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Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations.

Spaceborne LiDAR has been widely used to obtain forest canopy heights over large areas, but it is still a challenge to obtain spatio-continuous forest canopy heights with this technology. In order to make up for this deficiency and take advantage of the complementary for multi-source remote sensing data in forest canopy height mapping, a new method to estimate forest canopy height was proposed by synergizing the spaceborne LiDAR (ICESat-2) data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types. In this study, National Geographical Condition Monitoring (NGCM) data was used to extract the distributions of coniferous forest (CF), broadleaf forest (BF), and mixed forest (MF) in Hua’ nan forest area in Heilongjiang Province, China. Accordingly, the forest canopy height estimation models for whole forest (all forests together without distinguishing types, WF), CF, BF, and MF were established, respectively, by Radom Forest (RF) and Gradient Boosting Decision Tree (GBDT). The accuracy for established models and the forest canopy height obtained based on estimation models were validated consequently. The results showed that the forest canopy height estimation models considering forest types had better performance than the model grouping all types of forest together. Compared with GBDT, RF with optimal variables had better performance in forest canopy height estimation with Pearson’s correlation coefficient (R) and the root-mean-squared error (RMSE) values for CF, BF, and MF of 0.72, 0.59, 0.62, and 3.15, 3.37, 3.26 m, respectively. It has been validated that a synergy of ICESat-2 with other remote sensing data can make a crucial contribution to spatio-continuous forest canopy height mapping, especially for areas covered by different types of forest.

The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an R2 of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, kNN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future.

Forest canopy height is a basic metric characterizing forest growth and carbon sink capacity. Based on full-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) data, this study used Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technology to estimate forest canopy height. In total the four methods of differential DEM (digital elevation model) algorithm, coherent amplitude algorithm, coherent phase-amplitude algorithm and three-stage random volume over ground algorithm (RVoG_3) were proposed to obtain canopy height and their accuracy was compared in consideration of the impacts of coherence coefficient and range slope levels. The influence of the statistical window size on the coherence coefficient was analyzed to improve the estimation accuracy. On the basis of traditional algorithms, time decoherence was performed on ALOS/PALSAR data by introducing the change rate of Landsat NDVI (Normalized Difference Vegetation Index). The slope in range direction was calculated based on SRTM (Shuttle Radar Topography Mission) DEM data and then introduced into the s-RVoG (sloped-Random Volume over Ground) model to optimize the canopy height estimation model and improve the accuracy. The results indicated that the differential DEM algorithm underestimated the canopy height significantly, while the coherent amplitude algorithm overestimated the canopy height. After removing the systematic coherence, the overestimation of the RVoG_3 model was restrained, and the absolute error decreased from 23.68 m to 4.86 m. With further time decoherence, the determination coefficient increased to 0.2439. With the introduction of range slope, the s-RVoG model shows improvement compared to the RVoG model. Our results will provide a reference for the appropriate algorithm selection and optimization for forest canopy height estimation using full-polarized L-band synthetic aperture radar (SAR) data for forest ecosystem monitoring and management.

An assessment of the National Aeronautics and Space Administration NASA’s Cyclone Global Navigation Satellite System (CyGNSS) mission for biomass studies is presented in this work on rain, coniferous, dry, and moist tropical forests. The main objective is to investigate the capability of Global Navigation Satellite Systems Reflectometry (GNSS-R) for biomass retrieval over dense forest canopies from a space-borne platform. The potential advantage of CyGNSS, as compared to monostatic Synthetic Aperture Radar (SAR) missions, relies on the increasing signal attenuation by the vegetation cover, which gradually reduces the coherent scattering component σ coh , 0 . This term can only be collected in a bistatic radar geometry. This point motivates the study of the relationship between several observables derived from Delay Doppler Maps (DDMs) with Above-Ground Biomass (AGB). This assessment is performed at different elevation angles θ e as a function of Canopy Height (CH). The selected biomass products are obtained from data collected by the Geoscience Laser Altimeter System (GLAS) instrument on-board the Ice, Cloud, and land Elevation Satellite (ICESat-1). An analysis based on the first derivative of the experimentally derived polynomial fitting functions shows that the sensitivity requirements of the Trailing Edge TE and the reflectivity Γ reduce with increasing biomass up to ~ 350 and ~ 250 ton/ha over the Congo and Amazon rainforests, respectively. The empirical relationship between TE and Γ with AGB is further evaluated at optimum angular ranges using Soil Moisture Active Passive (SMAP)-derived Vegetation Optical Depth ( VOD ), and the Polarization Index ( PI ). Additionally, the potential influence of Soil Moisture Content (SMC) is investigated over forests with low AGB.

High spatial resolution imagery provided by unmanned aerial vehicles (UAVs) can yield accurate and efficient estimation of tree dimensions and canopy structural variables at the local scale. We flew a low-cost, lightweight UAV over an experimental Pinus pinea L. plantation (290 trees distributed over 16 ha with different fertirrigation treatments) to determine the tree positions and to estimate individual tree height (h), diameter (d), biomass (wa), as well as changes in these variables between 2015 and 2017. We used Structure from Motion (SfM) and 3D point cloud filtering techniques to generate the canopy height model and object-based image analysis to delineate individual tree crowns (ITC). ITC results were validated using accurate field measurements over a subsample of 50 trees. Comparison between SfM-derived and field-measured h yielded an R2 value of 0.96. Regressions using SfM-derived variables as explanatory variables described 79% and 86–87% of the variability in d and wa, respectively. The height and biomass growth estimates across the entire study area for the period 2015–2017 were 0.45 m ± 0.12 m and 198.7 ± 93.9 kg, respectively. Significant differences (t-test) in height and biomass were observed at the end of the study period. The findings indicate that the proposed method could be used to derive individual-tree variables and to detect spatio-temporal changes, highlighting the potential role of UAV-derived imagery as a forest management tool.

The Global Ecosystem Dynamics Investigation (GEDI), specifically designed to measure vertical forest structures, has acquired, since April 2019, more than 35 billion waveforms of Earth’s surface on a nearly global scale. GEDI is equipped with 3 identical 1064 nm lasers with a power of 10 mJ per shot, where 1 laser is split into 2 lasers, resulting in two 5 mJ coverage lasers and two 10 mJ full-power lasers. In this study, we evaluate the potential of GEDI’s four lasers to penetrate through canopies and detect the ground, and their capabilities to detect the top of the canopies over a tropical forest (in French Guiana) characterized by a dense canopy cover and tall trees. The accurate detection of both of these surfaces is the first step in characterizing vertical forest structures. The SRTM Digital Elevation Model (DEM) is used as a reference point for elevations while a canopy height model (CHM), derived from airborne and spaceborne LiDAR data, is used as a reference for canopy heights. In addition, the ground and canopy-top elevations estimated from NASA’s Land, Vegetation, and Ice Sensor (LVIS, 1064 nm full-waveform LiDAR, 5 mJ per shot, ~8 km altitude) are used as a benchmark for comparison with GEDI’s lasers. Results indicate that GEDI’s coverage and full-power lasers, even after the application of a preliminary filter that removes around 50% of acquisitions, tend to underestimate tree heights in densely vegetated, tall forests. Moreover, GEDI’s coverage lasers also exhibited a lower level of performance in comparison to both the full-power lasers and LVIS. Overall, the average estimated maximum canopy heights (RH100) for a CHM greater than 30 m was 24.4 m with the coverage lasers, 32.1 m with the full-power lasers, and 36.7 m with LVIS. The analysis of shots with high-beam sensitivity (sensitivity ≥ 98%) showed that they tend to have a better probability of reaching the ground and have better detection of canopy tops for both GEDI laser types. Nonetheless, GEDI’s coverage lasers still showed an underestimation of canopy heights with an average RH100 of 29.8 m, while for GEDI’s full-power lasers and LVIS, the average RH100 was 35.2 m and 37.7 m, respectively. Finally, the assessment of the acquisition time on the detection of the ground return and the top of the canopies showed that, for the coverage lasers, solar noise could affect the detection of the ground return as acquisitions made during early mornings or late afternoons have more penetration than shots acquired between 8 a.m. and 4 p.m. The effect of acquisition time on the detection of the tops of canopies showed that solar noise slightly affected the coverage lasers. Regarding the full-power lasers, the acquisition time of the shots seem to affect neither the penetration of the lasers, nor the detection of the tops of canopies.

In uneven-aged forests, trees of different diameters, heights, and ages are located in a small area, which is due to the felling of individual trees or groups of trees, as well as small-scale natural disturbances. In this article, we present an objective method for classifying forest stands into even- and uneven-aged stands based on freely available low-resolution (with an average recording density of 5 points/m2) national lidar data. The canopy closure, dominant height, and canopy height diversity from the canopy height model and the voxels derived from lidar data were used to classify the forest stands. Both approaches for determining forest structural diversity (canopy height diversity—CHDCHM and CHDV) yielded similar results, namely two clusters of even- and uneven-aged stands, although the differences in vertical diversity between even- and uneven-aged stands were greater when using CHM. The first analysis, using CHM for the CHD assessment, estimated the uneven-aged forest area as 49.3%, whereas the second analysis using voxels estimated it as 34.3%. We concluded that in areas with low laser scanner density, CHM analysis is a more appropriate method for assessing forest stand height heterogeneity. The advantage of detecting uneven-aged structures with voxels is that we were able to detect shade-tolerant species of varying age classes beneath a dense canopy of mature, dominant trees. The CHDCHM values were estimated to be 1.83 and 1.86 for uneven-aged forests, whereas they were 1.57 and 1.58 for mature even-aged forests. The CHDV values were estimated as 1.50 and 1.62 for uneven-aged forests, while they were 1.33 and 1.48 for mature even-aged forests. The classification of stands based on lidar data was validated with data from measurements on permanent sample plots. Statistically significantly lower average values of the homogeneity index and higher values of the Shannon–Wiener index from field measurements confirm the success of the classification of stands based on lidar data as uneven-aged forests.

Context The severity of damage to tropical forests by hurricanes is determined by the interplay between disturbance strength and resistance of forests. While major hurricanes often follow a warming period with severe drought, there has been no quantitative assessment of the legacy of drought on responses of tropical forests to major hurricanes. Objectives We aim to derive factors that determine landscape spatial distribution of canopy damages by hurricanes and to investigate how drought will affect forest responses to major hurricanes. Methods We used canopy height data from two LiDAR campaigns in Puerto Rico before and after Hurricane Maria in September 2017 after severe drought in 2014–2016 to analyze canopy damage during the hurricane. Spatial error models were applied to canopy height reduction in wet, moist, and dry forest zones on pre-hurricane canopy properties, hurricane forcing, geomorphology, and rainfall deficit during the drought. Results Pre-hurricane canopy properties dominated over the other variables in determining the spatial variation of canopy height reduction. Drought affected forest response to the hurricane in different ways: The rainfall deficit during the drought cut off the canopy height reduction of wet forest but elevated that of dry forest. Pooling the data from all three zones, drought alleviated significantly the canopy height reduction, and this alleviation of damage was stronger for forests with taller trees. Conclusions For island setting under major hurricanes, forest resistance dominates to control landscape spatial distribution of canopy damage. This study suggests that defoliation during pre-hurricane drought may have protected forests from hurricane damage.

Plant height (PH) is an important agronomical parameter to assess the growth status in upland rice fields. Recently, field-based phenotyping using unmanned aerial vehicles (UAVs) has received increasing attention as a cost-effective, well-suited sensing technology to measure PH. In this study, we evaluated feasibility of a low-cost small UAV for estimating PH in upland rice fields in Laos with a canopy height model (CHM). Images of the upland field, including 501 plots (= 167 accessions × 3 replicates), were captured by a commercial small UAV (DJI Phantom 4) before emergence and in the near-flowering stage to generate digital surface models (DSMs). The CHM was developed from the difference of the DSMs using UAV images obtained before emergence and before flowering. The CHM metrics of each plot were then calculated using 90-99th percentiles and the top 1-10% largest pixel values of CHM and were compared with the manually measured field PH (78.25-189.75 cm). The predictive accuracy was assessed in the 90-99th percentiles and top 1-10% values of CHM metrics with 5-fold cross-validation procedures. Simple linear regression analyses between the field PH and CHM metrics showed that the top 3% CHM metrics had the best correlation with the field PH (R 2  = 0.712, root-mean-square error (RMSE) = 9.142 cm, p < 0.001). Cross-validation procedures also confirmed that the top 3% CHM metrics were the best in terms of accuracy for estimating PH, with an error of 6.963% (8.823 cm) error.