Canopy Height Map Research Articles

Regionalization of an Existing Global Forest Canopy Height Model for Forests of the Southern United States

A large-scale global canopy height map (GCHM) is essential for global forest aboveground biomass estimation. Four GCHMs have recently been built using data from the Geoscience Laser Altimeter System (GLAS) sensor aboard the Ice, Cloud, and land Elevation Satellite (ICESat), along with auxiliary spatial and climate information. The main objectives of this research were to find out how well a selected GCHM agrees with airborne lidar data from locations in the southern United States and to recalibrate that GCHM to more closely match the forest canopy heights found in the region. The airborne lidar resource was built from data collected between 2010 and 2012, available from in-house and publicly available sources, for sites that included a variety of vegetation types across the southern United States. EPA ecoregions were used to provide ecosystem information for the southern United States. The airborne lidar data were pre-processed to provide lidar-derived metrics, and assigned to four height categories—namely, returns from above 0 m, 1 m, 3 m, and 5 m. The assessment phase results indicated that the 90th and 95th percentiles of the airborne lidar height values were well-suited for use in the recalibration phase of the study. Simple linear regression was used to generate a new, recalibrated GCHM. It was concluded that the characterization of the agreement of a selected GCHM with local data, followed by the recalibration of the existing GCHM to the local region, is both viable and essential for future GCHMs in studies conducted at large scales.

Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy

In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m3/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess the sensitivity of backscatter at C- and L-bands to forest biomass, an approach based on an artificial neural network (ANN) was implemented. The ANN was trained using the backscattering coefficient at L-band (ALOS PALSAR, HH and HV polarization) and C-band (Envisat ASAR in HH polarization) as inputs. Spatially distributed WV values for the entire test area were derived by the integration (fusion) of a canopy height map derived from the Ice, Cloud, and Land Elevation Geoscience Laser Altimeter System (ICESat GLAS) and the NFI data, in order to build a significant ground truth dataset for the training stage. The analysis of the backscattering sensitivity to WV showed a moderate correlation at L-band and was almost negligible at C-band. Despite this, the ANN algorithm was able to exploit the synergy of SAR frequencies and polarizations, estimating WV with average Pearson’s correlation coefficient (R) = 0.96 and root mean square error (RMSE) ≃ 39 m3/ha when applied to the test dataset and average R = 0.86 and RMSE ≃ 75 m3/ha when validated on the direct measurements from the NFI. Considering the heterogeneity of the scenario (Mediterranean mixed forests in hilly landscape) and the small amount of available ground measurements with respect to the spatial variability of different plots, the obtained results can be considered satisfactory. Moreover, the successful use of WV from global maps for implementing the algorithm suggests the possibility to apply the algorithm to wider areas or even to global scales.

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

Spaceborne and airborne polarimetric synthetic-aperture radar interferometry (PolInSAR) data have been extensively used for forest parameter retrieval. The PolInSAR models have proven their potential in the accurate measurement of forest vegetation height. Spaceborne monostatic multifrequency data of different SAR missions and the Global Ecosystem Dynamics Investigation (GEDI)-derived forest canopy height map were used in this study for vegetation height retrieval. This study tested the performance of PolInSAR complex coherence-based inversion models for estimating the vegetation height of the forest ranges of Doon Valley, Uttarakhand, India. The inversion-based forest height obtained from the three-stage inversion (TSI) model had higher accuracy than the coherence amplitude inversion (CAI) model-based estimates. The vegetation height values of GEDI-derived canopy height map did not show good relation with field-measured forest height values. It was found that, at several locations, GEDI-derived forest height values underestimated the vegetation height. The statistical analysis of the GEDI-derived estimates with field-measured height showed a high root mean square error (RMSE; 5.82 m) and standard error (SE; 5.33 m) with a very low coefficient of determination (R2; 0.0022). An analysis of the spaceborne-mission-based forest height values suggested that the L-band SAR has great potential in forest height retrieval. TSI-model-based forest height values showed lower p-values, which indicates the significant relation between modelled and field-measured forest height values. A comparison of the results obtained from different SAR systems is discussed, and it is observed that the L-band-based PolInSAR inversion gives the most reliable result with low RMSE (2.87 m) and relatively higher R2 (0.53) for the linear regression analysis between the modelled tree height and the field data. These results indicate that higher wavelength PolInSAR datasets are more suitable for tree canopy height estimation using the PolInSAR inversion technique.

Mapping global forest canopy height through integration of GEDI and Landsat data

Consistent, large-scale operational monitoring of forest height is essential for estimating forest-related carbon emissions, analyzing forest degradation, and quantifying the effectiveness of forest restoration initiatives. The Global Ecosystem Dynamics Investigation (GEDI) lidar instrument onboard the International Space Station has been collecting unique data on vegetation structure since April 2019. Here, we employed global Landsat analysis-ready data to extrapolate GEDI footprint-level forest canopy height measurements, creating a 30 m spatial resolution global forest canopy height map for the year 2019. The global forest height map was compared to the GEDI validation data (RMSE = 6.6 m; MAE = 4.45 m, R2 = 0.62) and available airborne lidar data (RMSE = 9.07 m; MAE = 6.36 m, R2 = 0.61). The demonstrated integration of GEDI data with time-series optical imagery is expected to enable multidecadal historic analysis and operational forward monitoring of forest height and its dynamics. Such capability is important to support global climate and sustainable development initiatives.

Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry

Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.

Discovering forest height changes based on spaceborne lidar data of ICESat-1 in 2005 and ICESat-2 in 2019: a case study in the Beijing-Tianjin-Hebei region of China

BackgroundThe assessment of change in forest ecosystems, especially the change of canopy heights, is essential for improving global carbon estimates and understanding effects of climate change. Spaceborne lidar systems provide a unique opportunity to monitor changes in the vertical structure of forests. NASA’s Ice, Cloud and Land Elevation Satellites, ICESat-1 for the period 2003 to 2009, and ICESat-2 (available since 2018), have collected elevation data over the Earth’s surface with a time interval of 10 years. In this study, we tried to discover forest canopy changes by utilizing the global forest canopy height map of 2005 (complete global coverage with 1 km resolution) derived from ICESat-1 data and the ATL08 land and vegetation products of 2019 (sampling footprints with 17 m diameter) from ICESat-2.ResultsOur study revealed a significant increase in forest canopy heights of China’s Beijing-Tianjin-Hebei region. Evaluations of unchanging areas for data consistency of two products show that the bias values decreased significantly from line-transect-level (− 8.0 to 6.2 m) to site-level (− 1.5 to 1.1 m), while RMSE values are still relatively high (6.1 to 15.2 m, 10.2 to 12.0 m). Additionally, 58% of ATL08 data are located in ‘0 m’ pixels with an average height of 7.9 m, which are likely to reflect the ambitious tree planting programs in China.ConclusionsOur study shows that it is possible, with proper calibrations, to use ICESat-1 and -2 products to detect forest canopy height changes in a regional context. We expect that the approach presented in this study is potentially suitable to derive a fine-scale map of global forest change.

Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

Mapping Canopy Heights of Poplar Plantations in Plain Areas Using ZY3-02 Stereo and Multispectral Data

Forest canopy height plays an important role in forest management and ecosystem modeling. There are a variety of techniques employed to map forest height using remote sensing data but it is still necessary to explore the use of new data and methods. In this study, we demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data from China’s latest civilian stereo mapping satellite ZY3-02. First, a digital surface model (DSM) was extracted using photogrammetry methods. Then, canopy samples and ground samples were selected through manual interpretation. Canopy height samples were obtained by calculating the DSM elevation differences between the canopy samples and ground samples. A regression model was used to correlate the reflectance of a ZY3-02 multispectral image with the canopy height samples, in which the red band and green band reflectance were selected as predictors. Finally, the model was extrapolated to the entire study area and a wall-to-wall forest canopy height map was obtained. The validation of the predicted canopy height map reported a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 1.58 m. This study demonstrates the capacity of ZY3-02 data for mapping the canopy height of pure plantations in plain areas.

TREE CANOPY HEIGHT ESTIMATION USING MULTI BASELINE RVOG INVERSION TECHNIQUE

Abstract. Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technique utilizes the characteristics of both SAR polarimetry and Interferometry. PolInSAR technique is proved to be very useful for vegetation parameters retrieval. Estimation of the tree canopy height parameter is very important for the estimation of the Above Ground Biomass (AGB). The baseline separation between different PolInSAR datasets has a very important role in the tree canopy height estimation due to the sensitivity of the baseline to the tree height and the forest structure. So for accurately estimating the tree canopy height of a forest with varying tree heights and species several pairs of PolInSAR datasets with different baselines separations are required. Multi-baseline Random Volume over Ground (RVoG) inversion technique is the most successful method for tree height inversion. UAVSAR, the Quad-Pol L-band airborne SAR of JPL/NASA acquired PolInSAR datasets over the Gabon forest as a part of the AfriSAR campaign. Nine PolInSAR SLC datasets of this campaign acquired over the Mondah Forest site of Gabon forest is used for this study. Tree canopy height map produced from this datasets shows that the tree height is varying at this site and has a maximum height of 50 m. The results obtained are validated using the field data collected by JPL/NASA during March 2016. The comparison of the results with the field data showed that both are in good agreement with an average deviation of 3.75 m.

The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data

Canopy height is an important forest structure parameter for understanding forest ecosystem and improving global carbon stock quantification accuracy. Light detection and ranging (LiDAR) can provide accurate canopy height measurements, but its application at large scales is limited. Using LiDAR-derived canopy height as ground truth to train the Random Forest (RF) algorithm and therefore predict canopy height from other remotely sensed datasets in areas without LiDAR coverage has been one of the most commonly used method in large-scale canopy height mapping. However, how variances in location, vegetation type, and spatial scale of study sites influence the RF modelling results is still a question that needs to be addressed. In this study, we selected 16 study sites (100 km2 each) with full airborne LiDAR coverage across the United States, and used the LiDAR-derived canopy height along with optical imagery, topographic data, and climate surfaces to evaluate the transferability of the RF-based canopy height prediction method. The results show a series of findings from general to complex. The RF model trained at a certain location or vegetation type cannot be transferred to other locations or vegetation types. However, by training the RF algorithm using samples from all sites with various vegetation types, a universal model can be achieved for predicting canopy height at different locations and different vegetation types with self-predicted R2 higher than 0.6 and RMSE lower than 6 m. Moreover, the influence of spatial scales on the RF prediction accuracy is noticeable when spatial extent of the study site is less than 50 km2 or the spatial resolution of the training pixel is finer than 500 m. The canopy height prediction accuracy increases with the spatial extent and the targeted spatial resolution.

Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory

AbstractAimEstimating the current spatial variation of biomass in the Amazon rain forest is a challenge and remains a source of substantial uncertainty in the assessment of the global carbon cycle. Precise estimates need to consider small‐scale variations of forest structures resulting from local disturbances, on the one hand, and require large‐scale information on the state of the forest that can be detected by remote sensing, on the other hand. In this study, we introduce a novel method that links a forest gap model and a canopy height map to derive the biomass distribution of the Amazon rain forest.LocationAmazon rain forest.MethodsAn individual‐based forest model was applied to estimate the variation of aboveground biomass across the Amazon rain forest. The forest model simulated individual trees; hence, it allowed the direct comparison of simulated and observed canopy heights from remote sensing. The comparison enabled the detection of disturbed forest states and the derivation of a simulation‐based biomass map at 0.16 ha resolution.ResultsSimulated biomass values ranged from 20 to 490 t (dry mass)/ha across 7.8 Mio km2 of Amazon rain forest. We estimated a total aboveground biomass stock of 76 GtC, with a coefficient of variation of 45%. We found mean differences of only 15% when comparing biomass values of the map with 114 field inventories. The forest model enables the derivation of additional estimates, such as basal area and stem density.Main conclusionsLinking a canopy height map with an individual‐based forest model captures the spatial variation of biomass in the Amazon rain forest at high resolution. The study demonstrates how this linkage allows for quantifying the spatial variation in forest structure caused by tree‐level to regional‐scale disturbances. It thus provides a basis for large‐scale analyses on the heterogeneous structure of tropical forests and their carbon cycle.

Exploring the relationship between canopy height and terrestrial plant diversity

A relatively small number of broad-scale patterns describe the distribution of biodiversity across the earth. All of them explore biodiversity focusing on a mono or bi-dimensional space. Conversely, the volume of the forests is rarely considered. In the present work, we tested a global correlation between vascular plant species richness (S) and average forest canopy height (H), the latter regarded as a proxy of volume, using the NASA product of Global Forest Canopy Height map and the global map of plant species diversity. We found a significant correlation between H and S both at global and macro-climate scales, with strongest confidence in the tropics. Hence, two different regression models were compared and discussed to provide a possible pattern of the H–S relation. We suggested that the volume of forest ecosystems should be considered in ecological studies as well as in planning and managing natural sites, although in this first attempt, we cannot definitively prove our hypothesis. Again, high-resolution spatial data could be highly important to confirm the H–S relation, even at different scales.

Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping

Abstract NASA's Global Ecosystem Dynamics Investigation (GEDI), to be deployed on the International Space Station (ISS) in 2018, will provide billions of measurements of ground elevation and forest vertical structure. GEDI will acquire data only along transects or tracks, with between-track spacing of about 500 m. To fill the gaps in between these tracks and potentially produce higher spatial resolution products, appropriate fusion strategies between GEDI observations and other spatially contiguous datasets should be explored. One source of global data on canopy structure comes from the TanDEM-X (TDX) mission, which uses the technique of Interferometric Synthetic Aperture Radar (InSAR) to estimate surface structure. The goal of this paper is to explore the fusion of GEDI data with TDX for canopy height retrievals. In particular we examined the improvement in TDX height retrievals from a widely used scattering model – the Random Volume over Ground (RVoG) model using ancillary topographic data from simulated GEDI observations of surface elevation. Our study site is a mountainous, mixed-temperate forest: Hubbard Brook Experimental Forest (HBEF). We started with a wall-to-wall lidar data set acquired by the Land Vegetation and Ice Sensor (LVIS) that provides a close analogue to anticipated GEDI waveforms. We derived a reference canopy height map and a reference bare earth digital terrain model (DTM) using LVIS. We next simulated GEDI ground tracks over HBEF for the nominal one-year period and extracted these observations from the reference DTM. A series of experiments were then conducted to examine the impact of ancillary topographic information. Using two different sets of TDX acquisitions, we compared height from RVoG respectively using no external DTM, the full LVIS DTM, and the DTM derived from simulated GEDI data against reference canopy heights at 90 m spatial resolution. With no external DTM to remove the ground phase, the RVoG model estimated heights with the best RMSE error (of the two TDX acquisitions dates) of 4.3 m and a bias of 2.5 m. Using the full LVIS DTM, results improved to 3.5 m RMSE and a bias of 1.3 m. Using the simulated GEDI DTM, the RMSE was 4.6 m with a bias of 1.8 m. The agreement between predicted and actual heights was good, ranging from an r2 of 0.39 (GEDI DTM) to 0.71 (p-value

Weak Environmental Controls of Tropical Forest Canopy Height in the Guiana Shield

Canopy height is a key variable in tropical forest functioning and for regional carbon inventories. We investigate the spatial structure of the canopy height of a tropical forest, its relationship with environmental physical covariates, and the implication for tropical forest height variation mapping. Making use of high-resolution maps of LiDAR-derived Digital Canopy Model (DCM) and environmental covariates from a Digital Elevation Model (DEM) acquired over 30,000 ha of tropical forest in French Guiana, we first show that forest canopy height is spatially correlated up to 2500 m. Forest canopy height is significantly associated with environmental variables, but the degree of correlation varies strongly with pixel resolution. On the whole, bottomland forests generally have lower canopy heights than hillslope or hilltop forests. However, this global picture is very noisy at local scale likely because of the endogenous gap-phase forest dynamic processes. Forest canopy height has been predictively mapped across a pixel resolution going from 6 m to 384 m mimicking a low resolution case of 3 points·km − 2 . Results of canopy height mapping indicated that the error for spatial model with environment effects decrease from 8.7 m to 0.91 m, depending of the pixel resolution. Results suggest that, outside the calibration plots, the contribution of environment in shaping the global canopy height distribution is quite limited. This prevents accurate canopy height mapping based only on environmental information, and suggests that precise canopy height maps, for local management purposes, can only be obtained with direct LiDAR monitoring.

A NEAR-GLOBAL BARE-EARTH DEM FROM SRTM

Abstract. The near-global elevation product from NASA’s Shuttle Radar Topographic Mission (SRTM) has been widely used since its release in 2005 at 3 arcsecond resolution and the release of the 1 arcsecond version in late 2014 means that the full potential of the SRTM DEM can now be realised. However the routine use of SRTM for analytical purposes such as catchment hydrology, flood inundation, habitat mapping and soil mapping is still seriously impeded by the presence of artefacts in the data, primarily the offsets due to tree cover and the random noise. This paper describes the algorithms being developed to remove those offsets, based on the methods developed to produce the Australian national elevation model from SRTM data. The offsets due to trees are estimated using the GlobeLand30 (National Geomatics Center of China) and Global Forest Change (University of Maryland) products derived from Landsat, along with the ALOS PALSAR radar image data (JAXA) and the global forest canopy height map (NASA). The offsets are estimated using several processes and combined to produce a single continuous tree offset layer that is subtracted from the SRTM data. The DEM products will be made freely available on completion of the first draft product, and the assessment of that product is expected to drive further improvements to the methods.

A NEAR-GLOBAL BARE-EARTH DEM FROM SRTM

The near-global elevation product from NASA’s Shuttle Radar Topographic Mission (SRTM) has been widely used since its release in 2005 at 3 arcsecond resolution and the release of the 1 arcsecond version in late 2014 means that the full potential of the SRTM DEM can now be realised. However the routine use of SRTM for analytical purposes such as catchment hydrology, flood inundation, habitat mapping and soil mapping is still seriously impeded by the presence of artefacts in the data, primarily the offsets due to tree cover and the random noise. This paper describes the algorithms being developed to remove those offsets, based on the methods developed to produce the Australian national elevation model from SRTM data. <br><br> The offsets due to trees are estimated using the GlobeLand30 (National Geomatics Center of China) and Global Forest Change (University of Maryland) products derived from Landsat, along with the ALOS PALSAR radar image data (JAXA) and the global forest canopy height map (NASA). The offsets are estimated using several processes and combined to produce a single continuous tree offset layer that is subtracted from the SRTM data. <br><br> The DEM products will be made freely available on completion of the first draft product, and the assessment of that product is expected to drive further improvements to the methods.

Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana

LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km.

A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height

Mapping the global distribution of forest canopy height is important for estimating forest biomass and terrestrial carbon flux. In this study, we present a global map of mean forest canopy height at 500 m spatial resolution obtained by combining Geoscience Laser Altimeter System (GLAS) data acquired from 2005 to 2006 and 13 ancillary variables, including seven climatic variables and six remote sensing variables (nadir BRDF-adjusted reflectance at red and NIR bands, tree cover, anisotropic factor, accumulated Enhanced Vegetation Index, and elevation). The original contributions of this study include the following: (1) The wavelet method was applied to complement the GLA14 product to identify the ground peak and the top-canopy peak. We found that it was useful for dealing with waveforms with low reconstruction accuracy. (2) GLAS data from the leafless season were not used for non-evergreen forest because the height retrieval results exhibited underestimation and strong variations. (3) The anisotropic factor (ANIF), an indicator related to surface structure, was included as an ancillary variable for the first time and was determined to be important for height modeling in the Asian and North American regions. (4) The balanced random forest (BRF) algorithm was applied to register GLAS mean forest canopy height to a 500 m grid considering the small proportion of extreme height classes (tall and short trees), and it achieved good performance in terms of modeling accuracy (RMSE = 2.75 to 4.45 m) and preserving data variation. An inter-comparison among three global forest height maps [the present study, Lefsky (2010), and Simard et al. (2011)] was implemented in a pixel-by-pixel manner. High agreement (R2 = 0.73, RMSE = 4.49 m) was determined between the present study and Simard et al., whereas the result from Lefsky was notably different from the other two results (R2 = 0.14, RMSE = 8.92 m, compared with the present study; R2 = 0.11, RMSE = 11.19 m, compared with Simard et al.). Large disparities were generally associated with evergreen broadleaf forests in South America, deciduous needleleaf forests in Europe and Russian North Asia, and evergreen needleleaf forests on the West Coast of North America. Differences in the height metric were a main factor affecting the disparities among the three results. Validation against field survey data acquired from the Distributed Active Archive Center indicated the accuracy of our mean forest canopy height map (R2 = 0.63, RMSE = 4.68 m, n = 59).

Mapping Forest Canopy Height Across Large Areas by Upscaling ALS Estimates with Freely Available Satellite Data

Operational assessment of forest structure is an on-going challenge for land managers, particularly over large, remote or inaccessible areas. Here, we present an easily adopted method for generating a continuous map of canopy height at a 30 m resolution, demonstrated over 2.9 million hectares of highly heterogeneous forest (canopy height 0–70 m) in Victoria, Australia. A two-stage approach was utilized where Airborne Laser Scanning (ALS) derived canopy height, captured over ~18% of the study area, was used to train a regression tree ensemble method; random forest. Predictor variables, which have a global coverage and are freely available, included Landsat Thematic Mapper (Tasselled Cap transformed), Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index time series, Shuttle Radar Topography Mission elevation data and other ancillary datasets. Reflectance variables were further processed to extract additional spatial and temporal contextual and textural variables. Modeled canopy height was validated following two approaches; (i) random sample cross validation; and (ii) with 108 inventory plots from outside the ALS capture extent. Both the cross validation and comparison with inventory data indicate canopy height can be estimated with a Root Mean Square Error (RMSE) of ≤ 31% (~5.6 m) at the 95th percentile confidence interval. Subtraction of the systematic component of model error, estimated from training data error residuals, rescaled canopy height values to more accurately represent the response variable distribution tails e.g., tall and short forest. Two further experiments were carried out to test the applicability and scalability of the presented method. Results suggest that (a) no improvement in canopy height estimation is achieved when models were constructed and validated for smaller geographic areas, suggesting there is no upper limit to model scalability; and (b) training data can be captured over a small percentage of the study area (~6%) if response and predictor variable variance is captured within the training cohort, however RMSE is higher than when compared to a stratified random sample.

High-resolution forest canopy height estimation in an African blue carbon ecosystem.

Mangrove forests are one of the most productive and carbon dense ecosystems that are only found at tidally inundated coastal areas. Forest canopy height is an important measure for modeling carbon and biomass dynamics, as well as land cover change. By taking advantage of the flat terrain and dense canopy cover, the present study derived digital surface models (DSMs) using stereo‐photogrammetric techniques on high‐resolution spaceborne imagery (HRSI) for southern Mozambique. A mean‐weighted ground surface elevation factor was subtracted from the HRSI DSM to accurately estimate the canopy height in mangrove forests in southern Mozambique. The mean and H100 tree height measured in both the field and with the digital canopy model provided the most accurate results with a vertical error of 1.18–1.84 m, respectively. Distinct patterns were identified in the HRSI canopy height map that could not be discerned from coarse shuttle radar topography mission canopy maps even though the mode and distribution of canopy heights were similar over the same area. Through further investigation, HRSI DSMs have the potential of providing a new type of three‐dimensional dataset that could serve as calibration/validation data for other DSMs generated from spaceborne datasets with much larger global coverage. HSRI DSMs could be used in lieu of Lidar acquisitions for canopy height and forest biomass estimation, and be combined with passive optical data to improve land cover classifications.