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The Forest Stewardship Council (FSC) aims to promote environmentally responsible forest management globally. However, quantifiable evidence of effects of FSC on forest management practices ...

Treatments to reduce forest fuels are often performed in forests to enhance forest health, regulate stand density, and reduce the risk of wildfires. Although commonly employed, there are concerns that these forest fuel treatments FTs may have negative impacts on certain wildlife species. Often FTs are planned across large landscapes, but the actual treatment extents can differ from the planned extents due to operational constraints and protection of resources e.g. perennial streams, cultural resources, wildlife habitats. Identifying the actual extent of the treated areas is of primary importance to understand the environmental influence of FTs. Light detection and ranging lidar is a powerful remote-sensing tool that can provide accurate measurements of forest structures and has great potential for monitoring forest changes. This study used the canopy height model CHM and canopy cover CC products derived from multi-temporal airborne laser scanning ALS data to monitor forest changes following the implementation of landscape-scale FT projects. Our approach involved the combination of a pixel-wise thresholding method and an object-of-interest OBI segmentation method. We also investigated forest change using normalized difference vegetation index NDVI and standardized principal component analysis from multi-temporal high-resolution aerial imagery. The same FT detection routine was then applied to compare the capability of ALS data and aerial imagery for FT detection. Our results demonstrate that the FT detection using ALS-derived CC products produced both the highest total accuracy 93.5% and kappa coefficient κ 0.70, and was more robust in identifying areas with light FTs. The accuracy using ALS-derived CHM products the total accuracy was 91.6%, and the κ was 0.59 was significantly lower than that using ALS-derived CC, but was still higher than using aerial imagery. Moreover, we also developed and tested a method to recognize the intensity of FTs directly from pre-and post-treatment ALS point clouds.

Collaborative adaptive management (CAM) is an appropriate management regime for social–ecological systems because it aims to reduce management uncertainties and fosters collaboration among diverse stakeholders. We evaluate the effectiveness of CAM in fostering collaboration among contentious multiparty environmental stakeholders based on the Sierra Nevada Adaptive Management Project (SNAMP). Our evaluation focuses on facilitated public multiparty discussions (2005–2012). Self-organizing maps (SOM), an unsupervised machine-learning method, were used to process, organize, and visualize the public meeting notes. We found that public discussion remained focused on the project content, yet the more contentious and critical issues dominated the discussions through time. Integration across topics could be improved. These results suggest that SNAMP collaborative adaptive management seems to have helped participants focus on the key issues, as well as advancing their discussions over time. Given the effectiveness ...

Ecosystem management requires cross-jurisdictional problem-solving and, when private lands are involved, cross-boundary cooperation from many individual landowners. Fragmented ownership patterns and variation in ownership values, as well as distrust and transaction costs, can limit cooperation. Results from a landowner survey in California were analyzed using an audience segmentation approach. Landowners were grouped into four clusters according to ownership motivations: rural lifestyle, working landscape, natural amenity, and financial investment. All clusters showed willingness to cooperate for all three topics addressed in the survey (pest and disease control, fire hazard reduction, and wildlife conservation), but their degree of willingness differed by cluster, who they were expected to cooperate with, and the natural resource problem addressed. All were more willing to cooperate with neighbors and local groups than with state and federal agencies. Landowners were most willing to cooperate to reduce f...

Monitoring productivity in coastal wetlands is important due to their high carbon sequestration rates and potential role in climate change mitigation. We tested agricultural- and forest-based methods for estimating the fraction of absorbed photosynthetically active radiation (f APAR), a key parameter for modelling gross primary productivity (GPP), in a restored, managed wetland with a dense litter layer of non-photosynthetic vegetation, and we compared the difference in canopy light transmission between a tidally influenced wetland and the managed wetland. The presence of litter reduced correlations between spectral vegetation indices and f APAR. In the managed wetland, a two-band vegetation index incorporating simulated World View-2 or Hyperion green and near-infrared bands, collected with a field spectroradiometer, significantly correlated with f APAR only when measured above the litter layer, not at the ground where measurements typically occur. Measures of GPP in these systems are difficult to capture...

Web 2.0 technologies including Public Participation Geographic Information Systems (PPGIS) and Volunteered Geographic Information (VGI) provide methods for engaging multiple publics in public lands management. We examined the effects of sampling in a PPGIS/VGI application for national forest planning in the US. A random sample (RS) of households and a volunteer public (VP) were invited to participate in an internet-based PPGIS to identify national forest values and use preferences. Spatial and non-spatial group responses were analysed. The VP group expressed stronger utilitarian values and consumptive use preferences while the RS group preferred forest amenities. These results would lead to different planning decisions. PPGIS/VGI methods should include scientific sampling to ground-truth voluntary participation.

The interface between neogeography and citizen science has great potential for environmental monitoring, but this nexus has been explored less often than each subject individually. In this article we review the emerging role of volunteered geographic information in citizen science and present a case study of an integrated tool set that engages multiple types of users (from targeted citizen-based observation networks, expert-driven focused monitoring, and opportunistic crowdsourcing efforts) in monitoring a forest disease in the western United States. We first introduce the overall challenge of data collection in environmental monitoring projects and then discuss the literature surrounding an emergent integration of citizen science and volunteered geographical information. We next explore how these methods characterize and underpin knowledge discovery and how multimodal interaction is supported so that a large spectrum of contributors can be included. These concepts are summarized in a conceptual model tha...

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.

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...

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.

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 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.

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.