Remote detection of invasive alien species

The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail

Monitoring the understory in eucalyptus plantations using airborne laser scanning

In eucalyptus plantations, the presence of understory increases the risk of fires, acts as an obstacle to forest operations, and leads to yield losses due to competition. The objective of this study was to develop an approach to discriminate the presence or absence of understory in eucalyptus plantations based on airborne laser scanning surveys. The bimodal canopy height profile was modeled by two Weibull density functions: one to model the canopy, and other to model the understory. The parameters used as predictor in the logistic model successfully discriminated the presence or absence of understory. The logistic model composed by gcanopy, gunderstory, and gunderstory showed higher values of accuracy (0.96) and kappa (0.92), which means an adequate classification of presence of understory and absence of understory. Weibull parameters could be used as input in the logistic regression to effectively identify the presence and absence of understory in eucalyptus plantation

Aliens, Aircraft, and Accuracies: Surveying for Understory Invasive Plants Using Unmanned Aerial Systems

Invasive (alien) plants are introduced species that can cause harm to native ecosystems, industries, or human health. Managing invasive species requires knowing where they are, and early detection of new populations increases the likelihood of local eradication. Unmanned aerial systems (UAS) are an emerging remote sensing technology that can capture very high spatial resolution imagery, are easily deployed, and may offer a more efficient alternative to extensive ground surveys to locate invasive plants. Imagery collected with UAS has been used to map invasive plants in open canopy habitats, but has yet to be tested for mapping invasive plants in forest understories. My aim was to explore the feasibility of UAS as an understory invasion monitoring tool, including tests of season, sensor type, and image classification method for reliable invasive detection. I collected imagery from a 21-hectare mixed and deciduous New Hampshire forest during spring and fall periods of phenology mismatch between native vegetation and two focal invasive plants, Berberis thunbergii (Japanese barberry) and Rosa multiflora (multiflora rose). I achieved up to 82% classification accuracy by grouping B. thunbergii and R. multiflora as an Invasive class. There were no significant differences in invasive detectability between sensors or classification methods, but spring imagery yielded the highest accuracies overall. Simpler pixel-based classifications are sufficient for achieving over 70% classification accuracy, though object-based segmentation can improve accuracy. UAS are promising technology with potential to reduce and target invasive plant ground surveys for temperate forest management

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

A Sense of Scale: Mapping Exotic Annual Grasses with Satellite Imagery Across a Landscape and Quantifying Their Biomass at a Plot Level with Structure-from-Motion in a Semi-Arid Ecosystem

The native vegetation communities in the sagebrush steppe, a semi-arid ecosystem type, are under threat from exotic annual grasses. Exotic annual grasses increase fire severity and frequency, decrease biodiversity, and reduce soil carbon storage amongst other ecosystem services. The invasion of exotic annual grasses is causing detrimental impacts to land use by eliminating forage for livestock and creating a huge economic cost from fire control and post-fire restoration. To combat invasion, land managers need to know what exotic annual grasses are present, where they are invading, and estimates of their biomass. Mapping exotic annual grasses is challenging because many areas in the sagebrush steppe are difficult to access; yet field measurements are the main method to identify and quantify their existence. In this study, we address this challenge by exploring the use of both landscape-scale and plot-scale observations with remote sensing. First, we use satellite imagery to map where exotic annual grasses are invading and identify the native species which are being encroached upon. Second, we investigate the use of fine-scale imagery for non-destructive measurements of biomass of exotic annual grasses. Understanding the location of exotic annual grasses is important for restoration efforts, e.g. large swath (~100m) herbicide spraying. Restoration efforts are expensive and often ineffective in areas already dominated by exotic annual grasses. Early detection of exotic annual grasses in sagebrush and native grasses communities will increase the chances of effective ecosystem restoration. We used Sentinel-2 satellite imagery in Google Earth Engine, a cloud computing platform, to train a random forest (RF) machine learning algorithm to map vegetation in ~150,000 acres in the sagebrush steppe in southeast Idaho. The result is a classification map of vegetation (overall accuracy of 72%) and a map of percent cover of annual grass (R2 = 0.58). The combination of these two maps will allow land managers to target areas of restoration and make informed decisions about where to allow grazing. In addition to knowing what exotic annual grasses exist and their percent cover, detailed information about their biomass is important for understanding fuel loads and forage quality. Structure from Motion (SfM) is a photogrammetry technique that uses digital images to develop 3-dimensional point clouds that can be transformed into volumetric measurements of biomass. The SfM technique has the potential to quantify biomass estimates across multiple plots while minimizing field work. We developed allometric equations relating SfM-derived volume (m3) to biomass (g/m2) for a study area in southeast Oregon. The resulting equation showed a positive relationship (R2 = 0.51) between the log transformed SfM-derived volume and log transformed biomass when litter was removed. This relationship shows promise in being upscaled to larger surveys using aerial platforms. This method can reduce the need for destructively harvesting biomass, and thus allow field work to cover a greater spatial extent. Ultimately, increasing spatial coverage for biomass will improve accuracy in quantifying fuel loads and carbon storage, providing insights to how these exotic plants are altering ecosystem services

FROM DRONES TO SOIL CORES: COMPREHENSIVE ECOLOGICAL ASSESSMENTS FOR ENHANCING CONSERVATION MANAGEMENT OF URBAN FORESTED NATURAL AREAS

Urban natural areas are vegetated areas within cities that exhibit characteristics of non-urban natural areas in that they have relatively high levels of self-regulation (low or no level of management) of natural ecosystem processes and exhibit high taxonomic, genetic, and structural diversity. When these areas take the form of urban parkland, they are often managed for their social benefits to urban residents, while their ecological potential remains underutilized. Growing interest in enhancing biodiversity conservation in cities has highlighted the importance of improving the ecological planning and management of urban natural areas, particularly forested natural areas. For the variety of agencies and non-profit organizations governing and managing urban natural areas, achieving conservation goals relies on comprehensive ecological data, but this information is often lacking in spatial resolution or altogether absent in most city parks and recreation departments across the U.S. Acquiring necessary data depends on extensive and time-consuming ground surveys, where city budgets and time constraints can present considerable obstacles. With over 2,000 acres of urban natural areas, government agencies and nonprofits in Knoxville, Tennessee are facing these same obstacles. The objectives of this research study were to 1) use drone remote sensing and traditional ecological field methods to quantify and characterize key indicators of urban forest health (vegetation, soils, and ecological impacts) of a 42-acre parcel of urban forested area in Knoxville, Tennessee, 2) investigate statistical relationships between forest health indicators and 4 vegetation indices derived from drone imagery to assess (“ground-truth”) a novel drone application in urban forest conservation management, and 3) investigate statistical relationships between forest health indicators and soil physical, textural, and chemical attributes. Key findings of the comprehensive ecological assessment reveal the dominance of 129 native plant species, invasion by 11 non-native plant species, acidic high-carbon soils sufficient in most plant available macro- and micro- nutrients, and significant relationships between both drone vegetation indices and soil attributes and key indicators of urban forest health. Findings from this study establish necessary baseline ecological and soils data and demonstrate a novel application of drone remote sensing in the conservation management of an urban forested natural area

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

The Effect of Site Characteristics on the Reproductive Output of Lesser Celandine (Ranunculus ficaria)

Ranunculus ficaria L., an ephemeral perennial invasive plant brought over from Europe, is becoming widespread throughout the Northeastern United States. This herbaceous buttercup is able to create extensive dense mats that limit native species growth. Taking advantage of an early growing season and rapid reproduction rates, this species can create dense monocultures, which threatens native communities and ecosystems. Elimination of native spring ephemerals results in decreased biodiversity. A better understanding of how R. ficaria responds to site characteristics is needed to prioritize management efforts toward high-risk sites.Ranunculus ficaria abundance and reproductive output (seed, bulbil and tuber production rates) were examined in plots spanning a disturbance gradient away from a river. Site characteristics (PAR, aspect, soil pH, soil moisture, texture and nutrient content) were investigated to examine their role in plant performance. I hypothesized that soil characteristics (pH and nutrient availability) drive R. ficaria plant performance; specifically I expected higher biomass and reproductive output to be associated with higher soil pH. I also expected reproductive output and R. ficaria biomass would be highest in moist floodplain at intermediate distances from rivers.Many soil nutrients and characteristics were significantly related to biomass and reproductive output; specifically phosphorus, calcium and LTI (Lime Test Index) all showed significantly positive relationships with plant biomass and bulbil counts, while soil pH was significantly positively related to biomass. Bulbil and tuber counts were significantly higher in soils of high percent silt. These findings suggest that soil characteristics (pH, texture) and nutrients (P, Ca) are strongly linked to plant performance, supporting my hypothesis. Reproductive output and R. ficaria biomass were not significantly greater at intermediate distances from rivers, in contrast to my hypothesis. A plant performance model was generated using object-based image analysis with the aim of creating an accurate classification of sites in terms of suitability for R. ficaria performance. A large scale field survey was used to assess model predictions, which were found be 68 % accurate. Overall, this study was able to expand on the current limited understanding of R. ficaria, which can prove helpful in aiding management to reduce population size and spread

Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

Riparian Vegetation Density Mapping of an Extremely Densely Vegetated Confined Floodplain

The most crucial function of lowland-confined floodplains with low slopes is to support flood conveyance and fasten floods; however, obstacles can hinder it. The management of riparian vegetation is often neglected, though woody species increase the vegetation roughness of floodplains and increase flood levels. The aims are (1) to determine the branch density of various riparian vegetation types in the flood conveyance zone up to the level of artificial levees (up to 5 m), and (2) to assess the spatial distribution of densely vegetated patches. Applying a decision tree and machine learning, six vegetation types were identified with an accuracy of 83%. The vegetation density was determined within each type by applying the normalized relative point density (NRD) method. Besides, vegetation density was calculated in each submerged vegetation zone (1–2 m, 2–3 m, etc.). Thus, the obstacles for floods with various frequencies were mapped. In the study area, young poplar plantations offer the most favorable flood conveyance conditions, whereas invasive Amorpha thickets and the dense stands of native willow forests provide the worst conditions for flood conveyance. Dense and very dense vegetation patches are common in all submerged vegetation zones; thus, vegetation could heavily influence floods