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Relatively little is known of how the world's largest vegetation transition zone – the Forest Tundra Ecotone (FTE) – is responding to climate change. Newly available, satellite-derived time-series of the photochemical reflectance index (PRI) across North America and Europe could provide new insights into the physiological response of evergreen trees to climate change by tracking changes in foliar pigment pools that have been linked to photosynthetic phenology. However, before implementing these data for such purpose at these evergreen dominated systems, it is important to increase our understanding of the fine scale mechanisms driving the connection between PRI and environmental conditions. The goal of this study is thus to gain a more mechanistic understanding of which environmental factors drive changes in PRI during late-season phenological transitions at the FTE – including factors that are susceptible to climate change (i.e., air- and soil-temperatures), and those that are not (photoperiod). We hypothesized that late-season phenological changes in foliar pigment pools captured by PRI are largely driven by photoperiod as opposed to less predictable drivers such as air temperature, complicating the utility of PRI time-series for understanding climate change effects on the FTE. Ground-based, time-series of PRI were acquired from individual trees in combination with meteorological variables and photoperiod information at six FTE sites in Alaska. A linear mixed-effects modeling approach was used to determine the significance (α = 0.001) and effect size (i.e., standardized slope b*) of environmental factors on late-seasonal changes in the PRI signal. Our results indicate that photoperiod had the strongest, significant effect on late-season changes in PRI (b* = 0.08, p

Digital terrain models (DTMs) and vegetation canopy height models (CHMs) are used in a wide range of earth and environmental sciences. An increasing number of CHM products are available from active, passive, and photogrammetric remotely sensed data; however, high-resolution (≤5 m), wall-to-wall CHMs for the arctic and northern boreal domains that are suitable for detailed spatial analysis are lacking. Recently, a 5-m spatial resolution pan-arctic digital surface model – the ArcticDEM – was created using automated stereopair analysis of high-resolution satellite data. The ArcticDEM is unprecedented in extent and spatial resolution, yet the product generally follows the uppermost surface elevation (i.e., representing a digital surface model, DSM) without regard to whether the surface is comprised of vegetation or bare-earth terrain. To address this limitation, we developed and tested an approach to map vegetation canopy height at a 5-m spatial resolution (hereafter called the local ArcticCHM), and then subtracted these estimated canopy heights from the ArcticDEM in order to create a 5-m resolution DTM (local ArcticDTM). We selected three pilot study areas (total 58 km2) across a north-south gradient in Alaska, representing a range of vegetation types and topographic conditions. We estimated and mapped canopy height using randomForest and imputation modeling approaches, with the ArcticDEM and high spatial resolution multispectral satellite data (WorldView-2) used as predictors. Airborne laser scanning (ALS) data was used for model calibration and independent validation. Canopy height was reliably predicted across the three study areas, with the best models ranging from root mean square errors (RMSE) 2.2 to 2.6 m and R2 ranging from 0.59 to 0.76 relative to ALS-based CHM reference data. Similarly, the RMSE between the new local ArcticDTM product and ALS-based DTM reference data was 45–68% less than similar comparisons with the ArcticDEM. Our results offer a means to extend these local ArcticDTM and CHM products to establish high-resolution products elsewhere in Alaska of high value for a wide range of earth and environmental sciences research investigations.

Standing dead trees (known as snags) are historically difficult to map and model using airborne laser scanning (ALS), or lidar. Specific snag characteristics are important for wildlife; for instance, a larger snag with a broken top can serve as a nesting platform for raptors. The objective of this study was to evaluate whether characteristics such as top intactness could be inferred from discrete-return ALS data. We collected structural information for 198 snags in closed-canopy conifer forest plots in Idaho. We selected 13 lidar metrics within 5 m diameter point clouds to serve as predictor variables in random forest (RF) models to classify snags into four groups by size (small (

Conserving multiple facets of biodiversity is important for sustaining ecosystems. However, understanding relationships between faunal diversity and measurable ecosystem quantities, such as heterogeneity and productivity, across continental scales can be complicated by disparate methods. We developed standardized approaches using lidar data and spectral greenness data (via NDVI; Normalized Difference Vegetation Index) from 637 sampling plots across four sites in North America, Europe, and Asia to test the local effects of habitat heterogeneity and productivity on taxonomic, functional, and phylogenetic diversity of breeding bird assemblages using boosted generalized additive models. Our results revealed the 3-D (three dimensional) vegetation structure (horizontal and vertical) to be of similar importance as NDVI in multiple biodiversity measures, and the importance of 3-D structure was higher for functional and phylogenetic biodiversity measures than for taxonomic measures. We found congruent responses between functional and phylogenetic diversity; however, patterns of taxonomic diversity differed from those of functional/phylogenetic diversity for most predictors. For example, NDVI had positive relationships with taxonomic diversity, but negative relationships with functional/phylogenetic diversity. The effect of canopy density on taxonomic diversity was generally bell-shaped, whereas the relationship was U-shaped for functional and phylogenetic diversity. As a result, this study supports a silviculture strategy with a high variety of canopy densities and vertical variabilities across forest stands to create maximum benefits for regional biodiversity. Here, early succession stands and closed stands sustain functionally-rich bird assemblages, while stands with a medium canopy density promote species-rich assemblages.

Northern herbivore ranges are expanding in response to a warming climate. Forage quality also influences herbivore distributions, but less is known about the effects of climate change on plant biochemical properties. Remote sensing could enable landscape-scale estimations of forage quality, which is of interest to wildlife managers. Despite the importance of integrated forage quality metrics like digestible protein (DP) and digestible dry matter (DDM), few studies investigate remote sensing approaches to estimate these characteristics. We evaluated how well DP and DDM could be estimated using hyperspectral remote sensing and assessed whether incorporating shrub structural metrics affected by browsing would improve our ability to predict DP and DDM. We collected canopy-level spectra, destructive-vegetation samples, and flew unoccupied aerial vehicles (UAVs) in willow (Salix spp.) dominated areas in north central Alaska in July 2019. We derived vegetation canopy structural metrics from 3D point cloud data obtained from UAV imagery using structure-from-motion photogrammetry. The best performing model for DP included a spectral vegetation index (SVI) that used a red-edge and shortwave infrared band, and shrub height variability (hvar; Nagelkerke R 2 = 0.81, root mean square error RMSE = 1.42%, cross validation ρ = 0.88). DDM’s best model included a SVI with a blue and a red band, the normalized difference red-edge index, and hvar (adjusted R 2 = 0.73, RMSE = 4.16%, cross validation ρ = 0.80). Results from our study demonstrate that integrated forage quality metrics may be successfully quantified using hyperspectral remote sensing data, and that models based on those data may be improved by incorporating additional shrub structural metrics such as height variability. Modern airborne sensor platforms such as Goddard’s LiDAR, Hyperspectral & Thermal Imager provide opportunities to fuse data streams from both structural and optical data, which may enhance our ability to estimate and scale important foliar properties.

Fungi are crucial to forest ecosystem function and provide important provisioning, regulating, supporting, and cultural ecosystem services. As major contributors to biomass decomposition, fungi are important to forest biogeochemical cycling and maintenance of vertebrate animal diversity. Many forest plant species live in a symbiotic relationship with a fungal partner that helps a host plant to acquire nutrients and water. In addition, edible fungi are recreationally as well as economically valuable. However, most fungi live in very cryptic locations (e.g. in soils and interior plant tissues) and are only visible when their ephemeral fruiting bodies are produced, making fungal occurrence difficult to detect and predict. While remote sensing has been used increasingly to identify and scale many forest characteristics (e.g. structure, function, and species diversity) related to myriad ecosystem services, the use of remotely sensed data in modelling the occurrence of fungi is largely unknown. We compared the performance of airborne lidar derived structural variables, including those associated with single tree detection, with variables derived from field inventories to model overall fungal species abundance as well as specific fungal guilds (i.e. a range of edibility from highly edible to very poisonous, and the number of fruiting bodies of saprotrophic and mutualistic ectomycorrhizal species) based on fruiting body sampling in a low range mountain forest (Bavarian Forest National Park). Lidar derived variables performed better than variables derived from field measurements to explain the abundance of all guilds combined, as well as the guilds of soil saprotrophic and ectomycorrhizal fungi, and the yield of highly edible fungi. Variables derived from field measurements performed better than lidar derived variables in explaining the yield of very poisonous fungi. Upscaling of yield and abundance of fruiting bodies to the whole study area opens the avenue for managers to identify areas of high interest by mushroom pickers, as opposed to those of potential danger to people and those that co-occur with sensitive species and habitats of conservation relevance. Moreover, the strong, guild-specific relationships found between the occurrence of fungi and lidar derived variables opens new avenues for scaling to large areas the occurrence of members of this cryptic kingdom.

Capturing and quantifying the world in three dimensions (x,y,z) using light detection and ranging (lidar) technology drives fundamental advances in the Earth and Ecological Sciences (EES). However, additional lidar dimensions offer the possibility to transcend basic 3-D mapping capabilities, including i) the physical time (t) dimension from repeat lidar acquisition and ii) laser return intensity (LRIλ) data dimension based on the brightness of single- or multi-wavelength (λ) laser returns. The additional dimensions thus add to the x,y, and z dimensions to constitute the five dimensions of lidar (x,y,z, t, LRIλ1… λn). This broader spectrum of lidar dimensionality has already revealed new insights across multiple EES topics, and will enable a wide range of new research and applications. Here, we review recent advances based on repeat lidar collections and analysis of LRI data to highlight novel applications of lidar remote sensing beyond 3-D. Our review outlines the potential and current challenges of time and LRI information from lidar sensors to expand the scope of research applications and insights across the full range of EES applications.

Crop biomass information is of great importance for a variety of applications, ranging from supporting farm management decisions to modeling the crop-environment system. Dimensionless spectral vegetation index values derived from satellite imagery are commonly used to derive crop biomass. However, the highly empirical nature of spectrally derived biomass estimates requires frequent and costly calibration with manually collected ground data. Recently, low cost, autonomously operating terrestrial laser scanners (ATLSs) have become available for near-surface applications. In contrast to the dimensionless nature of spectral index values, autonomous light detection and ranging (lidar) technology measures physical vegetation structure by recording the x, y, z coordinates of canopy components at very high spatial (

The Landsat 8 mission provides new opportunities for quantifying the distribution of above-ground carbon at moderate spatial resolution across the globe, and in particular drylands. Furthermore, coupled with structural information from space-based and airborne laser altimetry, Landsat 8 provides powerful capabilities for large-area, long-term studies that quantify temporal and spatial changes in above-ground biomass and cover. With the planned launch of ICESat-2 in 2017 and thus the potential to couple Landsat 8 and ICESat-2 data, we have unprecedented opportunities to address key challenges in drylands, including quantifying fuel loads, habitat quality, biodiversity, carbon cycling, and desertification. In this study, we explore the strengths of Landsat 8's Operational Land Imager (OLI) in estimating vegetation structure in a dryland ecosystem, and compare these results to Landsat 5's Thematic Mapper (TM). We also demonstrate the potential of OLI when coupled with light detection and ranging (lidar) in estimating vegetation cover and biomass in a dryland ecosystem. The OLI and TM predictions were similarly positive, indicating data from these sensors may be used in tandem for long-term time-series analysis. Results indicate shrub and herbaceous cover are well predicted with multi-temporal OLI data, and a combination of OLI and lidar derivatives improves most of these estimates and reduces uncertainty. For example, significant improvements were made for shrub cover (R2 = 0.64 and 0.78 using OLI only and both OLI and lidar data, respectively). Importantly, a time series of OLI, with some improvement from lidar, provides strong estimates of herbaceous cover (68% of the variance is explained with OLI alone). In contrast, OLI data explain roughly 59% of the variance in total shrub biomass, however approximately 71% of the variance is explained when combined with lidar derivatives. To estimate the potential synergies of OLI and ICESat-2 we used simulated ICESat-2 photon data to predict vegetation structure. In a shrubland environment with a vegetation mean height of 1 m and mean vegetation cover of 33%, vegetation photons are able to explain nearly 50% of the variance in vegetation height. These results, and those from a comparison site, suggest that a lower detection threshold of ICESat-2 may be in the range of 30% canopy cover and roughly 1 m height in comparable dryland environments and these detection thresholds could be used to combine future ICESat-2 photon data with OLI spectral data for improved vegetation structure. Overall, the synergistic use of Landsat 8 and ICESat-2 may improve estimates of above-ground biomass and carbon storage in drylands that meet these minimum thresholds, increasing our ability to monitor drylands for fuel loading and the potential to sequester carbon.

The effects of climate change are more acute in the Arctic than any other region, and as such, arctic tundra wildlife habitats are changing in ways that are not yet well understood. Remote sensing tools are capable of assessing dynamics in wildlife-habitat associations over a wide range of spatial scales and in a variety of ecosystems. As already well-established in other ecosystems, Light Detection and Ranging (LiDaR) technology has the potential to greatly expand our understanding of tundra wildlife-habitat associations because unlike the most commonly used spectral vegetation index - the Normalized Difference Vegetation Index (NDVI) - LiDaR directly quantifies three-dimensional (3-D) vegetation structure. Only recently has airborne laser scanning (ALS) technology evolved to having vertical resolution and point densities high enough to quantify small differences in vegetation structure, such as characterize arctic tundra ecosystems. Our objective was to employ ALS metrics, airborne spectral data (for NDVI), and Landsat 7 ETM + data (for snow cover) to determine the relative importance of a suite of ecological characteristics - considered at multiple spatial scales - on habitat use and reproductive success of two of the most common migratory songbird species breeding in northern Alaska. Our most important findings are that (1) combining ALS metrics, but not NDVI, with Landsat derived snow cover data provided useful information, revealing that while Gambel's white-crowned sparrows use breeding territories in areas with high canopy volume and large snow-free areas, Lapland longspurs will establish territories in areas with low canopy volume, small patches of snow-free tundra, and minimal surface wetness and; (2) while habitat characteristics were important determinants of habitat use for both species at the territory scale, those in the immediate vicinity of nest-sites were not important. Contrary to expectation, we also found that the reproductive success of both species was unaffected by variation in our hypothesized metrics of shelter and food availability (canopy volume, standard deviation of micro-terrain height) at both the nest-site or territory scales. Our study is the first arctic demonstration of how ALS yields novel insights into wildlife-habitat associations, suggesting it has great potential in other ecosystems with similarly small - yet often ecologically important - ranges in 3-D vegetation structure.

Accurate monitoring of climate-driven expansion of low-stature shrubs in Arctic tundra requires high-resolution maps of shrub biomass that can accurately quantify the current baseline over relevant spatial and temporal extents. In this study, our goal was to use airborne lidar and imagery to build accurate high-resolution shrub biomass maps for an important research landscape in the American Arctic. In a leave-one-out cross-validation analysis, optimized lidar-derived canopy volume was a good single predictor of harvested shrub biomass (R2 = 0.62; RMSD = 219 g m− 2; slope = 1.08). However, model accuracy was improved by incorporating additional lidar-derived canopy metrics and airborne spectral metrics in a Random Forest regression approach (pseudo R2 = 0.71; RMSD = 197 g m− 2; slope = 1.02). The best Random Forest model was used to map shrub biomass at 0.80 m resolution across three lidar collection footprints (~ 12.5 km2 total) near Toolik Field Station on Alaska's North Slope. We characterized model uncertainty by creating corresponding maps of the coefficient of variation in Random Forest shrub biomass estimates. We also explore potential benefits of incorporating lidar-derived topographic metrics, and consider tradeoffs inherent in employing different data sources for high-resolution vegetation mapping efforts. This study yielded maps that provide valuable, high-resolution spatial estimates of aboveground shrub biomass and canopy volume in a rapidly changing tundra ecosystem.

Ecosystem service‐based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot‐level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community‐weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.

Characterizing the wide range of light availability and photosynthetic properties throughout a plant canopy is important for modeling the exchanges of carbon, water, and nutrients between the biosphere and the atmosphere. Such characterization could be especially important in one of the world's most rapidly changing biomes – the Arctic tundra – where further warming-induced increases in the size, abundance and complexity of small arctic shrubs are projected. An improved understanding of their canopy organization could provide insights into associated climate feedbacks since multifaceted interactions between 3D canopy structure, environmental conditions, leaf physiology, and light availability affect the potentials and limitations of vegetation carbon assimilation and storage. The aim of this study was to explore new methods to elucidate evidence for photosynthetic partitioning according to light availability within a short canopy ( Salix pulchra exposed to near continuous sunlight at low solar angles in the Arctic tundra. Instantaneous photosynthetic photon flux density (PPFD) and daily integrated quantum flux density ( Q int ) were modeled from a ray-tracing algorithm for voxels (edge-length .01 m) within the canopy that were each assigned a physically based directional gap fraction (DGF) and extinction coefficient ( k ) from which each voxel's effective leaf area index (LAI e ) was calculated. Voxel parameters for the ray-tracing model were derived from the x , y , and z locations of high spatial resolution ( Q int and path-length (determined as the accumulated photon travel distance from the canopy-edge) were compared with two variables derived from traditional light quantification techniques – ceptometer derived leaf-area index (LAI), and manually measured vertical canopy depth. Insignificant relationships were observed between traditional measurements of light environment (LAI and vertical canopy depth) and TLS derived measurements ( Q int and path length), suggesting wide variability among these methods. When each of four light quantification variables were compared against leaf-level variables classically associated with photosynthetic partitioning (percent nitrogen (N%), chlorophyll a to b ratio (Chl a / b ), and photosynthetic capacity ( A max )), patterns suggesting photosynthetic partitioning emerged only when the LiDAR-derived 3D locations of the leaf samples were considered ( Q int and path length). Statistically significant ( p Q int ( r = 0.31, 0.46, 0.49 for N%, Chl a / b , and A max , respectively), while 2 of 3 showed a statistically significant response to LiDAR derived path length. Only 1 of 3 photosynthetic variables showed a statistically significant response to manually measured canopy depth or ceptometer derived LAI. Results from this study suggest that LiDAR-based techniques for quantifying the 3D light environment of small shrubs exposed to low solar angles reveal patterns of photosynthetic partitioning that may otherwise be overlooked using more traditional techniques.

Improving management practices in industrial forest plantations may increase production efficiencies, thereby reducing pressures on native tropical forests for meeting global pulp needs. This study aims to predict stem volume (V) in plantations of fast-growing Eucalyptus hybrid clones located in southeast Brazil using field plot and airborne Light Detection andRanging (LiDAR) data. Forest inventory attributes and LiDAR-derived metrics were calculated at 108 sample plots. The best LiDAR-based predictors of V were identified based on loadings calculated from a principal component analysis (PCA). After selecting these best predictors using PCA,we developed multiple regression models predicting V from selected LiDAR metrics. Metrics related to tree height and canopy depth were most effective for V prediction, with an overall model coefficient of determination (adj. R2) of 0.87, and a root mean squared error (RMSE) of 27.60 m3 ha-1 (i.e. relative RMSE = 9.99 per cent).We used this model to map stem V of Eucalyptus hybrid clones across the full LiDAR data extent. The accuracy and precision of our results show that LiDAR-derived V is appropriate for updating Eucalyptus forest base maps and registries in the paper and pulp supply chain. However, further studies are necessary to evaluate and compare the cost of acquisition and processing of LiDAR data against conventional V inventory in this system.

Remotely sensed data that are sensitive to rapidly changing plant physiology can provide real-time information about crop responses to abiotic conditions. The Photochemical Reflectance Index (PRI) has shown promise when measured at short timesteps to remotely estimate dynamics in xanthophyll pigment interconversion — a plant photoprotective mechanism that results in lowered photosynthetic efficiency. To gain a better understanding of this dynamic spectral response to environmental conditions, we investigated PRI over two seasons (2013 and 2014) in rainfed soft white spring wheat ( Triticum aestivum L. ). Highly temporally resolved (measurement frequency = five minutes) in-situ radiometric measurements of PRI were collected at field plots of varying nitrogen (N) and soil water conditions (n = 16). To represent the diurnal magnitude of xanthophyll pigment interconversion, we use a delta PRI (ΔPRI) derived from a midday PRI (xanthophyll de-epoxidation state) and an early morning PRI (xanthophyll epoxidation state). We hypothesize that ΔPRI can empirically deconvolve the diurnally changing (facultative) from the seasonally changing (constitutive) component of the PRI signal. In this study, ΔPRI demonstrated less sensitivity than an uncorrected PRI to leaf area index (LAI) and leaf chlorophyll content throughout the growing season. ΔPRI was correlated with continuous, unattended crop responses associated with vapor pressure deficit (0.50 > R 2 > 0.48), stomatal conductance (R 2 = 0.47), and air temperature (0.42 > R 2 > 35). Further, the sensitivity with which ΔPRI responded to solar radiation under varying N treatments and periods of soil water availability (surplus, depletion, and deficit) suggests that crop growth may be inhibited by a xanthophyll cycle mediated stress response, detectable by ΔPRI. A major implication of these findings is that highly temporally and spatially resolved ΔPRI data could be used to track plant status in response to changing environmental conditions.

Shrub proliferation across the Arctic from climate warming is expanding herbivore habitat but may also alter forage quality. Dietary fibers&mdash, an important component of forage quality&mdash, influence shrub palatability, and changes in dietary fiber concentrations may have broad ecological implications. While airborne hyperspectral instruments may effectively estimate dietary fibers, such data captures a limited portion of landscapes. Satellite data such as the multispectral WorldView-3 (WV-3) instrument may enable dietary fiber estimation to be extrapolated across larger areas. We assessed how variation in dietary fibers of Salix alaxensis (Andersson), a palatable northern shrub, could be estimated using hyperspectral and multispectral WV-3 spectral vegetation indices (SVIs) in a greenhouse setting, and whether including structural information (i.e., leaf area) would improve predictions. We collected canopy-level hyperspectral reflectance readings, which we convolved to the band equivalent reflectance of WV-3. We calculated every possible SVI combination using hyperspectral and convolved WV-3 bands. We identified the best performing SVIs for both sensors using the coefficient of determination (adjusted R2) and the root mean square error (RMSE) using simple linear regression. Next, we assessed the importance of plant structure by adding shade leaf area, sun leaf area, and total leaf area to models individually. We evaluated model fits using Akaike&rsquo, s information criterion for small sample sizes and conducted leave-one-out cross validation. We compared cross validation slopes and predictive power (Spearman rank coefficients ) between models. Hyperspectral SVIs (R2 = 0.48&ndash, 0.68, RMSE = 0.04&ndash, 0.91%) outperformed WV-3 SVIs (R2 = 0.13&ndash, 0.35, RMSE = 0.05&ndash, 1.18%) for estimating dietary fibers, suggesting hyperspectral remote sensing is best suited for estimating dietary fibers in a palatable northern shrub. Three dietary fibers showed improved predictive power when leaf area metrics were included (cross validation &rho, = +2&ndash, 8%), suggesting plant structure and the light environment may augment our ability to estimate some dietary fibers in northern landscapes. Monitoring dietary fibers in northern ecosystems may benefit from upcoming hyperspectral satellites such as the environmental mapping and analysis program (EnMAP).

An unprecedented array of observing systems, coupled with ever increasing computing capacity, makes this a golden era for ecologists to study and quantify ecosystem services using remote sensing technology. Here, we review recent studies that utilize remote sensing to understand the supply and demand of ecosystem services, with a specific focus on pollination services by bees in forested and agroforestry contexts. Pollination by bees is a globally threatened ecosystem service that supports the production of food crops and maintains plant biodiversity. We explore how studies that use remote sensing to characterize landscapes, monitor individual organisms, measure biodiversity proxies or species habitat, and describe ecosystem processes may improve modeling of pollination services on spatial scales that match large-scale management efforts, such as forest conservation policy. We then discuss future research opportunities, such as exploring LiDAR and radar for 3-D habitat measurements, mapping phenology in space and time, and direct measurement of pollination events and outcomes.

article i nfo Arctic tundra ecosystems are responding to effects of climatic warming via shifts in vegetation composition and increased woody biomass. In this sensitive ecosystem, minor increases in woody plant biomass may induce sig- nificant changes in ecosystem structure and function. However, establishing methods for quantifying and poten- tially scaling woody plant biomass in low-stature biomes is challenging. In this study, we investigated the potentialtouseterrestriallaserscanning(TLS)toremotelyestimateharvestedbiomassandleafareaoftwodom- inant low-stature (b1.5 m tall) Arctic shrub species in 0.64 m 2 subplots established in northern Alaskan tundra. We explored two biomass estimation approaches (volumetric surface differencing and voxel counting) applied topoint clouds obtained fromclose-range(2m)and variable-range(b50m)terrestriallaser scans.Relationships between harvested biomassand TLS metricswere strongfor all combinationsofapproaches, with voxel counting giving a marginally better result than surface differencing for close-range data (R 2 = 0.94 vs 0.92; RMSE= 102 g vs 117 g) and surface differencing proving stronger than voxel counting for variable-range data (R 2 = 0.91 vs 0.82; RMSE = 119 g vs 169 g). Strong relationships between total harvested biomass and total leaf dry mass (R 2 = 0.93; RMSE = 13.4 g), and between leaf dry mass and leaf wet area (R 2 = 0.99; RMSE = 9.01 cm 2 )j ustify estimation of shrub leaf area from TLS-derived shrub biomass. Our results show that rapidlyacquired, repeatable terrestrial laser scans taken at multiple ranges can be processed using simple biomass estimation approaches to yield aboveground biomass and leaf area estimates for low-stature shrubs at fine spatial scales (sub-meter to ~50 meters) with the fidelity required to monitor small but ecologically meaningful changes in tundra structure. Further, these data may be employed as ground reference data for broader scale remote sensing data collection, such as airborne LiDAR scanning, that would enable shrub biomass and leaf-area estimates at fine spatial resolution over large spatial extents.

Basal area (BA) is a good predictor of timber stand volume and forest growth. This study developed predictive models using field and airborne LiDAR (Light Detection and Ranging) data for estimation of basal area in Pinus taeda plantation in south Brazil. In the field, BA was collected from conventional forest inventory plots. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil.

Improvements in the management of pine plantations result in multiple industrial and environmental benefits. Remote sensing techniques can dramatically increase the efficiency of plantation management by reducing or replacing time-consuming field sampling. We tested the utility and accuracy of combining field and airborne lidar data with Random Forest, a supervised machine learning algorithm, to estimate stem total and assortment (commercial and pulpwood) volumes in an industrial Pinus taeda L. forest plantation in southern Brazil. Random Forest was populated using field and lidar-derived forest metrics from 50 sample plots with trees ranging from three to nine years old. We found that a model defined as a function of only two metrics (height of the top of the canopy and the skewness of the vertical distribution of lidar points) has a very strong and unbiased predictive power. We found that predictions of total, commercial, and pulp volume, respectively, showed an adjusted R2 equal to 0.98, 0.98 and 0.96, with unbiased predictions of −0.17%, −0.12% and −0.23%, and Root Mean Square Error (RMSE) values of 7.83%, 7.71% and 8.63%. Our methodology makes use of commercially available airborne lidar and widely used mathematical tools to provide solutions for increasing the industry efficiency in monitoring and managing wood volume.

Advanced technologies for improved nitrogen (N) fertilizer management are paramount for sustainably meeting future food demands. Green laser systems that measure pulse return intensity can provide more reliable information about foliar N than can traditional passive remote sensing devices during the critical early crop growth stages (e.g., before canopy closure when vegetation and soil signals are spectrally mixed) when further decisions regarding N management can be made. However, current green laser systems are not designed for agricultural applications and only employ a single green laser wavelength, which may limit applications because many factors that require normalization techniques can affect pulse return intensity. Here, we describe the design of a tractor-mountable, green (532 nm)- and red (658 nm) dual wavelength laser system and evaluate the potential of an additional red reference wavelength to improve laser based estimates of foliar N by calculating laser spectral indices based on ratio combinations of green laser return intensity (GLRI) and red laser return intensity (RLRI). We hypothesized that such laser spectral indices aid in accounting for factors that confound laser based foliar N estimates including variations in leaf angle, measurement distance, soil returns, and mixed edge returns. Leaf level measurements in winter wheat ( Triticum aestivum ) revealed that the two laser spectral indices improved the relationship with foliar N ( r 2 > 0.71, RMSE r 2 = 0.47, RMSE = 0.38%). Laboratory measurements also showed that laser spectral indices reduced the effect of measurement distance on laser readings and allowed leaf returns to be better separated from edge returns and soil returns. However, laboratory measurements showed that laser spectral indices did not account for variations in leaf angle, possibly explaining the weak relationships ( r 2 r 2 = 0.65, RMSE = 0.37%) alone. Laboratory measurements suggest that the better performance of GLRI compared to ratio-based laser spectral indices may result from pronounced differences in the leaf-level bidirectional reflectance distribution factor (BRDF leaf ) between the green and red laser wavelengths, thus confounding leaf angle effects so that they are not cancelled when calculating laser spectral indices. This finding suggests that the small spot size of the laser pulses (⩽5 mm diameter) interacts with BRDF leaf at very fine scales, therefore causing differential, wavelength-specific scattering effects. Additional study of BRDF leaf at the mm scale is therefore warranted, and should be carefully considered in future development and use of multi-wavelength laser systems for remotely sensing foliar biochemistry.

Pinon–juniper (PJ) woodlands have recently undergone dramatic drought-induced mortality, triggering broad scale structural changes in this extensive Southwestern US biome. Given that climate projections for the region suggest widespread conifer mortality is likely to continue into the next century, it is critical to better understand how this climate-induced change in vegetation structure alters ecosystem function (e.g., productivity, biogeochemical cycling, energy partitioning) in PJ woodlands. Data from satellite remote sensing could potentially help to gain some of this understanding. However, relatively little is known about the suitability of satellite remote sensing for monitoring mortality induced structural and functional changes in PJ woodlands, a semi-arid biome characterized by sparse vegetation that complicates remote sensing of vegetation properties. Here we examined the potential role of satellite remote sensing to better understand structural and functional changes that take place in PJ woodlands as they respond to and recover from climate induced mortality events. We used time series medium (30 × 30 m) and high (≤ 5 × 5 m) spatial resolution satellite data in concert with eddy covariance-based gross primary productivity (GPP) data collected in a PJ woodland mortality manipulation near Mountainair, NM. Our results showed that both high and medium resolution satellite remote sensing data can provide ecosystem level assessments of overall canopy mortality and subsequent regrowth in these semi-arid woodlands. However, high spatial resolution data allowed detecting the disturbance almost a full year earlier and enabled us to describe the spatially heterogeneous patterns of recovery typical for such complex. The ability of satellite remote sensing to be used to provide information about changes in ecosystem function was conditional on the presence of sufficient soil moisture; when soil moisture was present the strength of the linear relationship between GPP and spectral vegetation indices improved at the manipulated site (e.g. for high resolution NDVI data R2 = 0.13 during drought conditions, R2 = 0.46 when wet, P

article i nfo Usingremotely-sensedmetricstoidentifyregionscontaininghighanimaldiversityand/orspecificanimalspecies orguildscanhelpprioritize forest managementand conservation objectivesacrossactivelymanaged landscapes. We predicted avian species richness in two mixed conifer forests, Moscow Mountain and Slate Creek, containing different management contexts and located in north-central Idaho. We utilized general linear models and an AIC model selection approach to examine the relative importance of a wide range of remotely-sensed ecological variables,including LiDAR-derived metrics of vertical and horizontal structuralheterogeneitiesof both vegetation and terrain, and Landsat-derived vegetation reflectance indices. We also examined the relative importance of these remotely sensed variables in predicting nesting guild distributions of ground/understory nesters, mid- upper canopy nesters, and cavity nesters. All top models were statistically significant, with adjusted R 2

Bark beetles cause significant tree mortality in coniferous forests across North America. Mapping beetle-caused tree mortality is therefore important for gauging impacts to forest ecosystems and assessing trends. Remote sensing offers the potential for accurate, repeatable estimates of tree mortality in outbreak areas. With the advancement of multi-temporal disturbance detection methods using Landsat data, the capability exists for improvement in mapping methods, yet more information is needed to determine the accuracy of these methods for mapping forest disturbances and to quantify differences between these methods and single-date image classification methods. We compared single-date (using maximum likelihood classification) to multi-date (using time series of spectral indices) classification methods of Landsat imagery and investigated how detection accuracy changed with varying levels of mortality severity. For each method, we evaluated several bands and/or spectral vegetation indices and identified the one that resulted in the highest accuracy. A fine-resolution classified aerial image within the Landsat scene was used as reference data for evaluation and comparison between methods. For the single-date image classification, we achieved a 91.0% (kappa = 0.88) overall accuracy with 11.7% omission and 2.3% commission errors for the red stage (tree mortality) class using the tasseled cap transformation indices of brightness, greenness, and wetness. For the multi-date analysis, the Band5/Band4 anomaly produced the highest accuracy among spectral indices and resulted in a 89.6% (kappa = 0.86) classification accuracy with 12.6% omission and 7.1% commission errors for the red stage class. We compared accuracies between the best single- and multi-date methods across a range of tree mortality within a pixel. The multi-date method was more accurate at intermediate levels of tree mortality, whereas the single-date method was more accurate at high mortality levels. Our results indicate that Landsat-based mapping of forest disturbances that use either single-date or multi-date methods can result in high classification accuracy.

Sagebrush-steppe ecosystems across the United States Intermountain West are experiencing major structural and functional changes. Scientists and managers need effective technologies to understand such dynamic changes across broad spatial scales. We tested the capacity of terrestrial laser scanning (TLS) to automatically determine structural information of individual shrubs (principally Artemisia tridentata) and shrub canopies in eastern Washington, USA. Because current airborne LiDAR systems have technological constraints that may limit their utility in shrub-dominated ecosystems, we used the TLS data both in their basic form and to simulate high resolution discrete-return airborne LiDAR data with sample densities of 4 and 16 points m−2. Through spatial wavelet analysis we automatically detected the locations of up to 78% of all individual shrubs identified in the field and up to 88% of shrubs with a crown diameter > 1.5 m. Shrub height and canopy cover derived from TLS data were significantly correlated ...

Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM) and tree density (TD) of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Ranging (LiDAR) data and the non- k-nearest neighbor (k-NN) imputation. Forest inventory attributes and LiDAR derived metrics were calculated at 53 regular sample plots and we used imputation models to retrieve the forest attributes at plot and landscape-levels. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more effective for retrieving height than tree density. The model coefficients of determination (adj.R2) and a root mean squared difference (RMSD) for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.

Sound forest policy and management decisions to mitigate rising atmospheric CO 2 depend upon accurate methodologies to quantify forest carbon pools and fluxes over large tracts of land. LiDAR remote sensing is a rapidly evolving technology for quantifying aboveground biomass and thereby carbon pools; however, little work has evaluated the efficacy of repeat LiDAR measures for spatially monitoring aboveground carbon pools through time. Our study objective was therefore to evaluate the use of discrete return airborne LiDAR for quantifying biomass change and carbon flux from repeat field and LiDAR surveys. We collected LiDAR data in 2003 and 2009 across ~ 20,000 ha of an actively managed, mixed conifer forest landscape in northern Idaho. The Random Forest machine learning algorithm was used to impute aboveground biomass pools of trees, saplings, shrubs, herbaceous plants, coarse and fine woody debris, litter, and duff using field-based forest inventory data and metrics derived from the LiDAR collections. Separate predictive tree aboveground biomass models were developed from the 2003 and 2009 field and LiDAR data, and biomass change was estimated at the plot, pixel, and landscape levels by subtracting 2003 predictions from 2009 predictions. Traditional stand exam data were used to independently validate 2003 and 2009 tree aboveground biomass predictions and tree aboveground biomass change estimates at the stand level. Over this 6-year period, we found a mean increase in tree aboveground biomass due to forest growth across the non-harvested portions of 4.1 Mg/ha/yr. We found that 26.3% of the landscape had been harvested during this time period which outweighed growth at the landscape level, resulting in a net tree aboveground biomass change of − 5.7 Mg/ha/yr, and − 2.3 Mg/ha/yr in total aboveground carbon, summed across all the aboveground biomass pools. Change in aboveground biomass was related to forest successional status; younger stands gained two- to three-fold less biomass than did more mature stands. This result suggests that even the most mature forest stands are valuable carbon sinks, and implies that forest management decisions that include longer harvest rotation cycles are likely to favor higher levels of aboveground carbon storage in this system. A 30-fold difference in LiDAR sampling density between the 2003 and 2009 collections did not affect plot-scale biomass estimation. These results suggest that repeat LiDAR surveys are useful for accurately quantifying high resolution, spatially explicit biomass and carbon dynamics in conifer forests.

Remote sensing is a key technology that enables us to scale up our empirical, in situ measurements of carbon uptake made at the site level. In low leaf area index ecosystems typical of semi-arid regions however, many assumptions of these remote sensing approaches fall short, given the complexities of the heterogeneous landscape and frequent disturbance. Here, we investigated the utility of remote sensing data for predicting gross primary production (GPP) in piñon-juniper woodlands in New Mexico (USA). We developed a simple model hierarchy using climate drivers and satellite vegetation indices (VIs) to predict GPP, which we validated against in situ estimates of GPP from eddy-covariance. We tested the influence of pixel size on model fit by comparing model performance when using VIs from RapidEye (5 m) and the VIs from Landsat ETM+ (30 m). We also tested the ability of the normalized difference wetness index (NDWI) and normalized difference red edge (NDRE) to improve model fits. The best predictor of GPP at the undisturbed PJ woodland was Landsat ETM+ derived NDVI (normalized difference vegetation index), whereas at the disturbed site, the red-edge VI performed best (R2adj of 0.92 and 0.90 respectively). The RapidEye data did improve model performance, but only after we controlled for the variability in sensor view angle, which had a significant impact on the apparent cover of vegetation in our low fractional cover experimental woodland. At both sites, model performance was best either during non-stressful growth conditions, where NDVI performed best, or during severe ecosystem stress conditions (e.g., during the girdling process), where NDRE and NDWI improved model fit, suggesting the inclusion of red-edge leveraging and moisture sensitive VI in simple, data driven models can constrain GPP estimate uncertainty during periods of high ecosystem stress or disturbance.

Multiple plant stresses can affect the health, esthetic condition, and timber harvest value of conifer forests. To monitor spatial and temporal dynamic forest stress conditions, timely, accurate, and cost-effective information is needed that could be provided by remote sensing. Recently, satellite imagery has become available via the RapidEye satellite constellation to provide spectral information in five broad bands, including the red-edge region (690–730 nm) of the electromagnetic spectrum. We tested the hypothesis that broadband, red-edge satellite information improves early detection of stress (as manifest by shifts in foliar chlorophyll a + b) in a woodland ecosystem relative to other more commonly utilized band combinations of red, green, blue, and near infrared band reflectance spectra. We analyzed a temporally dense time series of 22 RapidEye scenes of a pinon-juniper woodland in central New Mexico acquired before and after stress was induced by girdling. We found that the Normalized Difference Red-Edge index (NDRE) allowed stress to be detected 13 days after girdling — between and 16 days earlier than broadband spectral indices such as the Normalized Difference Vegetation Index (NDVI) and Green NDVI traditionally used for satellite based forest health monitoring. We conclude that red-edge information has the potential to considerably improve forest stress monitoring from satellites and warrants further investigation in other forested ecosystems.

In-season, spatially variable nitrogen (N) fertilizer applications in agricultural systems can help to maximize crop N use efficiency and minimize N losses via hydrological leaching, runoff, and atmospheric volatilization. N fertilizer management often relies upon measurements of crop spectral reflectance using ground-based optical on-the-go sensors or hand-held chlorophyll meters. However, soil background reflectance can confound on-the-go sensing, especially during early crop growth stages, and hand-held chlorophyll meters are impractical for spatially explicit mapping at the field scale. Scanning laser technology is available that measures the intensity of the reflected laser light plus height information within a mm-scale ground instantaneous field of view at a very high sampling rate (up to 50,000 points s −1 in this study). We examined the ability to quantify foliar N status of spring wheat ( Triticum aestivum L.) using a green (532 nm) terrestrial laser scanner during an early stem extension growth stage (Zadoks growth stage 3.2). Laser data were processed by (1) removing soil background returns based on laser-determined height information, (2) standardizing green laser intensity based on white-reference panel readings, and (3) filtering noisy laser returns from leaf edges based on a laser return intensity threshold value. The return intensity of the reflected green laser light more accurately ( r 2 = 0.68, RMSE = 0.30 μg g −1 ) predicted foliar N concentration than conventional chlorophyll meter readings ( r 2 = 0.36, RMSE = 0.41 μg g −1 ) and spectral indices measured by a ground optical on-the-go sensor ( r 2 0.39 μg g −1 ). The results indicate that laser scanners are useful for measuring the N status of wheat during early growth stages, and provide justification for incorporating laser scanner based measurements into developing spatially-explicit estimates of foliar N during this critical growth period. Further research is needed to evaluate the operational practicality of a green scanning laser from a moving platform.

Insect outbreaks are major forest disturbances, causing tree mortality across millions of ha in North America. Resultant spatial and temporal patterns of tree mortality can profoundly affect ecosystem structure and function. In this study, we evaluated the classification accuracy of multispectral imagery at different spatial resolutions. We used four-band digital aerial imagery (30-cm spatial resolution and aggregated to coarser resolutions) acquired over lodgepole pine-dominated stands in central Colorado recently attacked by mountain pine beetle. Classes of interest included green trees and multiple stages of post-insect attack tree mortality, including dead trees with red needles (“red-attack”), dead trees without needles (“gray-attack”), and non-forest. The 30-cm resolution image facilitated delineation of trees located in the field, which were used in image classification. We employed a maximum likelihood classifier using the green band, Red–Green Index (RGI), and Normalized Difference Vegetation Index (NDVI). Pixel-level classification accuracies using this imagery were good (overall accuracy of 87%, kappa = 0.84), although misclassification occurred between a) sunlit crowns of live (green) trees and herbaceous vegetation, and b) sunlit crowns of gray- and red-attack trees and bare soil. We explored the capability of coarser resolution imagery, aggregated from the 30-cm resolution to 1.2, 2.4, and 4.2 m, to improve classification accuracy. We found the highest accuracy at the 2.4-m resolution, where reduction in omission and commission errors and increases in overall accuracy (90%) and kappa (0.88) were achieved, and visual inspection indicated improved mapping. Pixels at this resolution included more shadow in forested regions than pixels in finer resolution imagery, thereby reducing forest canopy reflectance and allowing improved separation between forest and non-forest classes, yet were fine enough to resolve individual tree crowns better than the 4.2-m imagery. Our results illustrate that a classification of an image with a spatial resolution similar to the area of a tree crown outperforms that of finer and coarser resolution imagery for mapping tree mortality and non-forest classes. We also demonstrate that multispectral imagery can be used to separate multiple postoutbreak attack stages (i.e., red-attack and gray-attack) from other classes in the image.

The Photochemical Reflectance Index (PRI) is used as an indicator of leaf and plant canopy photosynthetic efficiency. However, the photosynthetic efficiency–PRI relationship has been shown to be inconsistent over time, likely due to changes in foliar pigment content. We measured reflectance spectra and biochemical properties from 24 leaves of two deciduous tree species and acquired pigment and reflectance data from the Leaf Optical Properties EXperiment database for an additional nine species. These data were used as inputs for the PROSPECT-5 leaf optical model. We found measurements of PRI to be significantly (p < 0.05) correlated with chlorophyll content, carotenoid content, and the carotenoid/chlorophyll ratio. However, only the PRI–carotenoid/chlorophyll ratio relationship was consistent across all analyses. Two predictive equations were derived from PROSPECT-5 simulations: a curvilinear PRI model (PRI(clm)) predicted the carotenoid/chlorophyll ratio (r2 = 0.99), and a linear model using the chlorophyll index (CI(lm)) predicted chlorophyll content (r2 = 0.98). Multiplying PRI(clm) with CI(lm) canceled the influence of chlorophyll content on PRI(clm) and thus allowed for prediction of carotenoid content from 11 deciduous tree species (r2 = 0.83). Our results confirm that the PRI is significantly influenced by chlorophyll and carotenoid pools and demonstrate a new approach for non-destructive estimation of leaf carotenoid content using the PRI. Because variation in foliar physiological status is known to relate to leaf carotenoid content and the carotenoid/chlorophyll ratio, convolving the PRI with a chlorophyll index is likely to be useful for understanding the photosynthetic performance of deciduous vegetation across a wide range of temporal periods, ranging from daily to seasonal time scales.

Plant structure and chlorophyll content strongly affect rates of photosynthesis. Rapid, objective, and repeatable methods are needed to measure these vegetative parameters to advance our understanding and modeling of plant ecophysiological processes. Terrestrial laser scanners (TLS) can be used to measure structural and potentially chemical properties of objects by quantifying the x , y , z coordinates and intensity of laser light, respectively, returned from an object's surface. The objective of this study was to determine the potential usefulness of TLS with a green (532 nm) laser to simultaneously measure the spatial distribution of chlorophyll a and b content (Chl ab ), leaf area (LA), and leaf angle (LAN). The TLS measurements were obtained from saplings of two tree species ( Quercus macrocarpa and Acer saccharum ) and from an angle-adjustable cardboard surface. The green laser return intensity value was strongly correlated with wet-chemically determined Chl ab ( r 2 = 0.77). Strong agreement was shown between measured and TLS-derived LA ( r 2 = 0.95, intercept = − 1.43, slope = 0.97). The TLS derived LANs of both species followed a plagiophile LAN distribution, and the measured angles of the cardboard surface allowed us to quantify that these LAN values were strongly correlated with TLS derived angles ( r 2 = 1.0, intercept and slope = 0.98). Our results show that terrestrial laser scanners are feasible for simultaneous measurement of LA, LAN, and Chl ab in simple canopies of small broadleaved plants. Further research is needed in more complex and larger canopies.

Active ground optical remote sensing (AGORS) devices mounted on overhead irrigation booms could help to improve seedling quality by autonomously monitoring seedling stress. In contrast to traditionally used passive optical sensors, AGORS devices operate independently of ambient light conditions and do not require spectral reference readings. Besides measuring red (590–670 nm) and near-infrared (>760 nm) reflectance AGORS devices have recently become available that also measure red-edge (730 nm) reflectance. We tested the hypothesis that the additional availability of red-edge reflectance information would improve AGORS of plant stress induced chlorophyll breakdown in Scots pine (Pinus sylvestris). Our results showed that the availability of red-edge reflectance information improved AGORS estimates of stress induced variation in chlorophyll concentration (r2 > 0.73, RMSE < 1.69) when compared to those without (r2 = 0.57, RMSE = 2.11).

Recent advances in understanding relationships between spectral reflectance of vegetation canopies and the structural and physiological drivers of canopy-atmosphere carbon dioxide exchange highlight the potential for using narrowband spectral vegetation indices to spatially scale CO 2 fluxes beyond the area of a tower footprint. However, ground reference observations of narrowband spectral reflectance in support of satellite observations can be challenging to obtain because (1) automated sampling of both upwelling and downwelling radiation is required over extended time periods to characterize diurnal and seasonal variability, (2) hyperspectral spectroradiometer data and hardware can be sensitive to environmental factors such as temperature and moisture, and (3) hyperspectral spectroradiometers are expensive, greatly limiting prospects for widespread automated sampling. We have therefore developed the QuadPod: a simple, lightweight, relatively low cost and low power sensor capable of continuously measuring upwelling and downwelling radiation in 10 nm wavebands centered at 532 nm, 568 nm, 676 nm, and 800 nm. QuadPod measurements can be combined to calculate spectral reflectance indices (e.g., the photochemical reflectance index, PRI; and the normalized difference vegetation index, NDVI) useful for modeling canopy-atmosphere carbon exchange. The basic QuadPod instrument design described here can be implemented using any combination of optical filters in order to calculate other spectral vegetation indices.

Individual tree detection algorithms can provide accurate measurements of individual tree locations, crown diameters (from aerial photography and light detection and ranging (lidar) data), and tree heights (from lidar data). However, to be useful for forest management goals relating to timber harvest, carbon accounting, and ecological processes, there is a need to assess the performance of these image-based tree detection algorithms across a full range of canopy structure conditions. We evaluated the performance of two fundamentally different automated tree detection and measurement algorithms (spatial wavelet analysis (SWA) and variable window filters (VWF)) across a full range of canopy conditions in a mixed-species, structurally diverse conifer forest in northern Idaho, USA. Each algorithm performed well in low canopy cover conditions (

Characterizing shrub-steppe rangeland condition often requires fine-scale measurement of individual plants across broad areas. Advances in remote sensing to develop improved algorithms to census and monitor individual rangeland plants using image data are important for improving the efficiency with which these critical areas are monitored. Here, we performed and evaluated the first test of spatial wavelet analysis (SWA) to automatically detect the location and crown diameter of individuals of two species of shrubs (Artemisia tridentata and Purshia tridentata). Additionally, we quantified the aggregated cover of these shrubs at the plot scale. High spatial resolution (0.25 and 1 m) multispectral aerial imagery and field-based vegetation measurements were collected in both spring and fall 2005. We found that image- and field-based measures of individual shrubs and their crown areas were highly correlated in the fall imagery (r = 0.89). Image-based SWA prediction of shrub cover at the plot level correlated b...

article i nfo Leaf area index (LAI) is a key forest structural characteristic that serves as a primary control for exchanges of mass and energy within a vegetated ecosystem. Most previous attempts to estimate LAI from remotely sensed data have relied on empirical relationships between field-measured observations and various spectral vegetation indices (SVIs) derived from optical imagery or the inversion of canopy radiative transfer models. However, as biomass within an ecosystem increases, accurate LAI estimates are difficult to quantify. Here we use lidar data in conjunction with SPOT5-derived spectral vegetation indices (SVIs) to examine the extent to which integration of both lidar and spectral datasets can estimate specific LAI quantities over a broad range of conifer forest stands in the northern Rocky Mountains. Our results show that SPOT5-derived SVIs performed poorly across our study areas, explaining less than 50% of variation in observed LAI, while lidar-only models account for a significant amount of variation across the two study areas located in northern Idaho; the St. Joe Woodlands (R 2 =0.86; RMSE=0.76) and the Nez Perce Reservation (R 2 =0.69; RMSE=0.61). Further, we found

Vegetation structure is an important factor that influences wildlife-habitat selection, reproduction, and survival. However, field-based measurements of vegetation structure can be time consuming, costly, and difficult to undertake in areas that are remote and/or contain rough terrain. Light detection and ranging (lidar) is an active remote sensing technology that can quantify three-dimensional vegetation structure over large areas and thus holds promise for examining wildlife-habitat relationships. We used discrete-return airborne lidar data acquired over the Black Hills Experimental Forest in South Dakota, USA in combination with field-collected vegetation and bird data to assess the utility of lidar data in quantifying vegetation structural characteristics that relate to avian diversity, density, and occurrence. Indices of foliage height diversity calculated from lidar data were positively and significantly correlated with indices of bird species diversity, with the highest correlations observed when foliage height diversity categories contained proportionally more foliage layers near the forest floor (

Ecologists need data on animal–habitat associations in terrestrial and aquatic environments to design and implement effective conservation strategies. Habitat characteristics used in models typically incorporate (1) field data of limited spatial extent and/or (2) remote sensing data that do not characterize the vertical habitat structure. Remote sensing tools that directly characterize three-dimensional (3-D) habitat structure and that provide data relevant to organism–habitat interactions across a hierarchy of scales promise to improve our understanding of animal–habitat relationships. Laser altimetry, commonly called light detection and ranging (lidar), is a source of geospatial data that can provide fine-grained information about the 3-D structure of ecosystems across broad spatial extents. In this review, we present a brief overview of lidar technology, discuss recent applications of lidar data in investigations of animal–habitat relationships, and propose future applications of this technology to issues of broad species-management and conservation interest.

Ground-based laser scanners represent a relatively new technology that promises to enhance the ability to remotely sense biophysical properties of vegetation. In this study, we utilized a commercially available discrete-return ground-based laser scanning system to sample properties of western larch (Larix occidentalis) in a northern Idaho forest. Three young trees

While nadir-viewing passive multispectral sensors have limited utility for characterizing the full dimensionality of forest canopies, multi-angle remote sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) may improve detection of canopy architecture, canopy cover, and leaf area index (LAI) of forest canopy versus understory vegetation. Our objective was to determine whether data from the MISR sensor could improve estimates of LAI across a post-fire ponderosa pine forest located in the Black Hills of South Dakota. We measured LAI during the 2002 and 2003 growing seasons and created continuous LAI maps using Landsat TM and ETM+ data (mean R 2 =0.81). We fit linear regression models of total and canopy LAI, using a series of MISR at-nadir and off-nadir or anisotropic vegetation indices as predictor variables, for each of five sampling periods. We found the best LAI model fits using either a new hotspot-adjusted normalized difference vegetation index, NDVIHS, NDVI calculated from the −60° view angle, or NDVI at-nadir and either the Hotspot–DarkSpot Index (HDS) or the normalized difference anisotrophic index (NDAX) (R 2 =0.56–0.91). However, differences in fits of these best models and those including NDVI at-nadir ranged from only 1 to 8%. Reflectance anisotropy patterns related strongly to understory vegetation phenology. We found that the relationships among NDVI or the enhanced vegetation index (EVI) and canopy or total LAI showed little variation across view angles when the understory vegetation was senesced and significant anisotropy when understory green LAI was greatest. These findings demonstrate the value of multi-temporal measurements during periods of understory phenological change, even if the overstory LAI is relatively stable. We also evaluated the performance of the MISR LAI product and found moderate fits between the MISR LAI and our field- and Landsat-derived canopy LAI (R 2 =0.21–0.44).

The success of a local maximum (LM) tree detection algorithm for detecting individual trees from lidar data depends on stand conditions that are often highly variable. A laser height variance and percent canopy cover (PCC) classification is used to segment the landscape by stand condition prior to stem detection. We test the performance of the LM algorithm using canopy height model (CHM) smoothing decisions and crown width estimation for each stand condition ranging from open savannah to multi-strata stands. Results show that CHM smoothing improves stem predictions for low density stands and no CHM smoothing better detects stems in dense even-aged stands, specifically dominant and co-dominant trees (R 2 � 0.61, RMSE � 20.91 stems with smoothing; R 2 � 0.85, RMSE � 46.02 stems with no-smoothing; combined smoothed CHM for low density and unsmoothed CHM for high density stands R 2 � 0.88, RMSE � 28.59 stems). At a threshold of approximately 2,200 stems ha � 1 , stem detection accuracy is no longer obtainable in any

Tethered balloon remote sensing platforms can be used to study radiometric issues in terrestrial ecosystems by effectively bridging the spatial gap between measurements made on the ground and those acquired via airplane or satellite. In this study, the Short Wave Aerostat-Mounted Imager (SWAMI) tethered balloon-mounted platform was utilized to evaluate linear and nonlinear spectral mixture analysis (SMA) for a grassland-conifer forest ecotone during the summer of 2003. Hyperspectral measurement of a 74-m diameter ground instantaneous field of view (GIFOV) attained by the SWAMI was studied. Hyperspectral spectra of four common endmembers, bare soil, grass, tree, and shadow, were collected in situ, and images captured via video camera were interpreted into accurate areal ground cover fractions for evaluating the mixture models. The comparison between the SWAMI spectrum and the spectrum derived by combining in situ spectral data with video-derived areal fractions indicated that nonlinear effects occurred in the near infrared (NIR) region, while nonlinear influences were minimal in the visible region. The evaluation of hyperspectral and multispectral mixture models indicated that nonlinear mixture model-derived areal fractions were sensitive to the model input data, while the linear mixture model performed more stably. Areal fractions of bare soil were overestimated in all models due to the increased radiance of bare soil resulting from side scattering of NIR radiation by adjacent grass and trees. Unmixing errors occurred mainly due to multiple scattering as well as close endmember spectral correlation. In addition, though an apparent endmember assemblage could be derived using linear approaches to yield low residual error, the tree and shade endmember fractions calculated using this technique were erroneous and therefore separate treatment of endmembers subject to high amounts of multiple scattering (i.e. shadows and trees) must be done with caution. Including the short wave infrared (SWIR) region in the hyperspectral and multispectral endmember data significantly reduced the Pearson correlation coefficient values among endmember spectra. Therefore, combination of visible, NIR, and SWIR information is likely to further improve the utility of SMA in understanding ecosystem structure and function and may help narrow uncertainties when utilizing remotely sensed data to extrapolate trace glas flux measurements from the canopy scale to the landscape scale.

We describe a new remote sensing system called the Short Wave Aerostat-Mounted Imager (SWAMI). The SWAMI is designed to acquire co-located video imagery and hyperspectral data to study basic remote sensing questions and to link landscape level trace gas fluxes with spatially and temporally appropriate spectral observations. The SWAMI can fly at altitudes up to 2 km above ground level to bridge the spatial gap between radiometric measurements collected near the surface and those acquired by other aircraft or satellites. The SWAMI platform consists of a dual channel hyperspectral spectroradiometer, video camera, GPS, thermal infrared sensor, and several meteorological and control sensors. All SWAMI functions (e.g. data acquisition and sensor pointing) can be controlled from the ground via wireless transmission. Sample data from the sampling platform are presented, along with several potential scientific applications of SWAMI data.

Wavelet analysis represents a powerful set of image processing techniques that have considerable potential to quantify ecologically relevant patterns at multiple scales. This paper provides a preliminary assessment of whether two‐dimensional wavelets convolved with 1 m panchromatic aerial photography can be used to detect automatically the location and crown diameters of western juniper (Juniperus occidentalis) plants as they encroach upon a sagebrush (Artemisia spp.) steppe landscape. The juniper crown diameters derived from wavelet analysis produced a strong correlation with crown diameters measured via comparable hand‐digitizing in a geographic information system (r = 0.96, n = 69) with a 5% commission and an 8% omission error. Through comparison with historical photography, we found that juniper plant cover increased 2.7 fold (from 2.7% to 7.3% total cover) during the period from 1939 to 1998 within the 15 ha study area. This approach has considerable potential for the long‐term monitoring of vegetati...