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The continued spread of invasive weeds is threatening ecosystem health throughout North America. Understanding the relationships between invasive weeds’ key phenological phases and structural and/or functional canopy development is an essential step for making informed decisions regarding their management. We analyzed a three-year image archive obtained from an inexpensive webcam overlooking a perennial pepperweed (Lepidium latifoliumL.) infestation in California to explore the ability of red (R)‐green (G)‐blue (B) color space information to track the structural and functional development of the pepperweed.Wecharacterizedstructuralandfunctionalcanopydevelopmentthroughsurfaceroughness length (z 0m ; a proxy for canopy height and leaf area index) and canopy photosynthesis (F A ), respectively, both of which we derived from eddy covariance measurements. Here we demonstrate the use of cross-correlation functions to determine the temporal lags between chromatic coordinates and two color indices, all calculated fromRGBbrightness levels, withz 0m andF A . We found that these color metrics fail to represent the structural and/or functional state of the canopy. In contrast,relative luminance(CIE Y) appears to be a better indicator forz 0m and especially forF A . We calculatedCIE Yfrom pepperweedRGB brightness levels in relation to hypothetical horizontal referenceRGBbrightness levels. We obtained the latter by applying the ratio between horizontally measured and hypothetical incoming solar radiation on a vertical surface toRGBbrightness levels of a vertically oriented reference of invariant light-grey color. We conclude that webcam image archives may provide an inexpensive tool for making informed decisions regarding the timing but not for assessing the effectiveness of invasive plant control measures such as mowing.

a b s t r a c t Tidal salt marshes in the San Francisco Estuary region display heterogeneous vegetation patterns that influence wetland function and provide adequate habitat for native or endangered wildlife. In addition to analyzing the extent of vegetation, monitoring the dynamics of vegetation pattern within restoring wetlandscanoffervaluableinformationabouttherestorationprocess.Patternmetrics,derivedfromclas- sified remotely sensed imagery, have been used to measure composition and configuration of patches and landscapes, but they can be unpredictable across scales, and inconsistent across time. We sought to identify pattern metrics that are consistent across spatial scale and time - and thus robust measures of vegetation and habitat configuration - for a restored tidal marsh in the San Francisco Bay, CA, USA. We used high-resolution (20cm) remotely sensed color infrared imagery to map vegetation pattern over 2 years, and performed a multi-scale analysis of derived vegetation pattern metrics. We looked at the influence on metrics of changes in grain size through resampling and changes in minimum mapping unit (MMU) through smoothing. We examined composition, complexity, connectivity and heterogeneity metrics, focusing on perennial pickleweed (Sarcocornia pacifica), a dominant marsh plant. At our site, pickleweed patches grew larger, more irregularly shaped, and closely spaced over time, while the over- all landscape became more diverse. Of the two scale factors examined, grain size was more consistent than MMU in terms of identifyingrelativechange in composition and configuration of wetland marsh vegetation over time. Most metrics exhibited unstable behavior with larger MMUs. With small MMUs, most metrics were consistent across grain sizes, from fine (e.g. 0.16m 2 ) to relatively large (e.g. 16m 2 ) pixel sizes. Scale relationships were more variable at the landcover class level than at the landscape level (across all classes). This information may be useful to applied restoration practitioners, and adds to our general understanding of vegetation change in a restoring marsh.

Detailed vegetation mapping of wetlands, both natural and restored, can offer valuable information about vegetation diversity and community structure and provides the means for examining vegetation change over time. We mapped vegetation at six tidal marshes (two natural, four restored) in the San Francisco Estuary, CA, USA, between 2003 and 2004 using detailed vegetation field surveys and high spatial-resolution color-infrared aerial photography. Vegetation classes were determined by performing hierarchical agglomerative clustering on the field data collected from each tidal marsh. Supervised classification of the CIR photography resulted in vegetation class mapping accuracies ranging from 70 to 92%; 10 out of 12 classification accuracies were above 80%, demonstrating the potential to map emergent wetland vegetation. The number of vegetation classes decreased with salinity, and increased with size and age. In general, landscape diversity, as measured by the Shannon’s diversity index, also decreased with salinity, with an exception for the most saline site, a newly restored marsh. Vegetation change between years is evident, but the differences across sites in composition and pattern were larger than change within sites over two growing seasons.

Tidal marsh monitoring and restoration can benefit from the union of fine-scale remote sensing products and field-based survey data via spatial predictive models. As part of an interdisciplinary wetland monitoring project in San Francisco Bay, we developed a suite of 1-m pixel-level spatial metrics describing patterns in marsh vegetation and geomorphology for six sites across a large salinity gradient. These metrics, based on multi-spectral aerial imagery and derived vegetation maps, provided a basis for fine-scale spatial modeling of avian habitat potential. Using common yellowthroat (Geothlypis trichas), song sparrow (Melospiza melodia), and black rail (Later- allus jamaicensis) abundance data, we developed statistical models with relatively high explanatory power. In each case, models were improved by including vegetation-map variables, but variables directly extracted from aerial imagery were more reliable indicators of avian abundance. Although results varied by species, our models achieved reasonable within-site predictive success. When predicting to sites not used in the training set, however, validation results were inconsistent and often poor, suggesting that these models should be used with caution outside of the original study sites. As remotely sensed data become more readily available, our methods may be applied to a diverse range of sites, resulting in improved model generality and applicability.

a b s t r a c t Urbanization has been a significant cause of deforestation throughout the latter half of the 20th century, and given global demographic trends, the conversion of forested land to urban uses will likely continue. California has had a long history of converting forests, and urbanization has been one of the princi- ple drivers. While many studies have examined how urbanization alters forest landscape structure at landscape or regional scales, little is known about how urban development influences linear forest edge structure at local scales where individual homeowner decisions dominate. We studied how forest edges at two California coastal oak woodlands (Pacheco Valle (PV) and China Camp (CV)) in the San Francisco Bay Area in California changed in the decades following urbanization. Using remote sensing and object- based image analysis, we isolated 20 urban-forest edges per site and quantified each edge's complexity (measured by sinuosity) for three time points at each site. Edges exhibited low sinuosity immediately fol- lowing development (PV=1.584, CC=1.5625), but grew significantly more complex (in 2003 PV=1.8705, CC 1.906). Linear forest edge structure at both sites, despite different development dates, showed similar and statistically significant increases in sinuosity by 2003. We attribute the initial, more linear struc- ture to mortality and trunk or canopy damage caused by construction, while ascribing the later, more complex structure to tree recruitment, canopy expansion, and homeowner actions that influence natural processes.

Sudden oak death, caused byPhytophthora ramorum, is widely established in mesic forests of coastal central and northern California. In 2000, we placed 18 plots in two Marin County sites to monitor dis- ease progression in coast live oaks (Quercus agrifolia), California black oaks (Q. kelloggii), and tanoaks (Lithocarpus densiflorus), the species that are most consistently killed by the pathogen in these areas. Through early 2008, the numbers of newly infected trees increased for all species. The infection rate for trees that were asymptomatic in 2000 was 5.0%y "1 for coast live oaks, 4.1%y "1 for black oaks and

Sudden oak death (SOD) has caused widespread mortality in a number of tree and shrub species throughout coastal California. As a result, canopy changes are directly visible from remotely sensed imagery. To quantify changes in horizontal canopy structure to the oak woodlands in China Camp State Park, California, USA, a heavily hit area, we developed a novel change detection technique that tracks changes to individual objects. Using 4-band, 1 m spatial resolution aerial photography, we classified four annual images (2000 to 2003) with object-based image analysis (OBIA) and employed a GIS for our change detection technique. We identified 352 gaps that contained SOD mortality in 2000 and persisted through 2003. Their median areas and perimeters did not change significantly in that time. However, those gaps that increased in size tended to be smaller than those that decreased, indicating increased mortality in newly infected areas. Our new change detection method allowed us to monitor these gaps one-by-one, revealing ecologically meaningful results that would otherwise be obscured in a landscape-scale analysis.

Nicaragua, home to the largest remaining extent of rainforest in Central America (total surface area) and to a significant indigenous population, has lost approximately half of its forest cover since 1950. This major and rapid loss of forest cover has been explained as the consequence of an eastward moving agricultural frontier that cuts through the region of Jinotega. If the current deforestation rate continues, the country could lose its remaining forest cover over the course of the next two decades; therefore, it is essential that the dynamics and relationships of land-use and land-cover change (LUCC) in this region are understood. To examine LUCC in Nicaragua over time, Landsat imagery from the southern portion of the region of Jinotega, taken in 1978, 1987, and 1999 was utilized. A remote-sensing method, supervised classification, which allows for the grouping of spectrally similar values for each year, followed by an image change detection analysis (postclassification comparison) was conducted. Groundtruthing (field validation) was conducted in 2006 to validate the data, which yielded increasing overall accuracy rates of 71.68% for 1978, 82.35% for 1987, and 84.38% for 1999. The classification and change detection results showed that if the agricultural cultivation overtook this region, it happened before 1978. Therefore, the possibility that either deforestation did not actually occur along an agricultural frontier or that it was located further east exists; this would be an interesting subject for future studies. There was, however, clear evidence of increased forest cover from 1987 to 1999 near the urban center, correlating with the enforced reforestation law in the city of Jinotega.

We investigate interactions between successive naturally occurring fires, and assess to what extent the environments in which fires burn influence these interactions. Using mapped fire perimeters and satellite-based estimates of post-fire effects (referred to hereafter as fire severity) for 19 fires burning relatively freely over a 31-year period, we demonstrate that fire as a landscape process can exhibit self-limiting characteristics in an upper elevation Sierra Nevada mixed conifer forest. We use the term ‘self-limiting’ to refer to recurring fire as a process over time (that is, fire regime) consuming fuel and ultimately constraining the spatial extent and lessening fire-induced effects of subsequent fires. When the amount of time between successive adjacent fires is under 9 years, and when fire weather is not extreme (burning index

Sudden oak death is a disease affecting coastal forests in California and southern Oregon. The spatial pattern of disease dynamics is important for forest and landscape pathology; in this work we investigated the interaction across landscape scales of disease symptomology in coast live oaks, Quercus agrifolia, (trunk bleeding, presence of beetles, and presence of the fungus Hypoxylon thouarsianum) and tree mortality through time. We used two-dimensional spatial analysis tools with data gathered in point-centered-quarter format in 2001 and 2004 to quantify the population density of the disease through time; to examine the spatial pattern of tree mortality across scales through time; and to examine the spatial co-occurrence of disease symptoms with crown mortality through time. Early in the study period dead trees were strongly clustered at smaller scales (~300 m) and after three years this clustering was less pronounced. Bleeding on trees occurred in clusters away from dead trees, particularly in 2004, likely indicating a new cohort of infected trees. The presence of H. thouarsianum was strongly related to overstory mortality through time. Beetle-infested trees co-occurred with mortality in 2001. By 2004, they occurred throughout the forest, and were less strongly correlated with overstory tree mortality, suggesting a future peak of tree mortality.

Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiersfirst segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scaledependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasitopological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

Although remote sensing offers the ability to monitor wetland restoration, few have tested automated methods for quantifying vegetation change. We implemented a semiautomated technique using color infrared aerial photography and a common vegetation index, Normalized Difference Vegetation Index (NDVI), to document vegetation colonization in a restoring salt marsh. Change in vegetation over a period of 10 years was analyzed using a postclassification comparison technique where each image year was classified individually into vegetated and nonvegetated areas using NDVI thresholds and then differenced between years to identify areas of vegetation change. Vegetated and nonvegetated areas were identified using this technique, as were areas and time periods of vegetation change. By comparing classified NDVI imagery, we calculated that 90% of our study site was vegetated 10 years after restoration. This study demonstrated that high-resolution remotely sensed data can be analyzed with common geospatial software to monitor change in a rapidly vegetating wetland and that long time frames with yearly image acquisition are needed to quantify plant colonization rates. This method was effective at detecting change in vegetation over time in a variable tidal marsh environment using imagery that had inconsistent specifications and quality across years. Inconsistencies included interannual climate variation, phenology, and presence of algae, as well as differences in pixel size and image brightness. Our findings indicate that remote sensing is useful for postrestoration monitoring of tidal marsh ecosystems.

Fires constitute one major ecological disturbance which influences the natural cycle of vegetation succession and the structure and function of ecosystems. There is no single natural scale at which ecological phenomena are completely understood and thus the capacity to handle scale is beneficial to methodological frameworks for analyzing and monitoring ecosystems. Although satellite imagery has been widely applied for the assessment of fire related topics, there are few studies that consider fire at several spatial scales simultaneously. This research explores the relationships between fire occurrence and several families of environmental factors at different spatial observation scales by means of classification and regression tree models. Predictors accounting for vegetation status (estimated by spectral indices derived from Landsat imagery), fire history, topography, accessibility and vegetation types were included in the models of fire occurrence probability. We defined four scales of analysis by identifying four meaningful thresholds related to fire sizes in the study site. Sampling methodology was based on random points and the power-law distribution describing the local fire regime. The observation scale drastically affected tree size, and therefore the achieved level of detail, and the most explanatory variables in the trees. As a general trend, trees considering all the variables showed a spectral index ruling the most explicative split. According to the comparison of the four pre-determined analysis scales, we propose the existence of three eventual organization levels: landscape patch or ecosystem level, local level and the basic level, the most heterogeneous and complex scale. Rules with three levels of complexity and applicability for management were defined in the tree models: (i) the repeated critical thresholds (predictor values across which fire characteristics change rapidly), (ii) the meaningful final probability classes and (iii) the trees themselves.

Three sets of coast live oak (Quercus agrifolia) leaf samples were collected on three dates: 20 April 2002, 23 July 2002 and 11 September 2001, respectively, for Sudden Oak Death (SOD) monitoring. A total of 330 reflectance spectra (covering 350–2500 nm) were measured in the laboratory with a spectrometer FieldSpec®Pro FR. In this study, the spectroscopic determination of two health levels of the coast live oak leaves was conducted with three sets of spectra. We used two classification algorithms, penalized discriminant analysis (PDA) and cross correlogram spectral matching (CCSM), to discriminate between healthy and infected leaves. PDA is a penalized version of Fisher's linear discriminant analysis (LDA) and can considerably improve upon LDA when it is used for the classification of hyperspectral data. CCSM is practised by calculating the cross correlation at different match positions between a test spectrum and a reference spectrum and is also suitable for processing hyperspectral data. Experimental re...

Research on the determinants of land use change and its relationship to vulnerability (broadly defined), biotic diversity and ecosystem services (e.g. Gullison et al. 2007), health (e.g. Patz et al. 2004) and climate change (e.g. van der Werf et al. 2004) has accelerated. Evidence of this increased interest is demonstrated by several examples. Funding agencies in the US (National Institutes of Health, National Science Foundation, National Aeronautics and Space Administration and National Oceanic and Atmospheric Administration) and around the world have increased their support of land use science. In addition to research papers in disciplinary journals, there have been numerous edited volumes and special issues of journals recently (e.g. Gutman et al. 2004; Environment & Planning B 2005; Environment & Planning A 2006; Lambin and Geist 2006; Kok, Verburg and Veldkamp 2007). And in 2006, the Journal of Land Use Science was launched. Land use science is now at a crucial juncture in its maturation process. Much has been learned, but the array of factors influencing land use change, the diversity of sites chosen for case studies, and the variety of modeling approaches used by the various case study teams have all combined to make two of the hallmarks of science, generalization and validation, difficult within land use science. This introduction and the four papers in this themed issue grew out of two workshops which were part of a US National Institutes of Health (NIH) ‘Roadmap’ project. The general idea behind the NIH Roadmap initiative was to stimulate scientific advances by bringing together diverse disciplines to tackle a common, multi-disciplinary scientific problem. The specific idea behind our Roadmap project was to bring together seven multi-disciplinary case study teams, working in areas that could be broadly classified as inland frontiers, incorporating social, spatial and biophysical sciences, having temporal depth on both the social and biophysical sides, and having had long-term funding. Early in our Roadmap project, the crucial importance of modeling, particularly agent-based modeling, for the next phase of land-use science became apparent and additional modelers not affiliated with any of the seven case studies were brought into the project. Since agent-based simulations attempt to explicitly capture human behavior and interaction, they were of special interest. At the risk of oversimplification, it is worth briefly reviewing selected key insights in land use science in the past two decades to set the stage for the papers in this themed issue. One of the earliest realizations, and perhaps most fundamental, was accepting the crucial role that humans play in transforming the landscape, and concomitantly the distinction drawn between land cover (which can be seen remotely) and land use (which, in most circumstances, requires in situ observation; e.g. Turner, Meyer and Skole 1994). The complexity of factors influencing land use change became apparent and led to a variety of ‘box and arrow’ diagrams as conceptual frameworks, frequently put together by committees rarely agreeing with one another on all details, but agreeing among themselves that there were many components (social and biophysical) whose role needed to be measured and understood. A series of case studies emerged, recognizing the wide array of variables that needed to be incorporated, and typically doing so by assembling a multidisciplinary team (Liverman, Moran, Rindfuss and Stern 1998; Entwisle and Stern 2005). The disciplinary make-up of the team strongly influenced what was measured and how it was measured (see Rindfuss, Walsh, Turner, Fox and Mishra 2004; Overmars and Verburg 2005), with limited, if any, coordination across case studies (see Moran and Ostrom 2005 for an exception). In large part, the focus on case studies reflected the infancy of theory in land use science. Teams combined their own theoretical knowledge of social, spatial and ecological change with an inductive approach to understanding land use change – starting from a kitchen sink of variables and an in-depth knowledge of the site to generate theory on the interrelationships between variables and the importance of contextual effects. This lack of coordination in methods, documentation and theory made it very difficult to conduct meta-analyses of the driving factors of land use change across all the case studies to identify common patterns and processes (Geist and Lambin 2002; Keys and McConnell 2005). Recognizing that important causative factors were affecting the entire site of a case study (such as a new road which opens an entire area) and that experimentation was not feasible, computational, statistical and spatially explicit modeling emerged as powerful tools to understand the forces of land use change at a host of space–time scales (Veldkamp and Lambin 2001; Parker, Manson, Janssen, Hoffmann, and Deadman 2003; Verburg, Schot, Dijst and Veldkamp 2004). Increasingly, in recognition of the crucial role of humans in land use change, modeling approaches that represent those actors as agents have emerged as an important, and perhaps the dominant, modeling approach at local levels (Matthews, Gilbert, Roach, Polhil and Gotts 2007). In this introductory paper we briefly discuss some of the major themes that emerged in the workshops that brought together scientists from anthropology, botany, demography, developmental studies, ecology, economics, environmental science, geography, history, hydrology, meteorology, remote sensing, geographic information science, resource management, and sociology. A central theme was the need to measure and model behavior and interactions among actors, as well as between actors and the environment. Many early agent-based models focused on representing individuals and households (e.g. Deadman 1999), but the importance of other types of actors (e.g. governmental units at various levels, businesses, and NGOs) was a persistent theme. ‘Complexity’ was a term that peppered the conversation, and it was used with multiple meanings. But the dominant topic to emerge was comparison and generalization: with multiple case studies and agent-based models blooming, how do we compare across them and move towards generalization? We return to the generalization issue at the end of this introductory paper after a brief discussion of the other themes.

A Compact Airborne Spectrographic Imager-2 (CASI) dataset was used for detecting mortality and vegetation stress associated with a new forest disease. We first developed a multilevel classification scheme to improve classification accuracy. Then, the CASI raw data were transformed to reflectance and corrected for topography, and a principal component (PC) transformation of all 48 bands and the visible bands and NIR bands were separately conducted to extract features from the CASI data. Finally, we classified the calibrated and corrected CASI imagery using a maximum likelihood classifier and tested the relative accuracies of classification across the scheme. The multilevel scheme consists of four levels (Levels 0 to 3). Level 0 covered the entire study area, classifying eight classes (oak trees, California bay trees, shrub areas, grasses, dead trees, dry areas, wet areas, and water). At Level 1, the vegetated and non-vegetated areas were separated. The vegetated and nonvegetated areas were further subdivided into four vegetated (oak trees, California bay trees, shrub areas, grasses) and four non-vegetated (dead trees, dry areas, wet areas, and water) classes at Level 2. Level 3 identified stressed and non-stressed oak trees (two classes). The ten classes classified at different levels are defined as final classes in this study. The experimental results indicated that classification accuracy generally increased as the detailed classification level increased. When the CASI topographically corrected reflectance data were processed into ten PCs (five PCs from the visible region and five PCs from NIR bands), the classification accuracy for Level 2 vegetated classes (non-vegetated classes) increased to 80.15 percent (94.10 percent) from 78.07 percent (92.66 percent) at Level 0. The accuracy of separating stressed from non-stressed oak trees at Level 3 was 75.55 percent. When classified as a part of Level 0, the stressed and non-stressed were almost inseparable. Furthermore, we found that PCs derived from visible and NIR bands separately yielded more accurate results than the PCs from all 48 CASI bands.

© 2016, Copyright © CASI. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure characteristics, which are critical for differentiating vegetation communities in compositionally homogeneous forests. Light detection and ranging (LiDAR) can accurately measure the forest vertical and horizontal structures and provide a great opportunity for solving this problem. This study introduces a strategy using both multispectral aerial imagery and LiDAR data to map vegetation composition and structure over large spatial scales. Our approach included the use of a Bayesian information criterion algorithm to determine the optimized number of vegetation groups within mixed conifer forests in two study areas in the Sierra Nevada, California, and an unsupervised classification technique and post hoc analysis to map these vegetation groups across both study areas. The results show that the proposed strategy can recognize four and seven vegetation groups at the two study areas, respectively. Each vegetation group has its unique vegetation structure characteristics or vegetation species composition. The overall accuracy and kappa coefficient of the vegetation mapping results are over 78% and 0.64 for both study sites. Résumé. La cartographie précise de la végétation est essentielle entre autres pour la gestion des ressources naturelles, l’analyse écologique, et la modélisation hydrologique. Les approches d’imagerie multispectrale et hyperspectrale par télédétection se sont avérées de précieuses contributions au processus de la cartographie de la végétation, mais elles ne peuvent fournir qu’un nombre limité de caractéristiques sur la structure de la végétation, qui sont essentielles pour différencier les communautés végétales dans les forêts de composition homogènes. La télédétection par laser «light detection and ranging» (LiDAR) peut mesurer avec précision les structures verticales et horizontales de la forêt, et fournit une formidable opportunité de résoudre ce problème. Cette étude présente une stratégie qui utilise à la fois l’imagerie multispectrale aérienne et des données LiDAR pour cartographier la composition et la structure de la végétation à grandes échelles spatiales. Notre approche comprenait l’utilisation d’un algorithme du critère d’information Bayésien pour déterminer le nombre optimal de groupes de végétation dans les forêts mixtes de conifères sur deux zones d’étude dans les Sierra Nevada, en Californie, ainsi qu’une technique de classification non supervisée et une analyse post hoc pour cartographier ces groupes de végétation dans les deux zones d’étude. Les résultats montrent que la stratégie proposée peut reconnaitre quatre et sept groupes de végétation dans les deux zones d’étude respectivement. Chaque groupe de végétation a des caractéristiques uniques de structure de la végétation ou de composition des espèces de la végétation. La précision globale et le coefficient kappa des résultats de la cartographie de la végétation sont de plus de 78% et 0,64 pour les deux sites d’étude.

A new forest disease called Sudden Oak Death, caused by the pathogen Phytophthora ramorum, occurs in coastal hardwood forests in California and Oregon. In this paper, we analyzed the spatial–temporal patterns of overstory oak tree mortality in China Camp State Park, CA over 4 years using the point patterns mapped from high spatial resolution remotely sensed imagery. Both univariate and multivariate spatial point pattern analyses were performed with special considerations paid to the spatial trends illustrated in the mapped point patterns. In univariate spatial point pattern analyses, we investigated inhomogeneousK-functions and Neyman–Scott point processes to characterize and model the spatial dependence among dead oak trees in each year. The results showed that the point patterns of dead oak trees are significantly clustered at different scales and spatial extents through time; and that both the extent and the scale of the clustering patterns decrease with time. In multivariate spatial point pattern analyses, we developed two simulation methods to test the spatial–temporal dependence among dead oak trees over time and the spatial dependence between dead oak trees and California bay trees, an important host for the pathogen. The results showed that new dead oak trees tend to be located within up to 300 m of past dead oak trees; and that a strong spatial association between oak tree mortality and California bay trees exists 150 m away. Published by Elsevier B.V.

In California, a newly discovered virulent pathogen (Phytophthora ramorum) has killed thousands of trees, including tanoak (Lithocarpus densiflorus), coast live oak (Quercus agrifolia), and black oak (Quercus kelloggii). Mapping the distribution of overstory mortality associated with the pathogen is an important part of disease management. In this study, we developed an object-based approach, including an image segmentation process and a knowledge-based classifier, to detect individual tree mortality in imagery of 1 m spatial resolution. The combined seg- mentation and classification methods provided an easy and intuitive way to incorpo- rate human knowledge into the classification process. The object-based approach significantly outperformed a pixel-based maximum likelihood classification method in mapping the tree mortality on high-spatial-resolution multispectral imagery.

© 2015 Tempel et al. Fuels-reduction treatments are commonly implemented in the western U.S. to reduce the risk of high-severity fire, but they may have negative short-term impacts on species associated with older forests. Therefore, we modeled the effects of a completed fuels-reduction project on fire behavior and California Spotted Owl (Strix occidentalis occidentalis) habitat and demography in the Sierra Nevada to assess the potential short- and long-term trade-offs. We combined field-collected vegetation data and LiDAR data to develop detailed maps of forest structure needed to parameterize our fire and forest-growth models. We simulated wildfires under extreme weather conditions (both with and without fuels treatments), then simulated forest growth 30 years into the future under four combinations of treatment and fire: treated with fire, untreated with fire, treated without fire, and untreated without fire. We compared spotted owl habitat and population parameters under the four scenarios using a habitat suitability index developed from canopy cover and large-tree measurements at nest sites and from previously derived statistical relationships between forest structure and fitness (λ) and equilibrium occupancy at the territory scale. Treatments had a positive effect on owl nesting habitat and demographic rates up to 30 years after simulated fire, but they had a persistently negative effect throughout the 30-year period in the absence of fire. We conclude that fuels-reduction treatments in the Sierra Nevada may provide long-term benefits to spotted owls if fire occurs under extreme weather conditions, but can have long-term negative effects on owls if fire does not occur. However, we only simulated one fire under the treated and untreated scenarios and therefore had no measures of variation and uncertainty. In addition, the net benefits of fuels treatments on spotted owl habitat and demography depends on the future probability that fire will occur under similar weather and ignition conditions, and such probabilities remain difficult to quantify. Therefore, we recommend a landscape approach that restricts timber harvest within territory core areas of use (∼125 ha in size) that contain critical owl nesting and roosting habitat and locates fuels treatments in the surrounding areas to reduce the potential for high-severity fire in territory core areas.

© 2015. Tidal wetlands are highly productive and act as critical habitat for a wide variety of plants, fish, shellfish, and other wildlife. These ecotones between aquatic and terrestrial environments also provide protection from storm damage, run-off filtering, and recharge of aquifers. Many wetlands along coasts have been exposed to stress-inducing alterations globally, including dredge and fill operations, hydrologic modifications, pollutants, impoundments, fragmentation by roads/ditches, and sea level rise. For wetland protection and sensible coastal development, there is a need to monitor these ecosystems at global and regional scales. Recent advances in satellite sensor design and data analysis are providing practical methods for monitoring natural and man-made changes in wetlands. However, available satellite remote sensors have been limited to mapping primarily wetland location and extent. This paper describes how the HyspIRI hyperspectral and thermal infrared sensors can be used to study and map key ecological properties, such as species composition, biomass, hydrology, and evapotranspiration of tidal salt and brackish marshes and mangroves, and perhaps other major wetland types, including freshwater marshes and wooded/shrub wetlands.

© 2015 Wolters Kluwer Health, Inc. All rights reserved. Background: in previous research, neighborhood deprivation was positively associated with body mass index (BMi) among adults with diabetes. We assessed whether the association between neighborhood deprivation and BMi is attributable, in part, to geographic variation in the availability of healthful and unhealthful food vendors. Methods: Subjects were 16,634 participants of the Diabetes Study of northern California, a multiethnic cohort of adults living with diabetes. neighborhood deprivation and healthful (supermarket and produce) and unhealthful (fast food outlets and convenience stores) food vendor kernel density were calculated at each participant's residential block centroid. We estimated the total effect, controlled direct effect, natural direct effect, and natural indirect effect of neighborhood deprivation on BMi. Mediation effects were estimated using g-computation, a maximum likelihood substitution estimator of the g-formula that allows for complex data relations such as multiple mediators and sequential causal pathways. Results: We estimated that if neighborhood deprivation was reduced from the most deprived to the least deprived quartile, average BMi would change by-0.73 units (95% confidence interval:-1.05,-0.32); however, we did not detect evidence of mediation by food vendor density. in contrast to previous findings, a simulated reduction in neighborhood deprivation from the most deprived to the least deprived quartile was associated with dramatic declines in both healthful and unhealthful food vendor density. Conclusions: availability of food vendors, both healthful and unhealthful, did not appear to explain the association between neighborhood deprivation and BMi in this population of adults with diabetes.

This paper examines the spatial point pattern of industrial toxic substances and the associated environmental justice implications in the San Francisco Bay Area, California, USA. Using a spatial analysis method called Ripley’s K we assess environmental justice across multiple spatial scales, and we verify and quantify the West Oakland neighborhood as an environmental justice site as designated by the US Environmental Protection Agency. Further, we integrate the ISCST3 air dispersion model with Geographic Information Systems (GIS) to identify the number of people potentially affected by a particular facility, and engage the problem of non-point sources of diesel emissions with an analysis of the street network.

The effects of fire on vegetation vary based on the properties and amount of existing biomass (or fuel) in a forest stand, weather conditions, and topography. Identifying controls over the spatial patterning of fire-induced vegetation change, or fire severity, is critical in understanding fire as a landscape scale process. We use gridded estimates of fire severity, derived from Landsat ETM+ imagery, to identify the biotic and abiotic factors contributing to the observed spatial patterns of fire severity in two large natural fires. Regression tree analysis indicates the importance of weather, topography, and vegetation variables in explaining fire severity patterns between the two fires. Relative humidity explained the highest proportion of total sum of squares throughout the Hoover fire (Yosemite National Park, 2001). The lowest fire severity corresponded with increased relative humidity. For the Williams fire (Sequoia/Kings Canyon National Parks, 2003) dominant vegetation type explains the highest proportion of sum of squares. Dominant vegetation was also important in determining fire severity throughout the Hoover fire. In both fires, forest stands that were dominated by lodgepole pine (Pinus contorta) burned at highest severity, while red fir (Abies magnifica) stands corresponded with the lowest fire severities. There was evidence in both fires that lower wind speed corresponded with higher fire severity, although the highest fire severity in the Williams fire occurred during increased wind speed. Additionally, in the vegetation types that were associated with lower severity, burn severity was lowest when the time since last fire was fewer than 11 and 17 years for the Williams and Hoover fires, respectively. Based on the factors and patterns identified, managers can anticipate the effects of management ignited and naturally ignited fires at the forest stand and the landscape levels.

This study investigated how changes in salt marsh soil properties and topography on sediment fans related to shifts in salt marsh plant community composition in the Elkhorn Slough Watershed, California, USA. Several sediment fans have formed in this watershed as soil eroding from farms moved downslope, filling marshes, mudflats, and channels. Sandy sediment deposition increased marsh plain elevation and altered edaphic properties by increasing bulk density and decreasing soil moisture, salinity, and soil nitrogen compared to reference sites. These changes created a strong wetland- upland gradient and influenced the development of well-defined vegetation zones from wetland to upland: pickleweed (Salicornia virginica), cattail (Typha spp.) and bulrush (Scirpus spp.), and arroyo willow (Salix lasiolepis). Based on statistical analysis, arroyo willow grew in a distinct edaphic environment, and its expansion into the salt marsh was restricted by elevation in tidal areas greater than 1.80 m NAVD 88, spring soil moisture levels lower than 20%, and year-round salinity levels lower than 2.67 dS/m. Cattail and bulrush were present in transitional environmental conditions with fluctuating salinity and at an elevation similar to that of the pickleweed community. The hydrogeologic setting played a part in this change, as the contribution of upland sandy soils likely facilitated the emergence of new edaphic properties including lower salinity, lower soil moisture, and reduced soil nutrients. The findings in this study underline the importance of on-going erosion-control efforts to estuarine conservation in Central California.

In this paper, we evaluate the capability of the high spatial resolution airborne Digital Airborne Imaging System (DAIS) imagery for detailed vegetation classification at the alliance level with the aid of ancillary topographic data. Image objects as minimum classification units were generated through the Fractal Net Evolution Approach (FNEA) segmentation using eCognition software. For each object, 52 features were calculated including spectral features, textures, topographic features, and geometric features. After statistically ranking the importance of these features with the classification and regression tree algorithm (CART), the most effective features for classification were used to classify the vegetation. Due to the uneven sample size for each class, we chose a non-parametric (nearest neighbor) classifier. We built a hierarchical classification scheme and selected features for each of the broadest categories to carry out the detailed classification, which significantly improved the accuracy. Pixel-based maximum likelihood classification (MLC) with comparable features was used as a benchmark in evaluating our approach. The objectbased classification approach overcame the problem of saltand-pepper effects found in classification results from traditional pixel-based approaches. The method takes advantage of the rich amount of local spatial information present in the irregularly shaped objects in an image. This classification approach was successfully tested at Point Reyes National Seashore in Northern California to create a comprehensive vegetation inventory. Computer-assisted classification of high spatial resolution remotely sensed imagery has good potential to substitute or augment the present ground-based inventory of National Park lands.

Sudden Oak Death is a new and virulent disease affecting hardwood forests in coastal California. The spatial–temporal dynamics of oak mortality at the landscape scale are crucial indicators of disease progression. Modeling disease spread requires accurate mapping of the dynamic pattern of oak mortality in time through multi-temporal image analysis. Traditional mapping approaches using per-pixel, single-date image classifications have not generated consistently satisfactory results. Incorporation of spatial–temporal contextual information can improve these results. In this paper, we propose a spatial–temporally explicit algorithm to classify individual images using the spectral and spatial–temporal information derived from multiple co-registered images. This algorithm is initialized by a spectral classification using Support Vector Machines (SVM) for each individual image. Then, a Markov Random Fields (MRF) model accounting for ecological compatibility is used to model the spatial–temporal contextual prior probabilities of images. Finally, an iterative algorithm, Iterative Conditional Mode (ICM), is used to update the classification based on the combination of the initial SVM spectral classifications and MRF spatial–temporal contextual model. The algorithm was applied to two-year (2000, 2001) ADAR (Airborne Data Acquisition and Registration) images, from which three classes (bare, dead, forest) are detected. The results showed that the proposed algorithm achieved significantly better results (Year 2000: Kappa = 0.92; Year 2001: Kappa = 0.91), compared to traditional pixel-based single-date approaches (Year 2000: Kappa = 0.67; Year 2001: Kappa = 0.66). The improvement from the contributions of spatial–temporal contextual information indicated the importance of spatial–temporal modeling in multi-temporal remote sensing in general and forest disease modeling in particular.

Relationships between land use or land cover and embeddedness, a measure of fine sediment in spawning gravels, were examined at multiple scales across 54 streams in the Russian River Basin, California. The results suggest that coarse-scale measures of watershed land use can explain a large proportion of the variability in embeddedness and that the explanatory power of this relationship increases with watershed size. Agricultural and urban land uses and road density were positively associated with embeddedness, while the opposite was true for forest cover. The ability of land use and land cover to predict embeddedness varied among five zones of influence, with the greatest explanatory power occurring at the entire-watershed scale. Land use within a more restricted riparian corridor generally did not relate to embeddedness, suggesting that reach-scale riparian protection or restoration will have little influence on levels of fine sediment. The explanatory power of these models was greater when conducted among a subset of the largest watersheds (maximum r2 = 0.73) than among the smallest watersheds (maximum r2 = 0.46).

Sudden oak death (SOD), caused by Phytophthora ramorum, is killing oaks and tanoaks in the Coast Ranges of California, from Monterey County to Humboldt County. In March 2000, 20 disease progression plots were established in Marin County, California, to characterize the progress of disease symptoms, and to determine the fidelity of the association of three or more bark and ambrosia beetle species (Coleoptera: Scolytidae) with diseased oaks and tanoaks. Symptoms of sudden oak death and signs of associated organisms were recorded from coast live oaks (Quercus agrifolia), California black oaks (Q. kelloggii), valley oaks (Q. lobata), and tanoaks (Lithocarpus densiflorus), four times per year, from March 2000 through March 2003. Symptoms and signs in Q. agrifolia progressed from bleeding, to infestation by scolytid beetles, to the development of fruiting structures of the fungus Hypoxylon thouarsianum. Mortality of symptomatic trees increased from 2000 to 2003 as follows: Q. agrifolia (n = 668), 5.8‐17.4%; Q. kelloggii (n = 53), 3.8‐9.4%; and L. densiflorus (n = 164), 8.3‐22.2%. All 31 Q. lobata remained asymptomatic. From 2000 to 2003, bleeding trees were 25.0‐23.6% of living Q. agrifolia, 15.5‐25.0% of Q. kelloggii, and 39.0‐62.4% of L. densiflorus. Scolytid beetles colonized more than 95% of the living symptomatic Q. agrifolia that subsequently died. Same-symptom cohorts were followed from March 2000 through March 2003. In the asymptomatic Q. agrifolia cohort, 12.0% developed bleeding by 2003. For the bleeding only cohort, 22.7% of Q. agrifolia died, but 73.5% of the beetle-colonized bleeding cohort died. Bleeding developed in 40.9% of the initially asymptomatic L. densiflorus cohort. By 2003, 24.6% of the initially bleeding L. densiflorus cohort had died. Both Weibull and Cox Proportional Hazards regression were

In the 1920s and 1930s Albert Everett Wieslander and several others explored much of California’s wilderness sampling vegetation, taking photographs, collecting specimens, and drawing detailed maps of what they found. The collection is now known as the Wieslander Vegetation Type Mapping (VTM) collection, and the entire survey encompassed nearly seventy million acres of the state, covering most of the wild areas exclusive of the deserts and the larger agricultural areas. These data represent a valuable resource for comparative and conservation ecology. We are digitizing the entire project, and making it available to researchers and the public. The current ‘‘VTM Digitization’’ Project, as it is known, is described in this article, and is a collaborative effort by teams at University of California Berkeley and University of California Davis, funded by the U.S. Forest Service and U.S. Department of Agriculture’s Cooperative State Research, Education and Extension Service. The VTM Digitization Project aims to digitize the entire VTM collection for use in ecological and geospatial analyses, and to facilitate access and distribution of the data by researchers and interested parties. This article describes our efforts at making the VTM plot data, maps and photographs digital, spatially referenced, readily available, and web-accessible via an Internet-based Geographic Information System (webGIS) application. In addition, we discuss potential uses for the data and caveats associated with its use, particularly spatial accuracy.

A stratified method of separating dead tree crowns from the forest mosaic, especially bare soil, is proposed. Both spectral and spatial information extracted from 1 m high-resolution digital airborne imagery was used. First, spectrally homogeneous objects were recognized based on a Normalized Difference Vegetation Index (NDVI) image. From this image, large patches of bare soil and small patches of noise were excluded by object-size thresholding. Next, an incomplete bare soil image was generated from thresholding the red band using a histogram-based method. A region-based subtraction algorithm was developed and performed on the homogeneous object image and the bare soil image to remove most small patches of bare soil. Finally, medium-sized patches of bare soil were deleted using the assumption that bare soil areas are spatially much closer to larger, pure bare soil areas. The method proved to be effective. The classification accuracy of dead tree crowns was increased from 21% to 75%. Index Terms-Sudden Oak Death, hardwood forest, remote sensing, NDVI, object size, homogeneous object, region-based subtraction, high-resolution imagery.

In the central California coastal forests, a newly discovered virulent pathogen ( Phytophthora ramorum ) has killed hundreds of thousands of native oak trees. Predicting the potential distribution of the disease in California remains an urgent demand of regulators and scientists. Most methods used to map potential ranges of species (e.g. multivariate or logistic regression) require both presence and absence data, the latter of which are not always feasibly collected, and thus the methods often require the generation of ‘pseudo’ absence data. Other methods (e.g. BIOCLIM and DOMAIN) seek to model the presence-only data directly. In this study, we present alternative methods to conventional approaches to modeling by developing support vector machines (SVMs), which are the new generation of machine learning algorithms used to find optimal separability between classes within datasets, to predict the potential distribution of Sudden Oak Death in California. We compared the performances of two types of SVMs models: two-class SVMs with ‘pseudo’ absence data and one-class SVMs. Both models performed well. The one-class SVMs have a slightly better true-positive rate (0.9272 ± 0.0460 S.D.) than the two-class SVMs (0.9105 ± 0.0712 S.D.). However, the area predicted to be at risk for the disease using the one-class SVMs (18,441 km 2 ) is much larger than that of the two-class SVMs (13,828 km 2 ). Both models show that the majority of disease risk will occur in coastal areas. Compared with the results of two-class SVMs, the one-class SVMs predict a potential risk in the foothills of the Sierra Nevada mountain ranges; much greater risks are also found in Los Angles and Humboldt Counties. We believe the support vector machines when coupled with geographic information system (GIS) will be a useful method to deal with presence-only data in ecological analysis over a range of scales. © 2004 Elsevier B.V. All rights reserved.

Freshwater research and management efforts could be greatly enhanced by a better understanding of the relationship between landscape-scale factors and water quality indicators. This is particularly true in urban areas, where land transformation impacts stream systems at a variety of scales. Despite advances in landscape quantification methods, several studies attempting to elucidate the relationship between land use/ land cover (LULC) and water quality have resulted in mixed conclusions. However, these studies have largely relied on compositional landscape metrics. For urban and urbanizing watersheds in particular, the use of metrics that capture spatial pattern may further aid in distinguishing the effects of various urban growth patterns, as well as exploring the interplay between environmental and socioeconomic variables. However, to be truly useful for freshwater applications, pattern metrics must be optimized based on characteristic watershed properties and common water quality point sampling methods. Using a freely available LULC data set for the Santa Clara Basin, California, USA, we quantified landscape composition and configuration for subwatershed areas upstream of individual sampling sites, reducing the number of metrics based on: (1) sensitivity to changes in extent and (2) redundancy, as determined by a multivariate factor analysis. The first two factors, interpreted as (1) patch density and distribution and (2) patch shape and landscape subdivision, explained approximately 85% of the variation in the data set, and are highly reflective of the heterogeneous urban development pattern found in the study area. Although offering slightly less explanatory power, compositional metrics can provide important contextual information.

In Pacific Coast salt marshes, only color and color IR aerial photography provide the spatial, spectral, and temporal resolution required to conduct long-term historic time series analysis of wetland change at the plant community level. We used historic aerial photographs with manual and automated image classification techniques to discern decadal-scale changes to salt marshes in Elkhorn Slough, California caused by off-farm sedimentation from 1971 to 2001. Change detection identified a process of plant succession that led to arroyo willow encroachment into pickleweed marsh. Changes observed were considered within the context of additional land use changes over a greater regional and time scale.

Attacks by the oak ambrosia beetle (Monothrum scutellare) accelerated and increased the amount of wood decay in stems of downed coast live oak (Quercus agrifolia) trees. When permethrin insecticide was sprayed on oak bark surface, the ambrosia beetles produced only one-fourth as many galleries in the sapwood as compared to sapwood beneath the unsprayed bark surface. Although decay fungi initiated infection in oak in the absence of beetles, the amount of wood decay was much greater in trees with beetle activity. The ambrosia beetles deeply penetrated the sapwood and constructed extensive galleries that hastened wood decay by decay pathogens. The insecticide inhibited beetle attack and formation of egg galleries and greatly decreased the amount of wood decay.

Livestock grazing appears a viable and useful vegetation management tool in the Sierra Nevada Foothills. DOI:10.2458/azu_rangelands_v26i2_wacker

We investigated the ability of high spatial-resolution 4-band imagery (Airborne Digital Acquisition and Registration - ADAR) to discern moisture stress in trees affected by Sudden Oak Death (SOD). We wanted to test if the imagery could be used to distinguish between green oak trees with advanced SOD trunk symptoms, and green oaks with no SOD trunk symptoms. ADAR imagery of China Camp State Park in Marin County, California was flown in spring 2000 and 2001. Training samples from the field consisting of the locations green healthy oaks and green symptomatic oaks were used to derive spectral signatures for the two classes. Both hierarchical unsupervised classification (HUC) and maximum likelihood classification (MLC) were used to classify the imagery. Accuracy assessment and other spectral measurements were performed to analyze the separability of the two signatures. Poor overall accuracy 55.17% was obtained by the HUC method. A better overall accuracy 74.19% was obtained by MLC method, but the low transformed divergence (1448) indicated poor separability of the training samples. The poor accuracy results can be explained by the fact that ADAR image has relatively broad spectral bands that combine narrow moisture- stress-sensitive regions with broader stress-insensitive regions; such combination could decrease the capability of ADAR to detect moisture stress. In addition, healthy oaks in the area display a marked variability in canopy condition, making it difficult to separate healthy trees from those experiencing some stress. In conclusion, this research indicated the inability to automate mapping of moisture stress in oaks using ADAR imagery, and limited success in using methods that require extensive field data.

Question: Previous interpretations of the variance plot of paired quadrat variance method (PQV) have been incomplete. The objective of this study was to clarify the interpretation of PQV, and to shed additional light on how different quadrat variance methods can be used, in concert, to measure scale in transect data. Methods: We used artificial and real data to examine how the PQV method elucidates spatial pattern. Two-term local quadrat variance (TTLQV) and new local variance (NLV) methods, together with their three-term counterparts, were also applied to the same data sets, and the results from all methods were compared. Results: When the mean gap size equalled the mean patch size along a transect, the first peak of the variance of PQV, NLV and TTLQV corresponded with the gap size (or patch size). However, if the mean gap size and patch size were unequal, the variance plot of PQV displayed a flat-topped plateau, in which the first inflection represented the mean size of the smaller phase and the second inflection represented the mean size of the larger phase; TTLQV showed a clear peak and NLV displayed a distinct first peak while the second inflection was dampened. The results also indicated than the three-term versions of quadrat variance methods did not consistently outperform their two-term counterparts, and often confused the interpretation of scale. Conclusions: The quadrat variance methods associated with the patch-gap measurements were able to efficiently detect not only the size of patches, but also the size of gaps.

Whether tracking invasive species, assessing water quality, or monitoring the spread of disease, comprehensive data collection is a key component of sustainable natural resource management. Increasingly, fostering community-based monitoring is seen as a valuable way to augment data gathering and enhance public involvement in environmental management. However, growing quantities of data and increasing interest from the public and decision makers create technical data storage and access issues. Although not yet widely used in natural resource management, Web-based Geographic Information Systems (webGIS), a hybrid of GIS and Internet technologies, are a promising option for entering and storing heterogeneous datasets, indexed by location, and making them widely available in a visual, dynamic, and interactive format. Although webGIS has the potential to increase public participation in environmental management, there are technical, institutional, and social challenges to its implementation and usage that need to be addressed, including differential Internet access, training, and privacy.

© 2014 by the Society for Range Management. On the Ground California landownerships are changing-becoming smaller and more amenity-driven, with important implications for ecosystem service production. Residence on the property, larger property size, source of income from the land, having a long-term outlook, and using an advisory service are associated with landowner management for ecosystem services for the owner and for society. Advisory services like Cooperative Extension and the Natural Resources Conservation Service, as well as private consultants and professional organizations, have an important role in the future of ecosystem service production.

Background The role that environmental factors, such as neighborhood socioeconomics, food, and physical environment, play in the risk of obesity and chronic diseases is not well quantified. Understanding how spatial distribution of disease risk factors overlap with that of environmental (contextual) characteristics may inform health interventions and policies aimed at reducing the environment risk factors. We evaluated the extent to which spatial clustering of extreme body mass index (BMI) values among a large sample of adults with diabetes was explained by individual characteristics and contextual factors. Methods We quantified spatial clustering of BMI among 15,854 adults with diabetes from the Diabetes Study of Northern California (DISTANCE) cohort using the Global and Local Moran’s I spatial statistic. As a null model, we assessed the amount of clustering when BMI values were randomly assigned. To evaluate predictors of spatial clustering, we estimated two linear models to estimate BMI residuals. First we included individual factors (demographic and socioeconomic characteristics). Then we added contextual factors (neighborhood deprivation, food environment) that may be associated with BMI. We assessed the amount of clustering that remained using BMI residuals. Results Global Moran’s I indicated significant clustering of extreme BMI values; however, after accounting for individual socioeconomic and demographic characteristics, there was no longer significant clustering. Twelve percent of the sample clustered in extreme high or low BMI clusters, whereas, only 2.67% of the sample was clustered when BMI values were randomly assigned. After accounting for individual characteristics, we found clustering of 3.8% while accounting for neighborhood characteristics resulted in 6.0% clustering of BMI. After additional adjustment of neighborhood characteristics, clustering was reduced to 3.4%, effectively accounting for spatial clustering of BMI. Conclusions We found substantial clustering of extreme high and low BMI values in Northern California among adults with diabetes. Individual characteristics explained somewhat more of clustering of the BMI values than did neighborhood characteristics. These findings, although cross-sectional, may suggest that selection into neighborhoods as the primary explanation of why individuals with extreme BMI values live close to one another. Further studies are needed to assess causes of extreme BMI clustering, and to identify any community level role to influence behavior change. Electronic supplementary material The online version of this article (doi:10.1186/1476-072X-13-48) contains supplementary material, which is available to authorized users.

We investigated the relationship between LiDAR and ladder fuels in the northern Sierra Nevada, California USA. Ladder fuels are often targeted in hazardous fuel reduction treatments due to their role in propagating fire from the forest floor to tree crowns. Despite their importance, ladder fuels are difficult to quantify. One common approach is to calculate canopy base height, but this has many potential sources of error. LiDAR may be a way forward to better characterize ladder fuels, but has only been used to address this question peripherally and in only a few instances. After establishing that landscape fuel treatments reduced canopy and ladder fuels at our site, we tested which LiDAR-derived metrics best differentiated treated from untreated areas. The percent cover between 2 and 4 m had the most explanatory power to distinguish treated from untreated pixels across a range of spatial scales. When compared to independent plot-based measures of ladder fuel classes, this metric differentiated between high and low levels of ladder fuels. These findings point to several immediate applications for land managers and suggest new avenues of study that could lead to possible improvements in the way that we model wildfire behavior across forested landscapes in the US.

Tidal marshes maintain elevation relative to sea level through accumulation of mineral and organic matter, yet this dynamic accumulation feedback mechanism has not been modeled widely in the context of accelerated sea-level rise. Uncertainties exist about tidal marsh resiliency to accelerated sea-level rise, reduced sediment supply, reduced plant productivity under increased inundation, and limited upland habitat for marsh migration. We examined marsh resiliency under these uncertainties using the Marsh Equilibrium Model, a mechanistic, elevation-based soil cohort model, using a rich data set of plant productivity and physical properties from sites across the estuarine salinity gradient. Four tidal marshes were chosen along this gradient: two islands and two with adjacent uplands. Varying century sea-level rise (52, 100, 165, 180 cm) and suspended sediment concentrations (100%, 50%, and 25% of current concentrations), we simulated marsh accretion across vegetated elevations for 100 years, applying the results to high spatial resolution digital elevation models to quantify potential changes in marsh distributions. At low rates of sea-level rise and mid-high sediment concentrations, all marshes maintained vegetated elevations indicative of mid/high marsh habitat. With century sea-level rise at 100 and 165 cm, marshes shifted to low marsh elevations; mid/high marsh elevations were found only in former uplands. At the highest century sea-level rise and lowest sediment concentrations, the island marshes became dominated by mudflat elevations. Under the same sediment concentrations, low salinity brackish marshes containing highly productive vegetation had slower elevation loss compared to more saline sites with lower productivity. A similar trend was documented when comparing against a marsh accretion model that did not model vegetation feedbacks. Elevation predictions using the Marsh Equilibrium Model highlight the importance of including vegetation responses to sea-level rise. These results also emphasize the importance of adjacent uplands for long-term marsh survival and incorporating such areas in conservation planning efforts.

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (

Coastal marshes depend on belowground biomass of roots and rhizomes to contribute to peat and soil organic carbon, accrete soil and alleviate flooding as sea level rises. For nutrient-limited plants, eutrophication has either reduced or stimulated belowground biomass depending on plant biomass allocation response to fertilization. Within a freshwater wetland impoundment receiving minimal sediments, we used experimental plots to explore growth models for a common freshwater macrophyte, Schoenoplectus acutus. We used N-addition and control plots (4 each) to test whether remotely sensed vegetation indices could predict leaf N concentration, root:shoot ratios and belowground biomass of S. acutus. Following 5 months of summer growth, we harvested whole plants, measured leaf N and total plant biomass of all above and belowground vegetation. Prior to harvest, we simulated measurement of plant spectral reflectance over 164 hyperspectral Hyperion satellite bands (350-2500 nm) with a portable spectroradiometer. N-addition did not alter whole plant, but reduced belowground biomass 36% and increased aboveground biomass 71%. We correlated leaf N concentration with known N-related spectral regions using all possible normalized difference (ND), simple band ratio (SR) and first order derivative ND (FDN) and SR (FDS) vegetation indices. FDN(1235, 549) was most strongly correlated with leaf N concentration and also was related to belowground biomass, the first demonstration of spectral indices and belowground biomass relationships. While S. acutus exhibited balanced growth (reduced root:shoot ratio with respect to nutrient addition), our methods also might relate N-enrichment to biomass point estimates for plants with isometric root growth. For isometric growth, foliar N indices will scale equivalently with above and belowground biomass. Leaf N vegetation indices should aid in scaling-up field estimates of biomass and assist regional monitoring.

The tools of landscape ecology are used to compare oak woodland landscapes in California and Spain. The linkages between spatial patterns and functional processes are explored using Geographic Information System and remote sensing technologies, drawing on available databases in each country. The Spanish Rural Landscape Monitoring System (SISPARES) methodology is applied and tested in an attempt to answer questions about the comparative structure, patterns, and changes in oak woodlands in Spain and California. Landform at the macro scale was found to be the main driver for the mid and long-term for oak woodland pattern and distribution. Spanish woodlands are most frequently located in peneplains with acid bedrock, creating large contiguous areas of woodland, likely managed as dehesa. Californian oak woodland patches are smaller, highly dispersed, and intermixed with chaparral and dense forest in foothills with acid bedrock. Agro-silvo-pastoral practices are also important in shaping oak woodlands, especially in Spain. New land uses are increasing in both countries, such as agro-tourism, game hunting, golf courses, and suburban development, but their impacts are much greater in California. Oak woodlands in California are located in areas of much higher fire vulnerability because of the intermix with flammable brush, forest, and urban sprawl. The oak woodlands that are most similar in Spain and California, according to the TWINSPAN classification, are those in Spain close to the mountains, and in California located near the coast.

The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.

Introduction Small food stores are prevalent in urban neighborhoods, but the availability of nutritious food at such stores is not well known. The objective of this study was to determine whether data from 3 sources would yield a single, homogenous, healthful food store category that can be used to accurately characterize community nutrition environments for public health research. Methods We conducted in-store surveys in 2009 on store type and the availability of nutritious food in a sample of nonchain food stores (n = 102) in 6 predominantly urban counties in Northern California (Alameda, Contra Costa, Marin, Sacramento, San Francisco, and Santa Clara). We compared survey results with commercial database information and neighborhood sociodemographic data by using independent sample t tests and classification and regression trees. Results Sampled small food stores yielded a heterogeneous group of stores in terms of store type and nutritious food options. Most stores were identified as convenience (54%) or specialty stores (22%); others were small grocery stores (19%) and large grocery stores (5%). Convenience and specialty stores were smaller and carried fewer nutritious and fresh food items. The availability of nutritious food and produce was better in stores in neighborhoods that had a higher percentage of white residents and a lower population density but did not differ significantly by neighborhood income. Conclusion Commercial databases alone may not adequately categorize small food stores and the availability of nutritious foods. Alternative measures are needed to more accurately inform research and policies that seek to address disparities in diet-related health conditions.