Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired fromUnmannedAerialVehicles(UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model E ciency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant di erence was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantationsinfo:eu-repo/semantics/publishedVersio

Vegetation Dynamics in Ecuador

Global forest cover has suffered a dramatic reduction during recent decades, especially in tropical regions, which is mainly due to human activities caused by enhanced population pressures. Nevertheless, forest ecosystems, especially tropical forests, play an important role in the carbon cycle functioning as carbon stocks and sinks, which is why conservation strategies are of utmost importance respective to ongoing global warming. In South America the highest deforestation rates are observed in Ecuador, but an operational surveillance system for continuous forest monitoring, along with the determination of deforestation rates and the estimation of actual carbon socks is still missing. Therefore, the present investigation provides a functional tool based on remote sensing data to monitor forest stands at local, regional and national scales. To evaluate forest cover and deforestation rates at country level satellite data was used, whereas LiDAR data was utilized to accurately estimate the Above Ground Biomass (AGB; carbon stocks) at catchment level. Furthermore, to provide a cost-effective tool for continuous forest monitoring of the most vulnerable parts, an Unmanned Aerial Vehicle (UAV) was deployed and equipped with various sensors (RBG and multispectral camera). The results showed that in Ecuador total forest cover was reduced by about 24% during the last three decades. Moreover, deforestation rates have increased with the beginning of the new century, especially in the Andean Highland and the Amazon Basin, due to enhanced population pressures and the government supported oil and mining industries, besides illegal timber extractions. The AGB stock estimations at catchment level indicated that most of the carbon is stored in natural ecosystems (forest and páramo; AGB ~98%), whereas areas affected by anthropogenic land use changes (mostly pastureland) lost nearly all their storage capacities (AGB ~2%). Furthermore, the LiDAR data permitted the detection of the forest structure, and therefore the identification of the most vulnerable parts. To monitor these areas, it could be shown that UAVs are useful, particularly when equipped with an RGB camera (AGB correlation: R² > 0.9), because multispectral images suffer saturation of the spectral bands over dense natural forest stands, which results in high overestimations. In summary, the developed operational surveillance systems respective to forest cover at different spatial scales can be implemented in Ecuador to promote conservation/ restoration strategies and to reduce the high deforestation rates. This may also mitigate future greenhouse gas emissions and guarantee functional ecosystem services for local and regional populations

White paper – On the use of LiDAR data at AmeriFlux sites

Our aim is to inform the AmeriFlux community on existing and upcoming LiDAR technologies (atmospheric Doppler or Raman LiDAR often deployed at flux sites are not considered here), how it is currently used at flux sites, and how we believe it could, in the future, further contribute to the AmeriFlux vision. Heterogeneity in vegetation and ground properties at various spatial scales is omnipresent at flux sites, and 3D mapping of canopy, understory, and ground surface can help move the science forward

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

The Burning Bush: Linking LiDAR-derived Shrub Architecture to Flammability

Light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) sensors are powerful tools for characterizing vegetation structure and for constructing three-dimensional (3D) models of trees, also known as quantitative structural models (QSM). 3D models and structural traits derived from them provide valuable information for biodiversity conservation, forest management, and fire behavior modeling. However, vegetation studies and 3D modeling methodologies often only focus on the forest canopy, with little attention given to understory vegetation. In particular, 3D structural information of shrubs is limited or not included in fire behavior models. Yet, understory vegetation is an important component of forested ecosystems, and has an essential role in determining fire behavior. In this dissertation, I explored the use of TLS data and quantitative structure models to model shrub architecture in three related studies. In the first study, I present a semi-automated methodology for reconstructing architecturally different shrubs from TLS LiDAR. By investigating shrubs with different architectures and point cloud densities, I showed that occlusion, shrub complexity, and shape greatly affect the accuracy of shrub models. In my second study, I assessed the 3D architectural drivers of understory flammability by evaluating the use of architectural metrics derived from the TLS point cloud and 3D reconstructions of the shrubs. I focused on eight species common in the understory of the fire-prone longleaf pine forest ecosystem of the state of Florida, USA. I found a general tendency for each species to be associated with a unique combination of flammability and architectural traits. Novel shrub architectural traits were found to be complementary to the direct use of TLS data and improved flammability predictions. The inherent complexity of shrub architecture and uncertainty in the TLS point cloud make scaling up from an individual shrub to a plot level a challenging task. Therefore, in my third study, I explored the effects of lidar uncertainty on vegetation parameter prediction accuracy. I developed a practical workflow to create synthetic forest stands with varying densities, which were subsequently scanned with simulated terrestrial lidar. This provided data sets quantitatively similar to those created by real-world LiDAR measurements, but with the advantage of exact knowledge of the forest plot parameters, The results showed that the lidar scan location had a large effect on prediction accuracy. Furthermore, occlusion is strongly related to the sampling density and plot complexity. The results of this study illustrate the potential of non-destructive lidar approaches for quantifying shrub architectural traits. TLS, empirical quantitative structural models, and synthetic models provide valuable insights into shrub structure and fire behavior

Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure

Abrupt forest disturbances generating gaps \u3e0.001 km2 impact roughly 0.4–0.7 million km2a−1. Fire, windstorms, logging, and shifting cultivation are dominant disturbances; minor contributors are land conversion, flooding, landslides, and avalanches. All can have substantial impacts on canopy biomass and structure. Quantifying disturbance location, extent, severity, and the fate of disturbed biomass will improve carbon budget estimates and lead to better initialization, parameterization, and/or testing of forest carbon cycle models. Spaceborne remote sensing maps large-scale forest disturbance occurrence, location, and extent, particularly with moderate- and fine-scale resolution passive optical/near-infrared (NIR) instruments. High-resolution remote sensing (e.g., ∼1 m passive optical/NIR, or small footprint lidar) can map crown geometry and gaps, but has rarely been systematically applied to study small-scale disturbance and natural mortality gap dynamics over large regions. Reducing uncertainty in disturbance and recovery impacts on global forest carbon balance requires quantification of (1) predisturbance forest biomass; (2) disturbance impact on standing biomass and its fate; and (3) rate of biomass accumulation during recovery. Active remote sensing data (e.g., lidar, radar) are more directly indicative of canopy biomass and many structural properties than passive instrument data; a new generation of instruments designed to generate global coverage/sampling of canopy biomass and structure can improve our ability to quantify the carbon balance of Earth\u27s forests. Generating a high-quality quantitative assessment of disturbance impacts on canopy biomass and structure with spaceborne remote sensing requires comprehensive, well designed, and well coordinated field programs collecting high-quality ground-based data and linkages to dynamical models that can use this information

Testing the Applicability of the Official Croatian DTM for Normalization of UAV-based DSMs and Plot-level Tree Height Estimations in Lowland Forests

The Airborne Laser Scanning (ALS) technology has been implemented in operational forest inventories in a number of countries. At the same time, as a cost-effective alternative to ALS, Digital Aerial Photogrammetry (PHM), based on aerial images, has been widely used for the past 10 years. Recently, PHM based on Unmanned Aerial Vehicle (UAV) has attracted great attention as well. Compared to ALS, PHM is unable to penetrate the forest canopy and, ultimately, to derive an accurate Digital Terrain Model (DTM), which is necessary to normalize point clouds or Digital Surface Models (DSMs). Many countries worldwide, including Croatia, still rely on PHM, as they do not have complete DTM coverage by ALS (DTMALS). The aim of this study is to investigate if the official Croatian DTM generated from PHM (DTMPHM) can be used for data normalization of UAV-based Digital Surface Model (DSMUAV) and estimating plot-level mean tree height (HL) in lowland pedunculate oak forests. For that purpose, HL estimated from DSMUAV normalized with DTMPHM and with DTMALS were generated and compared as well as validated against field measurements. Additionally, elevation errors in DTMPHM were detected and eliminated, and the improvement by using corrected DTMPHM (DTMPHMc) was evaluated. Small, almost negligible variations in the results of the leave-oneout cross-validation were observed between HL estimated using proposed methods. Compared to field data, the relative root mean square error (RMSE%) values of HL estimated from DSMUAV normalized with DTMALS, DTMPHM, and DTMPHMc were 5.10%, 5.14%, and 5.16%, respectively. The results revealed that in the absence of DTMALS, the existing official Croatian DTM could be readily used in remote sensing based forest inventory of lowland forest areas. It can be noted that DTMPHMc did not improve the accuracy of HL estimates because the gross errors mainly occurred outside of the study plots. However, since the existence of the gross errors in Croatian DTMPHM has been confirmed by several studies, it is recommended to detect and eliminate them prior to using the DTMPHM in forest inventory

Doctor of Philosophy

dissertationWith increasing wildfire activity throughout the western United States comes an increased need for wildland firefighters to protect civilians, structures, and public resources. In order to mitigate threats to their safety, firefighters employ the use of safety zones (SZ: areas where firefighters are free from harm) and escape routes (ER: pathways for accessing SZ). Currently, SZ and ER are designated by firefighters based on ground-level information, the interpretation of which can be error-prone. This research aims to provide robust methods to assist in the ER and SZ evaluation processes, using remote sensing and geospatial modeling. In particular, I investigate the degree to which lidar can be used to characterize the landscape conditions that directly affect SZ and ER quality. I present a new metric and lidar-based algorithm for evaluating SZ based on zone geometry, surrounding vegetation height, and number of firefighters present. The resulting map contains a depiction of potential SZ throughout Tahoe National Forest, each of which has a value that indicates its wind- and slope-dependent suitability. I then inquire into the effects of three landscape conditions on travel rates for the purpose of developing a geospatial ER optimization model. I compare experimentally-derived travel rates to lidar-derived estimates of slope, vegetation density, and ground surface roughness, finding that vegetation density had the strongest negative effect. Relative travel impedances are then mapped throughout Levan Wildlife Management Area and combined with a route-finding algorithm, enabling the identification of maximally-efficient escape routes between any two known locations. Lastly, I explore a number of variables that can affect the accurate characterization of understory vegetation density, finding lidar pulse density, overstory vegetation density, and canopy height all had significant effects. In addition, I compare two widely-used metrics for understory density estimation, overall relative point density and normalized relative point density, finding that the latter possessed far superior predictive power. This research provides novel insight into the potential use of lidar in wildland firefighter safety planning. There are a number of constraints to widespread implementation, some of which are temporary, such as the current lack of nationwide lidar data, and some of which require continued study, such as refining our ability to characterize understory vegetation conditions. However, this research is an important step forward in a direction that has potential to greatly improve the safety of those who put themselves at risk to ensure the safety of life and property

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