Estimate Canopy Cover Research Articles

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation.

Multi-Spectral Imaging from an Unmanned Aerial Vehicle Enables the Assessment of Seasonal Leaf Area Dynamics of Sorghum Breeding Lines.

Genetic improvement in sorghum breeding programs requires the assessment of adaptation traits in small-plot breeding trials across multiple environments. Many of these phenotypic assessments are made by manual measurement or visual scoring, both of which are time consuming and expensive. This limits trial size and the potential for genetic gain. In addition, these methods are typically restricted to point estimates of particular traits, such as leaf senescence or flowering and do not capture the dynamic nature of crop growth. In water-limited environments in particular, information on leaf area development over time would provide valuable insight into water use and adaptation to water scarcity during specific phenological stages of crop development. Current methods to estimate plant leaf area index (LAI) involve destructive sampling and are not practical in breeding. Unmanned aerial vehicles (UAV) and proximal-sensing technologies open new opportunities to assess these traits multiple times in large small-plot trials. We analyzed vegetation-specific crop indices obtained from a narrowband multi-spectral camera on board a UAV platform flown over a small pilot trial with 30 plots (10 genotypes randomized within 3 blocks). Due to variable emergence we were able to assess the utility of these vegetation indices to estimate canopy cover and LAI over a large range of plant densities. We found good correlations between the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) with plant number per plot, canopy cover and LAI both during the vegetative growth phase (pre-anthesis) and at maximum canopy cover shortly after anthesis. We also analyzed the utility of time-sequence data to assess the senescence pattern of sorghum genotypes known as fast (senescent) or slow senescing (stay-green) types. The Normalized Difference Red Edge (NDRE) index which estimates leaf chlorophyll content was most useful in characterizing the leaf area dynamics/senescence patterns of contrasting genotypes. These methods to monitor dynamics of green and senesced leaf area are suitable for out-scaling to enhance phenotyping of additional crop canopy characteristics and likely crop yield responses among genotypes across large fields and multiple dates.

Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery

Canopy cover is an important forest structure parameter for many applications in ecology, hydrology, and forest management. Light detection and ranging (LiDAR) is a promising tool for estimating canopy cover because it can penetrate forest canopy. Various algorithms have been developed to calculate canopy cover from LiDAR data. However, little attention was paid to evaluating how different factors, such as estimation algorithm, LiDAR point density and scan angle, influence canopy cover estimates; and how LiDAR-derived canopy cover differs from estimates using traditional methods, such as field measurements, aerial and satellite imagery. In this study, we systematically compared canopy cover estimations from LiDAR data, quick field measurements, aerial imagery, and satellite imagery using different algorithms. The results show that LiDAR-derived canopy cover estimates are marginally influenced by the estimation algorithms. LiDAR data with a point density of 1 point/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> can generate comparable canopy cover estimates to data with a higher density. The uncertainty of canopy cover estimates from LiDAR data increased drastically as scan angles exceed 12°. Plot-level canopy cover estimates derived from quick field measurements do not have strong correlation with LiDAR-derived estimations. Both the aerial imagery-derived and satellite imagery-derived canopy cover estimates are comparable to LiDAR-derived canopy cover estimates at the forest stand scale, but tend to be overestimated in sparse forests and be underestimated in dense forests, particularly for the aerial imagery-derived estimates. The results from this study can provide practical guidance for the selection of data sources, sampling schemes, and estimation methods in regional canopy cover mapping.

Increase in stream water nitrate nitrogen concentrations caused by a disturbance to a forested catchment by Japanese oak wilt

ABSTRACTIn recent years, Japanese oak wilt (JOW) has become prevalent in Japan and has spread into almost all prefectures. To understand the effects of a JOW disturbance on nitrate nitrogen (NO3–) concentrations in a forest stream, we analyzed the changes in NO3– concentrations over a period of 7 years in a mixed broadleaved-conifer secondary forest in Japan. The JOW disturbance arose in the fifth year and lasted until the seventh year of the study period, resulting in a 4.7%, 3.0%, and 2.5% loss in the total basal area of the forest in the fifth, sixth, and seventh years, respectively, eventually resulting in an estimated canopy cover loss of 9.7%. The mean NO3– concentration after the appearance of JOW (3.99 μmol L–1) was significantly higher than before the start of the disturbance (1.99 μmol L–1). This study demonstrated that even the modest canopy cover loss by JOW-induced dieback of Quercus serrata trees could cause an increase in stream water NO3– concentration.

Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index

We present the first comparison of Sentinel-2A (S2) MSI (Multi-Spectral Instrument) and Landsat 8 (L8) OLI (Operational Land Imager) data in the retrieval of forest canopy cover (CC), effective canopy cover (ECC), and leaf area index (LAI). We used S2 and L8 images obtained from Suonenjoki, Finland on 17 and 22 August 2015, respectively. A combination of airborne lidar data and field plots was used to calculate CC, ECC and LAI for a set of 746 systematically placed lidar plots. Generalized additive models were used to link these variables with various types of spectral indices. Our results indicated that instead of using the native image resolution, the model accuracies were better for 60 m lidar plots comprising of nine (S2) or four (L8) image pixels. The best-case absolute root mean square errors of prediction (RMSEPs) (relative RMSEPs shown in parentheses) obtained from ten-fold cross validation of multivariate models for S2 CC, ECC and LAI were 0.126 (24.0%), 0.100 (20.8%), and 0.596 (19.6%), respectively. For L8, the corresponding RMSEPs were 0.128 (24.5%), 0.108 (22.4%), and 0.614 (20.2%). The marginally better performance of S2 models may be related to the 705 nm red edge band, which frequently occurred among the selected predictors. When testing indices that used bands available for both sensors, there were no systematic differences between S2 and L8.

Estimating vertical canopy cover using dense image-based point cloud data in four vegetation types in southern Sweden

ABSTRACTThis study had the aim of investigating the utility of image-based point cloud data for estimation of vertical canopy cover (VCC). An accurate measure of VCC based on photogrammetric matching of aerial images would aid in vegetation mapping, especially in areas where aerial imagery is acquired regularly. The test area is located in southern Sweden and was divided into four vegetation types with sparse to dense tree cover: unmanaged coniferous forest; pasture areas with deciduous tree cover; wetland; and managed coniferous forest. Aerial imagery with a ground sample distance of 0.24 m was photogrammetrically matched to produce dense image-based point cloud data. Two different image matching software solutions were used and compared: MATCH-T DSM by Trimble and SURE by nFrames. The image-based point clouds were normalized using a digital terrain model derived from airborne laser scanner (ALS) data. The canopy cover metric vegetation ratio was derived from the image-based point clouds, as well as from raster-based canopy height models (CHMs) derived from the point clouds. Regression analysis was applied with vegetation ratio derived from near nadir ALS data as the dependent variable and metrics derived from image-based point cloud data as the independent variables. Among the different vegetation types, vegetation ratio derived from the image-based point cloud data generated by using MATCH-T resulted in relative root mean square errors (rRMSE) of VCC ranging from 6.1% to 29.3%. Vegetation ratio based on point clouds from SURE resulted in rRMSEs ranging from 7.3% to 37.9%. Use of the vegetation ratio based on CHMs generated from the image-based point clouds resulted in similar, yet slightly higher values of rRMSE.

Assessing variability in post‐fire forest structure along gradients of productivity in the Canadian boreal using multi‐source remote sensing

AbstractAimForest regeneration following fire is an important component of the global carbon cycle, but it is difficult to monitor over large and remote forested regions, such as Canada's north. In this study, we aim to (1) characterize how forest regeneration following fire varies across the Canadian boreal and (2) determine if this variability is captured by satellite‐derived estimates of productivity.LocationCanadian boreal.MethodsWe relate structural measurements from light detection and ranging (lidar) data to gross primary productivity (GPP) estimates from the MODerate Resolution Imaging Spectroradiometer (MODIS) along a 25‐year chronosequence of forest regeneration following fire. Over 400 patches that burned from 1985–2009 were analysed, with fire information obtained from a national Landsat‐derived record of forest change.ResultsIn the first 15 years since fire (YSF), estimates of percent canopy cover (&gt; 2 m) were typically low regardless of GPP (mean = 11.0–16.0%, SD = 7.8–8.9%) and correlations to GPP were relatively weak (r = 0.18–0.48). Canopy cover was more variable between stands by 16–25 YSF (mean = 16.2–21.7%, SD = 16.0–17.1%), and correlations to GPP were stronger (r = 0.63–0.71, P &lt; 0.01). Conversely, variability in stand height (75th height percentile) remained low at 16–25 YSF (mean = 4.9–5.0 m, SD = 0.9–1.1 m) and weakly related to GPP (r = 0.16–0.21).Main conclusionsSatellite‐derived estimates of productivity capture differences in canopy structure across the boreal, but only after 15 YSF. While canopy cover varied strongly along gradients of productivity from 16–25 YSF, differences in vertical growth were less pronounced due to slow boreal growth rates. Our results provide important insights into how satellite‐derived estimates of productivity are realized structurally, as understanding regional variation in forest regeneration is critical to quantifying carbon dynamics in forests. Combining lidar‐derived estimates of structure with Landsat‐derived disturbance history is a valuable approach for characterizing variability in post‐fire structure over large forested areas.

Reconstruction of Pinon-Juniper Forest Structure to Examine Historic Wildlife Habitat Characteristics in the Davis Mountains, USA

Changes in pinon-juniper (Pinus spp., Juniperusspp.) communities across the southwestern United States have often decreased ecological diversity of the understory and increases of exotic species. Reconstructing age and establishment patterns provides essential understanding to guide treatments and management for anthropogenically-altered forests. The goal of this study was to determine how patterns of pinon and juniper growth in the Davis Mountains, Texas, varied over time and how this pattern influenced wildlife habitat of several indicator species. Establishment patterns and basal area growth progression were identified, canopy cover estimates regressed from pre-developed canopy regression equations to re-construct historic forest stand structure and canopy characteristics in twenty-year intervals and applied to known wildlife habitat requirements of Montezuma quail (Cyrtonyx montezumae), black bear (Ursus americanus) and White-tailed deer/Mule deer (Odocoileus virginianus/O. hemionus). The sites provided habitat for these wildlife species, but the specific habitat provided changed over time. Prescribed burning could promote better forage for black bear while fire exclusion could improve dense cover for escape and denning cover. Montezuma quail would use thinner, less-dense habitats for forage, loafing and escape cover and the denser stand dynamics for cover and shelter. If forage habitat for Montezuma quail is required, the more open habitats found in the early 1900’s could be re-established by prescribed burning and tree removal. Habitat for White-tailed deer transitioned from more open forage, loafing, fawning cover in the early 1900’s to denser thermal, and escape cover in the later 1900’s. Mule deer habitat transitioned from a preferred open habitat to a more dense cover habitat that would be utilized primarily for bedding. Prescribed burning and tree removal to open up the current habitat would benefit Mule deer and white-tailed deer to a lesser degree.

Variability in kelp forest structure along a latitudinal gradient in ocean temperature

Subtidal forests comprised of kelps and other canopy-forming macroalgae represent critically important marine habitats. Kelp forests exhibit high rates of primary productivity, magnified secondary productivity, support high levels of biodiversity and provide various ecosystem services. Compared with many other regions, kelp forests around the UK have been largely understudied despite their recognised importance and the possible impacts of environmental change factors. We conducted surveys at 12 kelp-dominated open-coast sites within four regions in the UK, spanning ~9° in latitude and ~3°C in mean sea temperature. We used a combination of quadrat-scale abundance and percent cover data as well as transect-scale canopy cover estimates to quantify ecological structure at multiple spatial scales. Kelp forest structure varied significantly between sites (nested within region) and also between regions. Regional-scale differences were principally driven by a higher abundance/cover of Alaria esculenta at the colder northern regions (i.e. north and west Scotland), and the presence of the Lusitanian kelp Laminaria ochroleuca at some sites in the southernmost region (i.e. southwest England) but nowhere else. The kelp Laminaria hyperborea dominated all sites and varied significantly between sites but not regions. All assemblage-level and population-level response variables were highly variable between sites within regions, suggesting that environmental factors varying at corresponding spatial scales (e.g. wave exposure, turbidity, sedimentation) are important drivers of pattern. The detection of regional-scale variability suggests that predicted changes in ocean climate, particularly increased sea temperature, may lead to changes in kelp forest structure in the future, with poleward range contractions (for A. esculenta) and expansions (for L. ochroleuca) likely. However, as the distribution-abundance patterns of the assemblage dominant L. hyperborea did not vary predictably with ocean temperature at this spatial scale, the fundamental structure of these habitats may be more influenced by localised factors, at least in the short-term. The relative importance of multiple, concurrent environmental change factors in structuring UK kelp forests remains largely unknown.

Checking the performance of point and plot sampling on aerial photoimagery of a large-scale population of trees outside forests

The present study investigates some sampling strategies for the estimation of abundance and canopy cover of trees outside forest (TOF) over large areas. A collection of about 53 000 TOF units in Ce...

Monitor key parameters of winter wheat using Crop model

Estimation of biomass, canopy cover and yield is very important to agricultural decision Precision Farming. During the winter wheat growing season of 2013/2014, field measurements were conducted at Yangling District, Shaanxi Province at the jointing stage, heading stage and filling stage. AquaCrop model and Particle swarm optimization algorithm was used to find the global optimal simulation when the intermediate variable was the biomass. Through the simulation for each of the experimental data, biomass, canopy coverage and soil moisture were verification by ground measurements. Based on 8 sets of data, the simulation accuracy was calculated. The RMSE, nRMSE, MAE and R2 between simulation and measured biomass were 1.06 ton/ha, 11.92%, 0.90 ton/ha and 0.92. The RMSE, nRMSE, MAE and R2 between simulation and measured canopy cover were 8.92%, 9.84%, 7.84% and 0.66, respectively. The simulation results show that the AquaCrop model can help the decision making of winter wheat field in arid areas.

The accuracy of large-area forest canopy cover estimation using Landsat in boreal region

Large area prediction of continuous field of tree cover i.e., canopy cover (CC) using Earth observation data is of high interest in practical forestry, ecology, and climate change mitigation activities. We report the accuracy of using Landsat images for CC prediction in boreal forests validated with field reference plots (N=250) covering large variation in latitude, forest structure, species composition, and site type. We tested two statistical models suitable for estimating CC: the beta regression (BetaReg) and random forest (RanFor). Landsat-based predictors utilized include individual bands, spectral vegetation indices (SVI), and Tasseled cap (Tass) features. Additionally, we tested an alternative model based on spectral mixture analysis (SMA). Finally, we carried out a first validation in boreal forests of the recently published Landsat Tree Cover Continuous (TCC) global product.Results showed simple BetaReg with red band reflectance provided the highest prediction accuracy (leave-site-out RMSECV 13.7%; R2CV 0.59; biasCV 0.5%). Spectral transformations into SVI and Tass did not improve accuracy. Including additional predictors did not significantly improve accuracy either. Nonlinear model RanFor did not outperform BetaReg. The alternative SMA model did not outperform the empirical models. However, empirical models cannot resolve the underestimation of high cover and overestimation of low cover. SMA prediction errors appeared less dependent on forest structure, while there seemed to be a potential for improvement by accounting for endmember variability of different tree species. Finally, using temporally concurrent observations, we showed the reasonably good accuracy of Landsat TCC product in boreal forests (RMSE 13.0%; R2 0.53; bias −2.1%), however with a tendency to underestimate high cover.

Estimation of urban tree canopy cover using random point sampling and remote sensing methods

Trees play an important role in urban areas by improving air quality, mitigating urban heat islands, reducing stormwater runoff and providing biodiversity habitat. Accurate and up-to-date estimation of urban tree canopy cover (UTC) is a basic need for the management of green spaces in cities, providing a metric from which variation can be understood, change monitored and areas prioritised. Random point sampling methods, such as i-Tree canopy, provide a cheap and quick estimation of UTC for a large area. Remote sensing methods using airborne Light Detection And Ranging (LiDAR) and multi-spectral images produce accurate UTC maps, although greater processing time and technical skills are required. In this paper, random point sampling and remote sensing methods are used to estimate UTC in Williamstown, a suburb of Melbourne, Australia. High resolution multi-spectral satellite images fused with LiDAR data with pixel-level accuracy are employed to produce the UTC map. The UTC is also estimated by categorising random points (a) automatically using the LiDAR derived UTC map and (b) manually using Google Maps and i-Tree canopy software. There was a minimum 1% difference between UTC estimated from the map derived from remotely sensed data and only 1000 random points automatically categorised by that same map, indicating the level of error associated with a random sampling approach. The difference between UTC estimated by remote sensing and manually categorised random point sampling varied in range of 4.5% using a confidence level of 95%. As monitoring of urban forest canopy becomes an increasing priority, the uncertainties associated with different UTC estimates should be considered when tracking change or comparing different areas using different methods.

Assessing the role of Mediterranean evergreen oaks canopy cover in land surface albedo and temperature using a remote sensing-based approach

Modifications in vegetation cover can have an impact on the climate through changes in biogeochemical and biogeophysical processes. In this paper, the tree canopy cover percentage of a savannah-like ecosystem (montado/dehesa) was estimated at Landsat pixel level for 2011, and the role of different canopy cover percentages on land surface albedo (LSA) and land surface temperature (LST) were analysed. A modelling procedure using a SGB machine-learning algorithm and Landsat 5-TM spectral bands and derived vegetation indices as explanatory variables, showed that the estimation of montado canopy cover was obtained with good agreement (R2 = 78.4%). Overall, montado canopy cover estimations showed that low canopy cover class (MT_1) is the most representative with 50.63% of total montado area. MODIS LSA and LST products were used to investigate the magnitude of differences in mean annual LSA and LST values between contrasting montado canopy cover percentages. As a result, it was found a significant statistical relationship between montado canopy cover percentage and mean annual surface albedo (R2 = 0.866, p < 0.001) and surface temperature (R2 = 0.942, p < 0.001). The comparisons between the four contrasting montado canopy cover classes showed marked differences in LSA (χ2 = 192.17, df = 3, p < 0.001) and LST (χ2 = 318.18, df = 3, p < 0.001). The highest montado canopy cover percentage (MT_4) generally had lower albedo than lowest canopy cover class, presenting a difference of −11.2% in mean annual albedo values. It was also showed that MT_4 and MT_3 are the cooler canopy cover classes, and MT_2 and MT_1 the warmer, where MT_1 class had a difference of 3.42 °C compared with MT_4 class. Overall, this research highlighted the role that potential changes in montado canopy cover may play in local land surface albedo and temperature variations, as an increase in these two biogeophysical parameters may potentially bring about, in the long term, local/regional climatic changes moving towards greater aridity.

Evaluation of Global Positioning System telemetry collar performance in the tropical Andes of southern Ecuador

The objective of this study was to evaluate if canopy cover, topographic obstruction of the sky, and differences among vegetation type affected performance of global positioning system (GPS) telemetry in southern Ecuador. A GPS collar was placed at 30 test sites in Podocarpus National Park, Ecuador, and we estimated canopy cover and topographic obstruction at each site. The mean fix success rate was 87.7% (SD=22.3%), and the mean location error for test sites was 9.7m (SD=4.17m). Canopy cover significantly reduced the performance of GPS telemetry in our study area both in terms of location error and fix acquisition rate. However, topographic obstruction did not significantly influence location errors or fix acquisition rate. Screening data to remove less accurate, two-dimensional fixes resulted in an 11% data loss and reduced the mean location error by 3.8m. Understanding how habitat variables influence fix acquisition and location errors of GPS collars will assist researchers in evaluating potential biases and developing methods to correct for the effect of such biases on analyses of habitat use and animal movements in the tropical Andes in southern Ecuador.

Calibration and Validation of Landsat Tree Cover in the Taiga−Tundra Ecotone

Monitoring current forest characteristics in the taiga−tundra ecotone (TTE) at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover &gt;80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010) by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE.

Characteristics Associated with Newell's Shearwater (Puffinus newelli) and Hawaiian Petrel (Pterodroma sandwichensis) Burrows on Kauai, Hawaii, USA

Abstract. Small-scale environmental characteristics associated with nesting burrows of the Newell's Shearwater (Puffinus newelli) and Hawaiian Petrel (Pterodroma sandwichensis) were documented in mesic and wet montane forest on the island of Kauai, Hawaii, USA. Most plots containing burrows were located on steep slopes, ranging from 28° to 48° (median = 39°) for the Newell's Shearwater and 0° to 67° (median = 34.5°) for the Hawaiian Petrel. Plots generally contained > 20% to 40% up to > 80% to 100% estimated vegetation cover 0–1 m above ground and > 0% to 20% up to > 60% to 80% cover 1–2 m above ground. Plots were also associated with estimated canopy cover from > 0% to 10% up to > 80% to 90% for the Newell's Shearwater and 0% up to > 70% to 80% for the Hawaiian Petrel. Soil in Newell's Shearwater plots tended to be harder 7.62–22.86 cm below ground, which might provide increased burrow stability. Additionally, maximum vegetation height tended to be greater above Newell's Shearwater plots (median = 6.00 m...

Incorporating Canopy Cover for Airborne-Derived Assessments of Forest Biomass in the Tropical Forests of Cambodia.

This research examines the role of canopy cover in influencing above ground biomass (AGB) dynamics of an open canopied forest and evaluates the efficacy of individual-based and plot-scale height metrics in predicting AGB variation in the tropical forests of Angkor Thom, Cambodia. The AGB was modeled by including canopy cover from aerial imagery alongside with the two different canopy vertical height metrics derived from LiDAR; the plot average of maximum tree height (Max_CH) of individual trees, and the top of the canopy height (TCH). Two different statistical approaches, log-log ordinary least squares (OLS) and support vector regression (SVR), were used to model AGB variation in the study area. Ten different AGB models were developed using different combinations of airborne predictor variables. It was discovered that the inclusion of canopy cover estimates considerably improved the performance of AGB models for our study area. The most robust model was log-log OLS model comprising of canopy cover only (r = 0.87; RMSE = 42.8 Mg/ha). Other models that approximated field AGB closely included both Max_CH and canopy cover (r = 0.86, RMSE = 44.2 Mg/ha for SVR; and, r = 0.84, RMSE = 47.7 Mg/ha for log-log OLS). Hence, canopy cover should be included when modeling the AGB of open-canopied tropical forests.

Mapping urban forest structure and function using hyperspectral imagery and lidar data

Cities measure the structure and function of their urban forest resource to optimize forest management and the provision of ecosystem services. Measurements made using plot sampling methods yield useful results including citywide or land-use level estimates of species counts, leaf area, biomass, and air pollution reduction. However, these quantities are statistical estimates made over large areas and thus are not spatially explicit. Maps of forest structure and function at the individual tree crown scale can enhance management decision-making and improve understanding of the spatial distribution of ecosystem services relative to humans and infrastructure. In this research we used hyperspectral imagery and waveform lidar data to directly map urban forest species, leaf area index (LAI), and carbon storage in downtown Santa Barbara, California. We compared these results to estimates produced using field-plot sampling and the i-Tree Eco model. Remote sensing methods generally reduced uncertainty in species-level canopy cover estimates compared to field-plot methods. This was due to high classification accuracy for species with large canopies (e.g., Platanus racemosa with ∼90% average accuracy, Pinus pinea at ∼93%, Quercus agrifolia at ∼83%) and high standard error of the plot-based estimates due to the uneven distribution of canopy throughout the city. Average LAI in canopy, based on lidar measurements was 4.47 while field measurements and allometry resulted in an LAI of 5.57. Citywide carbon storage, based on lidar measurements and allometry was estimated at 50,991 metric tons (t) and 55,900t from plot-sampling. As others have noted, carbon density varied substantially by development intensity based largely on differences in fractional cover but less so when only evaluating canopy biomass. Using separate biomass equations for each leaf type (broadleaf, needleleaf, palm) resulted in a more accurate carbon map but a less accurate citywide estimate.

Comparative assessment of methods for estimating tree canopy cover across a rural-to-urban gradient in the mid-Atlantic region of the USA.

Tree canopy cover significantly affects human and wildlife habitats, local hydrology, carbon cycles, fire behavior, and ecosystem services of all types. In addition, changes in tree canopy cover are both indicators and consequences of a wide variety of disturbances from urban development to climate change. There is growing demand for this information nationwide and across all land uses. The extensive inventory plot system managed by the USDA Forest Service Forest Inventory and Analysis (FIA) offers a unique opportunity for acquiring unbiased tree canopy cover information across broad areas. However, the estimates it produces had not yet been examined for comparative accuracy with other sources. In this study, we compared four different methods readily available and with significant potential for application over broad areas. The first two, field-collected and photointerpreted, are currently acquired by FIA on approximately 44,000 plots annually nationwide. The third method is a stem-mapping approach that models tree canopy cover from variables regularly measured on forested plots and is efficient enough to calculate nationwide. The fourth is a Geographic-Object-Based Image Analysis (GEOBIA) approach that uses both high-resolution imagery and leaf-off LiDAR data and has reported very high accuracies and spatial detail at state-wide levels of application. Differences in the spatial and temporal resolution and coverage of these four datasets suggest that they could provide complementary information if their relationships could be better understood. Plot- and county-level estimates of tree canopy cover derived from each of the four data sources were compared for 11 counties in Maryland, Pennsylvania, and West Virginia across a range of urbanization levels. We found high levels of systematic agreement between field and photointerpreted, stem-mapped and field, photointerpreted and GEOBIA estimates. In several cases, the relationship changed with the level of tree canopy cover. GEOBIA produced the highest tree cover estimates of all the methods compared. Results are discussed with respect to known differences between the methods and ground conditions found in both forest and nonforest areas.