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Recent developments in technology and data processing for Unoccupied Aerial Vehicles (UAVs) have revolutionized the scope of ecosystem monitoring, providing novel pathways to fill the critical gap between limited-scope field surveys and limited-customization satellite and piloted aerial platforms. These advances are especially ground-breaking for supporting management, restoration, and conservation of landscapes with limited field access and vulnerable ecological systems, particularly wetlands. This study presents a scoping review of the current status and emerging opportunities in wetland UAV applications, with particular emphasis on ecosystem management goals and remaining research, technology, and data needs to even better support these goals in the future. Using 122 case studies from 29 countries, we discuss which wetland monitoring and management objectives are most served by this rapidly developing technology, and what workflows were employed to analyze these data. This review showcases many ways in which UAVs may help reduce or replace logistically demanding field surveys and can help improve the efficiency of UAV-based workflows to support longer-term monitoring in the face of wetland environmental challenges and management constraints. We also highlight several emerging trends in applications, technology, and data and offer insights into future needs.

The fencing of protected areas is increasing worldwide. However, the implementation of fences for conservation has outpaced scientific assessment of their effectiveness, non-target impacts, and long-term costs. We assessed landscape predictors of fence crossing sites and employed camera traps over a one-year period to investigate wildlife responses to a conservation fence around Lake Nakuru National Park, Kenya. Specifically, we measured the impact of the fence on wild mammal movement, and the temporal impacts of fence maintenance on wildlife crossings and behavior. Cameras captured more than 65,000 detections of animals approaching fences, with 3626 observed crossings over 2818 trap nights at 19 sites. Using these data, we developed a guide to classifying fence-specific mammal behaviors. Thirty-eight wild mammal species approached known weak points in the fence, and 27 species were recorded crossing the fence. No single environmental variable predicted detection or fence crossing points for all species, but seasonality, human activity, habitat visibility, and proximity to an adjacent protected area were each correlated with species-specific crossing locations. Additionally, breaches of repaired fence-crossing locations occurred within days of maintenance. We conclude that popular, ‘one-size-fits-all’, conservation fence designs may be ineffective and costly for restraining movement of many wildlife species. We recommend that those deploying conservation fences start with clearly articulated management goals, that fence maintenance be informed by taxa-specific tendencies to breach fences, and that managers consider the strategic creation of wildlife corridors, overpasses, or ungulate-proof fences to link fenced protected areas with surrounding habitat.

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

Forest ecosystems in the American west have long been influenced by timber harvests and fire suppression, and recently through treatments that reduce fuel for fire management. Precisely quantifying the structural changes to forests caused by fuel treatments is an essential step to evaluate their impacts. Satellite imagery-derived vegetation indices, such as the normalized difference vegetation index (NDVI), have been widely used to map forest dynamics. However, uncertainties in using these vegetation indices to quantify forest structural changes have not been thoroughly studied, mainly due to the lack of wall-to-wall validation data. In this study we generated forest structural changes in aboveground biomass (AGB) and canopy cover as a result of fuel treatments using bi-temporal airborne light detection and ranging (LiDAR) data and field measurements in a mixed coniferous forest of northern Sierra Nevada, California, USA. These LiDAR-derived forest structural measures were used to evaluate the uncertainties of using Landsat-derived vegetation indices to quantify treatments. Our results confirmed that vegetation indices can accurately map the extents of forest disturbance and canopy cover changes caused by fuel treatments, but the accuracy in quantifying AGB changes varied by the pre-treatment forest densities and treatment intensity. Changes in vegetation indices had relatively weaker correlations (coefficient of determination 700 Mg/ha), than in forests with moderate-density (AGB between 100 Mg/ha and 700 Mg/ha) before the disturbances. Moreover, understory treatments (canopy height

In the western United States, Ixodes pacificus Cooley & Kohls (Acari: Ixodidae) is the primary vector of the agents causing Lyme disease and granulocytic anaplasmosis in humans. The geographic distribution of the tick is associated with climatic variables that include temperature, precipitation, and humidity, and biotic factors such as the spatial distribution of its primary vertebrate hosts. Here, we explore (1) how climate change may alter the geographic distribution of I. pacificus in California, USA, during the 21st century, and (2) the spatial overlap among predicted changes in tick habitat suitability, land access, and ownership. Maps of potential future suitability for I. pacificus were generated by applying climate-based species distribution models to a multi-model ensemble of climate change projections for the Representative Concentration Pathway (RCP) 4.5 (moderate emission) and 8.5 (high emission) scenarios for two future periods: mid-century (2026-2045) and end-of-century (2086-2099). Areas climatically-suitable for I. pacificus are projected to expand by 23% (mid-century RCP 4.5) to 86% (end-of-century RCP 8.5) across California, compared to the historical period (1980-2014), with future estimates of total suitable land area ranging from about 88 to 133 thousand km2, or up to about a third of California. Regions projected to have the largest area increases in suitability by end-of-century are in northwestern California and the south central and southern coastal ranges. Over a third of the future suitable habitat is on lands currently designated as open access (i.e. publicly available), and by 2100, the amount of these lands that are suitable habitat for I. pacificus is projected to more than double under the most extreme emissions scenario (from ~23,000 to >51,000 km2). Of this area, most is federally-owned (>45,000 km2). By the end of the century, 26% of all federal land in the state is predicted to be suitable habitat for I. pacificus. The resulting maps may facilitate regional planning and preparedness by informing public health and vector control decision-makers.

Fire suppression has been reported to homogenize landscapes in regions that historically experienced frequent wildfire. The Illilouette Creek Basin in Yosemite National Park experienced nearly 100 years of fire suppression, but after a change in management strategy it is now one of the few areas in the United States that has experienced a frequent fire regime for the past half-century. This study quantifies changing landscape properties in the Basin from its fire-suppressed state to the present. These landscape properties range from the relative dominance of different vegetation types to the spatial distribution of vegetation patches. This is the first detailed study of watershed-scale changes in overstory vegetation within a landscape transitioning from a fire suppressed condition to frequent, mixed severity wildfires. We mapped major vegetation types over time within Illilouette Creek Basin using high resolution aerial images from four different decades, starting with the final years of a fire-suppressed period and capturing multiple snapshots during forty years of repeated fires. We quantify landscape heterogeneity and vegetation patch shape properties using landscape metrics. From 1969 to 2012, conifer cover decreased by 24% while shrub area increased by 35%, sparse meadow area increased by 199% and dense meadows by 155%. The Shannon’s Evenness Index based on these four vegetation types increased from 0.4 to 0.7, indicating increased landscape heterogeneity. This study demonstrates that wildfires can return diversity to a fire-suppressed landscape, even after protracted fire suppression. Management of forests to restore fire regimes has the potential to maintain healthy, resilient landscapes in frequent fire-adapted ecosystems.

The increasing volume of remote sensing data with improved spatial and temporal resolutions generates unique opportunities for monitoring and mapping of crops. We compared multiple single-band and multi-band object-based time-constrained Dynamic Time Warping (DTW) classifications for crop mapping based on Sentinel-2 time series of vegetation indices. We tested it on two complex and intensively managed agricultural areas in California and Texas. DTW is a time-flexible method for comparing two temporal patterns by considering their temporal distortions in their alignment. For crop mapping, using time constraints in computing DTW is recommended in order to consider the seasonality of crops. We tested different time constraints in DTW (15, 30, 45, and 60 days) and compared the results with those obtained by using Euclidean distance or a DTW without time constraint. Best classification results were for time delays of both 30 and 45 days in California: 79.5% for single-band DTWs and 85.6% for multi-band DTWs. In Texas, 45 days was best for single-band DTW (89.1%), while 30 days yielded best results for multi-band DTW (87.6%). Using temporal information from five vegetation indices instead of one increased the overall accuracy in California with 6.1%. We discuss the implications of DTW dissimilarity values in understanding the classification errors. Considering the possible sources of errors and their propagation throughout our analysis, we had combined errors of 22.2% and 16.8% for California and 24.6% and 25.4% for Texas study areas. The proposed workflow is the first implementation of DTW in an object-based image analysis (OBIA) environment and represents a promising step towards generating fast, accurate, and ready-to-use agricultural data products.

This study proposed a new framework that combines pixel-level change detection and object-level recognition to detect changes of built-up land from high-spatial resolution remote sensing images. First, an adaptive differencing method was designed to detect changes at the pixel level based on both spectral and textural features. Next, the changed pixels were subjected to a set of morphological operations to improve the completeness and to generate changed objects, achieving the transition of change detection from the pixel level to the object level. The changed objects were further recognised through the difference of morphological building index in two phases to indicate changed objects on built-up land. The transformation from changed pixels to changed objects makes the proposed framework distinct with both the pixel-based and the object-based change detection methods. Compared with the pixel-based methods, the proposed framework can improve the change detection capability through the transformation and successive recognition of objects. Compared with the object-based method, the proposed framework avoids the issue of multitemporal segmentation and can generate changed objects directly from changed pixels. The experimental results show the effectiveness of the transformation from changed pixels to changed objects and the successive object-based recognition on improving the detection accuracy, which justify the application potential of the proposed change detection framework.

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.

Author(s): Beller, EE; Kelly, M; Larsen, LG | Abstract: Landscape changes such as urbanization can dramatically affect the provision of ecosystem services such as carbon storage. However, while cities have been shown to store substantial amounts of carbon in soils and vegetation, we have little information from long-term studies about how contemporary carbon storage in urban areas compares to carbon storage in the natural ecosystems that characterized these landscapes prior to urbanization. We used historical archival sources and land-cover data to quantify and map historical tree carbon storage in the now-urban Santa Clara Valley, California, USA prior to substantial Euro-American modification (ca. 1850) and to analyze change in the amount and distribution of carbon storage over the past ca. 160 years. We estimate that total tree carbon storage in the study area was ~784,000 to 2.2 million Mg (13.6–38.1 Mg C/ha) when the region was characterized by oak savanna and woodland habitats, compared to ~895,000 Mg C (15.5 Mg C/ha) today. This represents a non-significant gain of 14% to a significant loss of 60% depending on scenario. We also demonstrate changes in the spatial distribution of carbon on the landscape, as losses in carbon storage in areas of former oak woodland were partially offset by gains in carbon storage in historical habitat types that historically had few or no trees. This challenges the hypothesis that aboveground carbon storage increases with urbanization in Mediterranean-climate ecosystems due to irrigation and tree planting. Our study demonstrates the utility of using pre-1900s historical sources to reconstruct changes in ecosystem services such as carbon storage over century time scales.

Author(s): Blaschke, T; Kelly, M; Merschdorf, H | Abstract: Remote sensing, what it is and what it can be used for, is laid out in various chapters of this comprehensive book. We may only state here that remote sensing has a short history-when compared to traditional disciplines such as mathematics or physics. Contrarily, we may state that it has a long history when we compare it to recent Internet-based technology like social media or, closer to our field, the tracking of people and moving objects by means of cell phone signals. Remote sensing has been a domain for specialists for many years and to some degree it still is. Similarly, geographic information system (GIS) has for years been a field where professionals worked on designated workstations while not being fully integrated in standard corporate information technology infrastructures. The latter changed more than a decade ago, while for remote sensing only recently, one may still witness remnants of historical developments of Remote Sensing (RS)-specific hardware and software. The dominant concept in remote sensing has been the pixel, while GIS functionality has always been somehow splintered into the raster and vector domains. Blaschke and Strobl (2001) provocatively raised the question “What's wrong with pixels?” having identified an increasing dissatisfaction with pixelby-pixel image analysis. Although this critique was not new (Cracknell 1998; see also Blaschke and Strobl 2001; Burnett and Blaschke 2003; Blaschke 2010; Blaschke et al. 2014 for a more thorough discussion), these authors described a need for applications beyond pixels and for specific methods and methodologies that support this (Figure 14.1).

Ixodes pacificus Cooley & Kohls (Acari: Ixodidae), the primary vector of Lyme disease spirochetes to humans in the far-western United States, is broadly distributed across Pacific Coast states, but its distribution is not uniform within this large, ecologically diverse region. To identify areas of suitable habitat, we assembled records of locations throughout California where two or more I. pacificus were collected from vegetation from 1980 to 2014. We then employed ensemble species distribution modeling to identify suitable climatic conditions for the tick and restricted the results to land cover classes where these ticks are typically encountered (i.e., forest, grass, scrub-shrub, riparian). Cold-season temperature and rainfall are particularly important abiotic drivers of suitability, explaining between 50 and 99% of the spatial variability across California among models. The likelihood of an area being classified as suitable increases steadily with increasing temperatures >0°C during the coldest quarter of the year, and further increases when precipitation amounts range from 400 to 800 mm during the coldest quarter, indicating that areas in California with relatively warm and wet winters typically are most suitable for I. pacificus. Other consistent predictors of suitability include increasing autumn humidity, temperatures in the warmest month between 23 and 33°C, and low-temperature variability throughout the year. The resultant climatic suitability maps indicate that coastal California, especially the northern coast, and the western Sierra Nevada foothills have the highest probability of I. pacificus presence.

Accurate assessment of fire severity is essential for evaluating the economic loss and ecological impacts from fire, and forest restoration planning. Light Detection and Ranging (LiDAR) can delineate three-dimensional forest structure at individual tree to forest stand level. Here we proposed a new method (tree crown Profile Area Change, cPAC) to quantify fire severity for individual tree from pre- and post-fire LiDAR data. Individual tree crown was firstly segmented from pre-fire LiDAR derived canopy height model. Fire severity was then calculated as the profile area change between pre- and post-fire height percentile curves delineated from LiDAR point clouds within each tree segment. We applied this $c$ PAC method to assess the severity of the American fire, 2013 in Sierra Nevada forest, California, USA. Comparing with plot level field measurements, the fire severity quantified by our cPAC method was strongly correlated with in-situ basal area changes $(\mathbf{R}^{2}=0.75)$ , whereas other simple LiDAR-metrics (canopy cover change $\mathbf{R}^{2}=0.36$ , tree height change $\mathbf{R}^{2}=0.37$ ) or the Landsat imagery-derived difference in the Normalized Burn Ratio showed much weaker relationships $(\mathbf{R}^{2}=0.20)$ . Although the general fire spatial extents were consistent, our cPAC method outperformed Landsat-based and other simple LiDAR-metrics in illustrating the tree biomass loss at finer gradient with higher precision.

© 2018 Optical Society of America. Crown base height (CBH) is an essential tree biophysical parameter for many applications in forest management, forest fuel treatment, wildfire modeling, ecosystem modeling and global climate change studies. Accurate and automatic estimation of CBH for individual trees is still a challenging task. Airborne light detection and ranging (LiDAR) provides reliable and promising data for estimating CBH. Various methods have been developed to calculate CBH indirectly using regression-based means from airborne LiDAR data and field measurements. However, little attention has been paid to directly calculate CBH at the individual tree scale in mixed-species forests without field measurements. In this study, we propose a new method for directly estimating individual-tree CBH from airborne LiDAR data. Our method involves two main strategies: 1) removing noise and understory vegetation for each tree; and 2) estimating CBH by generating percentile ranking profile for each tree and using a spline curve to identify its inflection points. These two strategies lend our method the advantages of no requirement of field measurements and being efficient and effective in mixed-species forests. The proposed method was applied to a mixed conifer forest in the Sierra Nevada, California and was validated by field measurements. The results showed that our method can directly estimate CBH at individual tree level with a root-mean-squared error of 1.62 m, a coefficient of determination of 0.88 and a relative bias of 3.36%. Furthermore, we systematically analyzed the accuracies among different height groups and tree species by comparing with field measurements. Our results implied that taller trees had relatively higher uncertainties than shorter trees. Our findings also show that the accuracy for CBH estimation was the highest for black oak trees, with an RMSE of 0.52 m. The conifer species results were also good with uniformly high R2ranging from 0.82 to 0.93. In general, our method has demonstrated high accuracy for individual tree CBH estimation and strong potential for applications in mixed species over large areas.

© 2016 Elsevier Ltd. Rural electrification in developing countries is often hampered by major information gaps between local communities and urban centers, where technical expertise and funding are concentrated. The tool presented in this paper addresses these gaps to support the implementation of off-grid micro hydropower infrastructure. Specifically, we present a method to site, size and evaluate the potential for micro hydropower based on remote sensing data. The method improves on previous approaches by (i) incorporating the effect of hillslope topography on the optimal layout of the infrastructure, and (ii) accounting for the constraints imposed by streamflow variability and local electricity demand on the optimal size of the plants. An assessment of the method's performance against 148 existing schemes indicates that it correctly identifies the most promising locations for hydropower in Nepal, but does not generally reproduce the specific design features of constructed plants, which are affected by site-specific constraints. We develop a proof-of-concept computer tool to explore the potential of webGIS technology to account for these constraints by collecting site-specific information from local users.

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 ...

Some of the factors that can contribute to the success of collaborative adaptive management--such as social learning, open communication, and trust--are built upon a foundation of the open exchange of information about science and management between participants and the public. Despite the importance of information transparency, the use and flow of information in collaborative adaptive management has not been characterized in detail in the literature, and currently there exist opportunities to develop strategies for increasing the exchange of information, as well as to track information flow in such contexts. As digital information channels and networks have been increased over the last decade, powerful new information monitoring tools have also been evolved allowing for the complete characterization of information products through their production, transport, use, and monitoring. This study uses these tools to investigate the use of various science and management information products in a case study--the Sierra Nevada Adaptive Management Project--using a mixed method (citation analysis, web analytics, and content analysis) research approach borrowed from the information processing and management field. The results from our case study show that information technologies greatly facilitate the flow and use of digital information, leading to multiparty collaborations such as knowledge transfer and public participation in science research. We conclude with recommendations for expanding information exchange in collaborative adaptive management by taking advantage of available information technologies and networks.

Forests play a key role in the global carbon cycle, and forest above ground biomass (AGB) is an important indictor to the carbon storage capacity and the potential carbon pool size of a forest ecosystem. Accurate estimation of forest AGB has become increasingly important for a wide range of end-users. Although satellite remote sensing provides abundant observations to monitor forest coverage, validation of coarse-resolution AGB derived from satellite observations is difficult because of the scale mismatch between the footprints of satellite observations and field measurements. In this study, we use airborne Lidar to bridge the scale gaps between satellite-based and field-based studies, and evaluate satellite-derived indices to estimate regional forest AGB. We found that: (1) Lidar data can be used to accurately estimate forest AGB using tree height and tree quadratic height, (2) linear regression, among four tested models, achieve the best performance (R2 = 0.74; RMSE = 183.57 Mg/ha); (3) for MODIS-derived vegetation indices at varied spatial resolution (250–1000 m), accumulated NDVI, accumulated LAI, and accumulated FPAR could explain 53–74% variances of forest AGB, whereas accumulated NDVI derived from 1 km MODIS products gives higher R2 (74%) and lower RMSE (13.4 Mg/ha) than others. We conclude that Lidar data can be used to bridge the scale gap between satellite and field studies. Our results indicate that combining MODIS and Lidar data has the potential to estimate regional forest AGB.

Monitoring the effects of grazing on rangelands is crucial for ensuring sustainable rangeland ecosystem function and maintaining its conservation values. Residual dry matter (RDM), the dry grass biomass left on the ground at the end of the grazing season, is a commonly used proxy for rangeland condition in Mediterranean climates. Moderate levels of RDM are correlated with soil stability, forage production, wildlife habitat, and diversity of native plants. Therefore RDM is widely monitored on rangeland conservation properties. Current ground-based methods for RDM monitoring are expensive, are labor intensive, and provide information in the fall, after the effects of grazing have already occurred. In this paper we present a cost-effective, rapid, and robust methodology to monitor and predict RDM using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. We performed a time series analysis of three MODIS-based vegetation indices (VIs) measured over the period 2000–2012: Normalized D...

Light detection and ranging (Lidar) data can be used to create wall-to-wall forest structure and fuel products that are required for wildfire behavior simulation models. We know that Lidar-derived forest parameters have a non-negligible error associated with them, yet we do not know how this error influences the results of fire behavior modeling that use these layers as inputs. Here, we evaluated the influence of error associated with two Lidar data products—canopy height (CH) and canopy base height (CBH)—on simulated fire behavior in a case study in the Sierra Nevada, California, USA. We used a Monte Carlo simulation approach with expected randomized error added to each model input. Model 1 used the original, unmodified data, Model 2 incorporated error in the CH layer, and Model 3 incorporated error in the CBH layer. This sensitivity analysis showed that error in CH and CBH did not greatly influence the modeled conditional burn probability, fire size, or fire size distribution. We found that the expected error associated with CH and CBH did not greatly influence modeled results: conditional burn probability, fire size, and fire size distributions were very similar between Model 1 (original data), Model 2 (error added to CH), and Model 3 (error added to CBH). However, the impact of introduced error was more pronounced with CBH than with CH, and at lower canopy heights, the addition of error increased modeled canopy burn probability. Our work suggests that the use of Lidar data, even with its inherent error, can contribute to reliable and robust estimates of modeled forest fire behavior, and forest managers should be confident in using Lidar data products in their fire behavior modeling workflow.

We document changes in forest structure between historical (1930s) and contemporary (2000s) surveys of California vegetation through comparisons of tree abundance and size across the state and within several ecoregions. Across California, tree density in forested regions increased by 30% between the two time periods, whereas forest biomass in the same regions declined, as indicated by a 19% reduction in basal area. These changes reflect a demographic shift in forest structure: larger trees (>61 cm diameter at breast height) have declined, whereas smaller trees (

© 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees’ and shrubs’ variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.

Estimating aboveground biomass (AGB) is essential to quantify the carbon balance of terrestrial ecosystems, and becomes increasingly important under changing global climate. Volume metrics of individual trees, for example stem volume, have been proven to be strongly correlated to AGB. In this paper, we compared a range of airborne Lidar-derived volume metrics (i.e. stem volume, crown volume under convex hull, and crown volume under Canopy Height Model (CHM)) to estimate AGB. In addition, we evaluated the effect of horizontal crown overlap (which is often neglected in Lidar literature) on the accuracy of AGB estimation by using a hybrid method that combined marker-controlled watershed segmentation and point cloud segmentation algorithms. Our results show that: (1) when the horizontal crown overlap issue was not addressed, models based on point cloud segmentation outperformed models based on marker-controlled watershed segmentation; models using stem volume estimated AGB more accurately than models using crown volume under convex hull and crown volume under CHM. (2) Once the horizontal crown overlap issue was taken into consideration, the model using crown volume under CHM yielded a more accurate estimation of AGB. Our study provides a comprehensive evaluation of the use of airborne Lidar-derived volume metrics for AGB estimation and could help researchers choose the appropriate airborne Lidar-derived volume metric. Moreover, the results also indicate that horizontal crown overlap should be addressed when the airborne Lidar-derived forest crown volume is used for estimating AGB.

article i nfo There isaneedtoquantify large-scale plantproductivityincoastalmarshestounderstandmarshresiliencetosea level rise, to help define eligibility for carbon offset credits, and to monitor impacts from land use, eutrophication and contamination. Remote monitoring of aboveground biomass of emergent wetland vegetation will help address this need. Differences in sensor spatial resolution, bandwidth, temporal frequency and cost constrain the accuracy of biomass maps produced for management applications. In addition the use of vegetation indices to map biomass may not be effective in wetlands due to confounding effects of water inundation on spectral re- flectance. Toaddress these challenges,we used partial least squares regression to select optimal spectral features in situ and with satellite reflectance data to develop predictive models of aboveground biomass for common emergent freshwater marsh species,Typhaspp. andSchoenoplectus acutus, at two restored marshes in the Sacra- mento-San Joaquin River Delta, California, USA. We usedfield spectrometer data to test model errors associated with hyperspectral narrowbands and multispectral broadbands, the influence of water inundation on prediction accuracy, and the ability to develop species specific models. We used Hyperion data, Digital Globe World View-2 (WV-2) data, and Landsat 7 data to scale up the best statistical models of biomass. Field spectrometer-based models of the full dataset showed that narrowband reflectance data predicted biomass somewhat, though not significantly better than broadband reflectance data (R 2

Switchgrass (Panicum virgatum) has been targeted by the U.S. Department of Agriculture as an exemplary bioenergy crop, however it requires a significant amount of water and experiences reduced yields in water-stressed conditions. To avoid competition for prime agricultural areas, lands that receive adequate rainfall but are marginal due to highly variable timing of rain are potentially ideal locations to grow drought-tolerant biofuels. As scientists develop a modified variety of switchgrass that can withstand periods of drought while not substantially affecting overall yield, it is important to identify the potential geographical niche for this xerophytic crop to maximize its environmental and economic sustainability. This project uses a spatial suitability modeling approach that incorporates fuzzy logic and utilizes both physical and economic variables. We assess several fuzzy overlay techniques to identify and synthesize tradeoffs between suitability criteria. Our results highlight the Great Plains region of the United States as a suitable region, and within this area we focus on Kansas for a more detailed analysis to calculate land areas within varying dryness index thresholds. For this we develop a specialized dryness index using high resolution (spatial & temporal) weather and soil data to provide a spatially explicit measure of dry spell severity for switchgrass across a landscape. We estimate that 80% of the suitable land area in Kansas falls within a dryness index equivalent to about four 22-day long dry stretches, or one 45-day long dry stretch. By identifying the dryness threshold where land area is maximized, the results of this analysis inform the development of drought-tolerant varieties of switchgrass and identify marginal areas where efforts to plant such a species may prosper.

SARAH M. LEWIS*, STEPHEN GROSS†,AXEL VISEL†‡, MAGGI KELLY* andWILLIAM MORROW§*University of California, 130 Mulford Hall #3114, Berkeley, CA 94720-3114, USA, †DOE Joint Genome Institute, WalnutCreek, CA USA, ‡Genomics Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720,USA, §Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USAAbstractIn the United States, renewable energy mandates calling for increased production of cellulosic biofuels willrequire a diversity of bioenergy feedstocks to meet growing demands. Within the suite of potential energy crops,plants within the genus Agave promise to be a productive feedstock in hot and arid regions. The potential distri-butions of Agave tequilana and Agave deserti in the United States were evaluated based on plant growth parame-ters identified in an extensive literature review. A geospatial suitability model rooted in fuzzy logic wasdeveloped that utilized a suite of biophysical criteria to optimize ideal geographic locations for this new crop,and several suitability scenarios were tested for each species. The results of this spatially explicit suitabilitymodel suggest that there is potential for Agave to be grown as an energy feedstock in the southwestern region ofthe United States – particularly in Arizona, California, and Texas – and a significant portion of these areas areproximate to existing transportation infrastructure. Both Agave species showed the highest state-level renewableenergy benefit in Arizona, where agave plants have the potential to contribute 4.8–9.6% of the states’ ethanolconsumption, and 2.5–4.9% of its electricity consumption, for A. deserti and A. tequilana, respectively. This analy-sis supports the feasibility of Agave as a complementary bioenergy feedstock that can be grown in areas tooharsh for conventional energy feedstocks.

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...

OBJECTIVE It is unknown whether any association between neighborhood food environment and obesity varies according to individual income and/or race/ethnicity. The objectives of this study were to test whether there was an association between food environments and obesity among adults with diabetes and whether this relationship differed according to individual income or race/ethnicity. RESEARCH DESIGN AND METHODS Subjects (n = 16,057) were participants in the Diabetes Study of Northern California survey. Kernel density estimation was used to create a food environment score for each individual’s residence address that reflected the mix of healthful and unhealthful food vendors nearby. Logistic regression models estimated the association between the modeled food environment and obesity, controlling for confounders, and testing for interactions between food environment and race/ethnicity and income. RESULTS The authors found that more healthful food environments were associated with lower obesity in the highest income groups (incomes 301–600% and >600% of U.S. poverty line) among whites, Latinos, and Asians. The association was negative, but smaller and not statistically significant, among high-income blacks. On the contrary, a more healthful food environment was associated with higher obesity among participants in the lowest-income group ( CONCLUSIONS These findings suggest that the availability of healthful food environments may have different health implications when financial resources are severely constrained.

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...

Discrete airborne lidar is increasingly used to analyze forest structure. Technological improvements in lidar sensors have led to the acquisition of increasingly high pulse densities, possibly reflecting the assumption that higher densities will yield better results. In this study, we systematically investigated the relationship between pulse density and the ability to predict several commonly used forest measures and metrics at the plot scale. The accuracy of predicted metrics was largely invariant to changes in pulse density at moderate to high densities. In particular, correlations between metrics such as tree height, diameter at breast height, shrub height and total basal area were relatively unaffected until pulse densities dropped below 1 pulse/m2. Metrics pertaining to coverage, such as canopy cover, tree density and shrub cover were more sensitive to changes in pulse density, although in some cases high prediction accuracy was still possible at lower densities. Our findings did not depend on the type of predictive algorithm used, although we found that support vector regression (SVR) and Gaussian processes (GP) consistently outperformed multiple regression across a range of pulse densities. Further, we found that SVR yielded higher accuracies at low densities ( 1 pl/m2). Our results suggest that low-density lidar data may be capable of estimating typical forest structure metrics reliably in some situations. These results provide practical guidance to forest ecologists and land managers who are faced with tradeoff in price, quality and coverage, when planning new lidar data acquisition.

Feature Unmanned aerial systems for agriculture and natural resources Sean D. Hogan, Drone Service Coordinator, Informatics and GIS Statewide Program, UC Agriculture and Natural Resources Maggi Kelly, Director, Informatics and GIS Statewide Program, UC Agriculture and Natural Resources; Faculty Director, Geospatial Innovation Facility, College of Natural Resources, UC Berkeley; Professor and UC Cooperative Extension Specialist, Department of Environmental Sciences, Policy and Management, UC Berkeley Brandon Stark, Director of the Center of Excellence on Unmanned Aircraft System Safety, UC Merced and UC Office of the President YangQuan Chen, Director, Mechatronics, Embedded Systems and Automation (MESA) Lab, UC Merced S mall unmanned aerial systems (UAS), also known as drones or unmanned aerial vehicles, have a rapidly growing role in research and practice in agriculture and natural resources. Here, we present the parameters and key limitations of the technology, summarize current regulations and cover examples of University of California research enabled by UAS technology. Motorized UAS were introduced as a potential remote sensing tool for scientific research in the late 1970s. However, due to a variety of limitations (the weight and limited functionality of available sensors and cameras, the lack of GPS-guided autopi- lots and so on) these platforms had few practical applica- tions (Przybilla and Wester-Ebbinghaus 1979; Wester- Ebbinghaus 1980; cited by Colomina and Molina 2014). For years, UAS technology was led by military needs and ap- plications. The relatively few applications in research and agriculture included deployments in Japan for crop dusting and in Australia for meteorological studies (Colomina and Molina 2014). In the past decade, several factors have greatly increased the utility and ease of use of UAS, while prices have fallen. Consumer demand drove the hobby craft industry to make major improvements in UAS vehicles. Integrating improved battery tech- nology, miniature inertia measurement units (IMU, initially developed for smartphones), GPS and cus- tomizable apps for smartphones and tablets has deliv- ered improved flight longevity, reliability, ease of use and the ability to better utilize cameras and other sen- sors needed for applications in agriculture and natu- ral resources (see below, Types of UAS). Innovations in sensor technology now include dozens of models of lightweight visible-spectrum and multispectrum cameras capable of capturing reliable, scientifically valid data from UAS platforms (see UAS sensors) (Whitehead and Hugenholtz 2014). Meanwhile, the Federal Aviation Administration (FAA) has helped fa- cilitate increased UAS use, with rule changes adopted in August 2016 that lowered what have previously been significant regulatory obstacles to the legal use of UAS for research and commercial purposes (see Regulations sidebar). UC faculty throughout California are using UAS in a wide range of agricultural and environmental research projects — from grazed rangelands to field crops and orchards, forests, lakes and even the ice sheets of Greenland http://calag.ucanr.edu • JANUARY–MARCH 2017 5 Will Suckow The Inspire 1 drone, made by DJI, flies with an RGB camera over the UC Berkeley Blue Oak Ranch Reserve in Santa Clara County.

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.

We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m 2 ), discrete return, small-footprint lidar data, along with multispectral imagery. Stand structure metric predictions generally decreased with increased canopy penetration. For example, from the top of canopy, we predicted canopy height (r 2 0.87), canopy cover (r 2 0.83), basal area (r 2 0.82), shrub cover (r 2 0.62), shrub height (r 2 0.59), combined fuel loads (r 2 0.48), and fuel bed depth (r 2 0.35). While the general fuel types were predicted accurately, specific surface fuel model predictions were poor (76 percent and 50 percent correct classification, respectively) using all algorithms. These fuel components are critical inputs for wildfire behavior modeling, which ultimately support forest management decisions. This comprehensive examination of the relative utility of lidar and optical imagery will be useful for forest science and management.

Climate change impacts, such as accelerated sea‐level rise, will affect stress gradients, yet impacts on competition/stress tolerance trade‐offs and shifts in distributions are unclear. Ecosystems with strong stress gradients, such as estuaries, allow for space‐for‐time substitutions of stress factors and can give insight into future climate‐related shifts in both resource and nonresource stresses. We tested the stress gradient hypothesis and examined the effect of increased inundation stress and biotic interactions on growth and survival of two congeneric wetland sedges, Schoenoplectus acutus and Schoenoplectus americanus. We simulated sea‐level rise across existing marsh elevations and those not currently found to reflect potential future sea‐level rise conditions in two tidal wetlands differing in salinity. Plants were grown individually and together at five tidal elevations, the lowest simulating an 80‐cm increase in sea level, and harvested to assess differences in biomass after one growing season. Inundation time, salinity, sulfides, and redox potential were measured concurrently. As predicted, increasing inundation reduced biomass of the species commonly found at higher marsh elevations, with little effect on the species found along channel margins. The presence of neighbors reduced total biomass of both species, particularly at the highest elevation; facilitation did not occur at any elevation. Contrary to predictions, we documented the competitive superiority of the stress tolerator under increased inundation, which was not predicted by the stress gradient hypothesis. Multifactor manipulation experiments addressing plant response to accelerated climate change are integral to creating a more realistic, valuable, and needed assessment of potential ecosystem response. Our results point to the important and unpredicted synergies between physical stressors, which are predicted to increase in intensity with climate change, and competitive forces on biomass as stresses increase.

While stress has been linked to poor health outcomes, little is known about the impact of objective measures of neighborhood crime on stress in patients with chronic disease. Using the Kaiser Permanente Diabetes Study of Northern California (DISTANCE), we examined associations between police-recorded crime (2005-2007) and stress (Perceived Stress Scale-4) in four large Northern California cities (Oakland, Sacramento, San Francisco, and San Jose). We performed stratified analysis by gender and race/ethnicity using generalized linear regression models. In our study sample (n = 3188, mean age 59, range 30-77), 10% reported high stress. In adjusted analyses, higher neighborhood all crimes rate was associated with modest increase in high stress for African-American (OR = 1.10; 95% CI 1.02-1.22) and Latina women (OR = 1.36; 95% CI 1.10-1.67) and property crime showed similar associations with stress for these groups of women. Visible crime was associated with stress only for Latina women (OR = 1.43; 95% CI 1.14-1.78). We found no association between crime and stress among men or other racial/ethnic groups of women. High crime levels may disproportionately impact health among certain subpopulations. Studies using additional measures of stress are necessary to differentiate the health impact of crime-related stress from other forms of stressors among individuals living with diabetes.

Research efforts that synthesize historical and contemporary ecological data with modeling approaches improve our understanding of the complex response of species, communities, and landscapes to changing biophysical conditions through time and in space. Historical ecological data are particularly important in this respect. There are remaining barriers that limit such data synthesis, and technological improvements that make multiple diverse datasets more readily available for integration and synthesis are needed. This paper presents one case study of the Wieslander Vegetation Type Mapping project in California and highlights the importance of rescuing, digitizing and sharing historical datasets. We review the varied ecological uses of the historical collection: the vegetation maps have been used to understand legacies of land use change and plan for the future; the plot data have been used to examine changes to chaparral and forest communities around the state and to predict community structure and shifts under a changing climate; the photographs have been used to understand changing vegetation structure; and the voucher specimens in combination with other specimen collections have been used for large scale distribution modeling efforts. The digitization and sharing of the data via the web has broadened the scope and scale of the types of analysis performed. Yet, additional research avenues can be pursued using multiple types of VTM data, and by linking VTM data with contemporary data. The digital VTM collection is an example of a data infrastructure that expands the potential of large scale research through the integration and synthesis of data drawn from numerous data sources; its journey from analog to digital is a cautionary tale of the importance of finding historical data, digitizing it with best practices, linking it with other datasets, and sharing it with the research community.

Forests historically associated with frequent fire have changed dramatically due to fire suppression and past harvesting over the last century. The buildup of ladder fuels, which carry fire from the surface of the forest floor to tree crowns, is one of the critical changes, and it has contributed to uncharacteristically large and severe fires. The abundance of ladder fuels makes it difficult to return these forests to their natural fire regime or to meet management objectives. Despite the importance of ladder fuels, methods for quantifying them are limited and imprecise. LiDAR (Light Detection and Ranging), a form of active remote sensing, is able to estimate many aspects of forest structure across a landscape. This study investigates a new method for quantifying ladder fuel in the field (using photographs with a calibration banner) and remotely (using LiDAR data). We apply these new techniques in the Klamath Mountains of Northern California to predict ladder fuel levels across the study area. Our results demonstrate a new utility of LiDAR data to identify fire hazard and areas in need of fuels reduction.

a b s t r a c t Regression modeling of biomass estimates from an airborne, multiple return lidar system using regional biomass allometric equations differs significantly from those using national scale Jenkins allometric equa- tions with respect to the amount of variation explained, variables selected and variables of importance. Our discrete return lidar data were collected in September 2007 at 121 plots in a conifer dominated forest site in the Sierra Nevada mountains that include a full range of forest density. We regressed field plot-level estimates of biomass derived from field data and two different allometric equations with a range of lidar metrics. We compared regression performance across eight models: (1) point clouds alone, (2) point clouds with an empirical relationship between DBH and height (i.e., volume), (3) individual tree-level metrics, and (4) all data combined, and across two allometric equations - (A) Forest Inventory Analysis (FIA), and (B) Jenkins. In lower biomass plots, the reference above ground biomass (AGB) esti- mates from regional allometric equations and Jenkins equations were closely related; in plots with large biomass they were different. This finding suggests that published equations from large biomass plots are either not readily available or less represented in national scale allometric equation compiling. Models using reference AGBs calculated from regional allometric equations performed much better than those using reference AGBs calculated from Jenkins allometric equations. In these cases adjusted R 2 improve- ment ranged from 0.07 to 0.11. The regression model that used regional allometric equations with lidar metrics and individual tree data provided the best overall R 2 (0.79) with lowest RMSE suggesting that in most conditions regional biomass equations should be preferred over national equations. The inclu- sion of volumetric metrics shows that lidar variables are more sensitive to the reference AGBs calculated from regional allometric equations, and care should be taken when substituting regional equations using national scale compiled allometric equations in regional biomass studies. In addition, consistent with previous studies, the mean height of individual trees identified was chosen by both models with both reference AGBs calculated from regional allometric equations and those calculated from Jenkins equa- tions, supporting the need to identify individual trees for biomass prediction. Based on these results, we conclude that the selection of allometric equations can influence the capacity of lidar data to estimate biomass significantly, and a careful selection of allometric equations is required for regional lidar biomass studies.

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...

Participatory adaptive management is widely promoted as the new paradigm in public lands management. It is grounded in two underlying principles - that management experiments and diverse sources of information should be used to continually refine management in complex ecological systems, and that the public must be included throughout the adaptive management process. Access to scientific results and exchange of information is at the core of both of these principles. The recent proliferation of Internet communities and web-based participation tools raises the question of how the Internet might help facilitate information exchange in participatory adaptive management. Using a case study approach, the role of web technologies in facilitating the flow of transparent and useful information was examined in a participatory adaptive management project focused on Forest Service vegetation management treatments in California's Sierra Nevada. Three evaluation methods were used: analysis of web usage and content, a survey of active participants, and a review of comments posted to the project website. Results suggest that the web played an important role throughout the adaptive management cycle by supporting communication through disseminating information to the public and increasing the transparency of the scientific process. The web played a small, but important role in public consultation, by providing a forum for targeted questions and feedback from the public. Internet technology did not actively support the two-way flow of information necessary for mutual learning. Web technology complements face-to-face interactions and public meetings, rather than replacing them.

Lidar data provide information on the vertical and horizontal structure of forests, and lidar remote sensing can be effective in characterizing the forest habitats of mammals. This study explored the ability of lidar-derived metrics to capture topography and forest structure surrounding denning trees used by the Pacific fisher (Martes pennanti) as a case study to illustrate the utility of lidar remote sensing in studying mammal-habitat associations. Lidar data were collected in September 2007 across conifer dominated forests in the Sierra Nevada Mountains that include a full range of forest density. We used Classification and Regression Trees (CART) to statistically compare the slope and lidar-derived forest height and structure metrics in the circular area (with radius of 10–50 m) surrounding denning trees and randomly selected non-denning trees. We accessed our model accuracy using resubstitution and cross-validation methods. Our results show that there is a strong association between fisher denning activity and its surrounding forested environment across scales, with high classification accuracy (overall accuracies above 80% and cross-validation accuracies above 70%) at 20, 30 and 50 m ranges. The best classification accuracies were found at 20 m (optimal resubstitution accuracy 86.2% and cross-validation accuracy 78%). Tree height and slope were important variables in classifying the area immediately surrounding denning trees; at scales larger than 20 m, forest structure and complexity became more important. The variables identified were also consistent with those identified from previous studies describing fisher resting tree structure. Based on these findings, we conclude that lidar remote sensing has utility in forest characterization for fishers and has great potential for mammal studies.

"Working landscapes" is the concept of fostering effective ecosystem stewardship and conservation through active human presence and management and integrating livestock, crop, and timber production with the provision of a broad range of ecosystem services at the landscape scale. Based on a statewide survey of private landowners of "working" forests and rangelands in California, we investigated whether owners who are engaged in commercial livestock or timber production appreciate and manage biodiversity and ecosystem services on their land in different ways than purely residential owners. Both specific uses and management practices, as well as underlying attitudes and motivations toward biodiversity and ecosystem services, were assessed. Correlation analysis showed one bundle of ecosystem goods and services (e.g., livestock, timber, crops, and housing) that is supported by some landowners at the community level. Another closely correlated bundle of biodiversity and ecosystem services includes recreation, hunting/fishing, wildlife habitat, and fire prevention. Producers were more likely to ally with the first bundle and residential owners with the second. The survey further confirmed that cultural ecosystem services and quality-of-life aspects are among the primary amenities that motivate forest and rangeland ownership regardless of ownership type. To live near natural beauty was the most important motive for both landowner groups. Producers were much more active in management for habitat improvement and other environmental goals than residential owners. As the number of production-oriented owners decreases, developing strategies for encouraging environment-positive management by all types of landowners is crucial.

We report on methane (CH 4 ) concentrations and efflux densities that were measured over a drained and grazed, peatland pasture in the Sacramento-San Joaquin River Delta of California over a three year period. The site was ideal for micrometeorological flux measurements due to its very flat topography, its exposure to vigorous winds and its extended fetch along the predominant wind direction. Nevertheless, the interpretation of methane fluxes with eddy covariance proved to be extremely complicated by a number of geographical, biophysical, biogeochemical and site management factors. Initial inspection of the data revealed unexpected results—methane concentrations and efflux densities were greatest during the night rather than during the day. To explain this odd diurnal behavior in methane efflux densities and concentration, we tested two hypotheses. The prime hypothesis presupposed that the stable stratification of the nocturnal boundary layer elongated the flux footprint and enabled the flux tower to sense wetter fields at the western edge of the pasture, flooded drainage ditches, and/or a tidal marsh upwind of the pasture—these land forms emitted methane at rates 10–100 times greater than the drained portion of the peatland. And, this methane was emitted into a shallower volume of the atmosphere due to the collapse of the nocturnal boundary layer, causing methane concentrations to rise faster. The alternative hypothesis attributed the higher nocturnal methane fluxes to cattle, as they may have congregated near the tower at night. We investigated these hypotheses with: (1) a series of micrometeorological field measurements at companion sites upwind and downwind of the pasture; (2) a series of chamber-based flux measurements on the representative land classes; (3) through the lens of a one-dimensional planetary boundary layer (pbl), box model; and (4) via inspection of digital camera images for the presence or absence of cattle. Together, these pieces of data suggest that elevated methane fluxes and concentrations at night were due to the combined correlation between: (1) the collapse of the nocturnal boundary layer; (2) the elongation of the flux and concentration footprints; and (3) the preferential sampling of an elevated methane source, be it the cattle, wet proportions of the field or some combination. On the other hand, our flux measurements were not perturbed by methane emanating from the tidal marsh that was several kilometers upwind of the peatland pasture site.

Light Detection and Ranging (lidar) has been widely applied to characterize the 3-dimensional (3D) structure of forests as it can generate 3Dpoint data with high spatial resolution and accuracy. Individual tree segmentations, usually derived from the canopy height model, are used to derive individual tree structural attributes such as tree height, crown diameter, canopy-based height, and others. In this study, we develop a new algorithm to segment individual trees from the small footprint discrete return airborne lidar point cloud. We experimentally applied the new algorithm to segment trees in a mixed conifer forest in the Sierra Nevada Mountains in California. The results were evaluated in terms of recall, precision, and F-score, and show that the algorithm detected 86 percent of the trees (“recall”), 94 percent of the segmented trees were correct (“precision”), and the overall F-score is 0.9. Our results indicate that the proposed algorithm has good potential in segmenting individual trees in mixed conifer stands of similar structure using small footprint, discrete return lidar data.

Downed logs on the forest floor provide habitat for species, fuel for forest fires, and function as a key component of forest nutrient cycling and carbon storage. Ground-based field surveying is a conventional method for mapping and characterizing downed logs but is limited. In addition, optical remote sensing methods have not been able to map these ground targets due to the lack of optical sensor penetrability into the forest canopy and limited sensor spectral and spatial resolutions. Lidar (light detection and ranging) sensors have become a more viable and common data source in forest science for detailed mapping of forest structure. This study evaluates the utility of discrete, multiple return airborne lidar-derived data for image object segmentation and classification of downed logs in a disturbed forested landscape and the efficiency of rule-based object-based image analysis (OBIA) and classification algorithms. Downed log objects were successfully delineated and classified from lidar derived metrics using an OBIA framework. 73% of digitized downed logs were completely or partially classified correctly. Over classification occurred in areas with large numbers of logs clustered in close proximity to one another and in areas with vegetation and tree canopy. The OBIA methods were found to be effective but inefficient in terms of automation and analyst’s time in the delineation and classification of downed logs in the lidar data.