Estimating Stand Density in a Tropical Broadleaf Forest Using Airborne LiDAR Data

This paper investigates the possibilities of improving aboveground forest biomass and basal area weighted mean height estimates from optical multispectral satellite data by adding Canopy Height Models (CHMs) obtained from matching multiple view-angle satellite data. The analysis was carried out using data collected over the Remningstorp test site in southern Sweden from 2008–2011 and used training and validation data from airborne laser scanning and field data. CHMs were produced by subtracting a Digital Elevation Model (based on airborne laser scanning data) from the Digital Surface Models created by matching multiple view-angle SPOT-5 HRS and ALOS PRISM data. By modeling biomass and height using regression analysis on multispectral data from SPOT-5 HRG in combination with height metrics from the CHMs, an improved root mean squared error (RMSE) was attained, compared with using the individual satellite data sources alone. A comparison between SPOT-5 HRS and ALOS PRISM CHMs in combination with multispectral data was made at stand level using biomass and height estimates from laser scanning data as reference data. For biomass, the relative RMSE improved from 32.9% when using only multispectral data, to 29.2% and 22.4% when adding the CHM from SPOT-5 HRS and ALOS PRISM, respectively. The corresponding improvements for height were from 16.1% to 15.3% with the SPOT-5 HRS CHM and to 12.9% with the ALOS PRISM CHM. A further analysis of combining the ALOS PRISM CHM and multispectral data was made at sub-stand level with field measurements as reference data. This combination gave a relative RMSE of 20.6% for biomass and 10.5% for height. In conclusion, the estimation accuracy for aboveground biomass and basal area weighted mean height was improved by adding CHM data to multispectral data from optical satellites.

BackgroundDetermining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research. The geometric-optical mutual shadowing (GOMS) model can be used to invert the forest canopy structural parameters at the regional scale. However, this method can obtain only the ratios among the horizontal canopy diameter (CD), tree height, clear height, and vertical CD. In this paper, we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale. We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale.ResultsThe semi-variance model can be used to calculate the CD at the regional scale that closely matches (mainly with in a range from − 1 to 1 m) the CD derived from the canopy height model (CHM) data. The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small, with a root mean square error (RMSE) of 1.96 for a 500-m area with high fractional vegetation cover (FVC) (i.e., forest area coverage index values greater than 0.8). Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height. And the error caused by the unmatched spatial resolution is small in dense forest.ConclusionsThe semi-variance model can be used to calculate the CD at the regional scale, together with the canopy structure parameters inverted by the GOMS model, the mean tree height at the regional scale can be obtained. Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.

Canopy height change (CHC) is one of the key characteristics of forest dynamics, associated with the fluctuations in forest above-ground biomass and carbon stocks. Field measurements and Airborne Laser Scanning (ALS) point clouds can be used to detect CHC; however, they have limited availability in space and time, making it challenging to map CHC over large areas. Alternatively, very high-resolution (VHR) satellite stereo imagery plays an increasingly vital role in estimating fine-scaled digital surface models (DSMs) across landscapes. However, its capability and potential to estimate canopy height model (CHM) and CHC has not been widely explored. Using ALS-derived CHM in 2011 and 2015 and the four-year CHC as references, we evaluated stereo-based CHM and CHC from WorldView-2 over five woody parks in Columbus, Ohio, USA. We also integrated stereo-based CHM with vegetation indices from Landsat 7 to improve CHM and CHC estimation with machine learning methods. Our results showed that VHR stereo imagery captured similar spatial patterns of CHM with ALS data but significantly overestimated CHM. Moreover, the ALS-derived CHC ranged from 1.6±1.9 m (mean ± standard deviation) to 3.1±1.2 m, as compared to from -1.3±0.7 m to 1.1±1.7 m for stereo-based CHC, indicating the limitation of CHC estimation by stereo imagery alone. Among six widely used machine learning methods, Gradient Boosting Regression method provided the most reliable estimates of CHM, with a correlation coefficient R of 0.64 and a root-mean-square error (RMSE) of 3.1 m (11.1%). Stereo-based CHM and vegetation indices explained more than 70% of CHM variability, substantially improving the estimation of 4-year CHC. Our results suggested that VHR stereo imagery alone has limitations in estimating CHM and CHC. The combination of remote-sensing structural (stereo-based CHM) and spectral (vegetation indices) information improves the CHM and CHC estimations.

ABSTRACTIt was demonstrated in the past that radar data is useful to estimate aboveground biomass due to their interferometric capability. Therefore, the potential of a globally available TanDEM-X digital elevation model (DEM) was investigated for aboveground biomass estimation via canopy height models (CHMs) in a tropical peat swamp forest. However, CHMs based on X-band interferometers usually require external terrain models. High accurate terrain models are not available on global scale. Therefore, an approach exclusively based on TanDEM-X and the decrease of accuracy compared to an approach utilizing a high accurate terrain model is assessed. In addition, the potential of X-band interferometric heights in tropical forests needs to be evaluated. Therefore, two CHMs were derived from an intermediate TanDEM-X DEM (iDEM; as a precursor for WorldDEMTM) alone and in combination with lidar measurements used as terrain model. The analysis showed high accuracies (root mean square error [RMSE] = 5 m) for CHMs based on iDEM and reliable estimation of aboveground biomass. The iDEM CHM, exclusively based on TanDEM-X, achieved a poor R2 of 0.2, nonetheless resulted in a cross-validated RMSE of 54 t ha−1 (16%). The low R2 suggested that the X-band height alone was not sufficient to estimate an accurate CHM, and thus the need for external terrain models was confirmed. A CHM retrieved from the difference of iDEM and an accurate lidar terrain model achieved a considerably higher correlation with aboveground biomass (R2 = 0.68) and low cross-validated RMSE of 24.5 t ha−1 (7.5%). This was higher or comparable to other aboveground biomass estimations in tropical peat swamp forests. The potential of X-band interferometric heights for CHM and biomass estimation was thus confirmed in tropical forest in addition to existing knowledge in boreal forests.

AimsUncrewed aerial vehicles (UAV), or drones, have become more affordable and easier to use, resulting in increased UAV applications in ecology and conservation. However, solar illumination, vegetation phenology and prevailing weather conditions will impact the quality of the derived products to differing degrees. In this study, we investigate how seasonal differences in solar illumination, tree foliage and weather conditions impact the accuracy of digital elevation models (DEM) and canopy height models (CHM) in a heterogeneous boreal landscape.MethodsWe compared DEMs and CHMs derived from drone photogrammetry with DEMs and CHMs produced from a drone‐mounted laser scanner across three seasons with different solar illumination, tree foliage and weather conditions during leaf‐off and leaf‐on seasons. Photogrammetric height models were evaluated across three land‐cover classes consisting of open areas, sparse‐forest and forest. The most accurate CHM for sparse‐forest was produced during summer under overcast conditions, whereas for the forest class, summer under clear skies was best.ResultsStructure from motion (SfM) photogrammetry performed well against the LiDAR survey in most cases with correlations between sampled points of up to R2 = 0.995. Root mean square errors (RMSEs) were <1.5 m in all DEMs and as low as 0.31 m in autumn clear‐sky data over open terrain. CHM RMSEs were somewhat higher in all cases except under winter overcast conditions when the RMSE for sparse‐forest reached 6.03 m.ConclusionsWe have shown that SfM photogrammetry is surprisingly robust to variations in vegetation type, tree phenology and weather, and performs well in comparison with a reference LiDAR data set. Our results show that, in boreal forests, autumn is the preferred season under clear‐sky conditions for DEM generation from SfM photogrammetry across all land‐cover classes, whereas summer is preferred for CHM modelling with a small trade‐off between overcast and clear‐sky conditions over different vegetation types. These results can help potential SfM users in ecology and forestry plan missions and review the quality of products derived from drone photogrammetry products.

Airborne laser scanning (ALS) has demonstrated utility for forestry applications and has renewed interest in other forms of remotely sensed data, especially those that capture three-dimensional (3-D) forest characteristics. One such data source results from the advanced processing of high spatial resolution digital stereo imagery (DSI) to generate 3-D point clouds. From the derived point cloud, a digital surface model and forest vertical information with similarities to ALS can be generated. A key consideration is that when developing forestry related products such as a canopy height model (CHM), a high spatial resolution digital terrain model (DTM), typically from ALS, is required to normalize DSI elevations to heights above ground. In this paper we report on our investigations into the use of DSI-derived vertical information for capturing variations in forest structure and compare these results to those acquired using ALS. An ALS-derived DTM was used to provide the spatially detailed ground surface elevations to normalize DSI-derived heights. Similar metrics were calculated from the vertical information provided by both DSI and ALS. Comparisons revealed that ALS metrics provided a more detailed characterization of the canopy surface including canopy openings. Both DSI and ALS metrics had similar levels of correlation with forest structural attributes (e.g., height, volume, and biomass). DSI-based models predicted height, diameter, basal area, stem volume, and biomass with root mean square (RMS) accuracies of 11.2%, 21.7%, 23.6%, 24.5%, and 23.7%, respectively. The respective accuracies for the ALS-based predictions were 7.8%, 19.1%, 17.8%, 17.9%, and 17.5%. Change detection between ALS-derived CHM (time 1) and DSI-derived CHM (time 2) provided change estimates that demonstrated good agreement (r = 0.71) with two-date, ALS only, change outputs. For the single-layered, even-aged stands under investigation in this study, the DSI-derived vertical information is an appropriate and cost-effective data source for estimating and updating forest information. The accuracy of DSI information is based on a capability to measure the height of the upper canopy envelope with performance analogous to ALS. Forest attributes that are well captured and subsequently modeled from height metrics are best suited to estimation from DSI metrics, whereas ALS is more suitable for capturing stand density. Further investigation is required to better understand the performance of DSI-derived height products in more complex forest environments. Furthermore, the difference in variance captured between ALS and DSI-derived CHM also needs to be better understood in the context of change detection and inventory update considerations.

Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands required for estimating and frequently updating forest data. Here we demonstrate the capability of alternative solutions based on the use of low-cost color infrared (CIR) cameras to estimate tree-level parameters, providing a cost-effective solution for forest inventories. ALS data were acquired with a Leica ALS60 laser scanner and digital aerial imagery (DAI) was acquired with a consumer-grade camera modified for color infrared detection and synchronized with a GPS unit. In this paper we evaluate the generation of a DAI-based canopy height model (CHM) from imagery obtained with low-cost CIR cameras using structure from motion (SfM) and spatial interpolation methods in the context of a complex canopy, as in forestry. Metrics were calculated from the DAI-based CHM and the DAI-based Normalized Difference Vegetation Index (NDVI) for the estimation of tree height and LAI, respectively. Results were compared with the models estimated from ALS point cloud metrics. Field measurements of tree height and effective leaf area index (LAIe) were acquired from a total of 200 and 26 trees, respectively. Comparable accuracies were obtained in the tree height and LAI estimations using ALS and DAI data independently. Tree height estimated from DAI-based metrics (Percentile 90 (P90) and minimum height (MinH)) yielded a coefficient of determination (R2) = 0.71 and a root mean square error (RMSE) = 0.71 m while models derived from ALS-based metrics (P90) yielded an R2 = 0.80 and an RMSE = 0.55 m. The estimation of LAI from DAI-based NDVI using Percentile 99 (P99) yielded an R2 = 0.62 and an RMSE = 0.17 m2/m−2. A comparative analysis of LAI estimation using ALS-based metrics (laser penetration index (LPI), interquartile distance (IQ), and Percentile 30 (P30)) yielded an R2 = 0.75 and an RMSE = 0.14 m2/m−2. The results provide insight on the appropriateness of using cost-effective 3D photo-reconstruction methods for targeting single trees with irregular and heterogeneous tree crowns in complex open-canopy forests. It quantitatively demonstrates that low-cost CIR cameras can be used to estimate both single-tree height and LAI in forest inventories.

Monitoring spruce volume and biomass with InSAR data from TanDEM-X

<p>LiDAR (Light Detection and Ranging) systems such as ALS (Airborne Laser Scanning) are increasingly being used in studies that analyse the forest structure and for the characterisation of their ecosystem processes. The main reason is their ability to provide an accurate three-dimensional description of the canopy structure, compared to other existing methods, such as passive sensors or photogrammetry. In addition, the high positional accuracy of ALS and their capacity of penetrating the canopy through small gaps in the forest canopy allow the estimation of parameters such as aboveground biomass, vegetation height, or leaf area index, among others. In forestry applications, the acquisition of these parameters usually requires a pre-processing analysis of the point clouds, which includes ground point filtering, Digital Terrain Model (DTM) and Canopy Height Model (CHM) derivation, tree detection, and segmentation, among other processes.  In the last decades, point cloud processing has benefited by the development of dedicated software packages such as LAStools, FUSION, or Terrascan, focused on obtaining DTM/CHM and LiDAR-derived metrics. However, the recent development of more sophisticated software packages, such as LidR or Pycrown, allow implementing novel and state-of-the-art algorithms as well as specific user-created functions.</p><p>The wide variety of licensed and open-source software packages for ALS data processing, together with the increasing diversity of existing algorithms and methodologies, has provoked a multitude of comparative analysis of the most widely used algorithms in the scientific literature.  However, given the recent development of the field, a robust and exhaustive review of the current use of these software and the related algorithms is still missing. In this contribution, we present a synthesis review of 613 papers on the use of software packages and algorithms for ALS processing used between 2016 and 2020. The review focuses in forest environments with a complex structure where the difference in elevation, slope, and the existence of multiple vegetation strata usually requires more complex and specialised algorithms. Therefore, three specific steps of LiDAR processing workflow were considered: ground point filtering, DTM interpolation and crown detection and segmentation. The results showed that ground point filtering (84% of the studies) is the most common step in ALS processing, compared to DTM interpolation (71%) and tree segmentation (36%). For the DTM interpolation step, TIN construction was the most used method (13%) compared to other methods such as ordinary kriging (3%). Conventional software packages that employ algorithms based on progressive TIN densification or hieratical robust interpolation approaches were the most commonly used in ground point filtering for DTM generation. Meanwhile, other user developed advanced algorithms were used more frequently in canopy segmentation processing, especially in those articles using datasets with high point densities (165.93 p/m<sup>2</sup> on average), compared to datasets processed with more general software solutions as FUSION (13.81 p/m2).</p>

The forest inventory technique by applying remote sensing technology has become a new breakthrough in technological developments in forest inventory activities. Unmanned Aerial Vehicle (UAV) imagery with camera sensor is one of the inventory tools that produce data with high spatial resolution. The level of spatial resolution of the image is strongly influenced by the flying height of the UAV for a certain camera’s focus. In addition, flight height also affects the acquisition time and accuracy of inventory results, although there is still little research on this matter. The study aims to (a)evaluate the effect of various flying heights on the accuracy of tree height measurements through UAV imagery for every stand age class, (b).estimate the trees diameter and canopy cover for every stand age class. Stand height was estimated using Digital Surface Models (DSM), Digital Terrain Models (DTM) and Orthophoto. DSM and DTM were built by converting orthophoto to pointclouds using the PIX4Dmapper based on Structure From Motion (SFM) on the photogrammetric method to reconstruct topography automatically. Meanwhile, the tree cover canopy was estimated using the All Return Canopy Index (ARCI) formula. The results show that the flight height of 100 meters produces a stronger correlation than the flying height of 80 meters and 120 meters in estimating tree height, based on the high coefficient of determination (R2) and the low root mean square error (RMSE) value. In addition, tree canopy estimation analysis using ARCI has a maximum difference of 9.8% with orthophoto visual delineation. Key words: canopy height model (CHM), digital surface models (DSM), digital terrain models (DTM), forest inventory, UAV image

Quantitative data obtained from native forests is costly and time-consuming. Thus, alternative measurement methods need to be developed to provide reliable information, especially in Atlantic Rain Forests. In this study we evaluated the hypothesis that the combination of an Airborne Laser Scanner (ALS) and an Unmanned Aerial Vehicle (UAV) can provide accurate quantitative information on tree height, volume, and aboveground biomass of the Araucaria angustifolia species. The study was carried out in Atlantic Rain forest fragments in southern Brazil. We tested and evaluated 3 digital canopy height model (CHM) scenarios: 1) CHM derived from ALS models; 2) CHM derived from UAV models; and 3) CHM from a combined ALS digital terrain model and UAV digital surface model. The height value at each tree coordinate was extracted from the pixel in the three evaluated scenarios and compared with the field measured values. ALS and UAV+ALS obtained RMSE% of 6.38 and 12.82 for height estimates, while UAV was 49.91%. Volume and aboveground biomass predictions are more accurate by ALS and UAV+ALS, while the UAV produced biased estimates. Since the ALS is currently used, periodic monitoring can be carried out by a combination of active (ALS) and passive (UAV) sensors.

Light Detection and Ranging (LiDAR) is remote sensing technology using transmitted properties of scattered light to detect the intended target. This technology potentially used for spatial planning and management, including mangrove monitoring. The purpose of this research was to map the mangrove coverage and canopy height using airborne LiDAR data at mangrove areas of Sangkulirang district, East Kutai, East Borneo. The corrected point cloud LiDAR data inputed into 10 blocks boundary. This data is classified into 7 classes (ground, mangroves, non mangroves, water, vehicle, low point and isolated point). Ground class used as the data source for digital terrain model (DTM) while mangrove class used as the data source for digital surface model (DSM). The substraction from overlaying DTM - DSM will produce canopy height model (CHM) that represent the height of mangroves canopy from land. The result of this research showed that mangrove distribution had an area of 64.07 km2. The height of mangrove canopy was dominated by 10-30 meters height while the maximum height reached 54.04 meters.

In forestry research, for forest inventories or other applications which require accurate 3D information on the forest structure, a Terrestrial Laser Scanner (TLS) is an efficient tool for vegetation structure estimation. Light Detection and Ranging (LiDAR) can even provide high-resolution information in tree canopies due to its high penetration capability. Depending on the forest plot size, tree density, and structure, multiple TLS scans are acquired to cover the forest plot in all directions to avoid any voids in the dataset that are generated. However, while increasing the number of scans, we often tend to increase the data redundancy as we keep acquiring data for the same region from multiple scan positions. In this research, an extensive qualitative analysis was carried out to examine the capability and efficiency of TLS to generate canopy top points in six different scanning combinations. A total of nine scans were acquired for each forest plot, and from these nine scans, we made six different combinations to evaluate the 3D vegetation structure derived from each scan combination, such as Center Scans (CS), Four Corners Scans (FCS), Four Corners with Center Scans (FCwCS), Four Sides Center Scans (FSCS), Four Sides Center with Center Scans (FSCwCS), and All Nine Scans (ANS). We considered eight forest plots with dimensions of 25 m × 25 m, of which four plots were of medium tree density, and the other four had a high tree density. The forest plots are located in central Slovakia; European beech was the dominant tree species with a mixture of European oak, Silver fir, Norway spruce, and European hornbeam. Altogether, 487 trees were considered for this research. The quantification of tree canopy top points obtained from a TLS point cloud is very crucial as the point cloud is used to derive the Digital Surface Model (DSM) and Canopy Height Model (CHM). We also performed a statistical evaluation by calculating the differences in the canopy top points between ANS and the five other combinations and found that the most significantly different combination was FSCwCS respective to ANS. The Root Mean Squared Error (RMSE) of the deviations in tree canopy top points obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.89 m to 14.98 m and 0.61 m to 7.78 m, respectively. The relative Root Mean Squared Error (rRMSE) obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.15% to 2.48% and 0.096% to 1.22%, respectively.

Data acquisition and simultaneous generation of real-time digital terrain models (DTMs) is a demanding task, due to the vast amounts of observations collected by modern technologies and measurement instruments in a short time. Existing methods for generating DTMs with large datasets require significant time and high computing power. Furthermore, these methods often fail to consider fragmentary DTM generation to maintain model continuity by addressing overlaps. Additionally, storing the resulting datasets, generated 3D models, and backup copies consumes excessive memory on computer and server disks. In this study, a novel concept of generating DTMs based on real-time data acquisition using the principles of sequential estimation is proposed. Since DTM generation occurs simultaneously with data acquisition, the proposed algorithm also incorporates data reduction techniques to manage the large dataset. The reduction is achieved using the Optimum Dataset Method (OptD). The effect of the research is the characteristics file that stores information about the DTM. The results demonstrate that the proposed methodology enables the creation of 3D models described by mathematical functions in each sequence and allows for determining the height of any terrain point efficiently. Experimental validation was conducted using airborne LiDAR data. The results demonstrate that data reduction using OptD retains critical terrain features while reducing dataset size by up to 98%, significantly improving computational efficiency. The accuracy of the generated DTM was assessed using root mean square error (RMSE) metrics, with values ranging from 0.041 m to 0.121 m, depending on the reduction level. Additionally, statistical analysis of height differences (ΔZ) between the proposed method and conventional interpolation techniques confirmed the reliability of the new approach. Compared to existing DTM generation methods, the proposed approach offers real-time adaptability, improved accuracy representation per model fragment, and reduced computational overhead.

Information about forest structures is becoming crucial to earth's global carbon cycle, forest habitats, and biodiversity. The Global Ecosystem Dynamics Investigation (GEDI) provides 25-m diameter footprints of the surface for 3-D structure measurements. The main goal of this study is to compare 12 031 footprints of GEDI data with other airborne and spaceborne digital elevation models (DEMs) for Southwest Spain. Ground elevation differences [elevation of the lowest mode (ELM)] are analyzed by comparing GEDI measurements with airborne laser scanning (ALS) LiDAR- and TanDEM-X-derived DEMs. The vertical structure (RH100) is compared to the ALS LiDAR measurement. Ten zones are analyzed, considering different degrees of coverage and slopes. We achieved a root mean square error (RMSE) of 6.13 m for the ELM when comparing GEDI and LiDAR data and an RMSE of 7.14 m when comparing GEDI and TanDEM-X data. For some of the studied areas, these values were considerably smaller, with RMSE values even lower than 1 m. For the RH100 metric, an RMSE of 3.56 m was achieved when comparing GEDI and LiDAR data, but again with a minimum value of 2.09 m for one zone. The results show a clear relation to coverage and slope, especially for the latter. This work also evaluates the positional uncertainty of GEDI footprints, shifting them ±10 and ±5 m along and across the track of the satellite orbit and their intermediate angular positions. The outcomes reveal a strong tendency to obtain better results in the ELM when setting the footprint to 270° and displacing it within 10 m of its positional uncertainty in comparison with the LiDAR and TanDEM-X data.