Estimating montane forest above-ground biomass in the upper reaches of the Heihe River Basin using Landsat-TM data

In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. Estimates of AGB are relevant for sustainable forest management, monitoring global change, and carbon accounting. This is particularly true for the Qilian Mountains, which are a water resource protection zone. We combined forest inventory data from 133 plots with TM images and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) V2 products (GDEM) in order to analyse the influence of the sun-canopy-sensor plus C (SCS+C) topographic correction on estimations of forest AGB using the stepwise multiple linear regression (SMLR) and k-nearest neighbour (k-NN) methods. For both methods, our results indicated that the SCS+C correction was necessary for getting more reliable forest AGB estimates within this complex terrain. Remotely sensed AGB estimates were validated against forest inventory data using the leave-one-out (LOO) method. An optimized k-NN method was designed by varying both mathematical formulation of the algorithm and remote-sensing data input, which resulted in 3000 different model configurations. Following topographic correction, performance of the optimized k-NN method was compared to that of the regression method. The optimized k-NN method (R² = 0.59, root mean square error (RMSE) = 24.92 tonnes ha^–1) was found to perform much better than the regression method (R² = 0.42, RMSE = 29.74 tonnes ha^–1) for forest AGB retrieval over this montane area. Our results indicated that the optimized k-NN method is capable of operational application to forest AGB estimates in regions where few inventory data are available.     

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.     

Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass

Regression has been widely applied in Light Detection And Ranging (LiDAR) remote sensing to spatially extend predictions of total aboveground biomass (TAGB) and other biophysical properties over large forested areas. Sample (field) plot size has long been considered a key sampling design parameter and focal point for optimization in forest surveys, because of its impact on sampling effort and the estimation accuracy of forest inventory attributes. In this study, we demonstrate how plot size and co-registration error interact to influence the estimation of LiDAR canopy height and density metrics, regression model coefficients, and the prediction accuracy of least-squares estimators of TAGB. We made use of simulated forest canopies and synthetic LiDAR point clouds, so that we could maintain strict control over the spatial scale and complexity of forest scenes, as well as the magnitude and type of planimetric error inherent in ground-reference and LiDAR datasets. Our results showed that predictions of TAGB improved markedly as plot size increased from 314 (10 m radius) to 1964 m² (25 m radius). The co-registration error (spatial overlap) between ground-reference and LiDAR samples negatively impacted the estimation of LiDAR metrics, regression model fit, and the prediction accuracy of TAGB. We found that larger plots maintained a higher degree of spatial overlap between ground-reference and LiDAR datasets for any given GPS error, and were therefore more resilient to the ill effects of co-registration error compared to small plots. The impact of co-registration error was more pronounced in tall, spatially heterogeneous stands than short, homogeneous stands. We identify and briefly discuss three possible ways that LiDAR data could be used to optimize plot size, sample selection, and the deployment of GPS resources in forest biomass surveys.     

Calibrated tree counting on remotely sensed images of planted forests

The development of robust and accurate methods for counting trees from remotely sensed data could provide substantial cost savings in forest inventory. A new methodology that provides a framework for calibrating tree detection algorithms to obtain accurate tree counts for even-aged stands is described. The methodology was evaluated using two tree detection algorithms and two operators using airborne laser scanning (ALS) and orthophotograph images for four Pinus radiata D.Don stands ranging in age between 5 and 32 years with stand densities ranging between 204 and 826 stems ha^?1. For application of the methodology to ALS images the error of estimate on the total count was 4.7% when calibration counts from actual ground plots were used and 10.5% when calibration counts from virtual plots on the image were used. For orthophotographs, the error of estimate was 6.1% using ground calibration plots and 24.3% using calibration counts from virtual plots. The described methodology was shown to be robust to variations in the process from the two operators and two algorithms evaluated. The measure of accuracy determined using the methodology can be used to provide an objective basis for evaluating a wide range of tree counting and detection processes in future research.     

Estimating forest parameters using Landsat ETM+ spectral responses and monocultured plantation fieldwork measurements data

Forest parameters, such as mean diameter at breast height (DBH), mean stand height (H) or volume per hectare (V), are imperative for forest resources assessment. Traditional forest inventory that is usually based on fieldwork is often difficult, time-consuming, and expensive to conduct over large areas. Therefore, estimating forest parameters in large areas using a traditional inventory approach combined with satellite data analysis can improve the spatial estimates of forest inventory data, and hence be useful for sustainable forest management and natural resources assessment. However, extracting practical information from satellite imagery for such purpose is a challenging task mainly because of insufficient knowledge linking forest inventory data to satellite spectral response. Here, we present the use of a cost-free Landsat-7 Enhanced Thematic Mapper Plus (ETM+) in order to explore whether it is possible to combine all available optical bands from a specific sensor for improving forest parameter spatial estimates, based on fieldwork at Lahav and Kramim Forests, in the Israeli Northern Negev. A generic strategy, based on morphological structuring element, convex hall and spectral band linear combination algorithms, was developed in order to extract the mathematical dependencies between the forest inventory measurements and linear combination sets of Landsat-7 ETM+ spectral bands, which yields the highest possible correlation with the forest inventory measured data. Using the mathematical dependency functions, we then convert the entire Landsat-7 ETM+ scenes into forest inventory parameter values with sufficient accuracy and tolerance errors needed for sustainable forest management. The root mean square error obtained between the measured and the estimated values for Lahav Forest are 0.70 cm, 0.29 m, and 1.48 m³ ha^?1 for the mean DBH, H, and V, respectively, and for Kramim forest are 0.61 cm, 0.70 m, and 6.31 m³ ha^?1, respectively. Furthermore, the suggested strategy could also be applied with other satellites data sources.     

Using terrestrial laser scanning to support biomass estimation in densely stocked young tree plantations

The total area of short-rotation tree plantations is increasing globally, one reason being the need to grow sustainable biomass for bio-energy production. Such stands are usually established with a very high stem density, and inventories for biomass estimation require the adaptation of traditional methods. In this study, we tested a novel, efficient, and non-destructive method for biomass estimation relevant to a high-density, short-rotation oak stand of about 16,500 stems ha^?1. We used terrestrial laser scanning (TLS) in a single-scan design to measure diameter at breast height (DBH) of all trees within 2 m-radius sample plots. Allometric models were then used to predict the tree biomass from their diameter. Biomass estimates were compared to the true biomass determined after harvesting of the sample plots. Mean absolute error and mean relative error were 12.9 kg and 16.4%, respectively, and the coefficient of determination of the relationship between traditionally measured and scan-based biomass was r² = 0.65 (p < 0.001). This TLS-based approach is promising as it considerably reduces fieldwork efforts in dense stands compared with traditional diameter tallying by calipers or tapes.     

Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser

Regression models relating variables derived from airborne laser scanning (ALS) to above-ground and below-ground biomass were estimated for 1395 sample plots in young and mature coniferous forest located in ten different areas within the boreal forest zone of Norway. The sample plots were measured as part of large-scale operational forest inventories. Four different ALS instruments were used and point density varied from 0.7 to 1.2 m^− 2. One variable related to canopy height and one related to canopy density were used as independent variables in the regressions. The statistical effects of area and age class were assessed by including dummy variables in the models. Tree species composition was treated as continuous variables. The proportion of explained variability was 88% for above- and 85% for below-ground biomass models. For given combinations of ALS-derived variables, the differences between the areas were up to 32% for above-ground biomass and 38% for below-ground biomass. The proportion of spruce had a significant impact on both the estimated models. The proportion of broadleaves had a significant effect on above-ground biomass only, while the effect of age class was significant only in the below-ground biomass model. Because of local effects on the biomass-ALS data relationships, it is indicated by this study that sample plots distributed over the entire area would be needed when using ALS for regional or national biomass monitoring.     

Effects of satellite image spatial aggregation and resolution on estimates of forest land area

Satellite imagery is being used increasingly in association with national forest inventories (NFIs) to produce maps and enhance estimates of forest attributes. We simulated several image spatial resolutions within sparsely and heavily forested study areas to assess resolution effects on estimates of forest land area, independent of other sensor characteristics. We spatially aggregated 30 m datasets to coarser spatial resolutions (90, 150, 210, 270, 510 and 990 m) and produced estimates of forest proportion for each spatial resolution using both model‐ and design‐based approaches. Average‐based aggregation had no effect on per‐image estimates of forest proportion; image variability decreased with increasing spatial resolution and local variability peaked between 210 and 270 m. Majority‐based aggregation resulted in overestimation of forest land in a heavily forested landscape and underestimation of forest land in a sparsely forested landscape, with both trends following a natural log distribution. Of the spatial resolutions tested, 30 m was superior for obtaining estimates using model‐based approaches. However, standard errors of design‐based inventory estimates of forest proportion were smallest when accompanying stratification maps which were aggregated to between 90 and 150 m spatial resolutions and strata thresholds were optimized by study area. These results suggest that spatially aggregating existing 30 m land cover datasets can provide NFIs with gains in precision of their estimates of forest land area, while reducing image storage size and processing times; land cover datasets derived from coarser spatial resolution sensors may provide similar benefits.     

A multi-sensor approach for improving biodiversity estimation in the Hyrcanian mountain forest,Iran

ABSTRACT

Modelling tree biodiversity in mountainous forests using remote-sensing data is challenging because forest composition and structure change along elevation. Topographic variations also affect vegetation’s spectral and backscattering behaviour. We demonstrate the potential of multi-source integration to tackle this challenge in a mountainous part of the Hyrcanian forest in Iran. This forest is a remnant of a deciduous broadleaved forest with heterogeneous structure affected by natural and anthropogenic factors. The multi-source approach (i.e. Landsat Enhanced Thematic Mapper Plus (ETM +), Advanced Land Observing Satellite/ Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR), and topographic variables) allows us to propose a biodiversity estimation model using partial least square regression (PLSR) calibrated and validated with limited field data. The effective number of species was calculated based on field measurements of the biodiversity in the study area. In order to model species diversity in more homogeneous extrinsic environmental conditions, we divided data into two groups with relatively uniform slope values. In each slope group, we modelled the correlation between observed biodiversity and satellite-derived data. For that, we followed three scenarios: (A) multispectral Landsat ETM + alone, (B) ALOS/PALSAR alone, and (C) inclusion of both sensors. In each scenario, elevation and slope data were also considered as predictors. We observed that in all scenarios, coefficient of determination (R²) in gentler slopes was higher than that in areas with steeper slopes (average difference in R²: ?R² = 0.21). The highest correlation was achieved by inclusion of synthetic aperture radar (SAR) and ETM + (R² = 0.87). The results clearly confirm that the multi-source remote-sensing approach can provide a practical estimate of biodiversity across the Hyrcanian forest and potentially in other deciduous broadleaved forests in complex terrain.     

Area-based quality control of airborne laser scanning 3D models for different land classes using terrestrial laser scanning: sample survey in Houston,USA

Airborne laser scanning (ALS) is a remote-sensing technique that provides scale-accurate 3D models consisting of dense point clouds with x, y planimetric coordinates and altitude z. Using ALS, very high-resolution (VHR) digital surface models (DSMs) have been widely used for commercial and scientific applications since the early 1990s. Although there is widespread usage, there has been little comprehensive investigation of quality control for ALS DSMs in the literature, as most studies have been limited to assessing point-based vertical accuracy. This article is dedicated to investigating the quality of ALS DSMs for different land classes using statistical and visual approaches based on absolute and relative vertical accuracy metrics. Rather than a limited number of ground control points (GCP), the model-to-model-based approach is applied and DSMs derived from terrestrial laser scanning (TLS) point clouds that have around 5 mm absolute and 3 mm relative geolocation accuracy were used as the reference data for comparison. The results demonstrate that in open, grass, and building land classes, the ALS DSMs reached both standard deviation (σ) and normalized median absolute deviation (NMAD) of 3–5 cm after the elimination of any systematic biases. This result sufficiently satisfies the vertical accuracy requirements for 1/1000-scale topographic maps determined by National Digital Elevation Program (NDEP) specifications. In tall vegetation, a higher number of discrepancies larger than 0.5 m exist, reversing the relation between σ and NMAD. These vegetation errors also do not appear to be normally distributed. As an additional investigation, the performance of ALS DEMs under dense high-vegetation areas was assessed. These under-canopy ALS DEMs, created using only classified ground returns, offer both σ and NMAD of 12–14 cm, a performance level that is difficult to achieve under-canopy using photogrammetric techniques.     

Single tree species classification from Terrestrial Laser Scanning data for forest inventory

Due to the increasing use of Terrestrial Laser Scanning (TLS) systems in the forestry domain for forest inventory, the development of software tools for the automatic measurement of forest inventory attributes from TLS data has become a major research field. Numerous research work on the measurement of attributes such as the localization of the trees, the Diameter at Breast Height (DBH), the height of the trees, and the volume of wood has been reported in the literature. However, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. Most of the research work uses Airborne Laser Scanning (ALS) data and measures tree species attributes on large scales. In this paper we propose a method for individual tree species classification of five different species based on the analysis of the 3D geometric texture of the bark. The texture features are computed using a combination of the Complex Wavelet Transforms (CWT) and the Contourlet Transform (CT), and classification is done using the Random Forest (RF) classifier. The method has been tested using a dataset composed of 230 samples. The results obtained are very encouraging and promising.     

Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data

Most terrestrial carbon is stored in forest biomass, which plays an important role in local, regional, and global climate change. Monitoring of forests and their status, and accurate estimation of forest biomass are important in mitigating the impacts of climate change. Empirical models developed using remote-sensing and field-measured forest data are commonly used to estimate forest biomass. In the present study, we used a mechanistic model to estimate height and biomass in the Three Gorges reservoir region (China) based on the allometric scale and resource limits (ASRL) model. The forests in the Three Gorges reservoir region are important and unique in view of the vertical distribution of vegetation and mixed needleleaf. Detailed information about the forest in this region is available from the Geoscience Laser Altimeter System (GLAS) and field measurements from 714 forest plots. The ASRL model parameters were adjusted using GLAS-derived forest tree height to reduce the deviation between modelled and observed forest height. The predicted maximum forest tree height from the optimized ASRL model was compared to measured tree heights, and a good correlation (R² = 0.566) was found. The allometric scale function between forest height and diameter at breast height (DBH) is developed and the maximum forest tree height from the optimized ASRL model transferred to DBH. Moreover, the forest biomass was estimated from DBH according to the allometric scale function that was determined using DBH and biomass data. The results of maximum forest biomass using the ASRL model and the allometric scale function show a good accuracy (R² = 0.887) in the Three Gorges reservoir region. Here, we present the forest biomass estimation approach following allometric theory for accurate estimation of maximum forest tree height and biomass. The proposed approach can be applied to forest species in all types of environmental conditions.     

Using satellite imagery and the k-nearest neighbors technique as a bridge between strategic and management forest inventories

Strategic forest inventory programs produce forest resource estimates for large areas such as states and provinces using data collected for a large number of variables on a relatively sparse array of field plots. Management inventories produce stand-level estimates to guide management decisions using data obtained with sampling intensities much greater than for strategic inventories. The costs associated with these greater sampling intensities have motivated investigations of alternatives to traditional sample-based management inventories. This study focused on a relatively inexpensive alternative to management inventories that uses strategic forest inventory plot data, Landsat Thematic Mapper (TM) satellite imagery, and the k-Nearest Neighbors (k-NN) technique. The approach entailed constructing stem density and basal area per unit area maps from which stand-level means were estimated as averages of k-NN pixel predictions. The study included investigations of the benefits of selecting optimal combinations of k-NN feature space variables derived from the TM imagery and the benefits of modifying the k-NN technique to eliminate spurious nearest neighbors. For both the stem density and basal area per unit area training data, the selection of optimal feature space covariates produced less than 1.5% improvement in root mean square error relative to using all covariates. The k-NN modification improved the sum of mean squared deviations for stand-level stem density and basal area per unit area estimates by 7–20% depending on the k-NN feature space covariates. For the best combination of feature space covariates, estimates of stand-level means were within confidence intervals for validation estimates for 11 of 12 stands for stem density and for 10 of 12 stands for basal area per unit area.     

Remote-sensing assessment of forest damage by Typhoon Saomai and its related factors at landscape scale

The remote-sensing technique is a cost-effective tool for monitoring large-scale forest damage sustained by typhoon events. Taking Cangnan County as the study area, this study aimed to extract the spatial pattern of damaged forest and determine the influencing factors of Typhoon Saomai in 2006, using Landsat Enhanced Thematic Mapper Plus (ETM+) data before and after the typhoon event. The results showed that 183 km² of forest land were damaged by Typhoon Saomai. There was no obvious diverse influence on forest damage within 25 km of Saomai’s path, after that the area of damaged forest decreased rapidly. For the land uses of construction, crop, and grass, decrease in normalized difference vegetation index was considerable under 100 m elevation and the number of damaged forest pixels showed positive correlation with vegetation aggregation, because trees standing in isolation, alongside roads, or in small groupings were easily damaged. For forest land, the number of damaged forest pixels decreased with higher elevation and relative aspect; when the relative aspect was in the range 0–40°, the number of damaged forest pixels was highest. Considering the interactive effects of these factors on forest damage caused by the typhoon, vegetation aggregation had the strongest influence followed by elevation, land use, relative aspect, and distance from the typhoon’s path.     

The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs

Various studies have been presented within the last 10 years on the possibilities for predicting forest variables such as stand volume and mean height by means of airborne laser scanning (ALS) data. These have usually considered tree stock as a whole, even though it is tree species-specific forest information that is of primary interest in Finland, for example. We will therefore concentrate here on prediction of the species-specific forest variables volume, stem number, basal area, basal area median diameter and tree height, applying the non-parametric k-MSN method to a combination of ALS data and aerial photographs in order to predict these stand attributes simultaneously for Scots pine, Norway spruce and deciduous trees as well as total characteristics as sums of the species-specific estimates. The predictor variables derived from the ALS data were based on the height distribution of vegetation hits, whereas spectral values and texture features were employed in the case of the aerial photographs. The data covered 463 sample plots in 67 stands in eastern Finland, and the results showed that this approach can be used to predict species-specific forest variables at least as accurately as from the current stand-level field inventory for Finland. The characteristics of Scots pine and Norway spruce were predicted more accurately than those of deciduous trees.     

Predicting old-growth tropical forest attributes from very high resolution (VHR)-derived surface metrics

Old-growth tropical forests are increasingly vanishing worldwide. Although the accurate quantification of tropical old-growth forests attributes is essential to understand, manage, and conserve their high diversity and biomass, conducting this task over large areas and at fine detail is not only expensive and time consuming, but also often practically impossible. This calls for the search for more efficient alternatives, particularly those based on remote sensing. In this study, we evaluate the potential of several surface metrics (tone and texture) extracted from very high resolution (VHR) satellite imagery to model the structural and diversity attributes of a tropical dry forest (TDF) in southern Mexico. We constructed simple linear models that used each forest attribute as dependent variables, and the tone and texture metrics extracted from several bands, the panchromatic (resolution = 0.5 m), red (R), infrared, and two vegetation indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI); resolution = 2 m), of a VHR image (GeoEye-1) as predictive variables. The significance of the models including one, two, two and its interaction, and three image metrics was evaluated by comparing them with null models. The structural characteristics of the TDF (basal area (BA), mean height, stem density) showed the highest modelling potential, with the goodness-of-fit (R²) values ranging from 0.58 to 0.66. Conversely, no significant models were obtained for total crown area (TCA) and all diversity attributes. Our results show that remote-sensing metrics detect the spatial variation in the structural attributes of this old-growth TDF better than they detect the variation in its diversity. Our ability to model forest attributes at large scales at fine detail (sampling plots <0.2 ha) can be much improved by combining the use of VHR imagery with an array as wide as possible of the image surface metrics, including both tone and texture.     

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.     

Estimating stem volume by tree crown area and tree shadow area extracted from pan‐sharpened Quickbird imagery in open Crimean juniper forests

Although open forests represent approximately 30% of the world's forest resources, there is a clear lack of reliable inventory data to allow sustainable management of this valuable resource from semi‐arid areas. This paper demonstrates that the low ground cover of open forest offers a unique opportunity for deriving single tree attributes from high‐resolution satellite imagery, allowing reliable biomass estimation. More particularly, this study investigates the relationship between field‐measured stem volume and tree attributes, including tree crown area and tree shadow area, measured from pan‐sharpened Quickbird imagery with a 0.61 m resolution in a sparse Crimean juniper (Juniperus excelsa M.Bieb.) forest in south‐western Turkey. First tree shadows and crowns were identified and delineated as individual polygons. Both visual delineation and computer‐aided automatic classification methods were tested. After delineation, stem volume as a function of these image‐measured attributes was modelled using linear regression. The statistical analyses indicated that stem volume was correlated with both shadow area and crown area. The best model for stem volume using shadow area resulted in an adjusted R ² = 0.67, with a root mean square error (RMSE) of 12.5%. The model for stem volume using crown area resulted in an adjusted R ² = 0.51, with a RMSE of 15.2%. The results showed that pan‐sharpened Quickbird imagery is suitable for estimating stem volume and may be useful in reducing the time required for obtaining inventory data in open Crimean juniper forests and other similar open forests.     

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).     

Aboveground forest biomass derived using multiple dates of WorldView-2 stereo-imagery: quantifying the improvement in estimation accuracy

ABSTRACT

The aim of this study was to investigate the capabilities of two date satellite-derived image-based point clouds (IPCs) to estimate forest aboveground biomass (AGB). The data sets used include panchromatic WorldView-2 stereo-imagery with 0.46 m spatial resolution representing 2014 and 2016 and a detailed digital elevation model derived from airborne laser scanning data. Altogether, 332 field sample plots with an area of 256 m² were used for model development and validation. Predictors describing forest height, density, and variation in height were extracted from the IPC 2014 and 2016 and used in k-nearest neighbour imputation models developed with sample plot data for predicting AGB. AGB predictions for 2014 (AGB₂₀₁₄) were projected to 2016 using growth models (AGB_{Projected_2016}) and combined with the AGB estimates derived from the 2016 data (AGB₂₀₁₆). AGB prediction model developed with 2014 data was also applied to 2016 data (AGB_{2016_pred2014}). Based on our results, the change in the 90^th percentile of height derived from the WorldView-2 IPC was able to characterize forest height growth between 2014 and 2016 with an average growth of 0.9 m. Features describing canopy cover and variation in height derived from the IPC were not as consistent. The AGB₂₀₁₆ had a bias of ?7.5% (?10.6 Mg ha^?1) and root mean square error (RMSE) of 26.0% (36.7 Mg ha^?1) as the respective values for AGB_{Projected_2016} were 7.0% (9.9 Mg ha^?1) and 21.5% (30.8 Mg ha^?1). AGB_{2016_pred2014} had a bias of ?19.6% (?27.7 Mg ha^?1) and RMSE of 33.2% (46.9 Mg ha^?1). By combining predictions of AGB₂₀₁₆ and AGB_{Projected_2016} at sample plot level as a weighted average, we were able to decrease the bias notably compared to estimates made on any single date. The lowest bias of ?0.25% (?0.4 Mg ha^?1) was obtained when equal weights of 0.5 were given to the AGB_{Projected_2016} and AGB₂₀₁₆ estimates. Respectively, RMSE of 20.9% (29.5 Mg ha^?1) was obtained using equal weights. Thus, we conclude that combination of two date WorldView-2 stereo-imagery improved the reliability of AGB estimates on sample plots where forest growth was the only change between the two dates.