Response of a Rocky Mountain forest system to a shifting disturbance regime, The

2019 Fall.Includes bibliographical references.Climate change is likely to drive widespread forest declines and transitions as temperatures shift beyond historic ranges of variability. Warming temperatures and shifting precipitation patterns may lead to increasing disturbances from wildfire, insect outbreaks, drought, and extreme weather events, which may greatly accelerate rates of ecosystem change. However, the role of disturbance in shaping forest response to climate change is not well understood. Better understanding the impacts of changing disturbance patterns on forest decline and recovery will allow us to better predict how forest ecosystems may adapt to a warming world. Severe wildfires and bark beetle outbreaks are currently affecting large areas of forest throughout western North America, and increasing disturbance size and severity will have uncertain impacts on forest persistence. The goal of my dissertation was to investigate the factors shaping disturbance response in a region of the San Juan Mountains, Colorado, which has undergone impacts from a high-severity spruce beetle outbreak and wildfire in the last 15 years. I conducted three separate studies in the burn area of the West Fork Complex wildfire, which burned in 2013, and in surrounding beetle-affected spruce-fir forests. The goals of each study were to 1) assess whether the severity of spruce beetle outbreaks occurring before wildfire resulted in compounded disturbance interactions affecting vegetation recovery, 2) determine how the severity of each disturbance type influenced fine-scale below-canopy temperature patterns across the landscape, and 3) assess how conifer seedling regeneration densities were influenced by effects of disturbance severity on seed dispersal, temperature, and vegetation structure. I found that disturbances influenced seedling regeneration and ecosystem resilience through several mechanisms. First, pre-fire beetle outbreak severity was negatively correlated with post-fire vegetation cover, indicating that the combined disturbances were inhibiting regeneration beyond what may have been expected with fire alone. Second, disturbances had significant effects on below-canopy temperatures, with burned areas ~0.5 °C warmer than unburned forest areas and differences in overnight minimum temperatures resulting from loss of live canopy in unburned, beetle-killed forests. Third, the large fire size and high severity resulted in very little spruce seed dispersal or conifer regeneration in most of the burned area, while spruce regeneration in unburned forest was negatively correlated with increasing overstory mortality from the spruce beetle. My results indicate that disturbance is playing an important role in determining the future trajectory of the forest in my study area. The West Fork Complex fire has caused a severe ecosystem transformation, has increased landscape exposure to warming temperatures, and is preventing forest re-establishment as a result of a lack of seed sources. The spruce beetle outbreak has not resulted in such a severe transformation, but is possibly leading to reduced forest resilience by reducing spruce seedling re-establishment and by altering fuel structures to make forests more prone to high soil burn severity if fire follows within ~10 years. Warming of below-canopy microclimates is not exacerbated by spruce beetle outbreak, and is rather partially offset by cooling of overnight temperatures. These findings provide insights into how forest responses to climate change may be shaped by disturbance processes, which are occurring with increasing severity and frequency worldwide

Assessing spatio-temporal patterns of forest decline across a diverse landscape in the Klamath Mountains using a 28-year Landsat time-series analysis

Rates of tree mortality in California and the Pacific Northwest have greatly increased in recent years, driven largely by pest and pathogen outbreaks as well as the effects of hotter, warmer droughts. While there have been a multitude of regional-scale assessments of mortality and forest decline, landscape-level studies are necessary to better identify forests that are most vulnerable to decline and to anticipate future changes. This need is particularly notable in the remote and little-studied mountains of northwest California, which are renowned for their diverse, heterogeneous vegetation types. A recent observation of elevated levels of Shasta red fir (Abies magnifica var. shastensis) mortality in a central part of this region – the Russian Wilderness – appears to mirror the timing of these larger forest mortality events and has highlighted the need to investigate if recent levels of mortality are historically unusual. The main objectives of my study were to (1) characterize contemporary tree mortality and determine potential drivers of that mortality using field-measured data, (2) integrate both field-measured data and annual LandTrendr data to assess temporal and spatial patterns of the extent and magnitude of forest decline, (3) assess the relationship between topographic and structural attributes with forest decline, and (4) determine whether climate is a potential driver of forest decline. To characterize contemporary tree mortality and determine potential drivers of that mortality, I established 142 field plots in the summer of 2015 measuring tree health and presence of any pests and pathogens on canopy tree species. Next, I used annualized LandTrendr algorithms across a 28-year time period (1986-2014) coupled with a regional forest type map to determine the timing, extent, and magnitude of canopy decline within each forest type. To assess potential drivers of canopy decline and identify specific vulnerabilities to drought, I used PRISM climate data and random forest classification using topographic and stand structure attributes. Plot data showed the highest proportions of mortality occurred in subalpine fir (Abies lasiocarpa, 35.3%) and Shasta red fir (28.6%), with evidence of fir engraver beetle (Scolytus ventralis) and Wien’s dwarf mistletoe (Arceuthobium abietinum subsp. wiensii) on many Shasta red fir individuals (34.7% and 20.4%, respectively). Forest decline was five times higher in the last two years of the time series (2013-2014) than in the previous twenty-six years. The greatest magnitude of decline was found in the red fir and subalpine conifer forest types, findings supported by my field-measured data. Canopy decline was greater at higher elevations, in denser canopies and in stands with larger trees. I did not detect any relationships between annual climate variables and forest decline, possibly due to a discrepancy between the course spatial scale of the climate data and fine-grained scale of forest disturbance, or because only two years exhibited pronounced canopy decline. My study demonstrates effectiveness in characterizing forest decline in a highly diverse landscape using a remote sensing approach and highlights the complexity of climate, pests and pathogens, stand structure, and topography as they relate to tree mortality and forest decline

Leveraging big satellite image and animal tracking data for characterizing large mammal habitats

Die zunehmende Verfügbarkeit von Satellitenfernerkundungs- und Wildtier-Telemetriedaten eröffnet neue Möglichkeiten für eine verbesserte Überwachung von Wildtierhabitaten durch Habitatmodelle, doch fehlt es häufig an geeigneten Ansätzen, um dieses Potenzial voll auszuschöpfen. Das übergeordnete Ziel dieser Arbeit bestand in der Konzipierung und Weiterentwicklung von Ansätzen zur Nutzung des Potenzials großer Satellitenbild- und Telemetriedatensätze in Habitatmodellen. Am Beispiel von drei großen Säugetierarten in Europa (Eurasischer Luchs, Rothirsch und Reh) wurden Ansätze entwickelt, um (1) Habitatmodelle mit dem umfangreichsten global und frei verfügbaren Satellitenbildarchiv der Landsat-Satelliten zu verknüpfen und (2) Wildtier-Telemetriedaten über Wildtierpopulationen hinweg in großflächigen Analysen der Habitateignung und -nutzung zu integrieren. Die Ergebnisse dieser Arbeit belegen das enorme Potenzial von Landsat-basierten Variablen als Prädiktoren in Habitatmodellen, die es ermöglichen von statischen Habitatbeschreibungen zu einem kontinuierlichen Monitoring von Habitatdynamiken über Raum und Zeit überzugehen. Die Ergebnisse meiner Forschung zeigen darüber hinaus, wie wichtig es ist, die Kontextabhängigkeit der Lebensraumnutzung von Wildtieren in Habitatmodellen zu berücksichtigen, insbesondere auch bei der Integration von Telemetriedatensätzen über Wildtierpopulationen hinweg. Die Ergebnisse dieser Dissertation liefern neue ökologische Erkenntnisse, welche zum Management und Schutz großer Säugetiere beitragen können. Darüber hinaus zeigt meine Forschung, dass eine bessere Integration von Satellitenbild- und Telemetriedaten eine neue Generation von Habitatmodellen möglich macht, welche genauere Analysen und ein besseres Verständnis von Lebensraumdynamiken erlaubt und so Bemühungen zum Schutz von Wildtieren unterstützen kann.The growing availability of satellite remote sensing and animal tracking data opens new opportunities for an improved monitoring of wildlife habitats based on habitat models, yet suitable approaches for making full use of this potential are commonly lacking. The overarching goal of this thesis was to develop and advance approaches for harnessing the potential of big satellite image and animal tracking data in habitat models. Specifically, using three large mammal species in Europe as an example (Eurasian lynx, red deer, and roe deer), I developed approaches for (1) linking habitat models to the largest global and freely available satellite image record, the Landsat image archive, and (2) for integrating animal tracking datasets across wildlife populations in large-area assessments of habitat suitability and use. The results of this thesis demonstrate the enormous potential of Landsat-based variables as predictors in habitat models, allowing to move from static habitat descriptions to a continuous monitoring of habitat dynamics across space and time. In addition, my research underscores the importance of considering context-dependence in species’ habitat use in habitat models, particularly also when integrating tracking datasets across wildlife populations. The findings of this thesis provide novel ecological insights that help to inform the management and conservation of large mammals and more broadly, demonstrate that a better integration of satellite image and animal tracking data will allow for a new generation of habitat models improving our ability to monitor and understand habitat dynamics, thus supporting efforts to restore and protect wildlife across the globe

Normalized Projected Red & SWIR (NPRS): A New Vegetation Index for Forest Health Estimation and Its Application on Spruce Bark Beetle Attack Detection

Due to the ongoing global warming, European spruce bark beetles has become a serious threat to the spruce forests in Europe and caused serious environmental and economic issues. This study proposes a new vegetation index, Normalized Projected Red & SWIR (NPRS), for detection of spruce bark beetle attacks. 29 healthy and 24 bark beetle attacked plots in southern Sweden were used for evaluating the classification accuracy using NPRS at early-, intermediate- and late-stage attacks. The obtained kappa coefficients were 0.73, 0.80 and 0.88, respectively. It was concluded that the NPRS is a feasible method for continuous bark beetle mapping over large areas

Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS)

The European spruce bark beetle (Ips typographus [L.]) is one of the most damaging pest insects of European spruce forests. A crucial measure in pest control is the removal of infested trees before the beetles leave the bark, which generally happens before the end of June. However, stressed tree crowns do not show any significant color changes in the visible spectrum at this early-stage of infestation, making early detection difficult. In order to detect the related forest stress at an early stage, we investigated the differences in radar and spectral signals of healthy and stressed trees. How the characteristics of stressed trees changed over time was analyzed for the whole vegetation season, which covered the period before attacks (April), early-stage infestation ('green-attacks', May to July), and middle to late-stage infestation (August to October). The results show that spectral differences already existed at the beginning of the vegetation season, before the attacks. The spectral separability between the healthy and infested samples did not change significantly during the 'green-attack' stage. The results indicate that the trees were stressed before the attacks and had spectral signatures that differed from healthy ones. These stress-induced spectral changes could be more efficient indicators of early infestations than the 'green-attack' symptoms.In this study we used Sentinel-1 and 2 images of a test site in southern Sweden from April to October in 2018 and 2019. The red and SWIR bands from Sentinel-2 showed the highest separability of healthy and stressed samples. The backscatter from Sentinel-1 and additional bands from Sentinel-2 contributed only slightly in the Random Forest classification models. We therefore propose the Normalized Distance Red & SWIR (NDRS) index as a new index based on our observations and the linear relationship between the red and SWIR bands. This index identified stressed forest with accuracies from 0.80 to 0.88 before the attacks, from 0.80 to 0.82 in the early-stage infestation, and from 0.81 to 0.91 in middle- and late-stage infestations. These accuracies are higher than those attained by established vegetation indices aimed at 'green-attack' detection, such as the Normalized Difference Water Index, Ratio Drought Index, and Disease Stress Water Index. By using the proposed method, we highlight the potential of using NDRS with Sentinel-2 images to estimate forest vulnerability to European spruce bark beetle attacks early in the vegetation season

Operationalization of Remote Sensing Solutions for Sustainable Forest Management

The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry

Applying high-resolution remote sensing to quantify baboon damage at a sub-compartment level in pine stands in the Mpumalanga escarpment region of South Africa

Managing risk in intensively managed monoculture plantation forests is an essential task to ensure sustainable yield and a continuous flow of forest products. However, since risks can be either biotic or abiotic, not all of them have a predictable pattern of spread, which can cause severe losses if management does not have the chance to implement mitigation action. Monitoring the change in forest health is vital as this provides the opportunity for preventative management and quantifies the amount of damage that management has to deal with. To provide this window of opportunity for appropriate action, constant monitoring is required. Until recently, forest health was measured through field surveys which provided adequate data. This procedure, however, is time consuming. Remote sensing has become very popular as a monitoring tool, due to its ability to provide assessment data in a fraction of the time. In this study, baboon damage in plantations along the Mpumalanga escarpment area of South Africa was monitored using remote sensing methods. While there are many methods of forest health monitoring using remote sensing, some approaches are less suitable as they either monitor damage caused at a plantation level, use lower spatial resolution (>10m) datasets or map damage using one available time period. The purpose of this study was first to establish the impact of baboon damage through time, using Sentinel-2 satellite imagery with all vegetation indices available, and the Extreme Gradient Boosting (XGboost) algorithm. The second part focused on analysing the damage at a tree level using PlanetScope imagery using a deep Learning approach. Overall, the study found that the use of Sentinel-2 data and PlanetScope data could accurately distinguish between the varying severity of baboon damage, achieving an accuracy of 95% and 82%. The processing time of the deep learning Artificial Neural Network (ANN) was greatly affected by the number of hidden layers and neurons used. Implementation of techniques used in this study has the potential to improve the accuracy of forest health monitoring in compartment forestry in South Africa

Applying high-resolution remote sensing to quantify baboon damage at a sub-compartment level in pine stands in the Mpumalanga escarpment region of South Africa

Managing risk in intensively managed monoculture plantation forests is an essential task to ensure sustainable yield and a continuous flow of forest products. However, since risks can be either biotic or abiotic, not all of them have a predictable pattern of spread, which can cause severe losses if management does not have the chance to implement mitigation action. Monitoring the change in forest health is vital as this provides the opportunity for preventative management and quantifies the amount of damage that management has to deal with. To provide this window of opportunity for appropriate action, constant monitoring is required. Until recently, forest health was measured through field surveys which provided adequate data. This procedure, however, is time consuming. Remote sensing has become very popular as a monitoring tool, due to its ability to provide assessment data in a fraction of the time. In this study, baboon damage in plantations along the Mpumalanga escarpment area of South Africa was monitored using remote sensing methods. While there are many methods of forest health monitoring using remote sensing, some approaches are less suitable as they either monitor damage caused at a plantation level, use lower spatial resolution (>10m) datasets or map damage using one available time period. The purpose of this study was first to establish the impact of baboon damage through time, using Sentinel-2 satellite imagery with all vegetation indices available, and the Extreme Gradient Boosting (XGboost) algorithm. The second part focused on analysing the damage at a tree level using PlanetScope imagery using a deep Learning approach. Overall, the study found that the use of Sentinel-2 data and PlanetScope data could accurately distinguish between the varying severity of baboon damage, achieving an accuracy of 95% and 82%. The processing time of the deep learning Artificial Neural Network (ANN) was greatly affected by the number of hidden layers and neurons used. Implementation of techniques used in this study has the potential to improve the accuracy of forest health monitoring in compartment forestry in South Africa

Land of 10,000 pixels: applications of remote sensing & geospatial data to improve forest management in northern Minnesota, USA

2018 Summer.Includes bibliographical references.The use of remote sensing and geospatial data has become commonplace in a wide variety of ecological applications. However, the utility of these applications is often limited by field sampling design or the constraints on spatial resolution inherent in remote sensing technology. Because land managers require map products that more accurately reflect habitat composition at local, operational levels there is a need to overcome these limitations and improve upon currently available data products. This study addresses this need through two unique applications demonstrating the ability of remote sensing to enhance operational forest management at local scales. In the first chapter, remote sensing products were evaluated to improve upon regional estimates of the spatial configuration, extent, and distribution of black ash from forest inventory and analysis (FIA) survey data. To do this, spectral and topographic indices, as well as ancillary geospatial data were combined with FIA survey information in a non-parametric modeling framework to predict the presence and absence of black ash dominated stands in northern Minnesota, USA. The final model produced low error rates (Overall: 14.5%, Presence: 14.3%, Absence: 14.6%; AUC: 0.92) and was strongly informed by an optimized set of predictors related to soil saturation and seasonal growth patterns. The model allowed the production of accurate, fine-scale presence/absence maps of black ash stand dominance that can ultimately be used in support of invasive species risk management. In the second chapter, metrics from low-density LiDAR were evaluated for improving upon estimates of forest canopy attributes traditionally accessed through the LANDFIRE program. To do this, LiDAR metrics were combined with a Landsat time-series derived canopy cover layer in random forest k-nearest neighbor imputation approach to estimate canopy bulk density, two measures of canopy base height, and stand age across the Boundary Waters Canoe Area in northern Minnesota, USA. These models produced strong relationships between the estimates of canopy fuel attributes and field-based data for stand age (R2 = 0.82, RMSE = 10.12 years), crown fuel base height (R2 = 0.79, RMSE = 1.10 m.), live crown base height (R2 = 0.71, RMSE 1.60 m.), and canopy bulk density (R2 = 0.58, RMSE 0.09 kg/m3). An additional standard randomForest model of canopy height was less successful (R2 = 0.33, RMSE 2.08 m). The map products generated from these models improve upon the accuracy of national available canopy fuel products and provide local forest managers with cost-efficient and operationally ready data required to simulate fire behavior and support management efforts

Bridging structure and function in semi-arid ecosystems by integrating remote sensing and ground based measurements

The Southwestern US is projected to continue the current significant warming trend, with increased variability in the timing and magnitude of rainfall events. The effects of these changes in climate are already evident in the form of multi-year droughts which have resulted in the widespread mortality of woody vegetation across the region. Therefore, the need to monitor and model forest mortality and carbon dynamics at the landscape and regional scale is an essential component of regional and global climate mitigation strategies, and critical if we are to understand how the imminent state transitions taking place in forests globally will affect climate forcing and feedbacks. Remote sensing offers the only solution to multitemporal regional observation, yet many challenges exist with employing modern remote sensing solutions in highly stressed vegetation characteristic of semi-arid biomes, making one of the most expansive biomes on the globe also one of the most difficult to accu- rately monitor and model. The goal of this research was to investigate how changes in the structure of semi-arid woodlands following forest mortality impacts ecosystem function, and to determine how this question can be addressed using remotely sensed data sets. I focused primarily on Pinus edulis and Juniperous monosperma (piñon-juniper) woodlands, and took advantage of an existing manipulation experiment where mortality was imposed on all of the large piñon (¡ 7 cm dbh) in a 4 ha PJ woodland in 2009 and the ecosystem functional responses have been quantified using eddy covariance. A nearby intact PJ woodland, also instrumented with eddy covariance, was used as a control for this experiment. I tested the ability of high resolution remote sensing data to mechanistically describe the patterns in overstory mortality and understory green-up in this manipulated woodland by comparing it to the intact woodland, and observed the heterogeneous response of the understory as a function of cover type. I also investigated the relationship between changes in soil water content and the greenness of the canopy, noting that in the disturbed woodland, I observed a decoupling between how the canopy was measured remotely (e.g., via vegetation indices, VI) and photosynthesis. This is significant in that it potentially represents a significant source of error in using existing light use efficiency models of carbon uptake in these disturbed woodlands. This research also suggested that leveraging remote sensing data which measures in the red-edge portion of reflected light can provide increased sensitivity to the low leaf area, ephemeral pulses of greenup that were identified in the disturbed woodland, post-canopy mortality. Given these findings, I developed a hierarchy of simple linear models to test how well vegetation indices acquired through different spatial resolution sensors (Land- sat and RapidEye) were able to predict carbon uptake in both intact and disturbed piñon-juniper woodlands. The vegetation indices used were a moisture sensitive VI, and a red-edge leveraging VI from these sensors, and I compared estimates of carbon uptake derived from these models to the Gross Primary Productivity estimated from tower-based eddy covariance at both the manipulated and intact piñon-juniper sites. I determined that the red-edge VI and the moisture sensitive VI both constrained uncertainty associated with carbon uptake, but that the variability in satellite view angle from scene to scene can impose a significant amount of noise in sparse canopy ecosystems. Finally, given the extent and prevalence of J. monosperma across the region, and its complex growth morphology, I tested the ability of aerial lidar to quantify the biomass of juniper. In this simplified case study, I developed a method- ology to relate the volume of canopy measured via lidar to the equivalent stem area at the root crown. By working in a single species ecosystem, I circumvented many challenges associated with driving allometries remotely, but also present a work-flow that I intend to adapt to more complex systems, namely piñon-juniper woodlands. Together, this work describes and addresses existing challenges with respect to us- ing remote sensing to understand both the structure and function of piñon-juniper woodlands, and how it changes in response to widespread piñon mortality. It provides several new techniques to mitigate the difficulties associated with monitoring mortality / recovery dynamics, predicting canopy function, and determining ecosystem state parameters in these complex, sensitive biomes