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ISBN: 9781267767363
Category :
Languages : en
Pages : 198
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Themontane mixed-conifer forests of San Diego County are currently experiencing extensive tree mortality, which is defined as dieback where whole stands are affected. This mortality is likely the result of the complex interaction of many variables, such as altered fire regimes, climatic conditions such as drought, as well as forest pathogens and past management strategies. Conifer tree mortality and its spatial pattern and change over time were examined in three components. In component 1, two remote sensing approaches were compared for their effectiveness in delineating dead trees, a spatial contextual approach and an OBIA (object based image analysis) approach, utilizing various dates and spatial resolutions of airborne image data.For each approach transforms and masking techniques were explored, which were found to improve classifications, and an object-based assessment approach was tested. In component 2, dead tree maps produced by the most effective techniques derived from component 1 were utilized for point pattern and vector analyses to further understand spatio-temporal changes in tree mortality for the years 1997, 2000, 2002, and 2005 for three study areas: Palomar, Volcan and Laguna mountains. Plot-based fieldwork was conducted to further assess mortality patterns. Results indicate that conifer mortality was significantly clustered, increased substantially between 2002 and 2005, and was non-random with respect to tree species and diameter class sizes. In component 3, multiple environmental variables were used in Generalized Linear Model (GLM-logistic regression) and decision tree classifier model development, revealing the importance of climate and topographic factors such as precipitation and elevation, in being able to predict areas of high risk for tree mortality. The results from this study highlight the importance of multi-scale spatial as well as temporal analyses, in order to understand mixed-conifer forest structure, dynamics, and processes of decline, which can lead to more sustainable management of forests with continued natural and anthropogenic disturbance.
Author:
Publisher:
ISBN: 9781267767363
Category :
Languages : en
Pages : 198
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Book Description
Themontane mixed-conifer forests of San Diego County are currently experiencing extensive tree mortality, which is defined as dieback where whole stands are affected. This mortality is likely the result of the complex interaction of many variables, such as altered fire regimes, climatic conditions such as drought, as well as forest pathogens and past management strategies. Conifer tree mortality and its spatial pattern and change over time were examined in three components. In component 1, two remote sensing approaches were compared for their effectiveness in delineating dead trees, a spatial contextual approach and an OBIA (object based image analysis) approach, utilizing various dates and spatial resolutions of airborne image data.For each approach transforms and masking techniques were explored, which were found to improve classifications, and an object-based assessment approach was tested. In component 2, dead tree maps produced by the most effective techniques derived from component 1 were utilized for point pattern and vector analyses to further understand spatio-temporal changes in tree mortality for the years 1997, 2000, 2002, and 2005 for three study areas: Palomar, Volcan and Laguna mountains. Plot-based fieldwork was conducted to further assess mortality patterns. Results indicate that conifer mortality was significantly clustered, increased substantially between 2002 and 2005, and was non-random with respect to tree species and diameter class sizes. In component 3, multiple environmental variables were used in Generalized Linear Model (GLM-logistic regression) and decision tree classifier model development, revealing the importance of climate and topographic factors such as precipitation and elevation, in being able to predict areas of high risk for tree mortality. The results from this study highlight the importance of multi-scale spatial as well as temporal analyses, in order to understand mixed-conifer forest structure, dynamics, and processes of decline, which can lead to more sustainable management of forests with continued natural and anthropogenic disturbance.
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Category : Conifers
Languages : en
Pages : 0
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Shifting wildfire patterns and climate conditions, magnified by anthropogenic climate change, are threatening the resilience of conifer forests in North America and more specifically, the western US. If native conifer species are functionally maladapted to novel fire patterns and post-fire climate conditions, large-scale shifts in conifer forest structure, composition, and extent may occur as warming intensifies. Forest resilience in the context of fire and climate can be understood and quantified by the survival of trees through fire events and success of trees to regenerate post-fire and maintain population levels. In this dissertation, I use field observations and remote sensing to examine patterns of fire-induced tree mortality and post-fire tree regeneration as proxies of conifer forest resilience in the western US, across a range of environments and forest types, and particularly within the context of expansive high-severity, stand-replacing wildfires. In Chapter 1, I evaluate the interactions between climate-environment conditions and the spatial, structural, and temporal characteristics of fire refugia as drivers of subalpine forest recovery in the cool and moist Cascade Range of Oregon and Washington. In Chapter 2, I quantify large-scale patterns of post-fire delayed conifer tree mortality across three ecoregions and two broad forest types in the western US using high-resolution satellite imagery, and I evaluate whether post-fire delayed conifer tree mortality is a ubiquitous process across broad geographies, and if so, I ask i) what drives it? and ii) can it meaningfully affect seed dispersal and thus forest regeneration processes? Finally, in Chapter 3, I use an aggregated database of post-fire conifer establishment responses, across over 1800 sites and four ecoregions in the western US, to challenge the generalized notion that conifer species' shade-tolerance dictates their regenerative capacity within exposed early seral post-fire environments.
Author: Phillip John Van Mantgem
Publisher:
ISBN:
Category : Conifers
Languages : en
Pages : 222
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Author: Yanlei Chen
Publisher:
ISBN:
Category :
Languages : en
Pages : 104
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We developed a semi-automatic algorithm named Berkeley Indices Trajectory Extractor (BITE) to detect forest disturbances, especially slow-onset disturbances such as insect mortality, from time series of Landsat 5 Thematic Mapper (TM) images. BITE is a streamlined process that features trajectory extraction and interpretation of multiple spectral indices followed by an integration of all indices. The algorithm was tested over Grand County in Colorado, located in the Southern Rocky Mountains Ecoregion, where forests dominated by lodgepole pine have been under mountain pine beetle attack since 2000. We produced a disturbance map using BITE with an identification accuracy of 94.7% assessed from 602 validation sample pixels. The algorithm shows its robustness in deriving forest disturbance type and timing with the presence of different levels of atmospheric conditions, noises, pixel misregistration and residual cloud/snow cover in the imagery. Outputs of the BITE algorithm could be used in studies designed to increase understanding of the mechanisms of mountain pine beetle dispersal and tree mortality, as well as other types of forest disturbances. Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden for information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors. The clustering process takes advantage of the reduced size of the compressed data and thus reduces computational complexity. We name this approach Clustering Based on Eigen Space Transformation (CBEST). In our experiment with a subscene of Landsat Thematic Mapper (TM) imagery, CBEST was found to be able to improve speed considerably over conventional K-means as the volume of data to be clustered increases. We assessed information loss and several other factors. In addition, we evaluated the effectiveness of CBEST in mapping land cover/use with the same image that was acquired over Guangzhou City, South China and an AVIRIS hyperspectral image over Cappocanoe County, Indiana. Using reference data we assessed the accuracies for both CBEST and conventional K-means and we found that the CBEST was not negatively affected by information loss during compression in practice. We then applied CBEST in mapping the forest change from 1986-2011 for the entire state of California, USA with over 400 Landsat TM images. We discussed potential applications of the fast clustering algorithm in dealing with large datasets in remote sensing studies. We present an efficient approach for a practice of large-area mapping of forest changes based on the Clustering Based on Eigen Space Transformation (CBEST) algorithm using remote sensing. By analyzing 450 Landsat Thematic Mapper (TM) satellite images from 1986 to 2011 with a five-year interval covering the entire state of California, USA, we derived a forest change type map, a forest loss map and a forest gain map. Although California has 99.6 million acres land area in total and the spatial resolution of Landsat TM is 30m, the computing time of the task took only 10 hours in a computer with an Intel 2.8 Ghz i5 CPU and 8 Gigabytes RAM. The overall accuracy of the forest cover in year 2011 was reported as 92.9% " 1.6%. We found that the estimated forest area changed from 28.20 " 1.98 million acres to 28.05 " 1.98 million acres from 1986-2011. In particular, our rough estimate indicates that each year California's forest experienced loss of 92 thousand acres and recovery of 85 thousand acres, resulting in seven thousand acres forest loss per year. In addition, during 1986-2011, around 12% of the forestland experienced changes, in which the change was 4% each for deforestation, afforestation and deforestation then recovered respectively. We concluded that the forestland in California had been managed in a sustainable manner over the 25 years, since no significantly directional changes were observed. Our approach made a tighter estimate of the true canopy coverage such that 29% of land in California is forestland, comparing with the statistics of 33% and 40% made by previous studies that had lower spatial resolution and shorter temporal coverage.
Author: Patricia Ellen Maloney
Publisher:
ISBN:
Category : Forest health
Languages : en
Pages : 258
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"Logging and fire exclusion policies in the Lake Tahoe Basin have increased tree densities over the past 150 years. Current tree densities range up to 450 trees/ha. Cumulative tree mortality in logged (mean = 25%) and unlogged (mean = 21%) stand types were significantly and positively correlated with tree density. The synergistic effect of bark beetles, pathogens, and stand density linked with drought events largely explain mortality in these forests. Unlogged, mixed-conifer forests with the historical fire regime still intact were studied in the Sierra San Pedro Martir, Baja, Mexico. Average tree density was low at 160 trees/ha. Cumulative mortality was 12.7%, with the greatest amount of mortality occurring to larger trees. Most tree mortality (78%) was due to pathogens and bark beetles. Mistletoe and a bark beetle species were widespread on Abies concolor. Mistletoe severity was negatively correlated to A. concolor regeneration. White pine blister rust (WPBR) is a heteroecious rust fungus that alternates between 5-needle pines, and Ribes to complete its life-cycle. In mixed-conifer forests, WPBR prevalence on Pinus lambertiana was correlated with the nearness of Ribes and influenced by environmental conditions favorable for rust infection. Disease was spatially aggregated with new infections occurring annually. In subalpine forests, disease was not correlated with the presence of Ribes. In this exposed location, disease may be episodic rather than chronic. When conditions are favorable, wind allows for widespread dispersal of spores. Demographic effects of this disease on its pine hosts include juvenile mortality and reduced cone production. In two locations in the Sierra Nevada, we found the prevalence and severity of dwarf mistletoe (DWM) on A. concolor was not or weakly correlated to host density, but severity was positively correlated to host size. On Pinus jeffreyi, DWM prevalence and severity were positively correlated with host density. Individuals of all sizes were susceptible to DWM, with less than expected becoming infected in the seedling-10 cm diameter class. Both aggregated and random spatial patterns were found for DWM, suggesting that the degree of infection and logging history are important in the spatial dynamics of DWM species."--Abstract
Author: Frank C. Croft
Publisher:
ISBN:
Category : Aerial photography in forestry
Languages : en
Pages : 14
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Author: James F. Fowler
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ISBN:
Category : Douglas fir
Languages : en
Pages : 32
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This review focused on the primary literature that described, modeled, or predicted the probability of postfire mortality in ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii). The methods and measurements that were used to predict postfire tree death tended to fall into two general categories: those focusing on measuring important aspects of fire behavior, the indirect but ultimate cause of mortality; and those focusing on tissue damage due to fire, the direct effect of fire on plant organs. Of the methods reviewed in this paper, crown scorch volume was the most effective, easiest to use, and most popular measurement in predicting postfire mortality in both conifer species. In addition to this direct measure of foliage damage, several studies showed the importance and utility of adding a measurement of stem (bole) damage. There is no clear method of choice for this, but direct assessment of cambium condition near the tree base is widely used in Douglas-fir. Only two ponderosa pine studies directly measured fine root biomass changes due to fire, but they did not use these measurements to predict postfire mortality. Indirect measures of fire behavior such as ground char classes may be the most practical choice for measuring root damage. This review did not find clear postfire survivability differences between the two species. The literature also does not show a consistent use of terminology; we propose a standard set of terms and their definitions.
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Category :
Languages : en
Pages : 27
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This paper summarizes the 2012-2017 bark beetle epidemic in the Sierra Nevada and its implications for long-term changes in tree species composition and forest structure. Preliminary plot and landscape-scale data are reviewed, showing higher levels of mortality for pine species and greater impacts in the southern Sierra Nevada compared to the northern portions of the range. The federal government owns approximately three quarters of the forested area impacted by high levels of tree morality, with the remainder of the land controlled by nonindustrial (18%) and industrial (6%) ownerships. The accumulation of dead and downed fuel and standing dead trees is expected to increase fire intensity and severity, and pose significant hazards for fire control efforts. Potential long-term changes in Sierra Nevada forest composition were explored with a GIS analysis conducted for the Sierra National Forest, located in the southern Sierra. GIS layers included very high fire threat, aspect, high tree mortality, topographic position classification, and climatic exposure. A factor of one was assigned to each parameter (i.e., no weighting for any of the variables). The modeling showed that 4% of the Sierra National Forest is at very high risk for type conversion from mixed conifer to shrublands, and 12% is at high risk. This information can inform landowners regarding the general locations where successful reforestation will be most challenging, as well as illustrate the scale of concern for one national forest in the southern Sierra Nevada. Changes to disturbance regimes, continuing land use changes, and climate change with associated species shifts pose significant challenges for maintaining healthy and resilient forests in the Sierra Nevada. Significant unknowns exist regarding the future species composition for vast portions of this region, but type conversions from mixed conifer to shrublands or oak/grass/woodland appear likely for some areas. Recommended best management practices focus on reducing tree densities, achieving successful reforestation, and using adaptive management in the face of currently unknown future changes in growing conditions. With the exception of the bark beetle epidemic in southern California in the early 2000s, lessons learned from other locations in western North America that have had sustained bark beetle epidemics in the past decade are not directly applicable to Sierra Nevada, with its Mediterranean climate, complex topography, and mixed-conifer forests. For these reasons, ongoing research efforts to characterize and understand tree mortality drivers and changes in forest structure and composition in the Sierra Nevada are extremely important.
Author: Melissa Viola Eitzel Solera
Publisher:
ISBN:
Category :
Languages : en
Pages : 175
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In this work, I answer timely questions regarding tree growth, tree survival, and community change in California tree species, using a variety of sophisticated statistical and remote sensing tools. In Chapter 1, I address tree growth for a single tree species with a thorough explanation of hierarchical state-space models for forest inventory data. Understanding tree growth as a function of tree size is important for a multitude of ecological and management applications. Determining what limits growth is of central interest, and forest inventory permanent plots are an abundant source of long-term information but are highly complex. Observation error and multiple sources of shared variation (spatial plot effects, temporal repeated measures, and a mosaic of sampling intervals) make these data challenging to use for growth estimation. I account for these complexities and incorporate potential limiting factors (tree size, competition, and resource supply) into a hierarchical state-space model. I estimate the diameter growth of white fir (Abies concolor) in the Sierra Nevada of California from forest inventory data, showing that estimating such a model is feasible in a Bayesian framework using readily available modeling tools. In this forest, white fir growth depends strongly on tree size, total plot basal area, and unexplained variation between individual trees. Plot-level resource supply variables (representing light, water, and nutrient availability) do not have a strong impact on inventory-size trees. This approach can be applied to other networks of permanent forest plots, leading to greater ecological insights on tree growth. In Chapter 2, I expand my state-space modeling to examine survival in seven tree species, as well as investigating the results of modeling them in aggregate (at the community level) and comparing with the individual species models. Declining tree survival is a complex, well-recognized problem, but studies have been largely limited to relatively rare old-growth forests or low-diversity systems, and to models which are species-aggregated or cannot easily accommodate yearly climate variables. I estimate survival models for a relatively diverse second-growth forest in the Sierra Nevada of California using a hierarchical state-space framework. I account for a mosaic of measurement intervals and random plot variation, and I directly include yearly stand development variables alongside climate variables and topographic proxies for nutrient limitation. My model captures the expected dependence of survival on tree size. At the community level, stand development variables account for decreasing survival trends, but species-specific models reveal a diversity of factors influencing survival. Species time trends in survival do not always conform to existing theories of Sierran forest dynamics, and size relationships with survival differ for each species. Within species, low survival is concentrated in susceptible subsets of the population and single estimates of annual survival rates do not reflect this heterogeneity in survival. Ultimately only full population dynamics integrating these results with models of recruitment can address the potential for community shifts over time. In Chapter 3, I combine statistical modeling with remote sensing techniques to investigate whether topographic variables influence changes in woody cover. In the North Coast of California, changes in fire management have resulted in conversion of oak woodland into coniferous forest, but the controls on this slow transition are unknown. Historical aerial imagery, in combination with Object-Based Image Analysis (OBIA), allows us to classify land cover types from the 1940s and compare these maps with recent cover. Few studies have used these maps to model drivers of cover change, partly due to two statistical challenges: 1) appropriately accounting for spatial autocorrelation (ideally without throwing away data) and 2) appropriately modeling percent cover which is bounded between 0 and 100 and not normally distributed. I study the change in woody cover in California's North Coast using historical (1948) and recent (2009) high-spatial-resolution imagery. I classify the imagery using eCognition Developer and aggregate the resulting maps to the scale of a Digital Elevation Model (DEM) in order to understand topographic drivers of woody cover change. I use Generalized Additive Models (GAMs) with a quasi-binomial probability distribution to account for spatial autocorrelation and the boundedness of the percent woody cover variable. I explore the relative roles of elevation, topographic slope, aspect (Northness/Eastness), topographic wetness index, profile curvature, historical percent woody cover, and geographical coordinates in influencing current percent woody cover. I estimate these models for scales of 20, 30, 40, 50, 60, 70, 80, 90, and 100 m, reflecting both tree neighborhood scales and stand scales. I find that historical woody cover has a consistent positive effect on current woody cover, and that the spatial term in the model is significant even after controlling for historical cover. Specific topographic variables emerge as important for different sites at different scales, but no overall pattern emerges across sites or scales for any of the topographic variables I tested. This GAM framework for modeling historical data is flexible and could be used with more variables, more flexible relationships with predictor variables, and larger scales. Modeling drivers of woody cover change from historical ecology data sources can be a valuable way to plan restoration and enhance ecological insight into landscape change. I conclude that these techniques are promising but a framework is needed for sensitivity analysis, as modeling results can depend strongly on variable selection and model structure.
Author: Gerald J. Gottfried
Publisher:
ISBN:
Category : Conifers
Languages : en
Pages : 28
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