Analysis of a lidar voxel-derived vertical profile at the plot and individual tree scales for the estimation of forest canopy layer characteristics

The goal of the current study was to develop methods of estimating the height of vertical components within plantation coniferous forest using airborne discrete multiple return lidar. In the summer of 2008, airborne lidar and field data were acquired for Loblolly pine forest locations in North Carolina and Virginia, USA, which comprised a variety of stand conditions (e.g. stand age, nutrient regime, and stem density). The methods here implement both field plot-scale analysis and an automated approach for the delineation of individual tree crown (ITC) locations and horizontal extents through a marker-based region growing process applied to a lidar derived canopy height model. The estimation of vertical features was accomplished through aggregating lidar return height measurements into vertical height bins, of a given horizontal extent (plot or ITC), creating a vertical ‘stack’ of bins describing the frequency of returns by height. Once height bins were created the resulting vertical distributions were smoothed with a regression curve-line function and canopy layers were identified through the detection of local maxima and minima. Estimates from Lorey’s mean canopy height was estimated from plot-level curve-fitting with an overall accuracy of 5.9% coefficient of variation (CV) and the coefficient of determination (R²) value of 0.93. Estimates of height to the living canopy produced an overall R² value of 0.91 (11.0% CV). The presence of vertical features within the sub-canopy component of the fitted vertical function also corresponded to areas of known understory presence and absence. Estimates from ITC data were averaged to the plot level. Estimates of field Lorey’s mean canopy top height from average ITC data produced an R² value of 0.96 (7.9% CV). Average ITC estimates of height to the living canopy produced the closest correspondence to the field data, producing an R² value of 0.97 (6.2% CV). These results were similar to estimates produced by a statistical regression method, where R² values were 0.99 (2.4% CV) and 0.98 (4.9% CV) for plot average top canopy height and height to the living canopy, respectively. These results indicate that the characteristics of the dominant canopy can be estimated accurately using airborne lidar without the development of regression models, in a variety of intensively managed coniferous stand conditions.     

Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots

Many areas of forest across northern Canada are challenging to monitor on a regular basis as a result of their large extent and remoteness. Although no forest inventory data typically exist for these northern areas, detailed and timely forest information for these areas is required to support national and international reporting obligations. We developed and tested a sample-based approach that could be used to estimate forest stand height in these remote forests using panchromatic Very High Spatial Resolution (VHSR, < 1 m) optical imagery and light detection and ranging (lidar) data. Using a study area in central British Columbia, Canada, to test our approach, we compared four different methods for estimating stand height using stand-level and crown-level metrics generated from the VHSR imagery. ‘Lidar plots’ (voxel-based samples of lidar data) are used for calibration and validation of the VHSR-based stand height estimates, similar to the way that field plots are used to calibrate photogrammetric estimates of stand height in a conventional forest inventory or to make empirical attribute estimates from multispectral digital remotely sensed data. A k-nearest neighbours (k-NN) method provided the best estimate of mean stand height (R ² = 0.69; RMSE = 2.3 m, RMSE normalized by the mean value of the estimates (RMSE-%) = 21) compared with linear regression, random forests, and regression tree methods. The approach presented herein demonstrates the potential of VHSR panchromatic imagery and lidar to provide robust and representative estimates of stand height in remote forest areas where conventional forest inventory approaches are either too costly or are not logistically feasible. While further evaluation of the methods is required to generalize these results over Canada to provide robust and representative estimation, VHSR and lidar data provide an opportunity for monitoring in areas for which there is no detailed forest inventory information available.     

Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data

While forest inventories based on airborne laser scanning data (ALS) using the area based approach (ABA) have reached operational status, methods using the individual tree crown approach (ITC) have basically remained a research issue. One of the main obstacles for operational applications of ITC is biased results often experienced due to segmentation errors. In this article, we propose a new method, called “semi-ITC” that overcomes the main problems related to ITC by imputing ground truth data within crown segments from the nearest neighboring segment. This may be none, one, or several trees. The distances between segments were derived based on a set of explanatory variables using two nonparametric methods, i.e., most similar neighbor inference (MSN) and random forest (RF). RF favored the imputation of common observations in the data set which resulted in significant biases. Main conclusions are therefore based on MSN. The explanatory variables were calculated by means of small footprint ALS and multispectral data. When testing with empirical data the new method compared favorably to the well-known ABA. Another advantage of the new method over the ABA is that it allowed for the modeling of rare tree species. The results of predicting timber volume with the semi-ITC method were unbiased and the root mean squared error (RMSE) on plot level was smaller than the standard deviation of the observed response variables. The relative RMSEs after cross validation using semi-ITC for total volume and volume of the individual species pine, spruce, birch, and aspen on plot level were 17, 38, 40, 101, and 222%, respectively. Due to the unbiasedness of the estimation, this study is a showcase for how to use crown segments resulting from ITC algorithms in a forest inventory context.     

Estimating tree crown size with spatial information of high resolution optical remotely sensed imagery

Tree crown size is a critical biophysical parameter that influences carbon, water and energy exchanges between forest ecosystems and the atmosphere. This study explores the potential of using spatial information of high resolution optical imagery in estimating mean tree crown diameter on a stand basis with an Ikonos image in the Blackwood Division of Duke Forest and its surrounding areas. The theory is based on the disc scene model that the ratio of image variances at two spatial resolutions is determined by the scene structure only. The mean tree crown diameter of a stand on the ground was estimated with a circular sampling plot made in the middle of the stand. The stands were then delineated in the panchromatic band of the Ikonos image. The relationship between mean tree crown diameter with image variance at a single spatial resolution, the ratio of image variances at two spatial resolutions, and the difference of image variances at two spatial resolutions were studied for conifer and hardwood stands, respectively. It was found that the ratio of image variances at 2 m and 3 m spatial resolutions best estimate conifer tree crown diameter (R ² = 0.7282). Though the image variance at a single resolution and the difference of image variances at two spatial resolutions are also significantly correlated to conifer tree crown diameter, the R ² is lower. Due to the continuity in canopy structure, the approach does not work well for hardwood stands.     

Mapping canopy height using a combination of digital stereo‐photogrammetry and lidar

Ranging techniques such as lidar (LIght Detection And Ranging) and digital stereo‐photogrammetry show great promise for mapping forest canopy height. In this study, we combine these techniques to create hybrid photo‐lidar canopy height models (CHMs). First, photogrammetric digital surface models (DSMs) created using automated stereo‐matching were registered to corresponding lidar digital terrain models (DTMs). Photo‐lidar CHMs were then produced by subtracting the lidar DTM from the photogrammetric DSM. This approach opens up the possibility of retrospective mapping of forest structure using archived aerial photographs. The main objective of the study was to evaluate the accuracy of photo‐lidar CHMs by comparing them to reference lidar CHMs. The assessment revealed that stereo‐matching parameters and left–right image dissimilarities caused by sunlight and viewing geometry have a significant influence on the quality of the photo DSMs. Our study showed that photo‐lidar CHMs are well correlated to their lidar counterparts on a pixel‐wise basis (r up to 0.89 in the best stereo‐matching conditions), but have a lower resolution and accuracy. It also demonstrated that plot metrics extracted from the lidar and photo‐lidar CHMs, such as height at the 95th percentile of 20 m×20 m windows, are highly correlated (r up to 0.95 in general matching conditions).     

Extraction of small biotopes and ecotones from multi-temporal RapidEye data and a high-resolution normalized digital surface model

The conservation of habitats and habitat complexes in diverse landscapes is increasingly recognized as a crucial factor in sustaining biodiversity and ecosystems. For the successful management of landscapes, habitat monitoring is necessary, but often small biotopes, e.g. scattered trees, copses, tree rows, hedges, and the transition zones between ecosystems, are ignored. This is important as such small biotopes are recognized as keystone elements in landscape structure for habitat networks. Furthermore, the transition zones between different habitats, often called ecotones, are dynamic and play several functional roles in landscape ecology. This article presents an approach for the extraction of small biotopes and ecotones combining object-based and pixel-based image analysis. Both high-resolution digital elevation data from airborne laser scanning and multi-temporal RapidEye remote-sensing data were used to automatically detect landscape elements and landscape patterns. First, multi-temporal RapidEye images were used to classify the main land-use classes using object-based image analysis. In the second step, a high-resolution digital surface model was integrated with the main classes, and small biotopes and ecotones were delineated by means of pixel-based image analysis. Classification accuracy for main land-use classes is above 92%, and a visual assessment using aerial image and onsite investigation show that the identification results for small biotopes well match reality. The results show the effectiveness of the classification strategy developed and the potential for incorporating the detailed surface mapping in heterogeneous vegetated areas.     

Estimating plot volume using lidar height and intensity distributional parameters

This study explored the feasibility of height distributional metrics and intensity values extracted from low-density airborne light detection and ranging (lidar) data to estimate plot volumes in dense Korean pine (Pinus koraiensis) plots. Multiple linear regression analyses were performed using lidar height and intensity distributional metrics. The candidate variables for predicting plot volume were evaluated using three data sets: total, canopy, and integrated lidar height and intensity metrics. All intensities of lidar returns used were corrected by the reference distance. Regression models were developed using each data set, and the first criterion used to select the best models was the corrected Akaike Information Criterion (AIC_c). The use of three data sets was statistically significant at R² = 0.75 (RMSE = 52.17 m³ ha^?1), R² = 0.84 (RMSE = 45.24 m³ ha^?1), and R² = 0.91 (RMSE = 31.48 m³ ha^?1) for total, canopy, and integrated lidar distributional metrics, respectively. Among the three data sets, the integrated lidar metrics-derived model showed the best performance for estimating plot volumes, improving errors up to 42% when compared to the other two data sets. This is attributed to supplementing variables weighted and biased to upper limits in dense plots with more statistical variables that explain the lower limits. In all data sets, intensity metrics such as skewness, kurtosis, standard deviation, minimum, and standard error were employed as explanatory variables. The use of intensity variables improved the accuracy of volume estimation in dense forests compared to prior research. Correction of the intensity values contributed up to a maximum of 58% improvement in volume estimation when compared to the use of uncorrected intensity values (R² = 0.78, R² = 0.53, and R² = 0.63 for total, canopy, and integrated lidar distributional metrics, respectively). It is clear that the correction of intensity values is an essential step for the estimation of forest volume.     

Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers

Mapping of patterns and spatial distribution of land-use/cover (LULC) has long been based on remotely sensed data. In the recent past, efforts to improve the reliability of LULC maps have seen a proliferation of image classification techniques. Despite these efforts, derived LULC maps are still often judged to be of insufficient quality for operational applications, due to disagreement between generated maps and reference data. In this study we sought to pursue two objectives: first, to test the new-generation multispectral RapidEye imagery classification output using machine-learning random forest (RF) and support vector machines (SVM) classifiers in a heterogeneous coastal landscape; and second, to determine the importance of different RapidEye bands on classification output. Accuracy of the derived thematic maps was assessed by computing confusion matrices of the classifiers’ cover maps with respective independent validation data sets. An overall classification accuracy of 93.07% with a kappa value of 0.92, and 91.80 with a kappa value of 0.92 was achieved using RF and SVM, respectively. In this study, RF and SVM classifiers performed comparatively similarly as demonstrated by the results of McNemer’s test (Z = 1.15). An evaluation of different RapidEye bands using the two classifiers showed that incorporation of the red-edge band has a significant effect on the overall classification accuracy in vegetation cover types. Consequently, pursuit of high classification accuracy using high-spatial resolution imagery on complex landscapes remains paramount.     

Estimation of tree crown volume from airborne lidar data using computational geometry

Although simple geometrical shapes are commonly used to describe tree crowns, computational geometry enables calculation of the individual crown properties directly from airborne lidar point clouds. Our objective was to calculate crown volumes (CVs) using this technique and validate the results by comparing them with field-measured values and modelled ellipsoidal crowns. The CVs of standing trees were obtained by measuring the crown radii at different heights, integrating the obtained crown profiles as solids of revolution, and finally averaging the volumes obtained from the four separate profiles. With the lidar data, the CVs were extracted using 3D alpha shape and 3D convex hull techniques. Crown base heights (CBHs) were also estimated from the lidar data and used to exclude echoes from the understory, which was also done using field-based CBHs to exclude this error source. The results show that the field-measured CVs had a high correlation with lidar-based estimates (best R ² = 0.83), but the lidar-based estimates were generally smaller than the field values. The best correspondence (root mean square difference (RMSD) = 45.0%, average difference = –24.7%) was obtained using the convex hull of the point data and field-measured CBH. The CBHs were consistently overestimated (RMSD = 37.3%; average difference = –20.0%), especially in spruces with long crowns. Thus using lidar-based CBH also increased the inaccuracy of the CV estimates. While the underestimation of CV is mainly explained by the inadequate number of echoes from the lower regions of the crowns, the CVs obtained from the lidar were better than those obtained with ellipsoids fitted by using general models for crown dimensions. The utility of the estimated CVs in the prediction of stem diameter is also demonstrated.     

Integrating lidar with Landsat data for subalpine temperate forest aboveground carbon estimation

Accurate forest carbon accounting forms a basis for promoting the development of ecosystem service markets including forest carbon sinks. However, carbon assessments over large forest areas are challenging. Difficulties are compounded by the lack of adequate field observations especially in mountainous regions. In this study, we describe the development of a two-phase sampling framework to evaluate regional aboveground carbon density (ACD) of subalpine temperate forests in northwestern China that includes integrating ground plots, airborne lidar metrics, and Landsat images. During the first phase, an accurate, lidar-derived, ACD inventory network of a representative forested zone (Dayekou Basin) was established on the basis of a modified allometric model by adding crown coverage (CC) as a supplementary variable; cross-validated R² was 0.88 and root mean square error (RMSE) was 14.7 Mg C ha^?1. The outcomes of this step enabled the extension of quasi-field plots required for the representative carbon evaluations and the amplification of the range of observed values. Further integration of lidar measures and optical Landsat data by using the partial least squares regression (PLSR) method was conducted in the subsequent phase. The final model developed for broad-scale estimates explained 76% of the variance in forest ACD and produced a mean bias error of 27.9 Mg C ha^?1. Aboveground carbon stocks for the whole ecoregion averaged 77.2 Mg ha^?1, which generated an uncertainty of 13%. Visual patterns revealed a systematic overestimation for low ACD values and an underestimation in those regions with high carbon density. Potential errors in our carbon estimates could be associated with the saturation of optical signals, accuracy of land-cover map, and effects of topographic conditions. Overall, the double-sampling method demonstrated promising means for carbon accounting over large areas in a spatially-explicit manner and provided a good first approximation of carbon quantities for the forests in the ecoregion. Our study illustrated the potential for the use of lidar sampling in facilitating scaling of field surveys to a larger spatial extent than ground-based practices by supplying accurate biophysical measurements (e.g. heights).     

Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing

Among vegetated coastal habitats, mangrove forests are among the densest carbon pools. They store their organic carbon in the surrounding soil and thus the sequestered carbon stays in the sediment for a long time and cannot be easily returned to the atmosphere. Additionally, mangroves also provide various important ecosystem services in coastal areas and surroundings. Accordingly, it is important to understand the distribution of biomass carbon stock in mangrove habitats in a spatial and temporal context, not only to reduce CO₂ concentrations in the atmosphere, but also for their sustainability. The objectives of this research are to map the mangrove carbon stock and estimate the total biomass carbon stock sheltered by mangrove forests, with the Karimunjawa Islands as a study site, using the widely available passive remote sensing system ALOS AVNIR-2. The modelling and mapping of mangrove carbon stock incorporates the integration of image pixel values and mangroves field data via empirical modelling. Vegetation indices and PC bands at different levels of radiometric corrections were all used as the input in the mangrove carbon stock modelling so that the effectiveness and sensitivity of different image transformations to particular radiometric correction levels could be analysed and understood. Afterward, the accuracy and effectiveness of each mangrove carbon stock-mapping routine was compared and evaluated. The accuracy of the best mangrove above-ground carbon stock (AGC) map modelled from vegetation index is 77.1% (EVI1, SE 5.89 kg C m^?2), and for mangrove below-ground carbon stock (BGC) it is 60.0% (GEMI, SE 2.54 kg C m^?2). The mangrove carbon stock map from ALOS AVNIR-2 PC bands showed a maximum accuracy of 77.8% (PC2, SE 5.71 kg C m^?2) and 60.8% (PC2, SE 2.48 kg C m^?2) for AGC and BGC respectively. From the resulting maps, the Karimunjawa Islands are estimated to shelter 96,482 tonnes C of mangroves AGC with a mean value of 21.64 kg C m^?2 and 24,064 tonnes C of mangroves BGC with a mean value of 5.39 kg C m^?2. Potentially, there are approximately 120,546 tonnes C of mangrove biomass carbon stock in the Karimunjawa Islands. Remote-sensing reflectance can successfully model mangrove carbon stock based on the relationship between mangrove canopy properties, represented by leaf area index (LAI) and the tree or root biomass carbon stock. The accuracy of the mangrove carbon stock map is subject to errors, which are sourced mainly from: (1) the absence of a species-specific biomass allometric equation for several species present in the study area; (2) the generalized standard conversion value of mangrove biomass to mangrove carbon stock; (3) the relationship between mangrove reflectance and mangrove LAI; (4) the relationship between mangrove reflectance and above-ground mangrove biomass and carbon stock due to its relationship with LAI; (5) the relationship between mangrove LAI and mangrove below-ground parts; (6) the inability to perform mangrove carbon stock modelling at the species level due to the complexities of the mangrove forest in the study area; (7) background reflectance and atmospheric path radiance that could not be completely minimized using image radiometric corrections and transformations; and (8) spatial displacement between the actual location of the mangrove forest in the field and the corresponding pixel in the image. The availability of mangrove biomass carbon stock maps is beneficial for carrying out various management activities, and is also very important for the resilience of mangroves to changing environments.     

Evaluation of RapidEye data for mapping algal blooms in inland waters

The high spatial resolution multispectral imaging sensor onboard RapidEye (RE) has a red-edge band centred at 710 nm, which can be used to produce a product equivalent to the Maximum Chlorophyll Index (MCI) that was developed to detect algal blooms with Medium Resolution Imaging Spectrometer (MERIS) data. The RapidEye system, with five satellites, offers a greater repeat frequency than other high-resolution satellites. In this study, we compared RapidEye and MERIS derived MCI products for the Harris Chain of Lakes in central Florida, USA, to determine if RapidEye can produce an equivalent product similar to MERIS. Data from two RapidEye satellites (RapidEye-2 and RapidEye-5) were used. Band-by-band matchups used RapidEye Top of the Atmosphere (TOA) reflectance and MERIS ρ_s (reflectance corrected only for Raleigh scattering and molecular absorption). The RapidEye TOA reflectance data differed from MERIS, but when the bands were calibrated to the MERIS, the MCI products matched between the two RapidEye satellites and the MERIS MCI. Estimated chlorophyll-a concentrations using a relationship established for Lake Erie matched in situ chlorophyll-a concentrations with a median error of 1.09 mg m^?3. The results indicate that RapidEye is useful for this purpose, which also suggests that other high-resolution satellites with similar red-edge bands may also provide MCI-type products that would allow estimation of chlorophyll-a. RapidEye provides a context for applying future constellation of small satellites for monitoring water quality issues. Lake water quality managers and environmental agencies could effectively use such high-resolution products to assess and manage algal bloom events.     

A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data

The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000-2004, and was validated using observed GPP over the period 2005-2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km × 1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr^− 1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances.     

Modelling lidar-derived boreal forest canopy cover with SPOT 4 HRVIR data

Forest canopy cover (C) is needed in forest area monitoring and for many ecological models. Airborne scanning lidar sensors can produce fairly accurate C estimates even without field training data. However, optical satellite images are more cost-efficient for large area inventories. Our objective was to use airborne lidar data to obtain accurate estimates of C for a set of sample plots in a boreal forest and to generalize C for a large area using a satellite image. The normalized difference vegetation index (NDVI) and reduced simple ratio (RSR) were calculated from the satellite image and used as predictors in the regressions. RSR, which combines information from the red, near-infrared, and shortwave infrared bands, provided the best performance in terms of absolute root mean square error (RMSE) (7.3%) in the training data. NDVI produced a markedly larger RMSE (10.0%). However, in an independent validation data set, RMSE increased (13.0–17.1%) because the systematic sample of validation plots contained more variation than the training plots. Our results are better than those reported earlier, which is probably explained by more consistent C estimates derived from the lidar. Our approach provides an efficient method for creating C maps for large areas.     

Modelling above-ground live trees biomass and carbon stock estimation of tropical lowland Dipterocarp forest: integration of field-based and remotely sensed estimates

Carbon exists as carbon dioxide (CO₂) which is one of the greenhouse gases (GHG) in the atmosphere that has an enormous influence on the impact of climate change. Therefore, the forest plays an undeniably pivotal role as a carbon sink, which absorbs carbon dioxide from the atmosphere. This research aims to develop allometric equation for above-ground live tree biomass (AGB) by combining field-based, combination of field data observation and technology (WV-3 and light detection and ranging (lidar)) and by using only technology derivation. The independent predictor was induced based on the literature review and theories, and an ordinary least square (OLS) estimator will be used to develop multiple linear regression models. During model selection, the best model fit was selected by calculating statistical parameters such as residual of the coefficient of determination (R²) selection methods, adjusted coefficient of determination (R²_adj), root mean square error, graphical analysis of the residuals, standard error (S_yx), and Akaike information criterion. An allometric equation of this research was developed using carbon stocks as dependent variables, and four of the predictor’s variables: diameter at breast height (DBH); total height observed at field (h_F); total height derived from airborne lidar (h_L); and morphometric variables of the crown projection area (CPA). Based on the statistic indicators, the most suitable model is Model 1, ln (S_c) = – β₀ + β₁ ln (h_L) + β₂ ln (DBH) + β₃ ln (CPA) for the combination of remote sensing and field observation; ln (S_c) = – β₀ + β₁ ln (h_F) + β₂ ln (DBH) for field inventory only; and ln (S_c) = – β₀ + β₁ ln (h_L) + β₂ ln (CPA) for remote sensing only. This model is reliable in forest management to estimate the AGB and carbon stock estimation using a selection of variable sources.     

Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands

Full-waveform small-footprint laser scanning and airborne hyperspectral image data of a forest area in Germany were fused to get a detailed characterization of forest reflective properties and structure. Combining active laser scanning data with passive hyperspectral data increases the information content without adding much redundancy.

The small-footprint light detection and ranging (lidar) waveforms on the area of each 5 m × 5 m HyMap pixel were combined into quasi-large-footprint waveforms of 0.5 m vertical resolution by calculating the mean laser intensity in each voxel. As exemplary applications for this data set, we present the estimation of crown base heights and the ease of displaying vertical and horizontal slices through the three-dimensional data set.

As a consequence of the identical geometry of the voxel bases and the hyperspectral image, they could be joined as a multi-band image. The combined spectra are well suited for interpretations of pixel content. In a test classification of tree species and age classes, the joint image performed better than the hyperspectral image alone and also better than the hyperspectral image combined with lidar percentile images.     

Comparison of three individual tree crown detection methods

Three image processing methods for single tree crown detection in high spatial resolution aerial images are presented and compared using the same image material and reference data. The first method uses templates to find the tree crowns. The other two methods uses region growing. One of them is supported by fuzzy rules while the other uses an image produced by Brownian motion. All three methods detect around 80%, or more, of the visible sunlit trees in two pine Pinus Sylvestris L.) and two spruce stands Picea abies Karst.) in a boreal forest. For all methods, large tree crowns are easier to detect than small ones.     

Upscaling of sparse in situ soil moisture observations by integrating auxiliary information from remote sensing

Upscaling of sparse in situ soil moisture (SM) observations is essential for the validation of current and upcoming space-borne SM retrievals, and the successful application of SM observations in hydrological models or data assimilation. In this study, we construct a novel method based on Bayesian data fusion to upscale in situ SM observations to the coarse scale of microwave remote sensing. In the framework of Bayesian theory, the valuable auxiliary information obtained in Moderate Resolution Imaging Spectroradiometer (MODIS) apparent thermal inertia (ATI) is integrated into the upscaling process. The method is validated using SM wireless sensor network data in the Tibetan plateau, which covers an area of approximately 30 × 30 km² with 20 in situ stations. Results confirm that the upscaled SM using the method with randomly selected three stations from the 20 stations is extremely close to the mean of the 20 SMs. The mean root mean square error (RMSE) between the upscaled SM and the mean of the 20 in situ SMs was 0.02 m³ m^?3, and the max RMSE was less than 0.05 m³ m^?3. Furthermore, the sensitivity of the upscaling accuracy to the number of in situ observations is discussed. When the number of in situ observations is greater than nine, the increasing accuracy of the Bayesian method is limited by the uncertainty in the ATI of the remote sensing.     

A mixed pixel- and region-based approach for using airborne laser scanning data for individual tree crown delineation in Pinus radiata D. Don plantations

The aim of this study was to evaluate the use of high-resolution airborne laser scanner (ALS) data to detect and measure individual trees. We developed and tested a new mixed pixel- and region-based algorithm (using Definiens Developer 7.0) for locating individual tree positions and estimating their total heights. We computed a canopy height model (CHM) of pixel size 0.25 m from dense first-pulse point data (8 pulses m^?2) acquired with a small-footprint discrete-return lidar sensor. We validated the results of individual tree segmentation with accurate field measurements made in 37 plots of Monterey pine (Pinus radiata D. Don) distributed over an area of 36 km². Fieldwork consisted of labelling all of the trees in each plot and measuring their height and position, for posterior integration of the data from both sources (field and lidar). The proposed algorithm correctly detected and linked 59.8% of the trees in the 37 sample plots. We also manually located the trees by using FUSION software to visualize the raw lidar data cloud. However, because the latter method is extremely time-consuming, we only considered 10 randomly selected plots. Manual location correctly detected and linked 71.9% of the trees (in this subsample the algorithm correctly detected and measured 63.5% of the trees). The R² values for the linear model relating field- and lidar-measured heights of the linked trees located manually and with the automatic location algorithm were 0.90 and 0.88, respectively.