Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery

A methodology for the assessment of fruit quality in crops subjected to different irrigation regimes is presented. High spatial resolution multispectral and thermal airborne imagery were used to monitor crown temperature and the Photochemical Reflectance Index (PRI) over three commercial orchards comprising peach, nectarine and orange fruit trees during 2008. Irrigation regimes included sustained and regulated deficit irrigation strategies, leading to high variability of fruit quality at harvest. Stem water potential was used to monitor individual tree water status on each study site. Leaf samples were collected for destructive sampling of xanthophyll pigments to assess the relationship between the xanthophyll epoxidation state (EPS) and PRI at leaf and airborne-canopy level. At harvest, fruit size, Total Soluble Solids (TSS) and Tritatable Acidity (TA) were measured to characterize fruit quality. A statistically significant relationship between EPS and PRI was found at the leaf (r² = 0.81) and canopy level (r² = 0.41). Airborne-derived crown PRI calculated from the imagery acquired during the fruit growth was related to the ratio of the total soluble solids normalized by the tritatable acidity (TSS/TA), an indicator of fruit quality measured on the same trees, yielding a coefficient of determination of r² = 0.50. The relationship between the integral of PRI time-series and TSS/TA yielded a coefficient of determination of r² = 0.72 (peach) and r² = 0.61 (nectarines). On the contrary, the relation between TSS/TA and the time-series of crown thermal imagery was very weak (r² = 0.21 and 0.25 respectively). These results suggest that a physiological remote sensing indicator related to photosynthesis, such as PRI, is more appropriate for fruit quality assessment than crown temperature, the established method of water stress detection, which is more related to crown transpiration. A radiative transfer modelling study was conducted to assess the potential validity of this methodology for fruit quality assessment when using medium spatial resolution imagery. The analysis shows important effects of soil and shadows on the PRI vs EPS relationship used for fruit quality assessment if non-pure crown reflectance was extracted from the imagery.     

Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection

Progress in assessing the feasibility for imaging fluorescence using the O₂-A band with 1 nm full-width half-maximum (FWHM) bands centered at 757.5 and 760.5 nm is reported in this paper. Multispectral airborne data was acquired at 150 m above ground level in the thermal, visible and near infrared regions yielding imagery at 15 cm spatial resolution. Simultaneous field experiments conducted in olive, peach, and orange orchards (water stress trials), and an olive orchard (variety trial) enabled the detected variability in fluorescence emission to be examined as function of stress status. In a parallel modelling activity the coupled leaf-canopy reflectance-fluorescence model, FluorMOD, was used to assess fluorescence retrieval capability by the in-filling method, as well as by fluorescence indices from the published literature. Fluorescence retrievals using the in-filling method, the derivative index D702/D680 and reflectance indices R690/R630, R761-R757, and R761/R757 yielded the best results in the simulation study, while demonstrating insensitivity to leaf area index (LAI) variation. The fluorescence in-filling method, derivative index D702/D680, and R761-R757 were the indices least affected by chlorophyll a + b (Cab) variation. On the other hand, other published indices for fluorescence detection at leaf and canopy levels exhibited high sensitivity to variations in Cab and LAI, and therefore were considered less suitable for in-field fluorescence detection. The fluorescence signal extraction from airborne imagery using the in-filling method was validated through comparisons with field-measured steady-state fluorescence (Fs) using the PAM-2100 and GFS-3000 instruments, confirming simulation predictions. The water stress experiments conducted on olive and peach orchards demonstrated the feasibility of chlorophyll fluorescence (F) extraction at the tree level from the airborne imagery, yielding determination coefficients r² = 0.57 (olive), and r² = 0.54 (peach). Consistent results were obtained between airborne F and ground truth assimilation (A) measured in the olive variety field experiment under no water stress levels, yielding r² = 0.71.     

Modelling PRI for water stress detection using radiative transfer models

This paper presents a methodology for water stress detection in crop canopies using a radiative transfer modelling approach and the Photochemical Reflectance Index (PRI). Airborne imagery was acquired with a 6-band multispectral camera yielding 15 cm spatial resolution and 10 nm FWHM over 3 crops comprising two tree-structured orchards and a corn field. The methodology is based on the PRI as a water stress indicator, and a radiative transfer modelling approach to simulate PRI baselines for non-stress conditions as a function of leaf structure, chlorophyll concentration (Cab), and canopy leaf area index (LAI). The simulation work demonstrates that canopy PRI is affected by structural parameters such as LAI, Cab, leaf structure, background effects, viewing angle and sun position. The modelling work accounts for such leaf biochemical and canopy structural inputs to simulate the PRI-based water stress thresholds for non-stress conditions. Water stress levels are quantified by comparing the image-derived PRI and the simulated non-stress PRI (sPRI) obtained through radiative transfer. PRI simulation was conducted using the coupled PROSPECT-SAILH models for the corn field, and the PROSPECT leaf model coupled with FLIGHT 3D radiative transfer model for the olive and peach orchards. Results obtained confirm that PRI is a pre-visual indicator of water stress, yielding good relationships for the three crops studied with canopy temperature, an indicator of stomatal conductance (r² = 0.65 for olive, r² = 0.8 for peach, and r² = 0.72 for maize). PRI values of deficit irrigation treatments in olive and peach were consistently higher than the modelled PRI for the study sites, yielding relationships with water potential (r² = 0.84) that enabled the identification of stressed crowns accounting for within-field LAI and Cab variability. The methodology presented here for water stress detection is based on the visible part of the spectrum, and therefore it has important implications for remote sensing applications in agriculture. This method may be a better alternative to using the thermal region, which has limitations to acquire operationally high spatial resolution thermal imagery.     

Assessing structural effects on PRI for stress detection in conifer forests

The retrieval of indicators of vegetation stress from remote sensing imagery is an important issue for the accurate assessment of forest decline. The Photochemical Reflectance Index (PRI) has been demonstrated as a physiological index sensitive to the epoxidation state of the xanthophyll cycle pigments and to photosynthetic efficiency, serving as a proxy for short-term changes in photosynthetic activity, stress condition, and pigment absorption, but highly affected by illumination conditions, viewing geometry and canopy structure. In this study, a diurnal airborne campaign was conducted over Pinus sylvestris and Pinus nigra forest areas with the Airborne Hyperspectral Scanner (AHS) to evaluate the effects of canopy structure on PRI when used as an indicator of stress in a conifer forest. The AHS airborne sensor was flown at two times (8:00 GMT and 12:00 GMT) over forest areas under varying field-measured stress levels, acquiring 2 m spatial resolution imagery in 80 spectral bands in the 0.43-12.5 μm spectral range. Five formulations of PRI (based on R₅₃₁ as a xanthophyll-sensitive spectral band) were calculated using different reference wavelengths, such as PRI₅₇₀ (reference band R_REF = R₅₇₀), and the PRI modifications PRI_m1 (R_REF = R₅₁₂), PRI_m2 (R_REF = R₆₀₀), PRI_m3 (R_REF = R₆₇₀), and PRI_m4 (R_REF = R₅₇₀, R₆₇₀), along with other structural indices such as NDVI, SR, OSAVI, MSAVI and MTVI2. In addition, thermal bands were used for the retrieval of the land surface temperature. A radiative transfer modeling method was conducted using the LIBERTY and INFORM models to assess the structural effects on the PRI formulations proposed, studying the sensitivity of PRI_m indices to detect stress levels while minimizing the effects caused by the conifer architecture. The PRI indices were related to stomatal conductance, xanthophyll epoxidation state (EPS) and crown temperature. The modeling analysis showed that the coefficient of variation (CV) for PRI was 50%, whereas the CV for PRIm1 (band R512 as a reference) was only 20%. Simulation and experimental results demonstrated that PRI_m1 (R_REF = R₅₁₂) was less sensitive than PRI (R_REF = R₅₇₀) to changes in Leaf Area Index (LAI) and tree densities. PRI₅₁₂ was demonstrated to be sensitive to EPS at both leaf (r² = 0.59) and canopy level (r² = 0.40), yielding superior performance than PRI₅₇₀ (r² = 0.21) at the canopy level. In addition, PRI₅₁₂ was significantly related to water stress indicators such as stomatal conductance (Gs; r² = 0.45) and water potential (Ψ; r² = 0.48), yielding better results than PRI₅₇₀ (Gs, r² = 0.21; Ψ, r² = 0.21) due to the structural effects found on the PRI₅₇₀ index at the canopy level.     

Capturing tree crown formation through implicit surface reconstruction using airborne lidar data

Forest structure data derived from lidar is being used in forest science and management for inventory analysis, biomass estimation, and wildlife habitat analysis. Regression analysis dominated previous approaches to the derivation of tree stem and crown parameters from lidar. The regression model for tree parameters is locally applied based on vertical lidar point density, the tree species involved, and stand structure in the specific research area. The results of this approach, therefore, are location-specific, limiting its applicability to other areas. For a more widely applicable approach to derive tree parameters, we developed an innovative method called ‘wrapped surface reconstruction’ that employs radial basis functions and an isosurface. Utilizing computer graphics, we capture the exact shape of an irregular tree crown of various tree species based on the lidar point cloud and visualize their exact crown formation in three-dimensional space. To validate the tree parameters given by our wrapped surface approach, survey-grade equipment (a total station) was used to measure the crown shape. Four vantage points were established for each of 55 trees to capture whole-tree crown profiles georeferenced with post-processed differential GPS points. The observed tree profiles were linearly interpolated to estimate crown volume. These fieldwork-generated profiles were compared with the wrapped surface to assess goodness of fit. For coniferous trees, the following tree crown parameters derived by the wrapped surface method were highly correlated (p < 0.05) with the total station-derived measurements: tree height (R² = 0.95), crown width (R² = 0.80), live crown base (R² = 0.92), height of the lowest branch (R² = 0.72), and crown volume (R² = 0.84). For deciduous trees, wrapped surface-derived parameters of tree height (R² = 0.96), crown width (R² = 0.75), live crown base (R² = 0.53), height of the lowest branch (R² = 0.51), and crown volume (R² = 0.89) were correlated with the total station-derived measurements. The wrapped surface technique is less susceptible to errors in estimation of tree parameters because of exact interpolation using the radial basis functions. The effect of diminished energy return causes the low correlation for lowest branches in deciduous trees (R² = 0.51), even though leaf-off lidar data was used. The wrapped surface provides fast and automated detection of micro-scale tree parameters for specific applications in areas such as tree physiology, fire modeling, and forest inventory.     

A LiDAR-derived canopy density model for tree stem and crown mapping in Australian forests

The retrieval of tree and forest structural attributes from Light Detection and Ranging (LiDAR) data has focused largely on utilising canopy height models, but these have proved only partially useful for mapping and attributing stems in complex, multi-layered forests. As a complementary approach, this paper presents a new index, termed the Height-Scaled Crown Openness Index (HSCOI), which provides a quantitative measure of the relative penetration of LiDAR pulses into the canopy. The HSCOI was developed from small footprint discrete return LiDAR data acquired over mixed species woodlands and open forests near Injune, Queensland, Australia, and allowed individual trees to be located (including those in the sub-canopy) and attributed with height using relationships (r² = 0.81, RMSE = 1.85 m, n = 115; 4 outliers removed) established with field data. A threshold contour of the HSCOI surface that encompassed ∼ 90% of LiDAR vegetation returns also facilitated mapping of forest areas, delineation of tree crowns and clusters, and estimation of canopy cover. At a stand level, tree density compared well with field measurements (r² = 0.82, RMSE = 133 stems ha^− 1, n = 30), with the most consistent results observed for stem densities ≤ 700 stems ha^− 1. By combining information extracted from both the HSCOI and the canopy height model, predominant stem height (r² = 0.91, RMSE = 0.77 m, n = 30), crown cover (r² = 0.78, RMSE = 9.25%, n = 30), and Foliage & Branch Projective Cover (FBPC; r² = 0.89, RMSE = 5.49%, n = 30) were estimated to levels sufficient for inventory of woodland and open forest structural types. When the approach was applied to forests in north east Victoria, stem density and crown cover were reliably estimated for forests with a structure similar to those observed in Queensland, but less so for forests of greater height and canopy closure.     

Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California

Greenhouse gas inventories and emissions reduction programs require robust methods to quantify carbon sequestration in forests. We compare forest carbon estimates from Light Detection and Ranging (Lidar) data and QuickBird high-resolution satellite images, calibrated and validated by field measurements of individual trees. We conducted the tests at two sites in California: (1) 59 km² of secondary and old-growth coast redwood (Sequoia sempervirens) forest (Garcia-Mailliard area) and (2) 58 km² of old-growth Sierra Nevada forest (North Yuba area). Regression of aboveground live tree carbon density, calculated from field measurements, against Lidar height metrics and against QuickBird-derived tree crown diameter generated equations of carbon density as a function of the remote sensing parameters. Employing Monte Carlo methods, we quantified uncertainties of forest carbon estimates from uncertainties in field measurements, remote sensing accuracy, biomass regression equations, and spatial autocorrelation. Validation of QuickBird crown diameters against field measurements of the same trees showed significant correlation (r = 0.82, P < 0.05). Comparison of stand-level Lidar height metrics with field-derived Lorey's mean height showed significant correlation (Garcia-Mailliard r = 0.94, P < 0.0001; North Yuba R = 0.89, P < 0.0001). Field measurements of five aboveground carbon pools (live trees, dead trees, shrubs, coarse woody debris, and litter) yielded aboveground carbon densities (mean ± standard error without Monte Carlo) as high as 320 ± 35 Mg ha^− 1 (old-growth coast redwood) and 510 ± 120 Mg ha^− 1 (red fir [Abies magnifica] forest), as great or greater than tropical rainforest. Lidar and QuickBird detected aboveground carbon in live trees, 70-97% of the total. Large sample sizes in the Monte Carlo analyses of remote sensing data generated low estimates of uncertainty. Lidar showed lower uncertainty and higher accuracy than QuickBird, due to high correlation of biomass to height and undercounting of trees by the crown detection algorithm. Lidar achieved uncertainties of < 1%, providing estimates of aboveground live tree carbon density (mean ± 95% confidence interval with Monte Carlo) of 82 ± 0.7 Mg ha^− 1 in Garcia-Mailliard and 140 ± 0.9 Mg ha^− 1 in North Yuba. The method that we tested, combining field measurements, Lidar, and Monte Carlo, can produce robust wall-to-wall spatial data on forest carbon.     

Evaluation of satellite based primary production modelling in the semi-arid Sahel

The MODIS (Moderate Resolution Imaging Spectroradiometer) primary productivity products are evaluated against observed Above-ground Net Primary Production (AGNPP) in the semi-arid Senegal 2001. MODIS net primary productivity (NPP) modelling is a light use efficiency (LUE) based approach incorporating constraints on vegetation productivity arising from simulated radiation, water demand and temperature data from NASA's Data Assimilation Office (DAO). Annually integrated MODIS PSN (MOD17A2 net photosynthesis, Collection 4) explains more of the observed biomass variation (r² = 0.77) than MODIS fAPAR (fraction Absorbed Photosynthetically Active Radiation, Collection 4) (r² = 0.72), indicating the effect of including the canopy stress scalar (ε_s) based on DAO data combined with modelled maintenance respiration costs (of leaf and fine roots). Annual MODIS NPP (MOD17A3, Collection 4 (C4) and Collection 4.5 (C4.5)) including growth respiration and live wood maintenance respiration costs and modified DAO input (C4.5) however increases the residual unexplained observed AGNPP variance (C4 NPP; r² = 0.49) (C4.5 NPP; r² = 0.37). The overall quality of the annual NPP MODIS C4 and C4.5 products are moderate for the semi-arid Senegal because of the annual respiration cost modelling and a change in C4.5 biome-specific parameters stored in a Biome Properties Look-Up Table (BPLUT) is the main contributor to the observed discrepancy between C4 and C4.5 NPP. The dynamic range of the values of all MOD17 products was too low when compared to observed AGNPP. An estimate of canopy water stress (SIWSI; Shortwave Infrared Water Stress Index) derived from MODIS channels 2 and 6 and photosynthetically active radiation (PAR) irradiance derived from geostationary METEOSAT data were tested for primary production modelling using a stepwise linear regression analysis. PAR irradiance was combined with MODIS fAPAR into APAR (Absorbed Photosynthetically Active Radiation) explaining 79% of the observed AGNPP variation. Introducing SIWSI significantly increased the explained variance of observed AGNPP (r² = 0.89). MODIS-derived percentage tree cover was tested as a predictor based on the hypothesis that tree cover provides information on differences in respiratory costs between trees and grasses thereby accounting for variations in the LUE conversion efficiency ε. No significant reduction in residual unexplained AGNPP variance was found. Earth observation based derivation of PAR and canopy water stress from SIWSI suggest potential improvements to primary production models in semi-arid biomes that can be implemented in general NPP modelling LUE methodology.     

MODIS tree cover validation for the circumpolar taiga-tundra transition zone

A validation of the 2005 500 m MODIS vegetation continuous fields (VCF) tree cover product in the circumpolar taiga-tundra ecotone was performed using high resolution Quickbird imagery. Assessing the VCF's performance near the northern limits of the boreal forest can help quantify the accuracy of the product within this vegetation transition area. The circumpolar region was divided into 7 longitudinal zones and validation sites were selected in areas of varying tree cover where Quickbird imagery is available in Google Earth. Each site was linked to the corresponding VCF pixel and overlaid with a regular dot grid within the VCF pixel's boundary to estimate percent tree crown cover in the area. Percent tree crown cover was estimated using Quickbird imagery for 396 sites throughout the circumpolar region and related to the VCF's estimates of canopy cover for 2000-2005. Regression results of VCF inter-annual comparisons (2000-2005) and VCF-Quickbird image-interpreted estimates indicate that: (1) Pixel-level, inter-annual comparisons of VCF estimates of percent canopy cover were linearly related (mean R² = 0.77) and exhibited an average root mean square error (RMSE) of 10.1% and an average root mean square difference (RMSD) of 7.3%. (2) A comparison of image-interpreted percent tree crown cover estimates based on dot counts on Quickbird color images by two different interpreters were more variable (R² = 0.73, RMSE = 14.8%, RMSD = 18.7%) than VCF inter-annual comparisons. (3) Across the circumpolar boreal region, 2005 VCF-Quickbird comparisons were linearly related, with an R² = 0.57, a RMSE = 13.4% and a RMSD = 21.3%, with a tendency to over-estimate areas of low percent tree cover and anomalous VCF results in Scandinavia. The relationship of the VCF estimates and ground reference indicate to potential users that the VCF's tree cover values for individual pixels, particularly those below 20% tree cover, may not be precise enough to monitor 500 m pixel-level tree cover in the taiga-tundra transition zone.     

Estimating average tree crown size using spatial information from Ikonos and QuickBird images: Across-sensor and across-site comparisons

The forest canopy is the medium for energy, mass, and momentum exchanges between the forest ecosystem and the atmosphere. Tree crown size is a critical aspect of canopy structure that significantly influences these biophysical processes in the canopy. Tree crown size is also strongly related to other canopy structural parameters, such as tree height, diameter at breast height and biomass. But information about tree crown sizes is difficult to obtain and rarely available from traditional forest inventory. The study objective was to test the hypothesis that a model previously developed for estimation of tree crown size can be generalized across sensors and sites. Our study sites include the Racoon Ecological Management Area in southeast Ohio, USA and the Duke Forest in North Carolina Piedmont, USA. We sampled a series of circular plots in the summers of 2005 and 2007. We derived average tree crown diameter (CD) for trees with diameter at breast height (DBH) greater than 6.4 cm (2.5 in) for each sampling plot. We developed statistical models using image spatial information from Ikonos and QuickBird images as the independent variable and CD for stands in Ohio as the dependent variable. The models provide an explanation of tree crown size for the hardwood stands comparable to other approaches (R² = ∼ 0.5 and RMSE = 0.83 m). Moreover, the models that estimate tree crown size using the ratio of image variances at two spatial resolutions can be applied across sensors and sites, i.e. the statistical models developed with Ikonos images can be applied directly to estimate tree crown size with QuickBird image, and the statistical models developed in Ohio can be applied directly to estimate tree crown size with images in North Carolina. These results indicate that the model developed based on image variance ratio at two spatial resolutions can be used to take advantage of existing sampling plot data and images to estimate CD with more recent images, enhancing the efficiency of forest resources inventory and monitoring.     

Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation

High spatial resolution remotely sensed data has the potential to complement existing forest health programs for both strategic planning over large areas, as well as for detailed and precise identification of tree crowns subject to stress and infestation. The area impacted by the current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia, Canada, has increased 40-fold over the previous 5 years, with approximately 8.5 million ha of forest infested in 2005. As a result of the spatial extent and intensity of the outbreak, new technologies are being assessed to help detect, map, and monitor the damage caused by the beetle, and to inform mitigation of future beetle outbreaks. In this paper, we evaluate the capacity of high spatial resolution QuickBird multi-spectral imagery to detect mountain pine beetle red attack damage. ANOVA testing of individual spectral bands, as well as the Normalized Difference Vegetation Index (NDVI) and a ratio of red to green reflectance (Red-Green Index or RGI), indicated that the RGI was the most successful (p < 0.001) at separating non-attack crowns from red attack crowns. Based on this result, the RGI was subsequently used to develop a binary classification of red attack and non-attack pixels. The total number of QuickBird pixels classified as having red attack damage within a 50 m buffer of a known forest health survey point were compared to the number of red attack trees recorded at the time of the forest health survey. The relationship between the number of red attack pixels and observed red attack crowns was assessed using independent validation data and was found to be significant (r² = 0.48, p < 0.001, standard error = 2.8 crowns). A comparison of the number of QuickBird pixels classified as red attack, and a broader scale index of mountain pine beetle red attack damage (Enhanced Wetness Difference Index, calculated from a time series of Landsat imagery), was significant (r² = 0.61, p < 0.001, standard error = 1.3 crowns). These results suggest that high spatial resolution imagery, in particular QuickBird satellite imagery, has a valuable role to play in identifying tree crowns with red attack damage. This information could subsequently be used to augment existing detailed forest health surveys, calibrate synoptic estimates of red attack damage generated from overview surveys and/or coarse scale remotely sensed data, and facilitate the generation of value-added information products, such as estimates of timber volume impacts at the forest stand level.     

Identifying species of individual trees using airborne laser scanner

Individual trees can be detected using high-density airborne laser scanner data. Also, variables characterizing the detected trees such as tree height, crown area, and crown base height can be measured. The Scandinavian boreal forest mainly consists of Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.), and deciduous trees. It is possible to separate coniferous from deciduous trees using near-infrared images, but pine and spruce give similar spectral signals. Airborne laser scanning, measuring structure and shape of tree crowns could be used for discriminating between spruce and pine. The aim of this study was to test classification of Scots pine versus Norway spruce on an individual tree level using features extracted from airborne laser scanning data. Field measurements were used for training and validation of the classification. The position of all trees on 12 rectangular plots (50×20 m²) were measured in field and tree species was recorded. The dominating species (>80%) was Norway spruce for six of the plots and Scots pine for six plots. The field-measured trees were automatically linked to the laser-measured trees. The laser-detected trees on each plot were classified into species classes using all laser-detected trees on the other plots as training data. The portion correctly classified trees on all plots was 95%. Crown base height estimations of individual trees were also evaluated (r=0.84). The classification results in this study demonstrate the ability to discriminate between pine and spruce using laser data. This method could be applied in an operational context. In the first step, a segmentation of individual tree crowns is performed using laser data. In the second step, tree species classification is performed based on the segments. Methods could be developed in the future that combine laser data with digital near-infrared photographs for classification with the three classes: Norway spruce, Scots pine, and deciduous trees.     

Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data

The potential of canopy reflectance modelling to retrieve simultaneously several structural variables in managed Norway spruce stands was investigated using the “Invertible Forest Reflectance Model”, INFORM. INFORM is an innovative extension of the FLIM model, with crown transparency, infinite crown reflectance and understory reflectance simulated using physically based sub-models (SAILH, LIBERTY and PROSPECT). The INFORM model was inverted with hyperspectral airborne HyMap data using a neural network approach. INFORM based estimates of forest structural variables were produced using site-specific ranges of stand structural variables. A relatively simple three layer feed-forward backpropagation neural network with two input neurons, one neuron in the hidden layer and three output neurons was employed to map leaf area index (LAI), crown coverage and stem density.To identify the optimum 2-band spectral subset to be used in the inversion process, all 2-band combinations of the HyMap dataset were systematically evaluated for model inversion. Field measurements of structural variables from 39 forest stands were used to validate the maps produced from HyMap imagery. Using two HyMap wavebands at 837 nm and 1148 nm the obtained accuracy of the LAI map amounts to an rmse of 0.58 (relative rmse = 18% of mean, R² = 0.73). With HyMap data resampled to Landsat TM spectral bands and using two “optimum” bands at 840 nm and 1650 nm, rmse was 0.66 and relative rmse 21%. In contrast to approaches based on empirical relations between spectral vegetation indices and structural variables, the main advantage of the inversion approach is that it does not require previous calibration.     

Development of a simulation model to predict LiDAR interception in forested environments

Airborne scanning LiDAR systems are used to predict a range of forest attributes. However, the accuracy with which this can be achieved is highly dependent on the sensor configuration and the structural characteristics of the forest examined. As a result, there is a need to understand laser light interactions with forest canopies so that LiDAR sensor configurations can be optimised to assess particular forest types. Such optimisation will not only ensure the targeted forest attributes can be accurately and consistently quantified, but may also minimise the cost of data acquisition and indicate when a survey configuration will not deliver information needs.In this paper, we detail the development and application of a model to simulate laser interactions within forested environments. The developed model, known as the LiDAR Interception and Tree Environment (LITE) model, utilises a range of structural configurations to simulate trees with variable heights, crown dimensions and foliage clumping. We developed and validated the LITE model using field data obtained from three forested sites covering a range of structural classes. Model simulations were then compared to coincident airborne LiDAR data collected over the same sites. Results indicate that the LITE model can be used to produce comparable estimates of maximum height of trees within plots (differences < 2.42 m), mean heights of first return data (differences < 2.27 m), and canopy height percentiles (r² = 0.94, p < 0.001) when compared to airborne LiDAR data. In addition, the distribution of airborne LiDAR hits through the canopy profile was closely matched by model predictions across the range of sites. Importantly, this demonstrates that the structural differences between forest stands can be characterised by LITE. Models that are capable of interpreting the response of small-footprint LiDAR waveforms can facilitate algorithm development, the generation of corrections for actual LiDAR data, and the optimisation of sensor configurations for differing forest types, benefiting a range of experimental and commercial LiDAR applications. As a result, we also performed a scenario analysis to demonstrate how differences in forest structure, terrain, and sensor configuration can influence the interception of LiDAR beams.     

Generation of crown bulk density for Pinus sylvestris L. from lidar

The use of lidar data to estimate critical variables needed for modeling wildfire behavior was tested on a Scots pine forest (Pinus sylvestris L.) in central Spain. Lidar data accurately estimated crown bulk density at the plot level (r²=0.80). Lidar data could be used to directly estimate crown volume (r²=0.92) and foliage biomass (r²=0.84), which together produced better results than directly fitting the lidar data to crown bulk density. Incorporating equations that relate tree diameter at breast height and other forest parameters improved estimates of foliage biomass. Individual tree level analyses were not completely successful due to difficulty in accurately assigning laser pulses to the correct tree (r²=0.14).     

Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African Savannahs

The remote sensing of fire severity is a noted goal in studies of forest and grassland wildfires. Experiments were conducted to discover and evaluate potential relationships between the characteristics of African savannah fires and post-fire surface spectral reflectance in the visible to shortwave infrared spectral region. Nine instrumented experimental fires were conducted in semi-arid woodland savannah of Chobe National Park (Botswana), where fire temperature (T_max) and duration (dt) were recorded using thermocouples positioned at different heights and locations. These variables, along with measures of fireline intensity (FLI), integrated temperature with time (T_sum) and biomass (and carbon/nitrogen) volatilised were compared to post-fire surface spectral reflectance. Statistically significant relationships were observed between (i) the fireline intensity and total nitrogen volatilised (r² = 0.54, n = 36, p < 0.001), (ii) integrated temperature (T_sum−μ) and total biomass combusted (r² = 0.72, n = 32, p < 0.001), and (iii) fire duration as measured at the top-of-grass sward thermocouple (dt_T) and total biomass combusted (r² = 0.74, n = 34, p < 0.001) and total nitrogen volatilised (r² = 0.73, n = 34, p < 0.001). The post-fire surface spectral reflectance was found to be related to dt and T_sum via a quadratic relationship that varied with wavelength. The use of visible and shortwave infrared band ratios produced statistically significant linear relationships with fire duration as measured by the top thermocouple (dt_T) (r² = 0.76, n = 34, p < 0.001) and the mean of T_sum (r² = 0.82, n = 34, p < 0.001). The results identify fire duration as a versatile measure that relates directly to the fire severity, and also illustrate the potential of spectrally-based fire severity measures. However, the results also point to difficulties when applying such spectrally-based techniques to Earth Observation satellite imagery, due to the small-scale variability noted on the ground. Results also indicate the potential for surface spectral reflectance to increase following higher severity fires, due to the laying down of high albedo white mineral ash. Most current techniques for mapping burned area rely on the general assumption that surface albedo decreases following a fire, and so if the image spatial resolution was high enough such methods may fail. Determination of the effect of spatial resolution on a sensor's ability to detect white ash was investigated using a validated optical mixture modelling approach. The most appropriate mixing model to use (linear or non-linear) was assessed using laboratory experiments. A linear mixing model was shown most appropriate, with results suggesting that sensors having spatial resolutions significantly higher than those of Landsat ETM+ will be required if patches of white ash are to be used to provide EO-derived information on the spatial variation of fire severity.     

Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner

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 (r² = 0.77). Strong agreement was shown between measured and TLS-derived LA (r² = 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 (r² = 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.     

Effects of soil moisture and water depth on ERS SAR backscatter measurements from an Alaskan wetland complex

We conducted a preliminary investigation of the response of ERS C-band SAR backscatter to variations in soil moisture and surface inundation in wetlands of interior Alaska. Data were collected from 5 wetlands over a three-week period in 2007. Results showed a positive correlation between backscatter and soil moisture in sites dominated by herbaceous vegetation cover (r = 0.74, p < 0.04). ERS SAR backscatter was negatively correlated to water depth in all open (non-forested) wetlands when water table levels were more than 6 cm above the wetland surface (r = − 0.82, p < 0.001). There was no relationship between backscatter and soil moisture in the forested (black spruce-dominated) wetland site. Our preliminary results show that ERS SAR data can be used to monitor variations in hydrologic conditions in high northern latitude wetlands (including peatlands), particularly sites with sparse tree cover.