

The continued decline in forest cover across New England becomes more concerning when faced with the fact that these same forests may be playing an important role in the fight against climate change. New Hampshire, in particular, is experiencing a 0.27% annual net loss in forest cover as of 2018. Increased population growth and accompanied development has resulted in the removal of forest cover and the fragmentation of once continuous forest blocks. Fragmentation can lead to further degradation of the remaining forest stands via alterations of the biotic and abiotic process at their edges. The use of unmanned aerial systems (UAS) is becoming an important tool to ensure the sustainable management of current forests stands and may help to better understand the effects of fragmentation at forest edges. Because of the relatively recent arrival of this technology, effective and appropriate testing for accurate and efficient data collection is necessary. Furthermore, UAS have not been employed yet to detect edge effects.This research investigated the impacts of UAS flight parameters on the accuracy of canopy height estimates made from UAS data by comparing UAS estimates across twelve combinations of flying height and image overlap to ground measured canopy height. A multi-temporal approach to species level mapping with UAS imagery was tested by collecting multiple dates of UAS imagery from early spring to late summer and assessing whether the inclusion of one or more dates improved classification accuracy. Additional comparisons between RGB and multi-spectral cameras were carried out. Finally, UAS imagery was used to measure and assess the changes in canopy cover with increased distance from the edge. This trend was compared to trends in canopy cover measured on the ground. The results show that flying height had no impact of the accuracy of the height estimates made from UAS data and increasing forward image overlap resulted in a significant but minor increase in accuracy. Classification accuracy was improved with the use of multi-temporal data collection but no more than three dates of optimally timed imagery was necessary. Additionally, the RGB imagery produced maps with consistently higher accuracy than the multi-spectral sensor employed in this study. Finally, we were able to detect and measure a significant trend in canopy cover that mimicked the trends found on the ground. The results of the first two parts of this dissertation will go on to provide guidance to forestry practitioners on how to collect UAS that balances accuracy and efficiency, thus reducing project costs. The final result serves as an initial demonstration of utilizing UAS for understanding edge effects and opens the door to better understanding the impacts of fragmentation over larger areas

UNMANNED AERIAL SYSTEMS (UAS) AS A TOOL FOR INVESTIGATING EDGE INFLUENCES IN NEW HAMPSHIRE FORESTS

The continued decline in forest cover across New England becomes more concerning when faced with the fact that these same forests may be playing an important role in the fight against climate change. New Hampshire, in particular, is experiencing a 0.27% annual net loss in forest cover as of 2018. Increased population growth and accompanied development has resulted in the removal of forest cover and the fragmentation of once continuous forest blocks. Fragmentation can lead to further degradation of the remaining forest stands via alterations of the biotic and abiotic process at their edges. The use of unmanned aerial systems (UAS) is becoming an important tool to ensure the sustainable management of current forests stands and may help to better understand the effects of fragmentation at forest edges. Because of the relatively recent arrival of this technology, effective and appropriate testing for accurate and efficient data collection is necessary. Furthermore, UAS have not been employed yet to detect edge effects.This research investigated the impacts of UAS flight parameters on the accuracy of canopy height estimates made from UAS data by comparing UAS estimates across twelve combinations of flying height and image overlap to ground measured canopy height. A multi-temporal approach to species level mapping with UAS imagery was tested by collecting multiple dates of UAS imagery from early spring to late summer and assessing whether the inclusion of one or more dates improved classification accuracy. Additional comparisons between RGB and multi-spectral cameras were carried out. Finally, UAS imagery was used to measure and assess the changes in canopy cover with increased distance from the edge. This trend was compared to trends in canopy cover measured on the ground. The results show that flying height had no impact of the accuracy of the height estimates made from UAS data and increasing forward image overlap resulted in a significant but minor increase in accuracy. Classification accuracy was improved with the use of multi-temporal data collection but no more than three dates of optimally timed imagery was necessary. Additionally, the RGB imagery produced maps with consistently higher accuracy than the multi-spectral sensor employed in this study. Finally, we were able to detect and measure a significant trend in canopy cover that mimicked the trends found on the ground. The results of the first two parts of this dissertation will go on to provide guidance to forestry practitioners on how to collect UAS that balances accuracy and efficiency, thus reducing project costs. The final result serves as an initial demonstration of utilizing UAS for understanding edge effects and opens the door to better understanding the impacts of fragmentation over larger areas

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Forestry and Arboriculture Applications Using High-Resolution Imagery from Unmanned Aerial Vehicles (UAV)

Forests cover over one-third of the planet and provide unmeasurable benefits to the ecosystem. Forest managers have collected and processed countless amounts of data for use in studying, planning, and management of these forests. Data collection has evolved from completely manual operations to the incorporation of technology that has increased the efficiency of data collection and decreased overall costs. Many technological advances have been made that can be incorporated into natural resources disciplines. Laser measuring devices, handheld data collectors and more recently, unmanned aerial vehicles, are just a few items that are playing a major role in the way data is managed and collected. Field hardware has also been aided with new and improved mobile and computer software. Over the course of this study, field technology along with computer advancements have been utilized to aid in forestry and arboricultural applications. Three-dimensional point cloud data that represent tree shape and height were extracted and examined for accuracy. Traditional fieldwork collection (tree height, tree diameter and canopy metrics) was derived from remotely sensed data by using new modeling techniques which will result in time and cost savings. Using high resolution aerial photography, individual tree species are classified to support tree inventory development. Point clouds were used to create digital elevation models (DEM) which can further be used in hydrology analysis, slope, aspect, and hillshades. Digital terrain models (DTM) are in geographic information system (GIS), and along with DEMs, used to create canopy height models (CHM). The results of this study can enhance how the data are utilized and prompt further research and new initiatives that will improve and garner new insight for the use of remotely sensed data in forest management